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- Probability Estimation of Future Earthquakes in China Based on Improved PSHA Model
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- On Construction of China’s Space Information Network
- The Spatial-Temporal Pattern Analysis of City Development in Countries along the Belt and Road Initiative Based on Nighttime Light Data
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- Integrated Space-Air-Ground Early Detection, Monitoring and Warning System for Potential Catastrophic Geohazards
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2021, 46(9): 1336-1344.
doi: 10.13203/j.whugis20200595
Abstract:
Objectives In order to calculate the high precision gravity gradient in level 1b (L1b) data processing of gravity field and steady-state ocean circulation explorer(GOCE)satellite, it is necessary to accurately measure the satellite attitude angular velocity. In the coordinate system transformation of the attitude data of astrology, the low precision angular velocity components of a single astrology instrument will transmit the noise to other angular velocity components, which will affect the calculation accuracy of the whole gravity gradient. Methods In this paper, based on the noise characteristics of the astrological instrument, the weighted matrix of the noise distribution on each axis of the astrological instrument is constructed, and the optimal attitude quaternion is solved jointly for the attitude quaternion of two or more astrological instruments, so as to provide accurate attitude control for subsequent angular velocity recovery. Results Compared with the single astro-meter calculation method, the accuracy of the optimal attitude angular velocity components wy and wz obtained by the combination of multiple astro-meters can reach about 1 order of magnitude higher within 10-100 mHz, and the noise level is up to 10-6 rad/s, which can effectively suppress the noise propagation caused by the low precision angular velocity components in coordinate system transformation. Conclusions The results show that the satellite attitude angular velocity calculated by the combination of multiple astrological instruments can be significantly improved in the whole power spectral density, the power spectral density of gravity gradient trace values calculated by the combined method of multiple astrologers is also significantly improved compared with the single astrologer method.
2021, 46(9): 1345-1353.
doi: 10.13203/j.whugis20210328
Abstract:
Objectives In video-based multiple object tracking, the object detection and re-identification have a strong correlation. The existing methods generally train the object detection and re-identification networks separately, which makes the tracking speed fail to meet the real-time requirements. In this paper, we integrate the detection and re-identification into one network to accelerate the tracking process, and also solve the problems of identity switching and failure tracking. Methods This paper develops a pedestrian motion model and obtains the optimal state estimation of pedestrians using center point detection. The person re-identification model with deep layer features uses the Mahalanobis distance and cosine distance to enhance the ability of person identification. And the Hungary algorithm is used for data online association, where the state estimation results become more accurate using Kalman filtering, and the unrelated lost objects are predicted by motion. Results Experiments are conducted on MOT15, MOT16 and MOT17 datasets using the proposed algorithm and other multi-pedestrian tracking algorithms, and the multiple object tracking accuracy of tracking results using our proposed algorithm is 63.5, 72.4 and 70.9, respectively, and the identity F1-measure is optimal, with the real-time rate. Conclusions The proposed algorithm can accelerate the tracking speed by network parameter sharing, and improve the recognition accuracy by person re-identification training.
2021, 46(9): 1354-1361.
doi: 10.13203/j.whugis20210323
Abstract:
Objectives The spatial layout of vectorized complex architectural drawings is widely used in 5G base station construction, smart homes, and AR/VR(augmented reality/virtual reality). Methods To solve the difficulty of identifying the spatial layout of drawings caused by various irregular drawing elements, a high?performance fusion recognition method combining raster image and vector representation is proposed. First, the rasterized representation of architectural plan drawings is used to extract the main development direction, and several enclosed spaces are constructed. Then, the vectorized representation of architectural plan drawings is used and according to the directional and adjacency characteristics of space recognition, the half?wall structure is innovatively proposed to calculate geometric position and topological relationship. The wall layout can be rebuilt as a whole with high precision according to the principle of space duality. Finally, the proposed method and the traditional methods are analyzed on the vector building plan data set of various wall layouts. Results The experimental results show that the proposed fusion dual recognition algorithm is usable and effective for various types of building models. Conclusions It has higher robustness and is less interfered by specific architectural drawing types.
2021, 46(9): 1362-1369,1385.
doi: 10.13203/j.whugis20210308
Abstract:
Objectives Traversability analysis is one of the necessary parts for rovers on extraterrestrial surface to explore unknown environment. Methods In this paper, we propose a data-driven method for traversability analysis for rovers on extraterrestrial surface. Based on the inputting multi-dimensional terrain information, the proposed method models traversability analysis as a semantic segmentation problem, which can explicitly compute a traversability map of this circumstances for a specific rover. Meanwhile, we provide an algorithm for generating training dataset for the rover. We first run the rover in the field to collect directed traversability results at certain positions, and then fulfill the undirected traversability map with these results by converting this problem into a global optimization problem, since undirected traversability map is more intuitive and straightforward for path planning. We can get the dataset for a specific rover by linking this map with the data of environment. In order to get the data more efficiently, we design an algorithm to generate virtual extraterrestrial terrains randomly and to simulate the running of a specific rover. Results We generate a set of visible multi-dimensional terrain information and perform traversing test in virtual environment, which is used for generating traversibility labels in the optimization method. Based on the terrain information and labels, we train a U-Net-like network for predicting labels according to the given multi-dimensional information, and the network performs well on test dataset with the accuracy of 93.8% on average.
2021, 46(9): 1265-1272.
doi: 10.13203/j.whugis20210241
Abstract:
We make an in-depth analysis of the concept of resilient PNT (positioning, navigation, and timing) system put forward by the United States and China, and point out their differences.Especially in view of the main requirements of the future national comprehensive PNT system, the concept of resilience is expounded that the resilient index of the PNT system can be divided into three categories: external force interference characteristic, internal technical characteristic and performance effect characteristic.At the same time, considering the differences of different levels of PNT system design, PNT flexibility is divided into three levels: user-level resilience, system-level resilience and system-level resilience.Based on the above work, it tries to help promote the theoretical research in the field of PNT system in China.
We make an in-depth analysis of the concept of resilient PNT (positioning, navigation, and timing) system put forward by the United States and China, and point out their differences.Especially in view of the main requirements of the future national comprehensive PNT system, the concept of resilience is expounded that the resilient index of the PNT system can be divided into three categories: external force interference characteristic, internal technical characteristic and performance effect characteristic.At the same time, considering the differences of different levels of PNT system design, PNT flexibility is divided into three levels: user-level resilience, system-level resilience and system-level resilience.Based on the above work, it tries to help promote the theoretical research in the field of PNT system in China.
2021, 46(9): 1273-1283, 1297.
doi: 10.13203/j.whugis20200312
Abstract:
Objectives Partial errors-in-variables(PEIV) model is a more general form of errors in variables(EIV) model. It has been widely used in the data processing of global navigation satellite system(GNSS), coordinate conversion, deformation monitoring and data fitting in the past ten years. Methods First, the development history of PEIV model is reviewed. Then, five core problems of PEIV model are summarized and analyzed, including parameter estimation algorithm, precision estimation, random model estimation, expansion algorithm and data processing application. Finally, the applications of PEIV model are outlined. Results We point out three questions for further research, including how to process big data by PEIV parameter estimation method, how to correct the bias of weighted total least squares(WTLS) adjustment by sampling method, and how to derive the formula of higher order parameter estimation covariance matrix are. Conclusions This review paper aims to further promote the development of surveying and mapping data processing and provide readers with suggestions and references.
2021, 46(9): 1284-1290.
doi: 10.13203/j.whugis20200243
Abstract:
Objectives The universal errors-in-variables(EIV) model extends the EIV model to the most general form, and the weighted total least squares(WTLS) algorithm is proposed to take into account the random errors in observation vector, observation vector coefficient matrix and parameter coefficient matrix. Methods In this paper, the universal EIV function model is expanded and the secondorder term is included into the constant term of the model. Thus, the universal EIV model is represented as Gauss-Helmert model in linear form, and the linearized total least squares(LTLS) algorithm and approximate precision estimation formula of the universal EIV model are derived. Results The estimation results of LTLS algorithm are consistent with that of WTLS algorithm of universal EIV model, which verifies the correctness and feasibility of the proposed algorithm. Conclusions When the model contains a large number of estimators, the LTLS algorithm of the universal EIV model significantly improves the computational efficiency and converges faster.
2021, 46(9): 1291-1297.
doi: 10.13203/j.whugis20190196
Abstract:
Objectives The inequality constrained partial errors-in-variables(ICPEIV) model is mainly solved by linear approximation method and nonlinear programming algorithms. The linear approximation method is computationally inefficient and the nonlinear programming algorithms are complicated because they are based on optimization theory. The nonlinear programming algorithms are impracticable to apply in surveying fields because the connections between nonlinear programming methods and classical adjustment have not been established. Methods Under the total least squares(TLS) criterion, the inequality constrained TLS problem is transformed into the quadratic programming according to the Kuhn-Tucker condition. Then an improved Jacobian iteration approach is proposed to solve the quadratic programming. Results The proposed method does not require the linearization process and has the same form with the classical least squares which is easy to code. Conclusions The numerical examples show that the proposed method is efficient in computation and concise in form and it is a beneficial extension of classical least squares theory.
2021, 46(9): 1298-1308.
doi: 10.13203/j.whugis20200182
Abstract:
Objectives For global navigation satellite system(GNSS), the ranging performance of observation would influence the system service capability. Methods Parabolic antenna with large-diameter could greatly reduce the impact of the ground equipment and the environment on signal acquisition. Based on data from parabolic antenna, observation characteristics and ranging performance of BeiDou-3 global satellite navigation system(BDS-3) are analyzed. Results The results show that, regarding the pseudorange deviation on BeiDou-3 satellite, no systematic deviation of the same magnitude, compared with BeiDou-2, is found on the signal of B1 I, B3 I, B1 C, B2 a, and B2 b. There are fluctuations on some signals. However, the correlation with elevation angle is not strong, and the average magnitude of all satellite fluctuations is0.1 m. This paper analyzes the multipath variation and noise level of carrier observation for BeiDou-3 satellite of each signal using triple-frequency combinations. The fluctuations of carrier phase triple combination are of the millimeter level, and those fluctuations do not closely correlate with the elevation or time. The root mean square of the triple-frequency carrier combination from parabolic antenna is mostly less than0.6 mm. Conclusions The results indicate that no systematic bias occurs in code and phase observation of BDS-3, and the system has a good ranging performance.
2021, 46(9): 1309-1317.
doi: 10.13203/j.whugis20190353
Abstract:
Objectives The global navigation satellite system(GNSS) coordinate time series can be used to analyze the motion of GNSS station, and the colored noise in coordinate time series affects the estimation accuracy of the motion parameters. Therefore, in order to improve the reliability of the motion analysis, it is necessary to take effective measures to eliminate the colored noise. Methods This paper proposes a method to eliminate colored noise by combining wavelet packet transform and information entropy theory. First, the simulation time series containing flicker noise is estimated to verify the estimation accuracy of the flicker noise. Then, discrete wavelet transform and wavelet packet transform are respectively carried out on the residual series of the simulation time series, and the flicker noise is eliminated by using information entropy.On this basis, the denoising effects of these two methods are compared. Finally, the coordinate time series at KMIN station is used as the measured data to further verify the denoising effect of wavelet packet coefficient information entropy. Results The experimental results show that the power spectrum and the time series of colored noise eliminated by using wavelet packet coefficient information entropy are in good agreement with the original colored noise. Conclusions The proposed method can effectively eliminate colored noise in the residual series and only retain white noise.
2021, 46(9): 1318-1327.
doi: 10.13203/j.whugis20190387
Abstract:
Objectives: Global navigation satellite system(GNSS) tomography, characterized by reconstructing the three-dimensional distribution of the atmospheric water vapor, has proved its capacity for studying the extreme weather events. The ill-conditioned problem resulting from the GNSS acquisition geometry is the critical issue of the GNSS tomography system. Algebraic reconstruction techniques, with the advantages of simple iteration and fast convergence, has been widely applied to the tropospheric tomography. However, the error allocation principle based on the intersection length of GNSS rays with each ray-voxel affects the accuracy of tomographic results. An improved adaptive algebraic reconstruction techniques are proposed to address this problem.Methods: In view of the unreasonable error allocation in the traditional algorithm, we suggest a new error allocation principle based on the variation of water vapor density(WVD) in voxels for the adaptive algorithms, in which the product of the intersection length and WVD is considered as a new principle to redistribute the difference between the GNSS slant water vapor(SWV)and the reconstructed SWV. Besides, the weight matrix model of elevation angles is introduced to the improved algorithm to optimize the tomographic results.Results: The new algorithms are tested by measured data from Xuzhou continuously operating reference stations(CORS) network and radiosonde during July2016. Experimental results reveal that the WVD derived from the adaptive algorithms performs better than that of the general algorithms in root mean squared error(RMSE), standard deviation(STD) and mean absolute error(MAE), and the RMSE is decreased by 25.91%, 15.81%, and 24.64% for adaptive algebraic reconstruction technique(AART), adaptive multiplicative algebraic reconstruction technique(AMART)and adaptive simultaneous iterative reconstruction technique(ASIRT) respectively. Under the conditions of light rain, moderate rain and heavy rain, the adaptive algorithms retrieve better water vapor profile than the traditional ones.Conclusions: Overall, the water vapor profile derived from the adaptive algorithms agrees with the radiosonde distribution better than the traditional algorithms, which reveals the advantage of the proposed method on optimizing the tomography solutions.
Objectives: Global navigation satellite system(GNSS) tomography, characterized by reconstructing the three-dimensional distribution of the atmospheric water vapor, has proved its capacity for studying the extreme weather events. The ill-conditioned problem resulting from the GNSS acquisition geometry is the critical issue of the GNSS tomography system. Algebraic reconstruction techniques, with the advantages of simple iteration and fast convergence, has been widely applied to the tropospheric tomography. However, the error allocation principle based on the intersection length of GNSS rays with each ray-voxel affects the accuracy of tomographic results. An improved adaptive algebraic reconstruction techniques are proposed to address this problem.Methods: In view of the unreasonable error allocation in the traditional algorithm, we suggest a new error allocation principle based on the variation of water vapor density(WVD) in voxels for the adaptive algorithms, in which the product of the intersection length and WVD is considered as a new principle to redistribute the difference between the GNSS slant water vapor(SWV)and the reconstructed SWV. Besides, the weight matrix model of elevation angles is introduced to the improved algorithm to optimize the tomographic results.Results: The new algorithms are tested by measured data from Xuzhou continuously operating reference stations(CORS) network and radiosonde during July2016. Experimental results reveal that the WVD derived from the adaptive algorithms performs better than that of the general algorithms in root mean squared error(RMSE), standard deviation(STD) and mean absolute error(MAE), and the RMSE is decreased by 25.91%, 15.81%, and 24.64% for adaptive algebraic reconstruction technique(AART), adaptive multiplicative algebraic reconstruction technique(AMART)and adaptive simultaneous iterative reconstruction technique(ASIRT) respectively. Under the conditions of light rain, moderate rain and heavy rain, the adaptive algorithms retrieve better water vapor profile than the traditional ones.Conclusions: Overall, the water vapor profile derived from the adaptive algorithms agrees with the radiosonde distribution better than the traditional algorithms, which reveals the advantage of the proposed method on optimizing the tomography solutions.
2021, 46(9): 1328-1335.
doi: 10.13203/j.whugis20190192
Abstract:
Objectives: In order to make the best use of seafloor topographic information contained in gravity data and improve the quality of seafloor topographic inversion results based on gravity data, the South China Sea is selected as the experimental sea area.Methods: The effects of different order seafloor in frequency domain are discussed in detail when recovering the gravity anomaly and vertical gravity gradient anomaly in different seafloor topographic environments.Results: The results of numerical analysis and experiment show that the results of one-time seafloor topographic play a major role in the contribution of gravity information. With the increase of the order of seafloor topography, the amplitude of gravity information decreases continuously. The high order gravity anomaly/vertical gravity gradient anomaly varies obviously in the sea area where the seafloor topography fluctuates sharply and its amplitude is larger, while gravity information varies slightly in the flat sea area. The vertical gravity gradient anomaly is more sensitive to high frequency part of seafloor topography than gravity anomaly.Conclusions: In the process of inversion of seafloor topography based on gravity data, if the input data source is gravity anomaly, it is suggested that the second term of seafloor topography should be taken into account. If the input data source is vertical gravity gradient anomaly, it is suggested that the third term or the fourth term of seafloor topography should be taken into account so as to make full use of the information contained in gravity data.
Objectives: In order to make the best use of seafloor topographic information contained in gravity data and improve the quality of seafloor topographic inversion results based on gravity data, the South China Sea is selected as the experimental sea area.Methods: The effects of different order seafloor in frequency domain are discussed in detail when recovering the gravity anomaly and vertical gravity gradient anomaly in different seafloor topographic environments.Results: The results of numerical analysis and experiment show that the results of one-time seafloor topographic play a major role in the contribution of gravity information. With the increase of the order of seafloor topography, the amplitude of gravity information decreases continuously. The high order gravity anomaly/vertical gravity gradient anomaly varies obviously in the sea area where the seafloor topography fluctuates sharply and its amplitude is larger, while gravity information varies slightly in the flat sea area. The vertical gravity gradient anomaly is more sensitive to high frequency part of seafloor topography than gravity anomaly.Conclusions: In the process of inversion of seafloor topography based on gravity data, if the input data source is gravity anomaly, it is suggested that the second term of seafloor topography should be taken into account. If the input data source is vertical gravity gradient anomaly, it is suggested that the third term or the fourth term of seafloor topography should be taken into account so as to make full use of the information contained in gravity data.
2021, 46(9): 1370-1377.
doi: 10.13203/j.whugis20200507
Abstract:
Objectives The spatial delimitation of urban area is the foundation for monitoring the urbanization level and determining city scale from the perspective of geographical space. Methods Based on the requirements of urban area defined in Document No. 51 of the State Council, this paper clarifies the connotations of urban area with further understandings and proposes a process for delimiting urban area in geographical space by connecting map polygons of governmental residents and neighborhood committee, which can reduce the subjectivity and fuzziness in the spatialization of process of urban area. Results This paper develops the main idea and spatial delimitation about urban area and takes Wuhan as an example to illustrate the details about spatial delimitation of urban areas. Conclusions This paper mainly discusses how to unify urban delineation under the current technical conditions, and does not mention how to use other auxiliary data, such as the application of multi-source big data in urban delineation, because of the unbalanced development of big data infrastructure in different cities. But it does not mean that the application of population big data and urban big data is not important. On the contrary, the application of big data in the work of urban areadelimitation is the next research focus and the future development trend.
