Top Cited
1
2019, 44(7): 957-966.
DOI: 10.13203/j.whugis20190088
Abstract:
In China, traditional methodology on early detection of natural terrain to landslides is challenging as zones most prone to slope failure are usually inaccessible due to high location and dense vegetation. This can lead to underestimation of potential landslide events to the degree of wrongly identifying unstable areas as stable. This paper provides a solution for these cases by proposing an integrated space-air-ground investigation system that allows for the early detection, real-time prediction, and warning of catastrophic geohazards. Firstly, high-resolution optical images and interferometric synthetic aperture radar (InSAR) data from satellites are employed to obtain a global panorama of a region, highlighting these problematic locations; yet results are detailed enough to provide reliable estimates of deformations at particular points along time spans of days and weeks. As consequence, it makes the compilation of long displacement time-histories feasible, contributing to the understanding of long-term landslide-driving phenomena in regions where it has been underestimated. This is called the general investigation. Then, detailed assessments can be done through the deve-lopment of unmanned aerial vehicles (UAV) for elaborating high-resolution relief maps and photogrammetric representations based on both visual images and light laser detection and ranging (LiDAR) data. The system finally allows for precise tagging of locations that warrant real-time site monitoring of displacements using global navigation satellite system (GNSS) and crack gauges, validating expecting behavior of these critical, but previously hidden hazardous locations. The overall approach makes it possible to establish a four-level comprehensive early warning system, which meets the urgent needs of the country and promotes a practical and operational application of such system in the field of geohazard prevention.
In China, traditional methodology on early detection of natural terrain to landslides is challenging as zones most prone to slope failure are usually inaccessible due to high location and dense vegetation. This can lead to underestimation of potential landslide events to the degree of wrongly identifying unstable areas as stable. This paper provides a solution for these cases by proposing an integrated space-air-ground investigation system that allows for the early detection, real-time prediction, and warning of catastrophic geohazards. Firstly, high-resolution optical images and interferometric synthetic aperture radar (InSAR) data from satellites are employed to obtain a global panorama of a region, highlighting these problematic locations; yet results are detailed enough to provide reliable estimates of deformations at particular points along time spans of days and weeks. As consequence, it makes the compilation of long displacement time-histories feasible, contributing to the understanding of long-term landslide-driving phenomena in regions where it has been underestimated. This is called the general investigation. Then, detailed assessments can be done through the deve-lopment of unmanned aerial vehicles (UAV) for elaborating high-resolution relief maps and photogrammetric representations based on both visual images and light laser detection and ranging (LiDAR) data. The system finally allows for precise tagging of locations that warrant real-time site monitoring of displacements using global navigation satellite system (GNSS) and crack gauges, validating expecting behavior of these critical, but previously hidden hazardous locations. The overall approach makes it possible to establish a four-level comprehensive early warning system, which meets the urgent needs of the country and promotes a practical and operational application of such system in the field of geohazard prevention.
2
2016, 41(2): 221-227.
DOI: 10.13203/j.whugis20140092
Abstract:
The distribution pattern of urban facility POIs usually forms clusters (i.e. "hot spots") in local geographic space. The kernel density estimation (KDE), which has been usually utilized for expressing these spatial characteristics, is one of the most popular visualization tools. Considering the missing of quantitative statistical inference assessment in KDE, this paper proposes a novel method to detect the hot spots of urban facility POIs. First, this method computes the attribute value of geographic unit with the "distance decay effect", then by adopting the statistical index of Getis-Ord Gi*, we analysis the local spatial cluster characteristics of urban facilities. Comparing this method with the conventional spatial autocorrelation based on the Quadrat clustering, the attribute value of kernel density computing can preserve the local information of data, and the spatial cluster characteristics of urban facilities can reflect the continuity characteristics of urban services, for that the KDE considers the regional impact based on the First Law of Geography. The actual data experiment for analyzing the financial POIs' distribution patterns indicates that this approach is effective to extract the hot spots of urban facility POIs in city areas.
The distribution pattern of urban facility POIs usually forms clusters (i.e. "hot spots") in local geographic space. The kernel density estimation (KDE), which has been usually utilized for expressing these spatial characteristics, is one of the most popular visualization tools. Considering the missing of quantitative statistical inference assessment in KDE, this paper proposes a novel method to detect the hot spots of urban facility POIs. First, this method computes the attribute value of geographic unit with the "distance decay effect", then by adopting the statistical index of Getis-Ord Gi*, we analysis the local spatial cluster characteristics of urban facilities. Comparing this method with the conventional spatial autocorrelation based on the Quadrat clustering, the attribute value of kernel density computing can preserve the local information of data, and the spatial cluster characteristics of urban facilities can reflect the continuity characteristics of urban services, for that the KDE considers the regional impact based on the First Law of Geography. The actual data experiment for analyzing the financial POIs' distribution patterns indicates that this approach is effective to extract the hot spots of urban facility POIs in city areas.
3
2015, 40(1): 1-13.
4
2018, 43(12): 1885-1898.
DOI: 10.13203/j.whugis20180251
Abstract:
Change detection for remote sensing imagery is the process to determine difference of the same object or phenomenon at different times. Real-time automatic change detection technology is of great significance for excavating potential of image data and maintaining the current situation of geospatial data. With the development of remote-sensing earth observation technology, varieties of remote-sensing sensors for different tasks have emerged. Change detection is also diversified with the coming up of multi-resolution remote-sensing data, with advanced theories and techniques developed for continuously different sensors. This paper reviews the development of multi-temporal remote sen-sing image change detection technologies and summarizes the classification system of multi-temporal remote sensing image change detection. And the latest developments in change detection research are summarized from three aspects:pre-processing, change detection strategies and accuracy assessment. This paper also points out the challenges that change detection is facing and possible countermeasures, in the hope of deepening the research into change detection technology for remote sensing images.
Change detection for remote sensing imagery is the process to determine difference of the same object or phenomenon at different times. Real-time automatic change detection technology is of great significance for excavating potential of image data and maintaining the current situation of geospatial data. With the development of remote-sensing earth observation technology, varieties of remote-sensing sensors for different tasks have emerged. Change detection is also diversified with the coming up of multi-resolution remote-sensing data, with advanced theories and techniques developed for continuously different sensors. This paper reviews the development of multi-temporal remote sen-sing image change detection technologies and summarizes the classification system of multi-temporal remote sensing image change detection. And the latest developments in change detection research are summarized from three aspects:pre-processing, change detection strategies and accuracy assessment. This paper also points out the challenges that change detection is facing and possible countermeasures, in the hope of deepening the research into change detection technology for remote sensing images.
5
2018, 43(12): 2039-2049.
DOI: 10.13203/j.whugis20180181
Abstract:
As the most frequent and devastating geohazard next to earthquakes, landslides are widely distributed in mountainous areas of west China, which makes early detection of landslides a vital task for geologic disaster prevention. Although time series SAR interferometry (InSAR) based on repeat-pass satellite SAR observations has shown a great potential in landslide detection, its performance is usually limited by factors such as vegetation coverage, which leads to low reliability of detection results. Aiming at this problem, we carry out a case study by employing the coherent scatterer InSAR (CSI) method to successfully detect 17 unstable slopes in Danba County in the upper reach of Dadu River Basin from archived ALOS PALSAR and ENVISAT ASAR datasets. The effectiveness and advantage of the CSI method are demonstrated by comparisons with other observation data as well as validation against field survey. And, major impact factors for the performance of time series InSAR analysis in landslide investigations and future research topics of high priority are summarized.
As the most frequent and devastating geohazard next to earthquakes, landslides are widely distributed in mountainous areas of west China, which makes early detection of landslides a vital task for geologic disaster prevention. Although time series SAR interferometry (InSAR) based on repeat-pass satellite SAR observations has shown a great potential in landslide detection, its performance is usually limited by factors such as vegetation coverage, which leads to low reliability of detection results. Aiming at this problem, we carry out a case study by employing the coherent scatterer InSAR (CSI) method to successfully detect 17 unstable slopes in Danba County in the upper reach of Dadu River Basin from archived ALOS PALSAR and ENVISAT ASAR datasets. The effectiveness and advantage of the CSI method are demonstrated by comparisons with other observation data as well as validation against field survey. And, major impact factors for the performance of time series InSAR analysis in landslide investigations and future research topics of high priority are summarized.
6
2017, 42(6): 711-720.