2021, 46(9): 1378-1385.
doi: 10.13203/j.whugis20190274
Abstract:
Objectives It is usually necessary to eliminate non-combat targets for conducting marine combat situation analysis. Because non-combat targets have little impacts on situational analysis and the retained combat-correlated targets have high reference value. Most of the existing behavior mining methods, which are based on the idea of clustering, are complicated and ineffective in cleaning of non-combat targets. Methods Therefore, this paper defines multi-dimension record similarity(MDRS) and proposes a real-time cleaning method for marine non-combat targets(MNCT-RTCM). The proposed method realizes the real-time cleaning of non-combat targets by similar duplicate record detection of multi-dimensional track data. Results The experiments are carried out on simulated military scenarios, and the results are evaluated and analyzed by calculating the recall rate and the precision rate. Conclusions The results show that the MNCT-RTCM method can effectively detect non-combat targets and achieve real-time cleaning of noncombat targets in the marine combat environment.
2021, 46(9): 1386-1394.
doi: 10.13203/j.whugis20190204
Abstract:
Objectives Morphology of polar sea ice changes continuously under the influences of external driving forces, e.g., wind, flow and waves. Observations and change regularity analysis on morphology of polar sea ice not only is helpful for the optimization of sea ice thermodynamics numerical simulation and the inversion of sea ice thickness based on roughness parameters, but also provide distinct importance for understanding the response of polar sea ice morphology to climate change. Methods Based on the undulations profiles of sea ice bottom in Northwestern Weddell Sea measured by the helicopter-borne electromagneticinduction bird(EM-bird) system during the winter of 2006, this paper establishes a nonlinear statistical optimal model with the cutoff draft as the identified variable to separate the ridge keels and local undulations on sea ice bottom. Then, the keel morphology parameters and correlation between the keel draft and frequency are statistically analyzed. Finally, a new parameter T is hatched to assess the correlation between the ridge sail height and keel draft. Results The results suggest that the optimal cutoff draft is 3.8 m and the keel spacing is the main factor affecting the ridging intensity. Despite marked sea ice deformation in different regions in Northwestern Weddell Sea, morphology changes of the keel are not evident. There is a good log correlation between draft and frequency of the keel, which vividly describes morphology and distribution of the keel. The newly hatched parameter T has a significant linear correlation with keel depth(r=0.93). Conclusions The newly proposed modeling method can separate the keel from sea ice bottom undulations more accurately, which provides a new theoretical reference for morphological correlation analysis between surface and bottom of sea ice, and the inversion of sea ice thickness and bottom draft from its surface height.
2021, 46(9): 1395-1403.
doi: 10.13203/j.whugis20200402
Abstract:
Objectives The airborne laser bathymetry system transmitted laser pulse through the complex channel which consisted of atmosphere, air-sea interface and water channel. The signal power decreased exponential with the increment of the water depth, and the pulse waveform was stretched and distorted at the same time. These two types of transmission characteristics would limit the maximum detection depth and the measurement accuracy of depth, respectively. Methods The purpose of the proposed algorithm is to improve the measurement accuracy of laser bathymetry system on the premise of ensuring the maximum detection depth performance. First, the water quality and Monte Carlo simulation method are used to study the different depth of echo signal characteristics. It reveals the propagation properties of laser signal and establishes a new parameter design method for laser bathymetry system, which can meet the requirment of international airborne laser depth bathymetry system. Then, the waveform distortion of echo signals at different depths are simulated under the specific system parameters. The adaptive matching filter algorithm is designed to improve the measurement precision. Results The simulation results revealed that the designed system parameters and the adaptive matched filtering algorithm can achieve the performance with the maximum detection depth of 50 m with measurement accuracy of 166 mm in the daytime and with the maximum detection depth of 70 m with measurement accuracy of 172 mm at night for laser bathymetry system. Conclusions The results show that the adaptive matched filtering algorithm can improve the measurement accuracy and serve the marine surveying and mapping of China.
2021, 46(9): 1404-1411.
doi: 10.13203/j.whugis20190258
Abstract:
Objectives According to the spatial attitude characteristics of the indoor walls, a point cloud vector tracking algorithm for automatically drawing the indoor plan was proposed. Methods Firstly, the indoor wall point cloud with a certain thickness was intercepted and projected onto the horizontal plane, and square grids were established on the projection plane. The plane points after projection were segmented and managed, and the point cloud in every grid was clustered by the eight neighborhood algorithm. Then the grid center of gravity method was used to generate sparse points, which were tracked and sorted according to the distance between points in the neighborhood and the angle of connection vector. The vector sequence of tracking direction was established, and the point cloud segmentation of different walls was completed combined with mutation of angle between adjacent values in the vector sequence. Finally, the least squares algorithm was used to fit the line of the same wall point cloud, and the intersection point of the adjacent wall line was obtained. Results The plane projection line segment of the house wall and its spatial connection relationship were established. The wall projection line segment was output in sequence. The drawing of the house plan was completed and the DXF format data exchange file was exported. Conclusions The proposed algorithm was verified by the analysis of indoor scanning data of a residential building. The results show that the proposed algorithm can accurately and quickly analyzed and processed the indoor laser scanning point cloud, and completed the drawing of interior plan.
2021, 46(9): 1412-1422.
doi: 10.13203/j.whugis20200031
Abstract:
Objectives With the development of urbanization, the urban heat island has attracted a lot attention because of its adverse effects on health of residents and urban sustainability. Local climate zone(LCZ)provides a novel framework of land-cover classification for the study of urban heat island. In this research, we investigated the impact of urban morphology on the seasonal variations of land surface temperature(LST) in different LCZs in Shenzhen, which is a core city of Guangdong-Hong Kong-Macao Greater Bay Area and a pilot demonstration zone with Chinese characteristics. Methods Multi-view high-resolution remote sensing images were employed to map the LCZs in the neighborhood scale. Additionally, the Pearson partial correlation and stepwise regression models were applied to analyze the influence of urban morphology on the seasonal characteristics of LST in different LCZs. Results LST varies greatly among seasons in the different LCZs. In addition, urban morphology plays important roles in the variations of LST among LCZs. And the two-dimensional metrics correlate more strongly with LST than the three-dimensional metrics in the LCZ1-LCZ3. The effects of the three-dimensional metrics in the LCZ4-LCZ6 and LCZ8 are more pronounced than that in the LCZ1-LCZ3. Conclusions Urban morphological variables of LCZs in different seasons have different effects on the surface temperature. Compared with the three-dimensional metrics, the two-dimensional metrics have a stronger correlation with the surface temperature. Moreover, the influence of three-dimensional metrics is enhanced in non-compact construction.
Articles online first have been peer-reviewed and accepted, which are not yet assigned to volumes /issues, but are citable by Digital Object Identifier (DOI).
Display Method:
, Available online ,
doi: 10.13203/j.whugis20200702
Abstract:
Objectives: With the enrichment of heterologous image acquisition methods, heterologous image is widely used in many fields, such as change detection, target recognition and disaster assessment. However, matching is the premise of heterologous image fusion application. Simultaneously, due to the differences in imaging mechanisms of different sensors, heterologous images are more sensitive to differences in illumination, contrast, and nonlinear radiation distortion. Therefore, heterologous image matching still faces some problems. There are two main problems:(1) heterologous image feature detection is difficult due to the difference of imaging mechanism, which indirectly increases the difficulty of matching; (2) heterologous image has significant differences in illumination, contrast and nonlinear radiation distortion, which reduces the robustness of feature description and easily leads to matching failure directly. This paper proposes a new matching method considering anisotropic weighted moment and the histogram of the absolute phase orientation. Methods: a new method of heterologous image matching considering anisotropic weighted moment and the histogram of the absolute phase orientation is proposed. Firstly, anisotropic filtering is used for image nonlinear diffusion. Based on this, the maximum moment and minimum moment of image phase consistency are calculated, and the anisotropic weighted moment equation is constructed to obtain the anisotropic weighted moment map. Then, the phase consistency model is extended to establish the absolute phase consistency orientation gradient. Combined with the log polar description template, a histogram of absolute phase consistency gradients (HAPCG) is established. Finally, the Euclidean distance is used as the matching measure for corresponding point recognition. Result: Several groups of heterologous remote sensing images with illumination, contrast, and nonlinear radiation distortion are used as data sources of experiments with SIFT, PSO-SIFT, LGHD and RIFT algorithms, respectively. The results show that our proposed HAPCG algorithm is superior to SIFT, PSO-SIFT and LGHD in the comprehensive matching performance of heterologous remote sensing images, and the average matching number of corresponding points is increased by about 2 times, and the RMSE is less than 2 pixels. When compared with the RIFT algorithm, the HAPCG algorithm can achieve higher matching accuracy in the case of similar corresponding points and can realize the robust matching of heterologous remote sensing images. Conclusions: The proposed HAPCG algorithm can achieve robust matching performance in heterologous remote sensing images and provide stable data support for multi-source image data fusion and other tasks. At the same time, it should be noted that the threshold setting of feature point extraction can be more flexible, with the value between 0 and 1. For example, when more feature points need to be obtained, the threshold can be reduced; on the contrary, when there are not too many feature points, the threshold can be increased accordingly. In general, set the threshold to be 0.4 is enough for the matching requirements in most cases.
Objectives: With the enrichment of heterologous image acquisition methods, heterologous image is widely used in many fields, such as change detection, target recognition and disaster assessment. However, matching is the premise of heterologous image fusion application. Simultaneously, due to the differences in imaging mechanisms of different sensors, heterologous images are more sensitive to differences in illumination, contrast, and nonlinear radiation distortion. Therefore, heterologous image matching still faces some problems. There are two main problems:(1) heterologous image feature detection is difficult due to the difference of imaging mechanism, which indirectly increases the difficulty of matching; (2) heterologous image has significant differences in illumination, contrast and nonlinear radiation distortion, which reduces the robustness of feature description and easily leads to matching failure directly. This paper proposes a new matching method considering anisotropic weighted moment and the histogram of the absolute phase orientation. Methods: a new method of heterologous image matching considering anisotropic weighted moment and the histogram of the absolute phase orientation is proposed. Firstly, anisotropic filtering is used for image nonlinear diffusion. Based on this, the maximum moment and minimum moment of image phase consistency are calculated, and the anisotropic weighted moment equation is constructed to obtain the anisotropic weighted moment map. Then, the phase consistency model is extended to establish the absolute phase consistency orientation gradient. Combined with the log polar description template, a histogram of absolute phase consistency gradients (HAPCG) is established. Finally, the Euclidean distance is used as the matching measure for corresponding point recognition. Result: Several groups of heterologous remote sensing images with illumination, contrast, and nonlinear radiation distortion are used as data sources of experiments with SIFT, PSO-SIFT, LGHD and RIFT algorithms, respectively. The results show that our proposed HAPCG algorithm is superior to SIFT, PSO-SIFT and LGHD in the comprehensive matching performance of heterologous remote sensing images, and the average matching number of corresponding points is increased by about 2 times, and the RMSE is less than 2 pixels. When compared with the RIFT algorithm, the HAPCG algorithm can achieve higher matching accuracy in the case of similar corresponding points and can realize the robust matching of heterologous remote sensing images. Conclusions: The proposed HAPCG algorithm can achieve robust matching performance in heterologous remote sensing images and provide stable data support for multi-source image data fusion and other tasks. At the same time, it should be noted that the threshold setting of feature point extraction can be more flexible, with the value between 0 and 1. For example, when more feature points need to be obtained, the threshold can be reduced; on the contrary, when there are not too many feature points, the threshold can be increased accordingly. In general, set the threshold to be 0.4 is enough for the matching requirements in most cases.
, Available online ,
doi: 10.13203/j.whugis20200187
Abstract:
As BeiDou Satellite Navigation System-3 (BDS-3) starts to provide service for global users, it is possible to get global-coverage and all-time positioning service for space application using BDS alone. The performance of space-borne BDS positioning is thoroughly analyzed with the in-orbit data of GNSS Occultation Sounder (GNOS) aboard FengYun-3D (FY-3D) satellite. Firstly, the visibility and position dilution of precision (PDOP) of BDS satellites in different LEO orbits are calculated based on real BDS ephemeris, and the orbit and clock error of broadcast ephemeris and Signal-In-Space Range Error (SISRE) are studied. The results show that the global coverage usability from ground to 2000 Km height orbit has already been 100%. The mean visible BDS satellite number across the world is 50% larger than that of GPS. For BDS broadcast ephemeris, the 3-D orbit error is 1.5m and clock error is 2.4ns. SISRE is about 0.79m and the clock accuracy of BDS-3 has reached the same level of GPS. Second, the real visible satellite number, signal strength, precision of pseudo-range and position accuracy are verified with the measurement data of GNOS. The code biases of BDS-2 satellites are focused on. The in-orbit data results show that GNOS in FY-3D could get 100% positioning with BDS-2 signals in service areas, and 3-D position accuracy is 5.53m. All BDS-2 satellites including Geosynchronous Earth Orbit (GEO), Inclined Geosynchronous Orbit and Medium Earth Orbit satellites have code biases. When the elevation is less than 40 degree, the code bias of GEO is firstly measured directly. The total electron content above 836Km LEO orbit is measured using BDS dual-frequency measurements, which can cause relative pseudo-range delay of about 0.6m. The research in this paper is of great significance to the space-based application of BDS and lays the foundation for the design of space-based BDS receivers.
As BeiDou Satellite Navigation System-3 (BDS-3) starts to provide service for global users, it is possible to get global-coverage and all-time positioning service for space application using BDS alone. The performance of space-borne BDS positioning is thoroughly analyzed with the in-orbit data of GNSS Occultation Sounder (GNOS) aboard FengYun-3D (FY-3D) satellite. Firstly, the visibility and position dilution of precision (PDOP) of BDS satellites in different LEO orbits are calculated based on real BDS ephemeris, and the orbit and clock error of broadcast ephemeris and Signal-In-Space Range Error (SISRE) are studied. The results show that the global coverage usability from ground to 2000 Km height orbit has already been 100%. The mean visible BDS satellite number across the world is 50% larger than that of GPS. For BDS broadcast ephemeris, the 3-D orbit error is 1.5m and clock error is 2.4ns. SISRE is about 0.79m and the clock accuracy of BDS-3 has reached the same level of GPS. Second, the real visible satellite number, signal strength, precision of pseudo-range and position accuracy are verified with the measurement data of GNOS. The code biases of BDS-2 satellites are focused on. The in-orbit data results show that GNOS in FY-3D could get 100% positioning with BDS-2 signals in service areas, and 3-D position accuracy is 5.53m. All BDS-2 satellites including Geosynchronous Earth Orbit (GEO), Inclined Geosynchronous Orbit and Medium Earth Orbit satellites have code biases. When the elevation is less than 40 degree, the code bias of GEO is firstly measured directly. The total electron content above 836Km LEO orbit is measured using BDS dual-frequency measurements, which can cause relative pseudo-range delay of about 0.6m. The research in this paper is of great significance to the space-based application of BDS and lays the foundation for the design of space-based BDS receivers.
, Available online ,
doi: 10.13203/j.whugis20200596
Abstract:
Objectives: Time-series offset tracking is a critical technique for landslide monitoring with optical images, which has been applied worldwide successfully in the recent years. The continued monitoring of landslide can provide early warning and emergency informationfor the nature hazard prevention. However, the resolution in the temporal domain is restricted by the single-platform datasets, especially in the case of bad weather. To solve this issue, this paper used the multi-source optical images, including Sentinel-2 and Landsat 8, to generate the long time-series deformation of landslides. The high resolution of deformation in the temporal domain can help us know more about the development process of the landslide. Methods: The method is executed based on the idea of small baseline subset (SBAS) in the interferometric synthetic aperture radar (InSAR) technique, aiming to reduce the influence of the temporal baseline and noise on the signal noise ratio (SNR). Meanwhile, to obtain the time-series deformation of the multi-platform optical images, we used a weighted singular value decomposition (SVD) method to obtain the time-series deformation of the multi-platform optical images. In this paper, a collection of 11 Sentinel-2 and 16 Landsat 8 datasets are used to generate two-dimensional horizontal time-series deformations of Baige landslide from 2014-11 to 2018-09. First, we calculated the time-series offset tracking of the single platform, i.e., Sentinel-2 and Landsat 8, and the dual-platform by considering the selection of the image pair, search window size, step length, and the threshold of SNR respectively. And a time-series deformation of 27 images are obtained with offset tracking technique of the dual-platform. Second, the accuracy of the results for the single-platform and the dual-platforms is evaluated by two commonly used methods:stable area statistics with a priori knowledge and cross-validation of independent datasets. Then, the developmental stages of Baige landslide is divided based on the long time-series deformation of the dual-platform, the creep deformation theory, and the tangential angle of the time-series curve.Finally, the relationship between precipitation and deformation is discussedto analyze the causes of the landslide. Results: The results show that:①the time-series deformation of the dual-platform can obtain more details compared the results of the single platform. ②the deformation accuracy of the dual-platform is higher than that of the single platform. For example, the improvements of the dual-platform in the east-west direction (°E/°W) and the north-south (°N/°S) direction are 3.02% and 5.37% respectively compared to the Sentinel-2. In the Landsat 8 case, the results are 3.61% and 0.40% respectively. During the time span, the maximum accumulated deformation and the deformation rate reach to 42.9 m and 9.06 m/a respectivley. In addition, the deformation accuracy of the Sentinel-2 is higher than that of the Landsat8inthe similar situation of observations.③the pre-event of Baige landslide can be divided into the initial stage, the uniform stage, the initial acceleration, and the medium-term acceleration. The corresponding average deformation ratesof each stage are 7.05 mm/d, 10.27 mm/d, 26.80 mm/d, and 65.79 mm/d respectively. The landslide is going through the medium-term acceleration stage until the 5, Feb. 2018.④the correlation between the deformation and precipitation is 0.88, which indicates that heavy rainfall is one of the major causesfor the Baige landslide. Conclusions: Although the deformation resolution of the multi-platform optical image is higher than that of the single platform, the fusion accuracy is limited by the accuracy of different individual platforms caused by the sensitivity for the topography. Hence, in the future work, we will focus on the combined estimation of the multi-platform based on the variance component estimation.