DOI: 10.13203/j.whugis20170100
Abstract:
Based on the DMSP/OLS nighttime light data for the years 1993-2012 and spatial analysis methods including standard deviational ellipse and rank-size distribution, this paper systematically analyzes the spatial structure and spatiotemporal dynamics of the urban system in countries along B & R (The Belt and Road Initiative). We found that nighttime light increased in most countries along B & R. These fast growing countries are undergoing economic reforms and post-war reconstruction, while nighttime light reduction occurs in areas of social and economic unrest. The trend of size distribution of nighttime light in B&R is continuous spatial expansion, and the center of the nighttime light is moving to southeast Asia. The nighttime light distribution in the top 2000 urban places in B&R follows the rank-size distribution, thus urban land distribution is more concentrated than the past. The high rank cities are fairly well developed, but the development of the small cities is lagging behind. The general distribution trend toward, concentration is stronger than decentralization in B&R.
Based on the DMSP/OLS nighttime light data for the years 1993-2012 and spatial analysis methods including standard deviational ellipse and rank-size distribution, this paper systematically analyzes the spatial structure and spatiotemporal dynamics of the urban system in countries along B & R (The Belt and Road Initiative). We found that nighttime light increased in most countries along B & R. These fast growing countries are undergoing economic reforms and post-war reconstruction, while nighttime light reduction occurs in areas of social and economic unrest. The trend of size distribution of nighttime light in B&R is continuous spatial expansion, and the center of the nighttime light is moving to southeast Asia. The nighttime light distribution in the top 2000 urban places in B&R follows the rank-size distribution, thus urban land distribution is more concentrated than the past. The high rank cities are fairly well developed, but the development of the small cities is lagging behind. The general distribution trend toward, concentration is stronger than decentralization in B&R.
7
2019, 44(7): 949-956.
DOI: 10.13203/j.whugis20190094
Abstract:
Since 2017, many serious geological disasters have been reported, including the 2017 mountain collapse at high altitudes in Xinmo Village in Mao County, Sichuan Province, and the 2018 Baige landslide in Jinsha River, most of which are of great destructive power and hard to detect in advance. It is worth noting that although the geohazard prevention has been carried out extensively across the whole country which is supported by the state, many of these geological disasters occur outside the potential geohazard points estimated in advance. The early identification of these undetectable geohazards points remains a big challenge and a crucial task in current geohazard prevention work. In this paper, the characteristics of interferometric synthetic aperture radar (InSAR) and its inherited limitations are summarized. Based on the integrated remote sensing technologies (including optical, SAR/InSAR and LiDAR), the key observation concept with three forms "morphology, deformation, situation" is proposed. Through the integration of a range of remote sensing technologies, the locations of potential geohazards will be identified qualitatively, and their associated movements will be monitored quantitatively. Finally, a series of thoughts and recommendations are provided to guide our future work for the early detection of serious geological hazards.
Since 2017, many serious geological disasters have been reported, including the 2017 mountain collapse at high altitudes in Xinmo Village in Mao County, Sichuan Province, and the 2018 Baige landslide in Jinsha River, most of which are of great destructive power and hard to detect in advance. It is worth noting that although the geohazard prevention has been carried out extensively across the whole country which is supported by the state, many of these geological disasters occur outside the potential geohazard points estimated in advance. The early identification of these undetectable geohazards points remains a big challenge and a crucial task in current geohazard prevention work. In this paper, the characteristics of interferometric synthetic aperture radar (InSAR) and its inherited limitations are summarized. Based on the integrated remote sensing technologies (including optical, SAR/InSAR and LiDAR), the key observation concept with three forms "morphology, deformation, situation" is proposed. Through the integration of a range of remote sensing technologies, the locations of potential geohazards will be identified qualitatively, and their associated movements will be monitored quantitatively. Finally, a series of thoughts and recommendations are provided to guide our future work for the early detection of serious geological hazards.
8
2019, 44(9): 1342-1354.
DOI: 10.13203/j.whugis20190086
Abstract:
On October 11 and November 3, 2018, two large-scale landslides occurred in the upstream of the Jinsha River near the Baige Village, Polo Town, Jiangda County, Tibet Autonomous Region. Although the drainage was carried out by manual intervention, it still caused severe losses to the residents and transportation facilities in the downstream. Whether there are similar large-scale potential landslides in the upstream and downstream of the Jinsha River has become the social focus issue after the Baige Landslide disaster occurred. Firstly, high-resolution optical satellite images are used to interpret and qualitatively evaluate the potential landslide hazards in the upstream of the Baige Landslide within 30 km and the downstream of the Baige Landslide within 100 km. A total of 51 potential landslides are identified, of which 10 potential landslides in the downstream of the Baige Landslide within 70-100 km have the risk of blocking the river. On this basis, the ALOS PALSAR-1 and Sentinel-1A radar satellite data are collected in the key area (70-100 km downstream of the Baige Landslide) with the risk of river blockage. The quantitative detection and analysis of the potential surface deformation of landslides are carried out by using the small baseline subsets interferometric synthetic aperture radar (SBAS-InSAR). Seven potential landslides are detected to have significant deformation, of which three are at higher risk of river blockage. The research results have been submitted to the Ministry of Emergency Management, Sichuan Natural Resources Department, China Railway Second Academy Engineering Group Co. Ltd. and other departments and units, providing a reference for the prevention and control of geological hazards in the upstream and downstream of the Baige Landslide and the route selection of Sichuan-Tibet railway.
On October 11 and November 3, 2018, two large-scale landslides occurred in the upstream of the Jinsha River near the Baige Village, Polo Town, Jiangda County, Tibet Autonomous Region. Although the drainage was carried out by manual intervention, it still caused severe losses to the residents and transportation facilities in the downstream. Whether there are similar large-scale potential landslides in the upstream and downstream of the Jinsha River has become the social focus issue after the Baige Landslide disaster occurred. Firstly, high-resolution optical satellite images are used to interpret and qualitatively evaluate the potential landslide hazards in the upstream of the Baige Landslide within 30 km and the downstream of the Baige Landslide within 100 km. A total of 51 potential landslides are identified, of which 10 potential landslides in the downstream of the Baige Landslide within 70-100 km have the risk of blocking the river. On this basis, the ALOS PALSAR-1 and Sentinel-1A radar satellite data are collected in the key area (70-100 km downstream of the Baige Landslide) with the risk of river blockage. The quantitative detection and analysis of the potential surface deformation of landslides are carried out by using the small baseline subsets interferometric synthetic aperture radar (SBAS-InSAR). Seven potential landslides are detected to have significant deformation, of which three are at higher risk of river blockage. The research results have been submitted to the Ministry of Emergency Management, Sichuan Natural Resources Department, China Railway Second Academy Engineering Group Co. Ltd. and other departments and units, providing a reference for the prevention and control of geological hazards in the upstream and downstream of the Baige Landslide and the route selection of Sichuan-Tibet railway.
9
2016, 41(6): 711-721.
DOI: 10.13203/j.whugis20160099
Abstract:
In this paper, we proposed an automatic aerotriangulation method based on SWDC-5 oblique images. First of all, we designed an improved viewpoint-invariant matching method for oblique images based on the perspective transformation. Secondly, in order to reduce the amount of unknown adjustment parameters (overmuch unknown adjustment parameter may weaken the instability of adjustment solution), we offered a new bundle adjustment model for oblique images which took the relative attitude parameters of cameras into account, and also gave the application scope of the model. Experiments conducted on the typical SWDC-5 oblique images demonstrated that when the relative attitude of cameras (on same camera station) are stable and their cameras exposure are limited within a short time delay, the aero-triangulation accuracy of our method is high, the unit weight error is 0.46 pixels and the average residual of image points is 0.27 pixels. Thirdly, we applied the results of aero-triangulation to PMVS (patch-based multi-view stereo matching) algorithm to gain the dense point-cloud of the experimental city, used screened Poisson reconstruction algorithm to get its 3D mesh, and reconstruct its 3D surface with 3D texture algorithm. Experiments showed that the accuracy of the aero triangulation met the requirements of applications, and the obtained 3D city model had a natural, real texture, all this proved that the idea of automatic reconstruction of 3D city model is feasible and made a good reference for the large-scale 3D city model reconstruction.
In this paper, we proposed an automatic aerotriangulation method based on SWDC-5 oblique images. First of all, we designed an improved viewpoint-invariant matching method for oblique images based on the perspective transformation. Secondly, in order to reduce the amount of unknown adjustment parameters (overmuch unknown adjustment parameter may weaken the instability of adjustment solution), we offered a new bundle adjustment model for oblique images which took the relative attitude parameters of cameras into account, and also gave the application scope of the model. Experiments conducted on the typical SWDC-5 oblique images demonstrated that when the relative attitude of cameras (on same camera station) are stable and their cameras exposure are limited within a short time delay, the aero-triangulation accuracy of our method is high, the unit weight error is 0.46 pixels and the average residual of image points is 0.27 pixels. Thirdly, we applied the results of aero-triangulation to PMVS (patch-based multi-view stereo matching) algorithm to gain the dense point-cloud of the experimental city, used screened Poisson reconstruction algorithm to get its 3D mesh, and reconstruct its 3D surface with 3D texture algorithm. Experiments showed that the accuracy of the aero triangulation met the requirements of applications, and the obtained 3D city model had a natural, real texture, all this proved that the idea of automatic reconstruction of 3D city model is feasible and made a good reference for the large-scale 3D city model reconstruction.