Objectives: Time-series offset tracking is a critical technique for landslide monitoring with optical images, which has been applied worldwide successfully in the recent years. The continued monitoring of landslide can provide early warning and emergency informationfor the nature hazard prevention. However, the resolution in the temporal domain is restricted by the single-platform datasets, especially in the case of bad weather. To solve this issue, this paper used the multi-source optical images, including Sentinel-2 and Landsat 8, to generate the long time-series deformation of landslides. The high resolution of deformation in the temporal domain can help us know more about the development process of the landslide. Methods: The method is executed based on the idea of small baseline subset (SBAS) in the interferometric synthetic aperture radar (InSAR) technique, aiming to reduce the influence of the temporal baseline and noise on the signal noise ratio (SNR). Meanwhile, to obtain the time-series deformation of the multi-platform optical images, we used a weighted singular value decomposition (SVD) method to obtain the time-series deformation of the multi-platform optical images. In this paper, a collection of 11 Sentinel-2 and 16 Landsat 8 datasets are used to generate two-dimensional horizontal time-series deformations of Baige landslide from 2014-11 to 2018-09. First, we calculated the time-series offset tracking of the single platform, i.e., Sentinel-2 and Landsat 8, and the dual-platform by considering the selection of the image pair, search window size, step length, and the threshold of SNR respectively. And a time-series deformation of 27 images are obtained with offset tracking technique of the dual-platform. Second, the accuracy of the results for the single-platform and the dual-platforms is evaluated by two commonly used methods:stable area statistics with a priori knowledge and cross-validation of independent datasets. Then, the developmental stages of Baige landslide is divided based on the long time-series deformation of the dual-platform, the creep deformation theory, and the tangential angle of the time-series curve.Finally, the relationship between precipitation and deformation is discussedto analyze the causes of the landslide. Results: The results show that:①the time-series deformation of the dual-platform can obtain more details compared the results of the single platform. ②the deformation accuracy of the dual-platform is higher than that of the single platform. For example, the improvements of the dual-platform in the east-west direction (°E/°W) and the north-south (°N/°S) direction are 3.02% and 5.37% respectively compared to the Sentinel-2. In the Landsat 8 case, the results are 3.61% and 0.40% respectively. During the time span, the maximum accumulated deformation and the deformation rate reach to 42.9 m and 9.06 m/a respectivley. In addition, the deformation accuracy of the Sentinel-2 is higher than that of the Landsat8inthe similar situation of observations.③the pre-event of Baige landslide can be divided into the initial stage, the uniform stage, the initial acceleration, and the medium-term acceleration. The corresponding average deformation ratesof each stage are 7.05 mm/d, 10.27 mm/d, 26.80 mm/d, and 65.79 mm/d respectively. The landslide is going through the medium-term acceleration stage until the 5, Feb. 2018.④the correlation between the deformation and precipitation is 0.88, which indicates that heavy rainfall is one of the major causesfor the Baige landslide. Conclusions: Although the deformation resolution of the multi-platform optical image is higher than that of the single platform, the fusion accuracy is limited by the accuracy of different individual platforms caused by the sensitivity for the topography. Hence, in the future work, we will focus on the combined estimation of the multi-platform based on the variance component estimation.
, Available online ,
doi: 10.13203/j.whugis20200557
Abstract:
Penguins are representative organisms in Antarctica. Monitoring the population and distribution of penguins is of great significance to the study of environmental changes in Antarctica. In the past studies, due to the limitation of medium-high resolution images, the accuracy of penguin recognition is difficult to be further improved and the existing time series analysis of penguin distribution and population is based on indirect identification method. The Penguin Island in East Antarctica was selected as the study area where the Chinese Antarctic Scientific Research Team used remote sensing UAV to make aerial observations in January 2017, January 2018 and December 2019, and obtained centimeter-level resolution images. Based on object-oriented classification, the shadow pixels of penguins in 3 images were extracted, the penguin habitats were marked, and the penguin population was calculated. The overall accuracy is 91%. The experimental results showed the dynamic changes of penguin population of which the distribution of penguin habitat was relatively fixed, but the number of penguins fluctuated with 1,068 pairs, 1,003 pairs and 1,081 pairs in 3 images respectively.
Penguins are representative organisms in Antarctica. Monitoring the population and distribution of penguins is of great significance to the study of environmental changes in Antarctica. In the past studies, due to the limitation of medium-high resolution images, the accuracy of penguin recognition is difficult to be further improved and the existing time series analysis of penguin distribution and population is based on indirect identification method. The Penguin Island in East Antarctica was selected as the study area where the Chinese Antarctic Scientific Research Team used remote sensing UAV to make aerial observations in January 2017, January 2018 and December 2019, and obtained centimeter-level resolution images. Based on object-oriented classification, the shadow pixels of penguins in 3 images were extracted, the penguin habitats were marked, and the penguin population was calculated. The overall accuracy is 91%. The experimental results showed the dynamic changes of penguin population of which the distribution of penguin habitat was relatively fixed, but the number of penguins fluctuated with 1,068 pairs, 1,003 pairs and 1,081 pairs in 3 images respectively.
, Available online ,
doi: 10.13203/j.whugis20200711
Abstract:
The cutting section analysis of 3D geological model and excavation simulation of underground engineering are important functions of 3D geological modeling and visualization analysis software. The traditional explicit cutting method relies on the pre-establishment of a three-dimensional geological body model, and the topological problem of the model is likely to lead to the phenomenon of section cavity or triangle intersection. The cutting accuracy and maximum cutting depth are limited by the modeling accuracy and depth of the existing geological body model. This paper introduces implicit functions to express multiple types of cutting planes. With a reasonable sequence setting of cutting operations, and based on the explicit calculation and attribute definition methods of the cutting section grids of the moving tetrahedron, a method that does not require any pre-establishment of a complete three-dimensional geological body model is proposed. A new implicit cutting method for quickly generating geological section from geological survey source data or geotechnical survey borehole data. A cutting analysis test was carried out for 1:50,000 regional geological survey data and geotechnical survey borehole data. The results show that the proposed method can effectively avoid the instability problem of the cutting algorithm caused by incorrect topology of geological body model. Moreover, the proposed method supports more types of cutting planes and surfaces, controllable cutting depth and precision of cutting plane expression, which provides a new idea and reference for the spatial analysis method of 3D geological model.
The cutting section analysis of 3D geological model and excavation simulation of underground engineering are important functions of 3D geological modeling and visualization analysis software. The traditional explicit cutting method relies on the pre-establishment of a three-dimensional geological body model, and the topological problem of the model is likely to lead to the phenomenon of section cavity or triangle intersection. The cutting accuracy and maximum cutting depth are limited by the modeling accuracy and depth of the existing geological body model. This paper introduces implicit functions to express multiple types of cutting planes. With a reasonable sequence setting of cutting operations, and based on the explicit calculation and attribute definition methods of the cutting section grids of the moving tetrahedron, a method that does not require any pre-establishment of a complete three-dimensional geological body model is proposed. A new implicit cutting method for quickly generating geological section from geological survey source data or geotechnical survey borehole data. A cutting analysis test was carried out for 1:50,000 regional geological survey data and geotechnical survey borehole data. The results show that the proposed method can effectively avoid the instability problem of the cutting algorithm caused by incorrect topology of geological body model. Moreover, the proposed method supports more types of cutting planes and surfaces, controllable cutting depth and precision of cutting plane expression, which provides a new idea and reference for the spatial analysis method of 3D geological model.
, Available online ,
doi: 10.13203/j.whugis20200221
Abstract:
The development of ICT (Information and Communications Technology) provides new challeng⁃ es and opportunities for modern cartography. Totally different from the traditional cartography in the fields of the research methods and themes, modern cartography trends to digital cyberspace from paper map, and cartographic theories are urged to be improved. However, the understanding of the theories about carto⁃ graphic virtual space have great differences within the professional domain, and the internal contradictions and transboundary invasion make map gradually become an appurtenance of Internet products. To learning from certain domains to analyse and handle them, challenges have prompted cartographic scholars to ex⁃ plore new fields. The development of "digits and culture" has pushed the game to the forefront of hightech. Games are often geographically significant, and maps of game are a combination of ICT and maps. Based on the cyberspace in game map with instances as the research subject, the paper explores the charac⁃ teristics and essence of game map and its implications to the real physical world by the dimensionalities, in⁃ teractivity, narrativity, exploration and cultural dissemination of game maps to discover the extension of cartography, which would replenish and perfect modern cartographic theory and promote its development.
The development of ICT (Information and Communications Technology) provides new challeng⁃ es and opportunities for modern cartography. Totally different from the traditional cartography in the fields of the research methods and themes, modern cartography trends to digital cyberspace from paper map, and cartographic theories are urged to be improved. However, the understanding of the theories about carto⁃ graphic virtual space have great differences within the professional domain, and the internal contradictions and transboundary invasion make map gradually become an appurtenance of Internet products. To learning from certain domains to analyse and handle them, challenges have prompted cartographic scholars to ex⁃ plore new fields. The development of "digits and culture" has pushed the game to the forefront of hightech. Games are often geographically significant, and maps of game are a combination of ICT and maps. Based on the cyberspace in game map with instances as the research subject, the paper explores the charac⁃ teristics and essence of game map and its implications to the real physical world by the dimensionalities, in⁃ teractivity, narrativity, exploration and cultural dissemination of game maps to discover the extension of cartography, which would replenish and perfect modern cartographic theory and promote its development.
, Available online ,
doi: 10.13203/j.whugis20200172
Abstract:
Aiming at the mixed observation data of multiple distribution forms, a P-model distributed mixed model is established. Considering that the mixed number in the model belongs to incomplete data, the EM algorithm is introduced to estimate the parameters of the mixed model and the P-model mixed model parameters are derived in detail The estimated iteration formula and the corresponding iteration steps are given. Finally, the mixed Gaussian distribution data, Laplacian distribution and Gaussian distribution mixed data, and the residual data of measured GPS observations are used to verify the correctness and adaptability of the formula in this paper. The results of the calculation examples show that, compared with the single probability distribution, the P-parameter hybrid model can accurately reflect the actual situation of the data distribution, and the model parameters estimated by the EM algorithm have higher accuracy.
Aiming at the mixed observation data of multiple distribution forms, a P-model distributed mixed model is established. Considering that the mixed number in the model belongs to incomplete data, the EM algorithm is introduced to estimate the parameters of the mixed model and the P-model mixed model parameters are derived in detail The estimated iteration formula and the corresponding iteration steps are given. Finally, the mixed Gaussian distribution data, Laplacian distribution and Gaussian distribution mixed data, and the residual data of measured GPS observations are used to verify the correctness and adaptability of the formula in this paper. The results of the calculation examples show that, compared with the single probability distribution, the P-parameter hybrid model can accurately reflect the actual situation of the data distribution, and the model parameters estimated by the EM algorithm have higher accuracy.
, Available online ,
doi: 10.13203/j.whugis20200028
Abstract:
The tensor invariant method for determining the Earth's gravity field with satellite gravitational gradient data is studied. The tensor invariant method does not depend on the acquisition of the spatial orientation information of the gradiometer, so it has a special advantage over the traditional methods in determining the earth gravity field. Numerical analyses are made for the linearization of tensor invariant observation equation, the processing of non-full tensor observation data, and the accuracy of gravity field recovery with the gradient data containing white noise. The results show that the tensor invariant solution realizes the combine processing of the different tensor components. The tailored linearization effectively achieves the linearization of the tensor invariant observation model while the accuracy of gravity field model determined using tensor invariants is significantly higher than that of the single tensor component solution. Also, the choice of initial reference model exerts minor influence on the total iterative process. Due to the non-full tensor gradient observations, it is proposed to replace the low precision gradient component by using the spherical harmonic synthesis of a priori gravity field model, which will not change the convergence and the accuracy of the solution even if the white noise exists in the observations because of the nadir-pointing characteristics of the gradiometer..
The tensor invariant method for determining the Earth's gravity field with satellite gravitational gradient data is studied. The tensor invariant method does not depend on the acquisition of the spatial orientation information of the gradiometer, so it has a special advantage over the traditional methods in determining the earth gravity field. Numerical analyses are made for the linearization of tensor invariant observation equation, the processing of non-full tensor observation data, and the accuracy of gravity field recovery with the gradient data containing white noise. The results show that the tensor invariant solution realizes the combine processing of the different tensor components. The tailored linearization effectively achieves the linearization of the tensor invariant observation model while the accuracy of gravity field model determined using tensor invariants is significantly higher than that of the single tensor component solution. Also, the choice of initial reference model exerts minor influence on the total iterative process. Due to the non-full tensor gradient observations, it is proposed to replace the low precision gradient component by using the spherical harmonic synthesis of a priori gravity field model, which will not change the convergence and the accuracy of the solution even if the white noise exists in the observations because of the nadir-pointing characteristics of the gradiometer..
, Available online ,
doi: 10.13203/j.whugis20200004
Abstract:
In order to analyze the differences between the geodetic, geocentric, rectifying, authalic, conformal latitudes and reduced latitude, symbolic expressions of difference between the auxiliary and reduced latitudes were derived by the computer algebra system and expressed by several power series in terms of the first eccentricity e and the third flattening rate n. Theoretical and numerical analyses show that the symbolic expressions in terms of the third flattening n are more concise than those of the first eccentricity e and the extrema of the differences between the auxiliary latitudes and the reduced latitude are all near π/4. Particularly, the absolute minimum and maximum values of the differences to the reduced latitude are generated from the authalic and geodetic latitudes, respectively, the new formulas presented in this study are applicable to any Earth ellipsoid and, to some extent, they enriched the analytical theory of the various commonly used latitudes.
In order to analyze the differences between the geodetic, geocentric, rectifying, authalic, conformal latitudes and reduced latitude, symbolic expressions of difference between the auxiliary and reduced latitudes were derived by the computer algebra system and expressed by several power series in terms of the first eccentricity e and the third flattening rate n. Theoretical and numerical analyses show that the symbolic expressions in terms of the third flattening n are more concise than those of the first eccentricity e and the extrema of the differences between the auxiliary latitudes and the reduced latitude are all near π/4. Particularly, the absolute minimum and maximum values of the differences to the reduced latitude are generated from the authalic and geodetic latitudes, respectively, the new formulas presented in this study are applicable to any Earth ellipsoid and, to some extent, they enriched the analytical theory of the various commonly used latitudes.
, Available online ,
doi: 10.13203/j.whugis20200007
Abstract:
The priori studies have revealed that the Chandler and annual wobbles (CW and AW) in polar motion (PM) are unstable over time. In order to further enhance the prediction accuracy of polar motion (PM), a harmonic model for the linear trend, CW and AW with time-varying coefficients is developed in this paper. The developed model takes into account the variations in both amplitudes and phases of the CW and AW. The PM predictions are calculated by means of two schemes to valid the effectives of the developed model:the least-squares extrapolation of the variable harmonic model for the linear trend, time-varying CW and AW in combination with the autoregressive technique, denoted as VLS+AR, and the combination of the least-squares extrapolation of the invariable harmonic model and AR filtering, referred to LS+AR. The results show that the accuracies of the PM predictions obtained by the VLS+AR are better than those generated by the LS+AR, especially for medium- and long-term predictions.
The priori studies have revealed that the Chandler and annual wobbles (CW and AW) in polar motion (PM) are unstable over time. In order to further enhance the prediction accuracy of polar motion (PM), a harmonic model for the linear trend, CW and AW with time-varying coefficients is developed in this paper. The developed model takes into account the variations in both amplitudes and phases of the CW and AW. The PM predictions are calculated by means of two schemes to valid the effectives of the developed model:the least-squares extrapolation of the variable harmonic model for the linear trend, time-varying CW and AW in combination with the autoregressive technique, denoted as VLS+AR, and the combination of the least-squares extrapolation of the invariable harmonic model and AR filtering, referred to LS+AR. The results show that the accuracies of the PM predictions obtained by the VLS+AR are better than those generated by the LS+AR, especially for medium- and long-term predictions.
, Available online ,
doi: 10.13203/j.whugis20200016
Abstract:
The slope filtering algorithm of point cloud is simple and easy to implement. In order to further improve the adaptability of slope filtering algorithm, a multi-scale adaptive slope filtering algorithm of point cloud is proposed. Firstly, a pseudo-grid is introduced to divide the point cloud on the basis of data pre-processing. Then, the slope angle of grid points is calculated one by one using distance weighting, and the threshold value is determined adaptively by combining k-means clustering and normal distribution. Finally, the multi-scale strategy is used to reduce the grid size step by step to achieve fine filtering of point cloud data. The algorithm was verified using two point cloud data sets with different densities. Compared with the two slope filtering algorithms and the classical algorithms of ISPRS, the experiment shows that this algorithm has better overall fitting results and higher stability, which is suitable for point cloud in different scenes.
The slope filtering algorithm of point cloud is simple and easy to implement. In order to further improve the adaptability of slope filtering algorithm, a multi-scale adaptive slope filtering algorithm of point cloud is proposed. Firstly, a pseudo-grid is introduced to divide the point cloud on the basis of data pre-processing. Then, the slope angle of grid points is calculated one by one using distance weighting, and the threshold value is determined adaptively by combining k-means clustering and normal distribution. Finally, the multi-scale strategy is used to reduce the grid size step by step to achieve fine filtering of point cloud data. The algorithm was verified using two point cloud data sets with different densities. Compared with the two slope filtering algorithms and the classical algorithms of ISPRS, the experiment shows that this algorithm has better overall fitting results and higher stability, which is suitable for point cloud in different scenes.