10
2019, 44(7): 967-979.
DOI: 10.13203/j.whugis20190098
Abstract:
Satellite radar observations enable us not only to detect landslides with detailed sliding signals over broad spatial extents, but also to track landslide dynamics continuously, which has gradually been recognized by the earth observation and landslide communities. However, there are still several challenges in the landslide detection and monitoring with satellite radar observations due to their inherent limitations such as the phase decorrelation caused by heavy vegetation and/or large gradient surface movements, and the geometric distortion introduced by the side-looking orbit. In this paper, from landslide detection and monitoring perspective, the four major challenges of satellite radar technologies are discussed:①The phase decorrelation caused by heavy vegetation can be weakened by use of synthetic aperture radar (SAR) imagery with a long radar wavelength (e.g. S-band or L-band), a short temporal resolution, and/or a high spatial resolution (e.g. 1 m or even higher), and/or advanced interferometric SAR (InSAR) time series, and the phase decorrelation associated with large deformation gradients can be addressed by SAR offset tracking and range split-spectrum interferometry techniques.②Atmospheric effects represent a big challenge of conventional InSAR for landslide detection and monitoring, especially in mountain areas. The generic atmospheric correction online service (GACOS) which is developed at Newcastle University can be used to reduce atmospheric effects on radar observations and simplify the follow-on time series analysis.③The geometric distortions such as shadows and layovers can be pre-analyzed using an external digital elevation model (DEM) for medium-spatial-resolution SAR data; in contrast, for high-resolution SAR data, a machine learning approach can be used to identify water bodies, shadow and layover areas without a requirement of a high-spatial-resolution DEM.④Residual topographic phase exhibits in areas with high buildings or steep slopes, which could easily lead to phase unwrapping errors; this can be tackled by a baseline linear combination approach. In addition, a framework is proposed to combine satellite radar technologies with other earth observations (e.g. ground-based radar, LiDAR and GNSS) to develop an automated landslide detection and monitoring system. It is expected that this paper will help the earth observation and landslide communities clarify the technical pros and cons of the satellite radar technologies so as to promote them and guide their future development.
Satellite radar observations enable us not only to detect landslides with detailed sliding signals over broad spatial extents, but also to track landslide dynamics continuously, which has gradually been recognized by the earth observation and landslide communities. However, there are still several challenges in the landslide detection and monitoring with satellite radar observations due to their inherent limitations such as the phase decorrelation caused by heavy vegetation and/or large gradient surface movements, and the geometric distortion introduced by the side-looking orbit. In this paper, from landslide detection and monitoring perspective, the four major challenges of satellite radar technologies are discussed:①The phase decorrelation caused by heavy vegetation can be weakened by use of synthetic aperture radar (SAR) imagery with a long radar wavelength (e.g. S-band or L-band), a short temporal resolution, and/or a high spatial resolution (e.g. 1 m or even higher), and/or advanced interferometric SAR (InSAR) time series, and the phase decorrelation associated with large deformation gradients can be addressed by SAR offset tracking and range split-spectrum interferometry techniques.②Atmospheric effects represent a big challenge of conventional InSAR for landslide detection and monitoring, especially in mountain areas. The generic atmospheric correction online service (GACOS) which is developed at Newcastle University can be used to reduce atmospheric effects on radar observations and simplify the follow-on time series analysis.③The geometric distortions such as shadows and layovers can be pre-analyzed using an external digital elevation model (DEM) for medium-spatial-resolution SAR data; in contrast, for high-resolution SAR data, a machine learning approach can be used to identify water bodies, shadow and layover areas without a requirement of a high-spatial-resolution DEM.④Residual topographic phase exhibits in areas with high buildings or steep slopes, which could easily lead to phase unwrapping errors; this can be tackled by a baseline linear combination approach. In addition, a framework is proposed to combine satellite radar technologies with other earth observations (e.g. ground-based radar, LiDAR and GNSS) to develop an automated landslide detection and monitoring system. It is expected that this paper will help the earth observation and landslide communities clarify the technical pros and cons of the satellite radar technologies so as to promote them and guide their future development.