, Available online ,
doi: 10.13203/j.whugis20200226
Abstract:
Based on the method of time differenced carrier phase, the mathematical model of multi-GNSS velocity estimation is introduced and the error sources are analyzed. Using the real data, the accuracy and performance of velocity determination at each GNSS frequency point and multi-GNSS are tested and analyzed. The results show that the accuracy of velocity determination at different frequency is different, the velocity estimation at B1I, B1C, B3I frequency points in BDS and E1, E5a, E6, E5b, E5 frequency points in Galileo have the same precision, with the horizontal direction better than 1.5 mm/s and the vertical direction better than 3 mm/s; the velocity estimation accuracy at B2I frequency in BDS is the same as that at L1, L2 and L5 frequency points in GPS with the horizontal accuracy of 1.5-2 mm/s and the vertical accuracy of 3-4 mm/s; the velocity estimation accuracy at G1 and G2 frequency in GLONASS is the worst, with 3-4 mm/s in horizontal direction and 5-5.5 mm/s in vertical direction. The accuracy of double frequency ionospheric free combination is lower than that of single frequency due to the amplification of the observation noise. In addition, the combination of multiple GNSS increases the number of visible satellites, reduces the PDOP value, and can significantly improve the velocity measurement accuracy. Compared with the single GPS system, the accuracy of GPS/BDS/GLONASS/Galileo velocity estimation is improved by 40% in horizontal direction and 46% in vertical direction. At 40° elevation cut-off, the availability rate is improved from 48% to 98% under the condition of the horizontal velocity better than 1 cm/s and the vertical direction better than 2 cm/s.
Based on the method of time differenced carrier phase, the mathematical model of multi-GNSS velocity estimation is introduced and the error sources are analyzed. Using the real data, the accuracy and performance of velocity determination at each GNSS frequency point and multi-GNSS are tested and analyzed. The results show that the accuracy of velocity determination at different frequency is different, the velocity estimation at B1I, B1C, B3I frequency points in BDS and E1, E5a, E6, E5b, E5 frequency points in Galileo have the same precision, with the horizontal direction better than 1.5 mm/s and the vertical direction better than 3 mm/s; the velocity estimation accuracy at B2I frequency in BDS is the same as that at L1, L2 and L5 frequency points in GPS with the horizontal accuracy of 1.5-2 mm/s and the vertical accuracy of 3-4 mm/s; the velocity estimation accuracy at G1 and G2 frequency in GLONASS is the worst, with 3-4 mm/s in horizontal direction and 5-5.5 mm/s in vertical direction. The accuracy of double frequency ionospheric free combination is lower than that of single frequency due to the amplification of the observation noise. In addition, the combination of multiple GNSS increases the number of visible satellites, reduces the PDOP value, and can significantly improve the velocity measurement accuracy. Compared with the single GPS system, the accuracy of GPS/BDS/GLONASS/Galileo velocity estimation is improved by 40% in horizontal direction and 46% in vertical direction. At 40° elevation cut-off, the availability rate is improved from 48% to 98% under the condition of the horizontal velocity better than 1 cm/s and the vertical direction better than 2 cm/s.
, Available online ,
doi: 10.13203/j.whugis20210057
Abstract:
It is important to know the pattern of Pakistan's regional development for the construction of the China-Pakistan Economic Corridor. In this paper, we used the Visible Infrared Imager Radiometer Suite (VⅡRS) night-time light remote sensing image as the main data source, and implemented the Savitzky-Golay (SG) filtering algorithm to repair the missing values of the images. Three methods, Night-time Light Change Ratio (NLCR), Mann-Kendall (MK) trend test method, and rank-scale law, were carried out to study the nighttime light change patterns of Pakistan's provincial/city level, geographic grid level, and East-West zone in three geographic scales. The analysis of the NLCR shows that from 2012 to 2019, the entire Pakistan has night-time light growth by 47.96%; all provincial regions have different degrees of development, and with NLCR increasing by >15%; the NLCR of Gwadar Port is 126.94%, which is the highest among the 15 node cities on the east and west lines of the China-Pakistan Economic Corridor; more importantly, not all 15 node cities have similar positive night-time light growth, with two cities having night-time light decrease. This indicates that the development of the 15 node cities on the east and west lines of the China-Pakistan Economic Corridor was unbalanced. Using the MK trend test, Punjab has the most spatial grids (1905) with the total night-time light upward trend at the 5 km geographic grid-scale, while Sindh has the most grids (338) with the total night-time light downward trend. Through visual inspection of the grids with the total night-time light downward trend in Sindh, it is found that most of the grids are located in rural areas of Sindh, which is consistent to the fact that there is a serious power shortage problem in rural areas of Sindh. The rank-size analysis results show that the distribution of urban size across the country has undergone a process from a relatively concentrated distribution to a more balanced distribution; the evolution of urban size distribution in the western region is also consistent to the national pattern; while the distribution of urban size in the eastern region has shifted from balanced to more balanced development; the Pareto indexes of both the eastern zone and the western zone are gradually decreased, indicating that the gap between the urban development in Pakistan is reduced, and the entire country is developing and becoming more balanced. In addition, It is worth noting that Pakistan's frequent large-scale power outages and lack of stable power supply may be the factors explaining the low brightness of night-time light in some areas. This study reveals Pakistan's regional development patterns under long-term time series and multiple spatial scales, which can provide a certain scientific basis for the planning of the China-Pakistan Economic Corridor.
It is important to know the pattern of Pakistan's regional development for the construction of the China-Pakistan Economic Corridor. In this paper, we used the Visible Infrared Imager Radiometer Suite (VⅡRS) night-time light remote sensing image as the main data source, and implemented the Savitzky-Golay (SG) filtering algorithm to repair the missing values of the images. Three methods, Night-time Light Change Ratio (NLCR), Mann-Kendall (MK) trend test method, and rank-scale law, were carried out to study the nighttime light change patterns of Pakistan's provincial/city level, geographic grid level, and East-West zone in three geographic scales. The analysis of the NLCR shows that from 2012 to 2019, the entire Pakistan has night-time light growth by 47.96%; all provincial regions have different degrees of development, and with NLCR increasing by >15%; the NLCR of Gwadar Port is 126.94%, which is the highest among the 15 node cities on the east and west lines of the China-Pakistan Economic Corridor; more importantly, not all 15 node cities have similar positive night-time light growth, with two cities having night-time light decrease. This indicates that the development of the 15 node cities on the east and west lines of the China-Pakistan Economic Corridor was unbalanced. Using the MK trend test, Punjab has the most spatial grids (1905) with the total night-time light upward trend at the 5 km geographic grid-scale, while Sindh has the most grids (338) with the total night-time light downward trend. Through visual inspection of the grids with the total night-time light downward trend in Sindh, it is found that most of the grids are located in rural areas of Sindh, which is consistent to the fact that there is a serious power shortage problem in rural areas of Sindh. The rank-size analysis results show that the distribution of urban size across the country has undergone a process from a relatively concentrated distribution to a more balanced distribution; the evolution of urban size distribution in the western region is also consistent to the national pattern; while the distribution of urban size in the eastern region has shifted from balanced to more balanced development; the Pareto indexes of both the eastern zone and the western zone are gradually decreased, indicating that the gap between the urban development in Pakistan is reduced, and the entire country is developing and becoming more balanced. In addition, It is worth noting that Pakistan's frequent large-scale power outages and lack of stable power supply may be the factors explaining the low brightness of night-time light in some areas. This study reveals Pakistan's regional development patterns under long-term time series and multiple spatial scales, which can provide a certain scientific basis for the planning of the China-Pakistan Economic Corridor.
, Available online ,
doi: 10.13203/j.whugis20200177
Abstract:
Spatial co-location pattern discovery aims to mining subsets of geographic features which are frequently located in close proximity. Due to spatial heterogeneity, the frequency of different features being co-located usually varies across the space, thus forming the regional co-location patterns. Currently, most methods for discovering regional co-location patterns focus on planar geospatial spaces which can hardly support the corresponding analysis on network space such as urban roads. Therefore, this paper proposes a regional network co-location pattern mining method based on spatial scan statistics. First, a network-constrained path expansion method is developed to detect the candidate paths where co-location patterns could occur. These candidates are further validated using a significance test, where the null model is constructed using a network-constrained bivariate Poisson distribution. Experiments using simulated data and taxi datasets show that the proposed method is more effective for discovering regional co-location patterns on the network space than a baseline method.
Spatial co-location pattern discovery aims to mining subsets of geographic features which are frequently located in close proximity. Due to spatial heterogeneity, the frequency of different features being co-located usually varies across the space, thus forming the regional co-location patterns. Currently, most methods for discovering regional co-location patterns focus on planar geospatial spaces which can hardly support the corresponding analysis on network space such as urban roads. Therefore, this paper proposes a regional network co-location pattern mining method based on spatial scan statistics. First, a network-constrained path expansion method is developed to detect the candidate paths where co-location patterns could occur. These candidates are further validated using a significance test, where the null model is constructed using a network-constrained bivariate Poisson distribution. Experiments using simulated data and taxi datasets show that the proposed method is more effective for discovering regional co-location patterns on the network space than a baseline method.
, Available online ,
doi: 10.13203/j.whugis20200345
Abstract:
To determine the geologic age of the lunar surface is the foundation of the research on the formation and evolution of the Moon, and of the inversion of the processes of lunar geological events. Lack of lunar rock and soil samples limits the range of geological units that can used for isotopic dating. Therefore, the dating using crater size-frequency distribution (CSFD) is employed to obtain the geologic ages of broader regions on the lunar surface. However, the presence of secondary craters will lead to a deviation in geologic age which is obtained by CSFD method. Thus, to get a more accurate geologic age, secondary craters should be eliminated. This paper presents a method to identify secondary craters based on the distribution of iron element on lunar surface. First, the method assumes that the iron content in secondary craters is close to that in lunar regolith. Then, band ratio method is utilized to acquire the iron content. Finally, secondary craters are distinguished from primary ones in terms of the difference of iron content between craters and lunar regolith. The effectiveness and robustness of the proposed method were tested using MI multispectral data of the Japanese Selene Mission. The experimental results show that the geologic age deviation compared with the known isotopic dated ones is less than 0.04Ga, which shows good consistency. Compared with other secondary craters identification methods, the proposed approach is proven to be more effective and robust.
To determine the geologic age of the lunar surface is the foundation of the research on the formation and evolution of the Moon, and of the inversion of the processes of lunar geological events. Lack of lunar rock and soil samples limits the range of geological units that can used for isotopic dating. Therefore, the dating using crater size-frequency distribution (CSFD) is employed to obtain the geologic ages of broader regions on the lunar surface. However, the presence of secondary craters will lead to a deviation in geologic age which is obtained by CSFD method. Thus, to get a more accurate geologic age, secondary craters should be eliminated. This paper presents a method to identify secondary craters based on the distribution of iron element on lunar surface. First, the method assumes that the iron content in secondary craters is close to that in lunar regolith. Then, band ratio method is utilized to acquire the iron content. Finally, secondary craters are distinguished from primary ones in terms of the difference of iron content between craters and lunar regolith. The effectiveness and robustness of the proposed method were tested using MI multispectral data of the Japanese Selene Mission. The experimental results show that the geologic age deviation compared with the known isotopic dated ones is less than 0.04Ga, which shows good consistency. Compared with other secondary craters identification methods, the proposed approach is proven to be more effective and robust.
, Available online ,
doi: 10.13203/j.whugis20200195
Abstract:
Based on the first asteroid exploration plan announced by China Space Administration on April 19, 2019, we develop an optical orbit determination software for the main belt comet 133P/Elst-Pizarro (7968), which is one of the mission targets. The 133P/Elst-Pizarro's ground-based optical observation data from July 24, 1979 to October 28, 2019 are analyzed. Compared with the well-known OrbFit software system, it is found that the residual distribution is consistent, the measurement statistical residual RMS is less than 0.01″, and the internal coincidence accuracy of orbit determination is also consistent with each other. The results suggests the reliability of our software. Furthermore, we carry out a simulation orbit determination analysis aimed at 133P/Elst-Pizarro to discuss the orbit determination accuracy from ground-based optical data. When we use 20-year optical observation data measured once a mouth from Yunnan and Chile station and adding Gaussian white noise which is close to the current actual observation level, the results reflect that the optical orbit determination accuracy of the asteroid is at 50 km level. At the same time, it also shows that the optical orbit determination accuracy of the asteroid can be effectively improved by increasing the observation data or reducing the observation noise.
Based on the first asteroid exploration plan announced by China Space Administration on April 19, 2019, we develop an optical orbit determination software for the main belt comet 133P/Elst-Pizarro (7968), which is one of the mission targets. The 133P/Elst-Pizarro's ground-based optical observation data from July 24, 1979 to October 28, 2019 are analyzed. Compared with the well-known OrbFit software system, it is found that the residual distribution is consistent, the measurement statistical residual RMS is less than 0.01″, and the internal coincidence accuracy of orbit determination is also consistent with each other. The results suggests the reliability of our software. Furthermore, we carry out a simulation orbit determination analysis aimed at 133P/Elst-Pizarro to discuss the orbit determination accuracy from ground-based optical data. When we use 20-year optical observation data measured once a mouth from Yunnan and Chile station and adding Gaussian white noise which is close to the current actual observation level, the results reflect that the optical orbit determination accuracy of the asteroid is at 50 km level. At the same time, it also shows that the optical orbit determination accuracy of the asteroid can be effectively improved by increasing the observation data or reducing the observation noise.
, Available online ,
doi: 10.13203/j.whugis20200029
Abstract:
Objectives: Because the signal control with fixed phase duration can not be adjusted adaptively according to the real-time road conditions, the improvement of traffic congestion phenomenon is limited. In order to simulate the phase adaptation of traffic lights according to the real-time road conditions, reasonably configure the phase duration of traffic lights and alleviate the traffic congestion, a closed-loop system of traffic lights timing optimization and self-feedback adjustment which include vehicles flow video acquisition, flow statistics, timing calculation, timing feedback, phase regulation, flow statistics and timing optimization was constructed. Methods: Built on Webster algorithm, combining virtual simulation and computer vision technology, a road traffic simulation scene based on Unity3D is constructed to simulate vehicle starting and stopping, driving and signal lights control. Then, the OpenCV library is used to process the traffic flow video captured by Camera Object in the scene, count the traffic flow, calculate the optimal signal cycle, and determine the traffic phase. Finally, the result of timing calculation is applicable to the simulation scene, and further timing optimization is implemented according to the real-time scene to realize the signal phase adjustment and optimization cycle. Results: Traffic flow generation is built on the number of vehicles arriving at the intersection during peak hours. Vehicles are randomly generated in each direction of intersection every minute, including 15-30 vehicles in congestion direction and 5-15 vehicles in non-congestion direction. The initial setting of intersection signal lights is the traditional fixed timing schema, in which the left turn green light is on for 20 seconds in the north-south direction, the yellow light is on for 3 seconds, the straight green light is 30 seconds, and the red light is on for 56 seconds; the straight red light is on for 56 seconds, the left turn green light is on for 20 seconds in the east-west direction, the yellow light is on for 3 seconds, and the straight green light is on for 30 seconds. Report to experimental results of the fixed timing scheme, (1) the average waiting time of intersection vehicles has decreased by 17 seconds; (2) the traffic volume has increased by 190 vehicles per hour with real-time timing optimization scheme. Conclusions: From the results of simulation experiments, the simulation of traffic signal timing using unity3d provides a very effective method for the verification of traffic signal timing algorithm. The closed-loop system of signal lights adaptive timing optimization makes up for the limitations of existing traffic lights timing optimization schemes, and achieves real-time timing optimization. Simulation results show that the closed-loop feedback of signal lamp adaptive timing optimization can greatly reduce the waiting time of vehicles and effectively alleviate traffic congestion.
Objectives: Because the signal control with fixed phase duration can not be adjusted adaptively according to the real-time road conditions, the improvement of traffic congestion phenomenon is limited. In order to simulate the phase adaptation of traffic lights according to the real-time road conditions, reasonably configure the phase duration of traffic lights and alleviate the traffic congestion, a closed-loop system of traffic lights timing optimization and self-feedback adjustment which include vehicles flow video acquisition, flow statistics, timing calculation, timing feedback, phase regulation, flow statistics and timing optimization was constructed. Methods: Built on Webster algorithm, combining virtual simulation and computer vision technology, a road traffic simulation scene based on Unity3D is constructed to simulate vehicle starting and stopping, driving and signal lights control. Then, the OpenCV library is used to process the traffic flow video captured by Camera Object in the scene, count the traffic flow, calculate the optimal signal cycle, and determine the traffic phase. Finally, the result of timing calculation is applicable to the simulation scene, and further timing optimization is implemented according to the real-time scene to realize the signal phase adjustment and optimization cycle. Results: Traffic flow generation is built on the number of vehicles arriving at the intersection during peak hours. Vehicles are randomly generated in each direction of intersection every minute, including 15-30 vehicles in congestion direction and 5-15 vehicles in non-congestion direction. The initial setting of intersection signal lights is the traditional fixed timing schema, in which the left turn green light is on for 20 seconds in the north-south direction, the yellow light is on for 3 seconds, the straight green light is 30 seconds, and the red light is on for 56 seconds; the straight red light is on for 56 seconds, the left turn green light is on for 20 seconds in the east-west direction, the yellow light is on for 3 seconds, and the straight green light is on for 30 seconds. Report to experimental results of the fixed timing scheme, (1) the average waiting time of intersection vehicles has decreased by 17 seconds; (2) the traffic volume has increased by 190 vehicles per hour with real-time timing optimization scheme. Conclusions: From the results of simulation experiments, the simulation of traffic signal timing using unity3d provides a very effective method for the verification of traffic signal timing algorithm. The closed-loop system of signal lights adaptive timing optimization makes up for the limitations of existing traffic lights timing optimization schemes, and achieves real-time timing optimization. Simulation results show that the closed-loop feedback of signal lamp adaptive timing optimization can greatly reduce the waiting time of vehicles and effectively alleviate traffic congestion.