11
2018, 43(7): 1085-1091.
DOI: 10.13203/j.whugis20160515
Abstract:
The research area is located in Shazhenxi town and Xietan town of Three Gorges reservoir area in this paper. In order to obtain better results that discrete the continuous factors of landslide, entropy based on minimal description length principle(Ent-MDLP) method is used. To avoid the influence of correlation between factors, we calculate the Pearson correlation coefficient to remove high correlation factor. In order to obtain more accurate non-landslide sample points, the non-landslide sample points are randomly selected from the very low and low susceptible regions predicted by the entropy method. For the optimized random forests model, the optimal random features and its number are determined by iterative calculation of out-of-bag error estimation. Then the optimized random forest is evaluated for the landslide of the study area, and the landslide susceptibility level is divided. The model is compared with the methods of logistic regression, support vector machine and non-optimized random forest. The accuracy of each model is evaluated by plotting the receiver sensitivity curve of each algorithm. The optimized random forest's area is the highest, which the area under the curve is 91.8%. These show that the random forest model is optimized with more high-predictive power in landslide-prone assessment.
The research area is located in Shazhenxi town and Xietan town of Three Gorges reservoir area in this paper. In order to obtain better results that discrete the continuous factors of landslide, entropy based on minimal description length principle(Ent-MDLP) method is used. To avoid the influence of correlation between factors, we calculate the Pearson correlation coefficient to remove high correlation factor. In order to obtain more accurate non-landslide sample points, the non-landslide sample points are randomly selected from the very low and low susceptible regions predicted by the entropy method. For the optimized random forests model, the optimal random features and its number are determined by iterative calculation of out-of-bag error estimation. Then the optimized random forest is evaluated for the landslide of the study area, and the landslide susceptibility level is divided. The model is compared with the methods of logistic regression, support vector machine and non-optimized random forest. The accuracy of each model is evaluated by plotting the receiver sensitivity curve of each algorithm. The optimized random forest's area is the highest, which the area under the curve is 91.8%. These show that the random forest model is optimized with more high-predictive power in landslide-prone assessment.
12
2020, 45(9): 1356-1366.
DOI: 10.13203/j.whugis20190346
Abstract:
Spatial heterogeneity or non-stationarity in data relationships is one of the hot topics in spatial statistics or relative application fields, while the development of local techniques forms an essential part for the relative studies. Geographically weighted regression (GWR) provides spatially varying coefficient estimates via location-specific weighted regression model calibrations, to explore spatial heterogeneities or non-stationarities, quantitatively. It has been widely used in a number of fields, and become one of the most important tools for exploring spatial heterogeneities in data relationships. We summarized the GWR basics in model calibration, result interpretation, model diagnostics, reviewed its research progress and problems in its applications, respectively. Meanwhile, we sorted out the important extensions of the basic GWR technique, particularly in applying flexible distance metric choices in GWR model calibration, multiscale parameter estimates and spatiotemporal data modeling. In addition, we also introduced the main GWR tools or software accordingly to provide the users or readers comprehensive reference and knowledge on the GWR technique.
Spatial heterogeneity or non-stationarity in data relationships is one of the hot topics in spatial statistics or relative application fields, while the development of local techniques forms an essential part for the relative studies. Geographically weighted regression (GWR) provides spatially varying coefficient estimates via location-specific weighted regression model calibrations, to explore spatial heterogeneities or non-stationarities, quantitatively. It has been widely used in a number of fields, and become one of the most important tools for exploring spatial heterogeneities in data relationships. We summarized the GWR basics in model calibration, result interpretation, model diagnostics, reviewed its research progress and problems in its applications, respectively. Meanwhile, we sorted out the important extensions of the basic GWR technique, particularly in applying flexible distance metric choices in GWR model calibration, multiscale parameter estimates and spatiotemporal data modeling. In addition, we also introduced the main GWR tools or software accordingly to provide the users or readers comprehensive reference and knowledge on the GWR technique.
13
2018, 43(10): 1531-1537.
DOI: 10.13203/j.whugis20160566
Abstract:
DInSAR technique is easily influenced by decorrelation of time and space and atmosphere delay, SBAS(small baseline subset technique) was applied to process 13 scene of TerraSAR-X data. The residual DEM error, atmospheric delay error and orbit error are estimated and removed. The maximum subsidence rates of 2310 and 1301 working faces were 40 mm/a and 50 mm/a respectively by analyzing the subsidence rate of the coal mine area from 2012 to 2013. It found that the land subsidence of 2306, 2308, 2310 working faces is not obvious before November 15, 2012 by analyzing the timing cumulative subsidence map. Three candidate points of slowly decorrelation filter phase in the 2310 and the 1301 working faces were extracted respectively to be analyzed and we found that the relationship between sedimentation value and time was linear, moreover, the earlier the mining time was, the more the linear variation of the sedimentation characteristics was. Cumulative settlement values obtained by the SBAS and DInSAR were compared and analyzed and turned out that the difference between the two methods was less than 5 mm. The time sequence subsidences of several points in the trending and orientation of 2310 working face were selected and extracted, the surface subsidence of study area in different time period was quantitative analysis by analyzing the displacements of these points. Experiments show that SBAS-InSAR technology has a good application prospect in the monitoring and analysis of surface subsidence in mining area.
DInSAR technique is easily influenced by decorrelation of time and space and atmosphere delay, SBAS(small baseline subset technique) was applied to process 13 scene of TerraSAR-X data. The residual DEM error, atmospheric delay error and orbit error are estimated and removed. The maximum subsidence rates of 2310 and 1301 working faces were 40 mm/a and 50 mm/a respectively by analyzing the subsidence rate of the coal mine area from 2012 to 2013. It found that the land subsidence of 2306, 2308, 2310 working faces is not obvious before November 15, 2012 by analyzing the timing cumulative subsidence map. Three candidate points of slowly decorrelation filter phase in the 2310 and the 1301 working faces were extracted respectively to be analyzed and we found that the relationship between sedimentation value and time was linear, moreover, the earlier the mining time was, the more the linear variation of the sedimentation characteristics was. Cumulative settlement values obtained by the SBAS and DInSAR were compared and analyzed and turned out that the difference between the two methods was less than 5 mm. The time sequence subsidences of several points in the trending and orientation of 2310 working face were selected and extracted, the surface subsidence of study area in different time period was quantitative analysis by analyzing the displacements of these points. Experiments show that SBAS-InSAR technology has a good application prospect in the monitoring and analysis of surface subsidence in mining area.
14
2018, 43(12): 1861-1871.
DOI: 10.13203/j.whugis20180172
Abstract:
In recent years, the rapid development of the earth observation capability and the intelligent computing technology has provided opportunities for the advancement and even revolution of remote sensing information technology. Remote sensing data processing technology has experienced the Digi-tal Signal Processing Era from 60s to 80s of last century, which utilizes the Statistical Model as the core, and the Quantitative Remote Sensing Era from 90s marked by the Physical Model. Recently, it is developing towards Remotely Sensed Big Data Era which relies on Data Model by data-driven intelligent analysis. This paper summarizes the history of remote sensing information technology and presents the concept of remotely sensed big data and the characteristics of intelligent information extraction era. Firstly, from the view of remotely sensed big data, this paper discusses the construction of object-based remote sensing knowledge dataset and analyzes the data-driven intelligent information extraction strategy combined the knowledge of remote sensing and deep learning algorithm. Then the current status and development of intelligent algorithms represented by deep learning are introduced by typical applications on object detection, fine classification and parameter inversion based on remote sensing data. Consequently, the application potential of deep learning on intelligent information extraction in Remotely Sensed Big Data Era is discussed.
In recent years, the rapid development of the earth observation capability and the intelligent computing technology has provided opportunities for the advancement and even revolution of remote sensing information technology. Remote sensing data processing technology has experienced the Digi-tal Signal Processing Era from 60s to 80s of last century, which utilizes the Statistical Model as the core, and the Quantitative Remote Sensing Era from 90s marked by the Physical Model. Recently, it is developing towards Remotely Sensed Big Data Era which relies on Data Model by data-driven intelligent analysis. This paper summarizes the history of remote sensing information technology and presents the concept of remotely sensed big data and the characteristics of intelligent information extraction era. Firstly, from the view of remotely sensed big data, this paper discusses the construction of object-based remote sensing knowledge dataset and analyzes the data-driven intelligent information extraction strategy combined the knowledge of remote sensing and deep learning algorithm. Then the current status and development of intelligent algorithms represented by deep learning are introduced by typical applications on object detection, fine classification and parameter inversion based on remote sensing data. Consequently, the application potential of deep learning on intelligent information extraction in Remotely Sensed Big Data Era is discussed.