, Available online ,
doi: 10.13203/j.whugis20200008
Abstract:
The deep learning model such as convolutional neural network has achieved satisfactory results in hyperspectral images classification. However, because traditional neurons can only perform scalar computation, the existing deep learning models cannot model the instantiation parameters of hyperspectral image features, so they cannot achieve satisfactory classification results under the condition of restricted neighborhood scope. To solve the problem, this paper designs a new network model by introducing capsule network structure. This model can further improve the classification accuracy of hyperspectral images by using the vector calculation of capsule neurons and the spatial relationship between features encoded by the weight matrix. Through verification on Pavia University, Indian Pines and Salinas data sets, experiments show that the network model proposed in this paper has better classification performance than the traditional algorithm and the convolutional neural network model, and has better adaptability to the training sample number and pixel neighborhood scope.
The deep learning model such as convolutional neural network has achieved satisfactory results in hyperspectral images classification. However, because traditional neurons can only perform scalar computation, the existing deep learning models cannot model the instantiation parameters of hyperspectral image features, so they cannot achieve satisfactory classification results under the condition of restricted neighborhood scope. To solve the problem, this paper designs a new network model by introducing capsule network structure. This model can further improve the classification accuracy of hyperspectral images by using the vector calculation of capsule neurons and the spatial relationship between features encoded by the weight matrix. Through verification on Pavia University, Indian Pines and Salinas data sets, experiments show that the network model proposed in this paper has better classification performance than the traditional algorithm and the convolutional neural network model, and has better adaptability to the training sample number and pixel neighborhood scope.
, Available online ,
doi: 10.13203/j.whugis20200001
Abstract:
To deal with the high computation expense in data-intensive heat map generation, parallel computing technology based on Graphics Processing Unit(GPU) is introduced to the heat map generation filed. With combination of trajectory line model, we propose a novel method for trajectory line heat map generation and display based on GPU. First of all, in order to avoid discontinuity and inhomogeneity of heat value resulted from different dense of trajectory points or unreasonable neighborhood threshold, trajectory line model is adopted to improve the effect of generated heat map. Second, aiming at the problem of low efficiency of heat map generation caused by large-scale data calculation, the proposed method improve the efficiency greatly via a combination of GPU parallel computing, tuning of kernel function parameter, loop unrolling and Pixel Buffer Object(PBO). Experiment results show that processing speed of the proposed method is from 5 to 30 times faster than CPU-based implementation. Additionally, the accelerator ratio tends to be higher as the resolution of heat map or the increase of trajectory data.
To deal with the high computation expense in data-intensive heat map generation, parallel computing technology based on Graphics Processing Unit(GPU) is introduced to the heat map generation filed. With combination of trajectory line model, we propose a novel method for trajectory line heat map generation and display based on GPU. First of all, in order to avoid discontinuity and inhomogeneity of heat value resulted from different dense of trajectory points or unreasonable neighborhood threshold, trajectory line model is adopted to improve the effect of generated heat map. Second, aiming at the problem of low efficiency of heat map generation caused by large-scale data calculation, the proposed method improve the efficiency greatly via a combination of GPU parallel computing, tuning of kernel function parameter, loop unrolling and Pixel Buffer Object(PBO). Experiment results show that processing speed of the proposed method is from 5 to 30 times faster than CPU-based implementation. Additionally, the accelerator ratio tends to be higher as the resolution of heat map or the increase of trajectory data.
, Available online ,
doi: 10.13203/j.whugis20200508
Abstract:
Machine learning language has become an ideal modeling tool in the field of dam health monitoring with its powerful nonlinear data mining ability. However, the minimum fitting mean square error (MSE) is determined as the only optimization objective in the traditional modeling process, which is likely to cause over-fitting problems. To overcome this problem, based on the relevance vector machine (RVM), a probabilistic prediction model is established under the constraint of double optimization objectives, which integrates the deformation spatial association and MSE. The deformation spatial association is quantified by the shape similarity index (SSI) at first. The double objective is then established with the combination of the MSE and SSI, and is achieved by making the MSE as small as possible, while the SSI is as large as possible. Engineering example of the Jinping-I arch dam shows that the average decrease proportion of the root mean square error (RMSE) and maximum absolute error (ME) of the proposed double objective RVM model is 31.2% and 24.8%, respectively, and the prediction performance can be further improved by using the multi-kernel function. The prediction confidence bandwidth of the RVM model is significantly smaller than that of the traditional multiple linear regression model, with an average decrease proportion of 75.1%. Therefore, the multi-kernel double objective RVM model established for the displacement of super high arch dams can effectively improve the prediction performance and reduce the uncertainty.
Machine learning language has become an ideal modeling tool in the field of dam health monitoring with its powerful nonlinear data mining ability. However, the minimum fitting mean square error (MSE) is determined as the only optimization objective in the traditional modeling process, which is likely to cause over-fitting problems. To overcome this problem, based on the relevance vector machine (RVM), a probabilistic prediction model is established under the constraint of double optimization objectives, which integrates the deformation spatial association and MSE. The deformation spatial association is quantified by the shape similarity index (SSI) at first. The double objective is then established with the combination of the MSE and SSI, and is achieved by making the MSE as small as possible, while the SSI is as large as possible. Engineering example of the Jinping-I arch dam shows that the average decrease proportion of the root mean square error (RMSE) and maximum absolute error (ME) of the proposed double objective RVM model is 31.2% and 24.8%, respectively, and the prediction performance can be further improved by using the multi-kernel function. The prediction confidence bandwidth of the RVM model is significantly smaller than that of the traditional multiple linear regression model, with an average decrease proportion of 75.1%. Therefore, the multi-kernel double objective RVM model established for the displacement of super high arch dams can effectively improve the prediction performance and reduce the uncertainty.
, Available online ,
doi: 10.13203/j.whugis20200481
Abstract:
Objectives: Ship detection in large-scale, high-resolution optical remote sensing images, especially in visible spectral remote sensing images, plays an important role in both military and civilian fields. It has always been an on-going and challenging research topic in the past decades, due to the complexity and variability of the backgrounds, the multi-scale, multi-type, and multi-posture diversity of ship targets. Methods: In order to promote the development of ship detection based on high-resolution optical remote sensing images (HRORSI), this paper provides an overview of existing study on ship detection methods of HRORSIover the past decades. We start by introducing all the existing monitoring systems for ship detection, but subsequently focus only on HRORSI. Then, a number of topics have been discussed in this paper, including the methodology system, the development history, the recent state of the art detection methods, detection datasets and metrics. Results: It is shown that current ship detection methods based on deep learning in HRORSI, have greatly expanded the adaptability of the complexity of the detection scene and the variation of the target distribution, and the speed and accuracy of the results have been greatly improved.It is also found that different influence factors make a big difference in choosing the corresponding ship detection models. Conclusions: Existing methods from different perspectives such as extracting richer features, multi-level and multi-scale feature fusion, more accurate target locating, scale aware ship detection, have made vigorous performance.However, there is still a big gap between efficient and intelligent ship interpretation in actual complex applications.We suggest that the future ship detection methods should adaptively support a variety of backgrounds, scales and optical sensors; the detection model can be effectively transfer to out-distributed target domain scenarios as well as resource-constrained scenarios; and more in-depth target recognition capabilities.
Objectives: Ship detection in large-scale, high-resolution optical remote sensing images, especially in visible spectral remote sensing images, plays an important role in both military and civilian fields. It has always been an on-going and challenging research topic in the past decades, due to the complexity and variability of the backgrounds, the multi-scale, multi-type, and multi-posture diversity of ship targets. Methods: In order to promote the development of ship detection based on high-resolution optical remote sensing images (HRORSI), this paper provides an overview of existing study on ship detection methods of HRORSIover the past decades. We start by introducing all the existing monitoring systems for ship detection, but subsequently focus only on HRORSI. Then, a number of topics have been discussed in this paper, including the methodology system, the development history, the recent state of the art detection methods, detection datasets and metrics. Results: It is shown that current ship detection methods based on deep learning in HRORSI, have greatly expanded the adaptability of the complexity of the detection scene and the variation of the target distribution, and the speed and accuracy of the results have been greatly improved.It is also found that different influence factors make a big difference in choosing the corresponding ship detection models. Conclusions: Existing methods from different perspectives such as extracting richer features, multi-level and multi-scale feature fusion, more accurate target locating, scale aware ship detection, have made vigorous performance.However, there is still a big gap between efficient and intelligent ship interpretation in actual complex applications.We suggest that the future ship detection methods should adaptively support a variety of backgrounds, scales and optical sensors; the detection model can be effectively transfer to out-distributed target domain scenarios as well as resource-constrained scenarios; and more in-depth target recognition capabilities.
, Available online ,
doi: 10.13203/j.whugis20200653
Abstract:
The complex geological conditions in the mountainous areas of western China, with strong internal and external dynamics effects, make catastrophic landslides frequent. The analysis of landslide susceptibility has become a necessary means for scientific early warning and active prevention before disasters. In the traditional landslide susceptibility analysis method, the general calculation accuracy of the single knowledge-driven model is limited, and the weight of the impact factor is highly subjective. The data-driven model also relies too much on the quality and quantity of sample data, and the heterogeneity of the landslide disaster environment is prominent. To overcome these shortcomings, this paper proposes a regional landslide susceptibility method that couples the contribution weight of landslide disaster environmental factors and heuristic knowledge fuzzy logic model.The proposed method uses spatial statistical indicators such as the historical landslide frequency ratio and the information entropy weight of the landslide disaster environmental factors to explicitly describe the contribution and spatial distribution characteristics of the landslide disaster environmental factors, which measures the constraint relationship and the mapping structure between multi-factors and landslides, and realizes multi-factor coupling regional landslide susceptibility. The experiment selects the disaster-prone areas in FengJie, Chongqing for verification and evaluation; and the result is superior to the single Information Value Model and Information Value and Logistic Regression model, which verifies the reliability of the method in this paper. The proposed method can overcome the strict requirements of the existing landslide susceptibility analysis on the number of historical observation samples, spatial representativeness and stable relationship of the obtained variables to a certain extent, and can provide reliable technical support for the susceptibility analysis of large-scale.
The complex geological conditions in the mountainous areas of western China, with strong internal and external dynamics effects, make catastrophic landslides frequent. The analysis of landslide susceptibility has become a necessary means for scientific early warning and active prevention before disasters. In the traditional landslide susceptibility analysis method, the general calculation accuracy of the single knowledge-driven model is limited, and the weight of the impact factor is highly subjective. The data-driven model also relies too much on the quality and quantity of sample data, and the heterogeneity of the landslide disaster environment is prominent. To overcome these shortcomings, this paper proposes a regional landslide susceptibility method that couples the contribution weight of landslide disaster environmental factors and heuristic knowledge fuzzy logic model.The proposed method uses spatial statistical indicators such as the historical landslide frequency ratio and the information entropy weight of the landslide disaster environmental factors to explicitly describe the contribution and spatial distribution characteristics of the landslide disaster environmental factors, which measures the constraint relationship and the mapping structure between multi-factors and landslides, and realizes multi-factor coupling regional landslide susceptibility. The experiment selects the disaster-prone areas in FengJie, Chongqing for verification and evaluation; and the result is superior to the single Information Value Model and Information Value and Logistic Regression model, which verifies the reliability of the method in this paper. The proposed method can overcome the strict requirements of the existing landslide susceptibility analysis on the number of historical observation samples, spatial representativeness and stable relationship of the obtained variables to a certain extent, and can provide reliable technical support for the susceptibility analysis of large-scale.
, Available online ,
doi: 10.13203/j.whugis20200186
Abstract:
To solve the problems in pedestrian dead reckoning algorithms for indoor positioning, of which the step recognition accuracy for step detection is not high enough, the synchronous control is not precise enough, and there is a large location deviation. An algorithm of improved finite state machine step detection for the activity of flat holding smartphone was proposed. A finite number of states were set to correspond to the trend of resultant acceleration variation during walking. Step detection and step cycle estimation were realized based on adjacent resultant acceleration difference and several thresholds of climbing and descending times. Experimental tests were conducted by two testers in 211 meters corridors with flat holding smartphone, respectively. Experimental results show that the accuracy of two step detection tests are both 100% by using the improved algorithm. It is 0.004 seconds earlier on average than the actual time for each step. And the average location error is 0.384m. Compared to the auto-correction analysis and acceleration differential based on finite state machine algorithms, the accuracy of step recognition, synchronous control and location estimation are improved at least 0.7%, 60% and 21.15%, respectively. The proposed algorithm behaves better than the existing algorithms in the aspects of the step recognition, the synchronous control and location estimation.
To solve the problems in pedestrian dead reckoning algorithms for indoor positioning, of which the step recognition accuracy for step detection is not high enough, the synchronous control is not precise enough, and there is a large location deviation. An algorithm of improved finite state machine step detection for the activity of flat holding smartphone was proposed. A finite number of states were set to correspond to the trend of resultant acceleration variation during walking. Step detection and step cycle estimation were realized based on adjacent resultant acceleration difference and several thresholds of climbing and descending times. Experimental tests were conducted by two testers in 211 meters corridors with flat holding smartphone, respectively. Experimental results show that the accuracy of two step detection tests are both 100% by using the improved algorithm. It is 0.004 seconds earlier on average than the actual time for each step. And the average location error is 0.384m. Compared to the auto-correction analysis and acceleration differential based on finite state machine algorithms, the accuracy of step recognition, synchronous control and location estimation are improved at least 0.7%, 60% and 21.15%, respectively. The proposed algorithm behaves better than the existing algorithms in the aspects of the step recognition, the synchronous control and location estimation.
, Available online ,
doi: 10.13203/j.whugis20200115
Abstract:
Integration of GNSS and INS can provide continuous and accurate positioning information for vehicles. However, the accuracy of low-cost GNSS/INS vehicle integrated navigation systems is unreliable during GNSS outages, which are common in urban areas. So, a long and short-term memory (LSTM) networks-aided GNSS/INS integrated navigation system based on extended Kalman filter (EKF) is proposed in this paper. LSTM networks are trained to learn the relationship between position error and INS output when GNSS available. When GNSS outage occurs, LSTM networks predict and correct errors of the integrated navigation system to improve location precision. The experiment shows that the north error and east error of the GNSS/INS integrated navigation systems based on EKF is 1.93m and 13.92m during the 15s GNSS outage. Meanwhile, the north error and east error of the GNSS/INS integrated navigation systems based on LSTM-EKF is 1.17m and 0.84m. The comparison results indicate that the proposed system can effectively improve location precision during GNSS outages.
Integration of GNSS and INS can provide continuous and accurate positioning information for vehicles. However, the accuracy of low-cost GNSS/INS vehicle integrated navigation systems is unreliable during GNSS outages, which are common in urban areas. So, a long and short-term memory (LSTM) networks-aided GNSS/INS integrated navigation system based on extended Kalman filter (EKF) is proposed in this paper. LSTM networks are trained to learn the relationship between position error and INS output when GNSS available. When GNSS outage occurs, LSTM networks predict and correct errors of the integrated navigation system to improve location precision. The experiment shows that the north error and east error of the GNSS/INS integrated navigation systems based on EKF is 1.93m and 13.92m during the 15s GNSS outage. Meanwhile, the north error and east error of the GNSS/INS integrated navigation systems based on LSTM-EKF is 1.17m and 0.84m. The comparison results indicate that the proposed system can effectively improve location precision during GNSS outages.
, Available online ,
doi: 10.13203/j.whugis20190148
Abstract:
The echo simulation of traditional synthetic aperture sonar (SAS) often considers the signal within 3 dB beam width. Besides, it neglects the fact that the echo signal is modulated by the wideband beam pattern of the transmitter and receiver. To solve this issue, this paper investigates the echo simulation based on the modulation of the transmitter/receiver beam pattern. On the basis of that, the influence of the transmitter/receiver beam pattern on the SAS imagery is discussed using the back projection (BP) algorithm. Comparing with the processing result of the weighted array signal, the influence of the beam pattern on the imaging result is further verified. The result in this paper is useful for the suppression of the spectrum alias in azimuth. Moreover, it is also useful for the demonstration and analysis of the performance indicators related to the designed SAS system.
The echo simulation of traditional synthetic aperture sonar (SAS) often considers the signal within 3 dB beam width. Besides, it neglects the fact that the echo signal is modulated by the wideband beam pattern of the transmitter and receiver. To solve this issue, this paper investigates the echo simulation based on the modulation of the transmitter/receiver beam pattern. On the basis of that, the influence of the transmitter/receiver beam pattern on the SAS imagery is discussed using the back projection (BP) algorithm. Comparing with the processing result of the weighted array signal, the influence of the beam pattern on the imaging result is further verified. The result in this paper is useful for the suppression of the spectrum alias in azimuth. Moreover, it is also useful for the demonstration and analysis of the performance indicators related to the designed SAS system.
, Available online ,
doi: 10.13203/j.whugis20200698
Abstract:
Most of the existing building simplification algorithmsfocus on one or two aspects of the three principles:area preservation, shape characteristics enhancement and drawing rectangles if possible. But in practice, map generalization is a process of mutual restriction and trade-off of various influencing conditions, cartographic experts consider all the three aspects and balance them to reach reasonable results, sometimes they delete the small details on building's outlines, but it results in the area difference; sometimes they displace some points of the edges so that the area stays unchanged, but the vertices' location changes. It depends on the short-edge structures and distributions. By imitatingthis thinking and operation process of cartographers, an algorithm based on short-edge structure recognition and progressive simplification is proposed. First, the building outline is preprocessed; then the contour segments are grouped, and the edge structure is identified and analyzed. After this, some specific situations are analyzed in detail, and the "calculation region" (the sequence of vertices associated with the simplification of a set of edges less than the tolerance) is used to determine the optimal simplificationmethod. Lastly, the area difference is distributed to the whole building so that the building's area difference is reduced while the shape is maintained. Open Street Map (OSM) buildings are selected in the experiment, the results show that the proposed method can effectively reduce the number of graphic vertices, enhances the shape characteristics like parallel, right angle, etc. Also, the algorithm can control the area differences after generalization. It gives consideration to and balances three aspects of the building simplification principles, which provides a new way for building simplification.
Most of the existing building simplification algorithmsfocus on one or two aspects of the three principles:area preservation, shape characteristics enhancement and drawing rectangles if possible. But in practice, map generalization is a process of mutual restriction and trade-off of various influencing conditions, cartographic experts consider all the three aspects and balance them to reach reasonable results, sometimes they delete the small details on building's outlines, but it results in the area difference; sometimes they displace some points of the edges so that the area stays unchanged, but the vertices' location changes. It depends on the short-edge structures and distributions. By imitatingthis thinking and operation process of cartographers, an algorithm based on short-edge structure recognition and progressive simplification is proposed. First, the building outline is preprocessed; then the contour segments are grouped, and the edge structure is identified and analyzed. After this, some specific situations are analyzed in detail, and the "calculation region" (the sequence of vertices associated with the simplification of a set of edges less than the tolerance) is used to determine the optimal simplificationmethod. Lastly, the area difference is distributed to the whole building so that the building's area difference is reduced while the shape is maintained. Open Street Map (OSM) buildings are selected in the experiment, the results show that the proposed method can effectively reduce the number of graphic vertices, enhances the shape characteristics like parallel, right angle, etc. Also, the algorithm can control the area differences after generalization. It gives consideration to and balances three aspects of the building simplification principles, which provides a new way for building simplification.
, Available online ,
doi: 10.13203/j.whugis20200518
Abstract:
Objectives: Differential Interferometric Synthetic Aperture Radar (D-InSAR) has been widely used in large-scale surface deformation monitoring. However, the surface deformation obtained from spaceborne SAR data is easily affected by atmospheric noise, and the long revisit period leads to the incoherence. In order to effectively reduce these effects, this paper proposes a method to monitor the deformation of highway slope by car-borneInSAR system. Methods: Due to the orbit control of thecar-borne dual-antenna InSAR system, the spatial baseline of the interferometric pairs is close to zero. Therefore, when the D-InSAR data withthis system is used to extract the deformation information, it can avoid the flat phase, which greatly simplifies the differential interference processing process. Results: A certain area of Wuhan, Hubei Province is chosenasthe experimental area. Seven corner reflectors are deployed in the test area to evaluate the accuracy of deformation information extracted using the car-borne dual-antenna InSAR system.The deformation of seven reflectors is calculated by using the method proposed in this paper. The root mean square error of deformation between the real value and thecalculated value is 2.206 mm. Conclusions: In this paper, a deformation monitoring method of zero space baseline usingthecar-borne dual-antenna InSAR system is proposed and verified in Wuhan city. The D-InSAR method based on zero space baseline can avoid the process of the flat phase. At the same time, our system is small in size, and the design of the trajectory according to the actual needsis flexibly, which is very practical for small-scale highway slope deformation monitoring. Because of the small revisit period, the car-borne InSAR Data will not produce phase error due to atmospheric delay, and the deformation measurement accuracy is high.
Objectives: Differential Interferometric Synthetic Aperture Radar (D-InSAR) has been widely used in large-scale surface deformation monitoring. However, the surface deformation obtained from spaceborne SAR data is easily affected by atmospheric noise, and the long revisit period leads to the incoherence. In order to effectively reduce these effects, this paper proposes a method to monitor the deformation of highway slope by car-borneInSAR system. Methods: Due to the orbit control of thecar-borne dual-antenna InSAR system, the spatial baseline of the interferometric pairs is close to zero. Therefore, when the D-InSAR data withthis system is used to extract the deformation information, it can avoid the flat phase, which greatly simplifies the differential interference processing process. Results: A certain area of Wuhan, Hubei Province is chosenasthe experimental area. Seven corner reflectors are deployed in the test area to evaluate the accuracy of deformation information extracted using the car-borne dual-antenna InSAR system.The deformation of seven reflectors is calculated by using the method proposed in this paper. The root mean square error of deformation between the real value and thecalculated value is 2.206 mm. Conclusions: In this paper, a deformation monitoring method of zero space baseline usingthecar-borne dual-antenna InSAR system is proposed and verified in Wuhan city. The D-InSAR method based on zero space baseline can avoid the process of the flat phase. At the same time, our system is small in size, and the design of the trajectory according to the actual needsis flexibly, which is very practical for small-scale highway slope deformation monitoring. Because of the small revisit period, the car-borne InSAR Data will not produce phase error due to atmospheric delay, and the deformation measurement accuracy is high.
, Available online ,
doi: 10.13203/j.whugis20200232
Abstract:
To find a high accuracy method for SCB prediction based on characteristics of SCB data, a preprocessing strategy (WMAD) of a wavelet threshold method based on the median absolute deviation(MAD) is proposed to preprocess the SCB, aiming at the small error of the middleweight of the SCB data. Firstly, the wavelet threshold method is used to decompose the SCB data to obtain the decomposed high frequency coefficient and low frequency coefficient. Then the MAD method is used to deal with the high frequency coefficient of each layer affecting the threshold setting, and the processed high frequency coefficient is used to calculate the threshold, so as to improve the ability of eliminating small outliers by the wavelet threshold method. Finally, the clock bias data of beidou-2 satellite are used to verify. The results show that this method can effectively eliminate the small error of median magnitude in the historical observation sequence of clock difference and predicting the satellite clock bias.
To find a high accuracy method for SCB prediction based on characteristics of SCB data, a preprocessing strategy (WMAD) of a wavelet threshold method based on the median absolute deviation(MAD) is proposed to preprocess the SCB, aiming at the small error of the middleweight of the SCB data. Firstly, the wavelet threshold method is used to decompose the SCB data to obtain the decomposed high frequency coefficient and low frequency coefficient. Then the MAD method is used to deal with the high frequency coefficient of each layer affecting the threshold setting, and the processed high frequency coefficient is used to calculate the threshold, so as to improve the ability of eliminating small outliers by the wavelet threshold method. Finally, the clock bias data of beidou-2 satellite are used to verify. The results show that this method can effectively eliminate the small error of median magnitude in the historical observation sequence of clock difference and predicting the satellite clock bias.
, Available online ,
doi: 10.13203/j.whugis20190286
Abstract:
In order to analsis the applicability and accuracy of TEC modelling for regional ionosphere from observations at a single station, the TEC models of 16 single-station regions in Europe are established and the TEC grids of the region are generated based on the spherical harmonic function of order from 2 to 15. The products are compared with the authoritative ionospheric products such as CODE, iGMAS and IGS. The results show that the single station regional ionospheric TEC model based on low order spherical harmonic function (2×2 or 3×3) has the same accuracy as that of global ionospheric TEC such as CODE, iGMAS and IGS, which is about 1.0TECU in the longitude and latitude range of 10×10 (radius < 600km) centered on a single station. Thus, the global ionospheric TEC model can be replaced by the ionospheric TEC model established by a single station in a certain region (radius less than 600 km), and the high-precision ionospheric delay correction can be efficiently provided for the single frequency users in the region.
In order to analsis the applicability and accuracy of TEC modelling for regional ionosphere from observations at a single station, the TEC models of 16 single-station regions in Europe are established and the TEC grids of the region are generated based on the spherical harmonic function of order from 2 to 15. The products are compared with the authoritative ionospheric products such as CODE, iGMAS and IGS. The results show that the single station regional ionospheric TEC model based on low order spherical harmonic function (2×2 or 3×3) has the same accuracy as that of global ionospheric TEC such as CODE, iGMAS and IGS, which is about 1.0TECU in the longitude and latitude range of 10×10 (radius < 600km) centered on a single station. Thus, the global ionospheric TEC model can be replaced by the ionospheric TEC model established by a single station in a certain region (radius less than 600 km), and the high-precision ionospheric delay correction can be efficiently provided for the single frequency users in the region.
, Available online ,
doi: 10.13203/j.whugis20200600
Abstract:
Objectives: As an important part of cultural heritage, immovable cultural relics are facing increasing risks of rainstorm and flood disaster. In order to improve the capabilities of prevention on rainstorm and flood disaster for immovable cultural relics, this study proposed a risk assessment method based on the theory of natural disaster risk assessment considering the seasonal variation of rainstorm and flood disaster, and took 24 National Ancient Sites in 18 counties in Fujian province as an example. Methods: The regional L-moment method was used to acquire the rainfall values of various return periods in different season, and analyze the characteristics of seasonal rainstorm and flood risk. We estimated the risk from three aspects:hazard factors, disaster formative environment and hazard bearing body of cultural heritages. The coefficient of variation method, entropy weight method and Delphi method were adopted to calculate the weight of those three aspects. Results: The results show that the seasonal difference of the risk assessment results is obvious, which indicates that the assessment method is feasible. The risk of rainstorm and flood was the highest in the second quarter, followed by the first and third quarter, and the spatial distribution of risk in different seasons was significantly different. In the first and second quarters, the risk of rainstorm and flood was high in coastal and northern counties, and low in central counties. The risks in the third and fourth quarters were high in the coastal areas and low in the inland areas. Conclusions: The proposed model for immovable cultural relics can well reflect the seasonal difference of rainstorm and flood risk, and is suitable for regions with large seasonal difference of rainstorm and flood disaster. The results of seasonal differences can provide scientific reference for disaster prevention and mitigation planning for immovable cultural relics.
Objectives: As an important part of cultural heritage, immovable cultural relics are facing increasing risks of rainstorm and flood disaster. In order to improve the capabilities of prevention on rainstorm and flood disaster for immovable cultural relics, this study proposed a risk assessment method based on the theory of natural disaster risk assessment considering the seasonal variation of rainstorm and flood disaster, and took 24 National Ancient Sites in 18 counties in Fujian province as an example. Methods: The regional L-moment method was used to acquire the rainfall values of various return periods in different season, and analyze the characteristics of seasonal rainstorm and flood risk. We estimated the risk from three aspects:hazard factors, disaster formative environment and hazard bearing body of cultural heritages. The coefficient of variation method, entropy weight method and Delphi method were adopted to calculate the weight of those three aspects. Results: The results show that the seasonal difference of the risk assessment results is obvious, which indicates that the assessment method is feasible. The risk of rainstorm and flood was the highest in the second quarter, followed by the first and third quarter, and the spatial distribution of risk in different seasons was significantly different. In the first and second quarters, the risk of rainstorm and flood was high in coastal and northern counties, and low in central counties. The risks in the third and fourth quarters were high in the coastal areas and low in the inland areas. Conclusions: The proposed model for immovable cultural relics can well reflect the seasonal difference of rainstorm and flood risk, and is suitable for regions with large seasonal difference of rainstorm and flood disaster. The results of seasonal differences can provide scientific reference for disaster prevention and mitigation planning for immovable cultural relics.
, Available online ,
doi: 10.13203/j.whugis20200324
Abstract:
For the difficult problem of balanced control of the computational efficiency and boundary position accuracy of the existing Voronoi algorithms based on the idea of figure discretization, we proposed an adaptive Voronoi diagram algorithm for multi-scale areal residential area matching based on interpolation strategy according to neighbor pair type——AVARA. Firstly, it uses the Delaunay triangle network composed of the centroids of the residential areas to calculate the neighbor pairs of the residential areas. Secondly, it classifies neighbor pairs according to the size relationship between the minimum distance between the neighbor pairs and the minimum side length of their minimum area bounding rectangles. Then it adaptively interpolates points on the boundary of the residential area according to the neighbor pair type. Finally, it constructs the Voronoi diagram for residential areas based on the interpolated point set and the vertex set of the residential area. We conducted the experiments of creating Voronoi diagram by using the residential area dataset which map scale is 1:10,000 and 1:50,000. The results showed that:compared with the intervisibility point method, the 3m and 6m interval interpolation point method of 1:10,000 data, AVARA outperformed the local position accuracy and time performance, compared with the 30m interval interpolation point method of 1:50,000 data, AVARA achieved higher local position accuracy, and compared with the 15m interval interpolation point method of 1:50,000 data, AVARA time performance has been improved by 34.6%.The problem of balance control between computational efficiency and boundary position accuracy of Voronoi diagram was alleviated effectively.
For the difficult problem of balanced control of the computational efficiency and boundary position accuracy of the existing Voronoi algorithms based on the idea of figure discretization, we proposed an adaptive Voronoi diagram algorithm for multi-scale areal residential area matching based on interpolation strategy according to neighbor pair type——AVARA. Firstly, it uses the Delaunay triangle network composed of the centroids of the residential areas to calculate the neighbor pairs of the residential areas. Secondly, it classifies neighbor pairs according to the size relationship between the minimum distance between the neighbor pairs and the minimum side length of their minimum area bounding rectangles. Then it adaptively interpolates points on the boundary of the residential area according to the neighbor pair type. Finally, it constructs the Voronoi diagram for residential areas based on the interpolated point set and the vertex set of the residential area. We conducted the experiments of creating Voronoi diagram by using the residential area dataset which map scale is 1:10,000 and 1:50,000. The results showed that:compared with the intervisibility point method, the 3m and 6m interval interpolation point method of 1:10,000 data, AVARA outperformed the local position accuracy and time performance, compared with the 30m interval interpolation point method of 1:50,000 data, AVARA achieved higher local position accuracy, and compared with the 15m interval interpolation point method of 1:50,000 data, AVARA time performance has been improved by 34.6%.The problem of balance control between computational efficiency and boundary position accuracy of Voronoi diagram was alleviated effectively.
, Available online ,
doi: 10.13203/j.whugis20200023
Abstract:
Buildings can be divided into physical cluster and property right cluster from different perspectives, and the latter attached to the former. The existing method can construct physical cluster and property right cluster of the same building automatically, but the two resulting clusters are independent of each other. This not only increases the cost of modeling, but also is not conducive to the update and maintenance of model data in the later period. For the problem, the paper studies the relationship between physical cluster and property right cluster of apartment buildings, and finds that the hierarchy of connected boundaries determines the property right solids aggregated by cells, and presents a method to transform physical clustering into property right clustering automatically. With existing physical clusters, the method transforms the cells of physical clusters into dual points, and the connected boundaries between cells into semantic edges, and the whole physical cluster into Node Relation Graph with Poincaré duality transformation. A segmenting algorithm is designed for Node Relation Graph, which can divide Node Relation Graph into sub Node Relation Graph representing proprietary and co-owned property according to the semantic information of the edges. Furthermore, the non-common boundary surfaces of the cell set corresponded by sub Node Relation Graph are extracted to construct the property right solids, and the aggregation of the property right solids forms the property right cluster. Instead of building two separate clusters, the method only builds a physical cluster and property right cluster is generated by the transformation. The results show that the proposed method can identify the property right solids and construct property right cluster in the existing physical cluster automatically. It saves the modeling cost and facilitates the update and maintenance of the later data.
Buildings can be divided into physical cluster and property right cluster from different perspectives, and the latter attached to the former. The existing method can construct physical cluster and property right cluster of the same building automatically, but the two resulting clusters are independent of each other. This not only increases the cost of modeling, but also is not conducive to the update and maintenance of model data in the later period. For the problem, the paper studies the relationship between physical cluster and property right cluster of apartment buildings, and finds that the hierarchy of connected boundaries determines the property right solids aggregated by cells, and presents a method to transform physical clustering into property right clustering automatically. With existing physical clusters, the method transforms the cells of physical clusters into dual points, and the connected boundaries between cells into semantic edges, and the whole physical cluster into Node Relation Graph with Poincaré duality transformation. A segmenting algorithm is designed for Node Relation Graph, which can divide Node Relation Graph into sub Node Relation Graph representing proprietary and co-owned property according to the semantic information of the edges. Furthermore, the non-common boundary surfaces of the cell set corresponded by sub Node Relation Graph are extracted to construct the property right solids, and the aggregation of the property right solids forms the property right cluster. Instead of building two separate clusters, the method only builds a physical cluster and property right cluster is generated by the transformation. The results show that the proposed method can identify the property right solids and construct property right cluster in the existing physical cluster automatically. It saves the modeling cost and facilitates the update and maintenance of the later data.
, Available online ,
doi: 10.13203/j.whugis20200633
Abstract:
Objectives: National park is the main body of China's natural protected area system, and land cover mapping in national parks plays an important role in understanding the status of natural resources, identifying existing ecological security threats and responding to them quickly. This analysis attempts to develop an accurate and cost-effective model for mapping land cover dataset in national parks. Methods: Two land cover classification methods were developed based on Google Earth Engine (GEE) environment by combining Sentinel-1 and Sentinel-2 images, topographic and textural derivatives to map land cover types in the Qianjiangyuan National Park. One used pixel-based random forest (RF) classification algorithm, the other used object-oriented simple non-iterative clustering (SNIC) segmentation in partnership with RF algorithm, which indicated that the input featureswere first segmented into superpixel image objects then classified by RF algorithm. To optimize classification features, a method taking the advantages of cloud computing platform to design different groups of comparative experiments to finalize feature combination with the highest classification accuracy was proposed, followed by using a recursive feature elimination method to further screen the features. Results: Experimental results showed that both the pixel-based method and the object-oriented method had good performance in land cover classification, and the corresponding highest overall classification accuracies of the two methods were estimated at 92.37% and 93.98%, respectively. Furthermore, integration of synthetic aperture radar (SAR) data into the classifications could substantially improve the classification accuracy when using the pixel-based method, but there was no apparent escalating effect for the object-oriented classification. Conclusions: Experiments showed that the land cover classification map generated from the SNIC+RF algorithm in GEE platform was more complete and the algorithm requires fewer features (15 features, including multispectral bands and spectral indices) and runs quickly in the GEE platform. Thus, this algorithm deserves to be popularized in national park management practices.
Objectives: National park is the main body of China's natural protected area system, and land cover mapping in national parks plays an important role in understanding the status of natural resources, identifying existing ecological security threats and responding to them quickly. This analysis attempts to develop an accurate and cost-effective model for mapping land cover dataset in national parks. Methods: Two land cover classification methods were developed based on Google Earth Engine (GEE) environment by combining Sentinel-1 and Sentinel-2 images, topographic and textural derivatives to map land cover types in the Qianjiangyuan National Park. One used pixel-based random forest (RF) classification algorithm, the other used object-oriented simple non-iterative clustering (SNIC) segmentation in partnership with RF algorithm, which indicated that the input featureswere first segmented into superpixel image objects then classified by RF algorithm. To optimize classification features, a method taking the advantages of cloud computing platform to design different groups of comparative experiments to finalize feature combination with the highest classification accuracy was proposed, followed by using a recursive feature elimination method to further screen the features. Results: Experimental results showed that both the pixel-based method and the object-oriented method had good performance in land cover classification, and the corresponding highest overall classification accuracies of the two methods were estimated at 92.37% and 93.98%, respectively. Furthermore, integration of synthetic aperture radar (SAR) data into the classifications could substantially improve the classification accuracy when using the pixel-based method, but there was no apparent escalating effect for the object-oriented classification. Conclusions: Experiments showed that the land cover classification map generated from the SNIC+RF algorithm in GEE platform was more complete and the algorithm requires fewer features (15 features, including multispectral bands and spectral indices) and runs quickly in the GEE platform. Thus, this algorithm deserves to be popularized in national park management practices.
, Available online ,
doi: 10.13203/j.whugis20200432
Abstract:
At present, Low Earth Orbit (LEO) satellites with missions, such as LEO navigation enhancement, earth observation and scientific application, require higher accuracy and real-time performance of orbit parameters. The accuracy of the traditional real-time orbit determination using space-borne GPS measurements is still a long way to catch up with real-time centimeter-level orbit demand due to the limitations of the GPS broadcast ephemeris errors. The use of real-time orbit and clock corrections for precise positioning applications will be extended into orbit if they are available via a satellite link to Geostationary(GEO) satellites. However, the data link to the GEO constellation may not be available over the polar regions. In this study, the centimeter-level Real-Time Precise Orbit Determination (RTPOD) method using space-borne GPS measurements with IGS-RTS data receiving interruption in the polar regions is studied. Firstly, the characteristics of ephemeris comprehensive error caused by real-time corrections and its short-time extrapolation are analyzed. Based on the analysis, a pseudo-ambiguity is modeled as a stochastic process with piecewise random walk and estimated in the mathematical model of RTPOD to reduce the ephemeris comprehensive error, and then the centimeter-level accuracy can be realized. Finally, using GPS broadcast ephemeris and IGS-RTS products(CLK93), the on broad RTPOD data processing for one week GPS measurements from the GRACE-C satellite is simulated by the SATODS software which is self-developed. The experimental results show that, considering the receiving interruption of real-time corrections in polar regions, the position and velocity accuracy of 7.04cm (3DRMS) and 0.20 mm/s, respectively, are achieved with the RTPOD method proposed in this paper.
At present, Low Earth Orbit (LEO) satellites with missions, such as LEO navigation enhancement, earth observation and scientific application, require higher accuracy and real-time performance of orbit parameters. The accuracy of the traditional real-time orbit determination using space-borne GPS measurements is still a long way to catch up with real-time centimeter-level orbit demand due to the limitations of the GPS broadcast ephemeris errors. The use of real-time orbit and clock corrections for precise positioning applications will be extended into orbit if they are available via a satellite link to Geostationary(GEO) satellites. However, the data link to the GEO constellation may not be available over the polar regions. In this study, the centimeter-level Real-Time Precise Orbit Determination (RTPOD) method using space-borne GPS measurements with IGS-RTS data receiving interruption in the polar regions is studied. Firstly, the characteristics of ephemeris comprehensive error caused by real-time corrections and its short-time extrapolation are analyzed. Based on the analysis, a pseudo-ambiguity is modeled as a stochastic process with piecewise random walk and estimated in the mathematical model of RTPOD to reduce the ephemeris comprehensive error, and then the centimeter-level accuracy can be realized. Finally, using GPS broadcast ephemeris and IGS-RTS products(CLK93), the on broad RTPOD data processing for one week GPS measurements from the GRACE-C satellite is simulated by the SATODS software which is self-developed. The experimental results show that, considering the receiving interruption of real-time corrections in polar regions, the position and velocity accuracy of 7.04cm (3DRMS) and 0.20 mm/s, respectively, are achieved with the RTPOD method proposed in this paper.
, Available online ,
doi: 10.13203/j.whugis20200688
Abstract:
Backscatter coefficient (sigma0) is one of observations of satellite radar altimetry. It is related to the physical and geometric characteristics of land surface under the influence of global/regional climate change. Sigma0 can be used for monitoring surface features under climate change, data calibration and verification of satellite altimeters, inversion of surface features (e.g. soil moisture, snow thickness, etc.) and other fields. GlobeLand30, as the global land cover information with the resolution of 30 m, was produced in China. The Geophysical Data Record (GDR) data of Jason-2 was used to extract and isolate the Ku-band sigma0 data of the Tibetan Plateau (TP). By using the GlobeLand30 2020 version as the basis for surface classification, the latitude and longitude paired sigma0 data give surface attributes, and then we obtain the time-varying sequence of sigma0 under different types of surface features. The singular spectrum analysis (SSA) interpolation is used to fill in missing data. The sigma0 time change trend and period information of the entire TP and different surface attributes are extracted and identified with SSA, and the period results are analyzed by FFT. From the analysis of sigma0 under different surface attributes, its time-varying sequence has different characteristic results:(1) The sigma0 is higher in waters and wetland areas, and the sigma0 is lower in permanent snow and ice areas. (2) There are stable annual, semi-annual and quarterly signals for sigma0 in the TP. The surface properties of the artificial surfaces, bare land, and shrubland area are stable, and the annual sigma0 change is not significant. The changes of sigma0 in other regions have significant annual and semi-annual periods. The amplitude of the semi-annual and quarterly signal is varies with the nature of the surface. (3) The sigma0 changes in the TP show an increasing trend. It is caused by climate change on the TP and wet surface. The sigma0 data of forest, grassland, and shrubland have the increasing trend, the sigma0 of wetland has a trend of decreasing. Besides geophysics and ocean dynamics research, satellite radar altimeter is also feasible for monitoring land environment. The sigma0 obtained by altimeter is closely related to ground properties and climate change. The effects of different geographical attributes on sigma0 in the TP show different time-varying status:(1) the change cycle of sigma0 is mainly annual cycle, and different land surface states have different periodic attributes, which is related to the response state of different land surface to climate change; (2) The sigma0 value in water and wetland is significantly higher than other areas, which may be caused by the difference of surface complex dielectric constant.
Backscatter coefficient (sigma0) is one of observations of satellite radar altimetry. It is related to the physical and geometric characteristics of land surface under the influence of global/regional climate change. Sigma0 can be used for monitoring surface features under climate change, data calibration and verification of satellite altimeters, inversion of surface features (e.g. soil moisture, snow thickness, etc.) and other fields. GlobeLand30, as the global land cover information with the resolution of 30 m, was produced in China. The Geophysical Data Record (GDR) data of Jason-2 was used to extract and isolate the Ku-band sigma0 data of the Tibetan Plateau (TP). By using the GlobeLand30 2020 version as the basis for surface classification, the latitude and longitude paired sigma0 data give surface attributes, and then we obtain the time-varying sequence of sigma0 under different types of surface features. The singular spectrum analysis (SSA) interpolation is used to fill in missing data. The sigma0 time change trend and period information of the entire TP and different surface attributes are extracted and identified with SSA, and the period results are analyzed by FFT. From the analysis of sigma0 under different surface attributes, its time-varying sequence has different characteristic results:(1) The sigma0 is higher in waters and wetland areas, and the sigma0 is lower in permanent snow and ice areas. (2) There are stable annual, semi-annual and quarterly signals for sigma0 in the TP. The surface properties of the artificial surfaces, bare land, and shrubland area are stable, and the annual sigma0 change is not significant. The changes of sigma0 in other regions have significant annual and semi-annual periods. The amplitude of the semi-annual and quarterly signal is varies with the nature of the surface. (3) The sigma0 changes in the TP show an increasing trend. It is caused by climate change on the TP and wet surface. The sigma0 data of forest, grassland, and shrubland have the increasing trend, the sigma0 of wetland has a trend of decreasing. Besides geophysics and ocean dynamics research, satellite radar altimeter is also feasible for monitoring land environment. The sigma0 obtained by altimeter is closely related to ground properties and climate change. The effects of different geographical attributes on sigma0 in the TP show different time-varying status:(1) the change cycle of sigma0 is mainly annual cycle, and different land surface states have different periodic attributes, which is related to the response state of different land surface to climate change; (2) The sigma0 value in water and wetland is significantly higher than other areas, which may be caused by the difference of surface complex dielectric constant.
, Available online ,
doi: 10.13203/j.whugis20200328
Abstract:
Sampling inspection is a fundamental approach in quality management and control of surveying and mapping achievements. Sampling scheme,which includes sampling plan and lot-judging rules, is the critical contents of sampling inspection as it directly determine the result of quality inspection. This paper focuses on the sampling schemes and investigates the validity and suitability of sampling inspection by attributes. A new conception of QUI (Quality Uncertain Interval), which is used to descript sampling scheme's characters, is constructed based on the theory of probability and hypothesis test. Because one sampling scheme can only determinate one QUI, both the QUI length and the quality limits of TYPE I and Ⅱ errors are used to evaluate the effectiveness and validity of sampling schemes. Four typical sampling schemes adopted in GB/T24356-2009 and six simulated sampling schemes are selected to test and analyze. Numerical results show QUI is rational and appropriate for inspection risk controlling of both Geographic products and all kinds of attributes-based sampling industrial products.
Sampling inspection is a fundamental approach in quality management and control of surveying and mapping achievements. Sampling scheme,which includes sampling plan and lot-judging rules, is the critical contents of sampling inspection as it directly determine the result of quality inspection. This paper focuses on the sampling schemes and investigates the validity and suitability of sampling inspection by attributes. A new conception of QUI (Quality Uncertain Interval), which is used to descript sampling scheme's characters, is constructed based on the theory of probability and hypothesis test. Because one sampling scheme can only determinate one QUI, both the QUI length and the quality limits of TYPE I and Ⅱ errors are used to evaluate the effectiveness and validity of sampling schemes. Four typical sampling schemes adopted in GB/T24356-2009 and six simulated sampling schemes are selected to test and analyze. Numerical results show QUI is rational and appropriate for inspection risk controlling of both Geographic products and all kinds of attributes-based sampling industrial products.
, Available online ,
doi: 10.13203/j.whugis20200382
Abstract:
To explore the relationship between urban ecological environment and human activities is an important research content in the current urbanization process. With the in-depth development of the era of big data, multi-source data ubiquitous in the Internet has been fully excavated, which has played an important role in promoting the research of urban ecological environment.Based on multi-source data, the paper proposed toconstruct human activity indicators (residential area walkability index, street vitality index, urban function mixing index) by using Point of Interests (POIs), Open Street Map (OSM) and residential area data, and urban ecological environment indicator (remote sensing ecological index) by using remote sensing images. Combing machine learning models such as polynomial regression(PLR), random forest regression(RFR), eXtreme Gradient Boosting Regression(XGB) and support vector regression machine(SVR), it is effective to make regression analysis on urban ecological environment and human activity indicators.By comparing the performance of different models in this data set, the relationship between the urban ecological environment and human activities is revealed.We demonstrate theapplication of our method using a case study of Nanchang city. The results show that:①The three indexes of human activities all present a central high and gradually decrease to the surroundings, while the urban ecological environment indicators show an opposite trend.②From the analysis of the performance results of each model on the data set, XGB has the best regression effect, followed by PLR.③There is a strong negative correlation between the urban ecological environment and human activities, and the street vitality index, the urban function mixing index are more relevant to the urban ecological environment, and the walkability index of the residential area is less relevant to the urban ecological environment.④In areas where human activities have less impact, the urban ecological environment will be disturbed by other factors, resulting in the low prediction accuracy, while the prediction accuracy in areas with strong human activities is high.
To explore the relationship between urban ecological environment and human activities is an important research content in the current urbanization process. With the in-depth development of the era of big data, multi-source data ubiquitous in the Internet has been fully excavated, which has played an important role in promoting the research of urban ecological environment.Based on multi-source data, the paper proposed toconstruct human activity indicators (residential area walkability index, street vitality index, urban function mixing index) by using Point of Interests (POIs), Open Street Map (OSM) and residential area data, and urban ecological environment indicator (remote sensing ecological index) by using remote sensing images. Combing machine learning models such as polynomial regression(PLR), random forest regression(RFR), eXtreme Gradient Boosting Regression(XGB) and support vector regression machine(SVR), it is effective to make regression analysis on urban ecological environment and human activity indicators.By comparing the performance of different models in this data set, the relationship between the urban ecological environment and human activities is revealed.We demonstrate theapplication of our method using a case study of Nanchang city. The results show that:①The three indexes of human activities all present a central high and gradually decrease to the surroundings, while the urban ecological environment indicators show an opposite trend.②From the analysis of the performance results of each model on the data set, XGB has the best regression effect, followed by PLR.③There is a strong negative correlation between the urban ecological environment and human activities, and the street vitality index, the urban function mixing index are more relevant to the urban ecological environment, and the walkability index of the residential area is less relevant to the urban ecological environment.④In areas where human activities have less impact, the urban ecological environment will be disturbed by other factors, resulting in the low prediction accuracy, while the prediction accuracy in areas with strong human activities is high.
, Available online ,
doi: 10.13203/j.whugis20200165
Abstract:
Water body plays an extremely important role in regional climate regulation. In recent decades,due to the unreasonable utilization and intensive development of the Aral Sea water resources by human beings, the water level and area of the Aral Sea have experienced a sharp decline and shrinkage, which inevitably has an important impact on the regional climate of the Aral Sea. So analyzing the changes of surface temperature variation in the Aral Sea region is of great significance for studying the relationship between the water body and the regional surface temperature. In order to reveal the variation characteristics of surface temperature affected by the Aral Sea, the MODIS surface temperature product (MOD11A2) in the Aral Sea area from 2000 to 2015 was used to analyze the affection on the regional surface temperature by the Area Sea based on the spatial analysis method. The results show that:(1) Compared with the surface temperature of the Aral Sea, 93% and 69% of the study area showed an upward trend respectively in the daytime and nighttime during the study period. (2) The direct impact range that the Aral Sea on the regional surface temperature is roughly within 30 km along the lake boundary. (3) As the area of the Aral Sea shrinks rapidly, the surface temperature difference between the various land cover types and the Aral Sea shows an increasing change trend, and the regional temperature regulation function of the Aral Sea gradually weakens.
Water body plays an extremely important role in regional climate regulation. In recent decades,due to the unreasonable utilization and intensive development of the Aral Sea water resources by human beings, the water level and area of the Aral Sea have experienced a sharp decline and shrinkage, which inevitably has an important impact on the regional climate of the Aral Sea. So analyzing the changes of surface temperature variation in the Aral Sea region is of great significance for studying the relationship between the water body and the regional surface temperature. In order to reveal the variation characteristics of surface temperature affected by the Aral Sea, the MODIS surface temperature product (MOD11A2) in the Aral Sea area from 2000 to 2015 was used to analyze the affection on the regional surface temperature by the Area Sea based on the spatial analysis method. The results show that:(1) Compared with the surface temperature of the Aral Sea, 93% and 69% of the study area showed an upward trend respectively in the daytime and nighttime during the study period. (2) The direct impact range that the Aral Sea on the regional surface temperature is roughly within 30 km along the lake boundary. (3) As the area of the Aral Sea shrinks rapidly, the surface temperature difference between the various land cover types and the Aral Sea shows an increasing change trend, and the regional temperature regulation function of the Aral Sea gradually weakens.
, Available online ,
doi: 10.13203/j.whugis20200142
Abstract:
High-precision precipitation field is important data support for hydrological, meteorological, and environmental analysis, which directly affects the accuracy of relevant services. The traditional simulation of precipitation distribution mostly relies on the spatial driving factors but neglects the impact of the temporal characteristics of precipitation on its spatial distribution. This work employed the average cumulative precipitation data from 83 national meteorological stations and 28 provincial meteorological stations in Hubei Province from 2015 to 2017. According to the correlation analysis, the arch height (C) and nugget value (C0) of the theoretical semivariogram model of precipitation time series are introduced as the influencing factors. This work applies the Geographically Weighted Regression (GWR) model to simulate the monthly average precipitation distribution model of Hubei Province. The results show that:1) The time-series semivariogram curve of monthly precipitation at each station is consistent with the seasonality of precipitation. 25.3% of the theoretical variation function model of site time series become stable in 4 months, and 36.14% in 6 months. 2) Arch height (C) and nugget value (C0) of the theoretical variation function model of precipitation time series at each station are correlated with the annual average cumulative precipitation in December of 0.01 level (both sides). The average correlation coefficients are 0.745 and 0.526, which are greater than the influence of geographical location and elevation on precipitation.3) The introduction of C and C0 is helpful to improve the global goodness of fit and interpolation accuracy of GWR model. By comparing the GWR model with latitude and longitude and the GWR model with variation function features of time series theory, the three-year average overall goodness of fit increased from 0.852 to 0.912. The accuracy evaluation of interpolation in the verification set site showed that the Mean Absolute Error(MAE), the Root Mean Square Error(RMSE) and the Mean Absolute Percentage Error(MAPE) all declined by more than 60% in three years. Therefore, the spatial-temporal weighted regression model with time-series theory variational function features can obtain high precision precipitation simulation results and is more suitable for the estimation of precipitation spatial distribution in the regions with abundant historical precipitation data.
High-precision precipitation field is important data support for hydrological, meteorological, and environmental analysis, which directly affects the accuracy of relevant services. The traditional simulation of precipitation distribution mostly relies on the spatial driving factors but neglects the impact of the temporal characteristics of precipitation on its spatial distribution. This work employed the average cumulative precipitation data from 83 national meteorological stations and 28 provincial meteorological stations in Hubei Province from 2015 to 2017. According to the correlation analysis, the arch height (C) and nugget value (C0) of the theoretical semivariogram model of precipitation time series are introduced as the influencing factors. This work applies the Geographically Weighted Regression (GWR) model to simulate the monthly average precipitation distribution model of Hubei Province. The results show that:1) The time-series semivariogram curve of monthly precipitation at each station is consistent with the seasonality of precipitation. 25.3% of the theoretical variation function model of site time series become stable in 4 months, and 36.14% in 6 months. 2) Arch height (C) and nugget value (C0) of the theoretical variation function model of precipitation time series at each station are correlated with the annual average cumulative precipitation in December of 0.01 level (both sides). The average correlation coefficients are 0.745 and 0.526, which are greater than the influence of geographical location and elevation on precipitation.3) The introduction of C and C0 is helpful to improve the global goodness of fit and interpolation accuracy of GWR model. By comparing the GWR model with latitude and longitude and the GWR model with variation function features of time series theory, the three-year average overall goodness of fit increased from 0.852 to 0.912. The accuracy evaluation of interpolation in the verification set site showed that the Mean Absolute Error(MAE), the Root Mean Square Error(RMSE) and the Mean Absolute Percentage Error(MAPE) all declined by more than 60% in three years. Therefore, the spatial-temporal weighted regression model with time-series theory variational function features can obtain high precision precipitation simulation results and is more suitable for the estimation of precipitation spatial distribution in the regions with abundant historical precipitation data.
, Available online ,
doi: 10.13203/j.whugis20200216
Abstract:
When using variance component estimation to determine the relative weight ratio, the least square solution is generally used as the initial value of the iteration. In geodetic joint inversion, the least squares method will cause ill-posed problems, so the regularized solution is used instead of the least squares solution. Regularization introduces bias to reduce variance, but when using variance component estimation to determine the relative weight ratio, the influence of bias is not considered, and the introduction of bias will cause inaccurate variance component estimation. This paper adopts the bias-corrected variance component estimation method to eliminate the influence of bias introduced by regularization. Simultaneously, the method in this paper, the residual-based bias-corrected variance component estimation and the variance component estimation method are used for simulation experiments. The experiment shows that the variance component estimation method after bias correction can better reverse the slip distribution. The bias-corrected variance component estimation method takes into account the bias introduced by the iterative initial value, and the theory is more rigorous. The Visso earthquake and Norcia earthquake are used for verification, the results show that the bias correction is reasonable and advantageous.
When using variance component estimation to determine the relative weight ratio, the least square solution is generally used as the initial value of the iteration. In geodetic joint inversion, the least squares method will cause ill-posed problems, so the regularized solution is used instead of the least squares solution. Regularization introduces bias to reduce variance, but when using variance component estimation to determine the relative weight ratio, the influence of bias is not considered, and the introduction of bias will cause inaccurate variance component estimation. This paper adopts the bias-corrected variance component estimation method to eliminate the influence of bias introduced by regularization. Simultaneously, the method in this paper, the residual-based bias-corrected variance component estimation and the variance component estimation method are used for simulation experiments. The experiment shows that the variance component estimation method after bias correction can better reverse the slip distribution. The bias-corrected variance component estimation method takes into account the bias introduced by the iterative initial value, and the theory is more rigorous. The Visso earthquake and Norcia earthquake are used for verification, the results show that the bias correction is reasonable and advantageous.
, Available online ,
doi: 10.13203/j.whugis20200170
Abstract:
Addressing the problem of high complexity of trajectory sub-segment similarity matching and result sensitivity of trajectory noise, this paper proposes a multi-level trajectory code tree structure that integrates adaptive Hilbert geographic grid coding. A hierarchical organizational form and sub-segment subordinate relationship expression structure are formed from the entire segment of the trajectory to the smallest segment, and a sub-segment similarity matching algorithm is designed on the basis of the trajectory segment code tree to transform complex spatial calculation into string matching operation, which greatly reduces the computational complexity of similar matching of sub-segments. Experiments on actual trajectory data show that the efficiency of the proposed method achieved more than an order of magnitude improvement over the classical distance-based similarity measurement method without affecting accuracy.
Addressing the problem of high complexity of trajectory sub-segment similarity matching and result sensitivity of trajectory noise, this paper proposes a multi-level trajectory code tree structure that integrates adaptive Hilbert geographic grid coding. A hierarchical organizational form and sub-segment subordinate relationship expression structure are formed from the entire segment of the trajectory to the smallest segment, and a sub-segment similarity matching algorithm is designed on the basis of the trajectory segment code tree to transform complex spatial calculation into string matching operation, which greatly reduces the computational complexity of similar matching of sub-segments. Experiments on actual trajectory data show that the efficiency of the proposed method achieved more than an order of magnitude improvement over the classical distance-based similarity measurement method without affecting accuracy.
, Available online ,
doi: 10.13203/j.whugis20210172
Abstract:
Objectives: The middle and lower reaches of Yangtze River, including Hubei, Hunan, Jiangxi, Anhui, Jiangsu, Zhejiang and Shanghai, are important planting bases of commercial grain in China. However, at present, there are relatively few studies on agricultural drought in this regions, and there is a lack of attention to the response of land cover types to drought. Moreover, in the context of climate change, the evolution and trend of agricultural drought in the middle and lower reaches of Yangtze River need further discussion. Methods: This study used MODIS V6 products to construct Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI) to monitor the temporal and spatial evolution of agricultural drought in the middle and lower reaches of Yangtze River from 2001 to 2019, and further explored the drought sensitivity of different vegetative types. Finally, based on the concept of climate change, this study analyzed the drought trends in six provinces and one city (Hubei, Hunan, Anhui, Jiangxi, Jiangsu, Zhejiang, and Shanghai) in the middle and lower reaches of Yangtze River. The results of the above three indices were further evaluated by Standardized Precipitation Index (SPI) on different time scales, obtained and calculated from the CHIRPS V2.0 dataset. Results: The results showed that the VCI and TCI could monitor the long-term abnormal vegetation growth and heat anomalies, respectively, but neither index could provide comprehensive overview of drought conditions. Combining the advantages of both indices with the weights of 0.7 and 0.3 for VCI and TCI, respectively, the VHI, was more effective in agricultural drought monitoring in the middle and lower reaches of Yangtze River. Different vegetation showed different drought sensitivity in study. Crops have the highest sensitivity to drought, forests are the lowest, and grasslands are somewhere in between. In the context of climate change, Jiangxi, Hunan, Hubei, Zhejiang, and Anhui show an intense wet trend in the past 20 years (0.0140/year, 0.0139/year, 0.0118/year, 0.0107/year and 0.0098/year, respectively), while the Jiangsu and Shanghai show a weak wet trend (0.0015/year and 0.0009/year, respectively). Conclusions: Drought indices should be integrated to provide comprehensive evaluation of agricultural drought in the middle and lower reaches of Yangtze River. Jiangsu and Shanghai are still at drought risk due to the weak wet trend and the local agricultural department should take drought mitigation measures to prevent economic losses. In the middle and lower reaches of Yangtze River, croplands have the most obvious response to drought, indicating that crops are most sensitive to drought than grasses and forests, more attention should be paid to agriculture management. The relevant results can provide reference for the early warning of drought in various provinces and cities in the middle and lower reaches of Yangtze River and help the management of regional agricultural production.
Objectives: The middle and lower reaches of Yangtze River, including Hubei, Hunan, Jiangxi, Anhui, Jiangsu, Zhejiang and Shanghai, are important planting bases of commercial grain in China. However, at present, there are relatively few studies on agricultural drought in this regions, and there is a lack of attention to the response of land cover types to drought. Moreover, in the context of climate change, the evolution and trend of agricultural drought in the middle and lower reaches of Yangtze River need further discussion. Methods: This study used MODIS V6 products to construct Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI) to monitor the temporal and spatial evolution of agricultural drought in the middle and lower reaches of Yangtze River from 2001 to 2019, and further explored the drought sensitivity of different vegetative types. Finally, based on the concept of climate change, this study analyzed the drought trends in six provinces and one city (Hubei, Hunan, Anhui, Jiangxi, Jiangsu, Zhejiang, and Shanghai) in the middle and lower reaches of Yangtze River. The results of the above three indices were further evaluated by Standardized Precipitation Index (SPI) on different time scales, obtained and calculated from the CHIRPS V2.0 dataset. Results: The results showed that the VCI and TCI could monitor the long-term abnormal vegetation growth and heat anomalies, respectively, but neither index could provide comprehensive overview of drought conditions. Combining the advantages of both indices with the weights of 0.7 and 0.3 for VCI and TCI, respectively, the VHI, was more effective in agricultural drought monitoring in the middle and lower reaches of Yangtze River. Different vegetation showed different drought sensitivity in study. Crops have the highest sensitivity to drought, forests are the lowest, and grasslands are somewhere in between. In the context of climate change, Jiangxi, Hunan, Hubei, Zhejiang, and Anhui show an intense wet trend in the past 20 years (0.0140/year, 0.0139/year, 0.0118/year, 0.0107/year and 0.0098/year, respectively), while the Jiangsu and Shanghai show a weak wet trend (0.0015/year and 0.0009/year, respectively). Conclusions: Drought indices should be integrated to provide comprehensive evaluation of agricultural drought in the middle and lower reaches of Yangtze River. Jiangsu and Shanghai are still at drought risk due to the weak wet trend and the local agricultural department should take drought mitigation measures to prevent economic losses. In the middle and lower reaches of Yangtze River, croplands have the most obvious response to drought, indicating that crops are most sensitive to drought than grasses and forests, more attention should be paid to agriculture management. The relevant results can provide reference for the early warning of drought in various provinces and cities in the middle and lower reaches of Yangtze River and help the management of regional agricultural production.
, Available online ,
doi: 10.13203/j.whugis20210224
Abstract:
The access, integrated management, and service of sensing devices is the primary prerequisite for smart cities. First of all, this context analyzes the requirement and complexity of smart city sensing to discuss sensing technology widely used currently. And then compares the development process of smart city sensing technology and analyzes the network architectures used in smart city observation nowadays. On this basis, the challenge of accessing the massive number of sensors in the future smart city is pointed out, and the next-generation solution of the Smart City Sensing Base Station (SCSBS) is proposed. This context also discusses the critical role of SCSBS as an integrated sensing infrastructure for future smart cities and introduces the operation principle of the whole system in terms of its prototype. Finally, this article analyzes and envisions its application capabilities in block-scale scenarios and emergency scenarios around the SCSBS.
The access, integrated management, and service of sensing devices is the primary prerequisite for smart cities. First of all, this context analyzes the requirement and complexity of smart city sensing to discuss sensing technology widely used currently. And then compares the development process of smart city sensing technology and analyzes the network architectures used in smart city observation nowadays. On this basis, the challenge of accessing the massive number of sensors in the future smart city is pointed out, and the next-generation solution of the Smart City Sensing Base Station (SCSBS) is proposed. This context also discusses the critical role of SCSBS as an integrated sensing infrastructure for future smart cities and introduces the operation principle of the whole system in terms of its prototype. Finally, this article analyzes and envisions its application capabilities in block-scale scenarios and emergency scenarios around the SCSBS.
, Available online ,
doi: 10.13203/j.whugis20200609
Abstract:
Individual location prediction is of great significance in applications such as precise prevention and control of infectious diseases and scientific planning of public facilities. Existing location prediction algorithms mainly focus on mining and modeling individual longitudinal historical trajectory characteristics, and realize location prediction, and less consider the regular characteristics of users with horizontal similarity. For this reason, based on the framework of Graph Convolution Network (GCN) and Long Short-Term Memory (LSTM) model, this paper proposes an individual location prediction algorithm that takes into account the characteristics of horizontally similar user trajectories and the characteristics of vertical historical regularity. First, construct a user trajectory similarity algorithm and screen users with high similarity, then use the graph convolution model to extract user trajectory features with high similarity of users to be predicted, and finally use the long and short-term memory model framework to extract historical trajectory features and integrate similar user trajectory features, So as to achieve individual location prediction. Based on the data of more than 80,000 users in a city for 4 consecutive working days, the results show that the accuracy of the method proposed in this paper decreases with the increase of the prediction time step, and the accuracy of night prediction is significantly higher than that of the day, but compared to the previous All models have an improvement of more than 10%. When 15 minutes is used as the prediction time step, the model accuracy rate reaches 80.45%.
Individual location prediction is of great significance in applications such as precise prevention and control of infectious diseases and scientific planning of public facilities. Existing location prediction algorithms mainly focus on mining and modeling individual longitudinal historical trajectory characteristics, and realize location prediction, and less consider the regular characteristics of users with horizontal similarity. For this reason, based on the framework of Graph Convolution Network (GCN) and Long Short-Term Memory (LSTM) model, this paper proposes an individual location prediction algorithm that takes into account the characteristics of horizontally similar user trajectories and the characteristics of vertical historical regularity. First, construct a user trajectory similarity algorithm and screen users with high similarity, then use the graph convolution model to extract user trajectory features with high similarity of users to be predicted, and finally use the long and short-term memory model framework to extract historical trajectory features and integrate similar user trajectory features, So as to achieve individual location prediction. Based on the data of more than 80,000 users in a city for 4 consecutive working days, the results show that the accuracy of the method proposed in this paper decreases with the increase of the prediction time step, and the accuracy of night prediction is significantly higher than that of the day, but compared to the previous All models have an improvement of more than 10%. When 15 minutes is used as the prediction time step, the model accuracy rate reaches 80.45%.
, Available online ,
doi: 10.13203/j.whugis20200229
Abstract:
Focusing on the problem of global absolute positioning of drones under global navigation satellite system (GNSS) denied environment, a reference satellite image retrieval method that aggregates deep learning features is proposed. First, the pre-trained deep learning model is used to extract local convolution features of the drone images and satellite images. Then, the local aggregation descriptor vector is used to generate the global expression of the images. Finally, the global feature of the image is used to perform similarity retrieval and the post-processing method of matching precisely and reranking the retrieval results is used, which further improves the retrieval accuracy. A new satellite reference image data set for absolute positioning of drone is designed and tested. The results show that the accuracy of the method used to retrieve the satellite reference image in the drone image adaptation area is 76.07%, which can provide a reference for the subsequent absolute positioning of vision-based drones.
Focusing on the problem of global absolute positioning of drones under global navigation satellite system (GNSS) denied environment, a reference satellite image retrieval method that aggregates deep learning features is proposed. First, the pre-trained deep learning model is used to extract local convolution features of the drone images and satellite images. Then, the local aggregation descriptor vector is used to generate the global expression of the images. Finally, the global feature of the image is used to perform similarity retrieval and the post-processing method of matching precisely and reranking the retrieval results is used, which further improves the retrieval accuracy. A new satellite reference image data set for absolute positioning of drone is designed and tested. The results show that the accuracy of the method used to retrieve the satellite reference image in the drone image adaptation area is 76.07%, which can provide a reference for the subsequent absolute positioning of vision-based drones.
, Available online ,
doi: 10.13203/j.whugis20200651
Abstract:
Deep learning based super-resolution reconstruction technology has been widely used in multi-temporal hyperspectral images, high-resolution image reconstruction. However, multi angle remote sensing images have rich complementary information, which is very suitable for super-resolution reconstruction. Therefore, this paper proposed an end-to-end super-resolution reconstruction algorithm based on dynamic upsampling filter network for high-resolution multi-angle remote sensing images. The network mainly includes two branches. One branch utilized dynamic upsampling filter block to improve the image resolution. The other branch is used to learn the high frequency information. The final super-resolution reconstruction image is obtained by adding the results of two branches. The validity of the network super-resolution reconstruction is verified by utilizing the WorldView2 multi-angle remote sensing image data sets. Experimental results show that the proposed method can effectively improve the spatial resolution of the target image while taking into account the angle dimension information of multi angle images, and effectively maintain the image details.
Deep learning based super-resolution reconstruction technology has been widely used in multi-temporal hyperspectral images, high-resolution image reconstruction. However, multi angle remote sensing images have rich complementary information, which is very suitable for super-resolution reconstruction. Therefore, this paper proposed an end-to-end super-resolution reconstruction algorithm based on dynamic upsampling filter network for high-resolution multi-angle remote sensing images. The network mainly includes two branches. One branch utilized dynamic upsampling filter block to improve the image resolution. The other branch is used to learn the high frequency information. The final super-resolution reconstruction image is obtained by adding the results of two branches. The validity of the network super-resolution reconstruction is verified by utilizing the WorldView2 multi-angle remote sensing image data sets. Experimental results show that the proposed method can effectively improve the spatial resolution of the target image while taking into account the angle dimension information of multi angle images, and effectively maintain the image details.
, Available online ,
doi: 10.13203/j.whugis20200179
Abstract:
The consistency of spatial distribution pattern before and after generalization is an important factor to evaluate the quality of land use data. Considering the lack of quantitative analysis and visualization in spatial distribution model evaluation of data, this paper puts forward a new evaluation method based on the unique autocorrelation of spatial data. Firstly, we establish the spatial weight matrix by using the semantic distance of data and calculate Global and local autocorrelation of data before and after processing by Moran'i index, then use Moran's scatterplot and LISA (local indicators of spatial association) aggregation map to visualize the quality of spatial distribution patterns. Compared with the traditional data quality evaluation method, this method can evaluate the consistency of spatial distribution pattern of global map better by taking into account the semantic relation of the data, obtaining the quantifiable aggregation degree and contrasting by the visual method. In cognitive experiments, we take the land type data been scale transformed which cause data quality problems mostly as an example. The experimental results accord with human cognitions and the experimental method is practical.
The consistency of spatial distribution pattern before and after generalization is an important factor to evaluate the quality of land use data. Considering the lack of quantitative analysis and visualization in spatial distribution model evaluation of data, this paper puts forward a new evaluation method based on the unique autocorrelation of spatial data. Firstly, we establish the spatial weight matrix by using the semantic distance of data and calculate Global and local autocorrelation of data before and after processing by Moran'i index, then use Moran's scatterplot and LISA (local indicators of spatial association) aggregation map to visualize the quality of spatial distribution patterns. Compared with the traditional data quality evaluation method, this method can evaluate the consistency of spatial distribution pattern of global map better by taking into account the semantic relation of the data, obtaining the quantifiable aggregation degree and contrasting by the visual method. In cognitive experiments, we take the land type data been scale transformed which cause data quality problems mostly as an example. The experimental results accord with human cognitions and the experimental method is practical.
Articles just accepted have been peer-reviewed and accepted, which are not yet assigned to volumes /issues, but are citable by Digital Object Identifier (DOI).
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, Available online ,
doi: 10.13203/j.whugis20200098
Abstract:
Unmanned Aerial Vehicle; Antarctic Expedition; Climate Change; Photogrammetry; Glaciology; Ecology; Geomorphology
Unmanned Aerial Vehicle; Antarctic Expedition; Climate Change; Photogrammetry; Glaciology; Ecology; Geomorphology
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