15
2015, 40(6): 711-715.
DOI: 10.13203/j.whugis20150021
Abstract:
Using space platforms as carriers,spatial information network(SIN)is a new kind of net-work system that implements real-time data acquisition,fast network transmission and informationprocessing.Through real-time data access/transmission,networks interconnection and cooperativedata processing,SIN could realize the integrated application and collaborative service of satellite re-mote sensing,satellite navigation and satellite communication.Firstly,the concept,function andcharacteristic of space information network are introduced.Then the significance and necessity of con-struction of China’s SIN are represented after an analysis of development of SIN at home and abroad.Finally,the research objectives and scientific issues for China’s SIN construction are discussed.
Using space platforms as carriers,spatial information network(SIN)is a new kind of net-work system that implements real-time data acquisition,fast network transmission and informationprocessing.Through real-time data access/transmission,networks interconnection and cooperativedata processing,SIN could realize the integrated application and collaborative service of satellite re-mote sensing,satellite navigation and satellite communication.Firstly,the concept,function andcharacteristic of space information network are introduced.Then the significance and necessity of con-struction of China’s SIN are represented after an analysis of development of SIN at home and abroad.Finally,the research objectives and scientific issues for China’s SIN construction are discussed.
16
2017, 42(2): 143-149.
DOI: 10.13203/j.whugis20160526
Abstract:
As we entered the 21 century, after more than ten years of rapid development of high resolution remote sensing satellite, the earth observation satellite system has developed from the original single satellite observation model to the present light and small satellite constellation observation model. All-weather and all-directional earth observation can be realized. The satellite constellation, communication satellites, navigation satellites and aircrafts are linked through dynamic linking network to form space-based information network to realize intelligent earth observation in the future. To make the system more intelligent and improve perception and cognition of system as well as quick response ability, earth observation brain (EOB) is proposed in this paper. EOB is the intelligent earth observation system based on events perception. In this paper, the concept model of EOB and the key technologies needed to be solved are introduced in detail, and an example is given to illustrate the process of perceptual cognition in the primary stage of the EOB. In the future, EOB can observe when, where, what change of what object to push these right information to right people in the right time and right place. Globally all kinds of users will obtain related geospatial data, information and knowledge in real time through EOB.
As we entered the 21 century, after more than ten years of rapid development of high resolution remote sensing satellite, the earth observation satellite system has developed from the original single satellite observation model to the present light and small satellite constellation observation model. All-weather and all-directional earth observation can be realized. The satellite constellation, communication satellites, navigation satellites and aircrafts are linked through dynamic linking network to form space-based information network to realize intelligent earth observation in the future. To make the system more intelligent and improve perception and cognition of system as well as quick response ability, earth observation brain (EOB) is proposed in this paper. EOB is the intelligent earth observation system based on events perception. In this paper, the concept model of EOB and the key technologies needed to be solved are introduced in detail, and an example is given to illustrate the process of perceptual cognition in the primary stage of the EOB. In the future, EOB can observe when, where, what change of what object to push these right information to right people in the right time and right place. Globally all kinds of users will obtain related geospatial data, information and knowledge in real time through EOB.
17
2018, 43(7): 1113-1121.
DOI: 10.13203/j.whugis20160192
Abstract:
With the rapid development of economy, urban internal space structure has been optimized significantly. It is of great significance to identify the spatial distribution and interaction rules of diffe-rent functional regions (DFR) for urban structure analysis and rational planning. We identify the spatial distribution of DFR by analyzing points of interest(POI) data based on kernel density estimation and head/tail breaks. On this basis, we analyze the spatio-temporal discipline of attraction and mutual relationship between typical DFR based on taxi trajectory data. Inside the 5th ring road of urban region of Beijing, the study reveals that:①Typical DFR Xidan, Guomao, Zhongguancun are business-oriented districts, Wangjing is a residential district with a significantly commuting characteristic. ②Guomao has the robust gravity (39.4%) on itself, which indicates that Guomao has more comprehensive urban functions. ③The attraction of DFR within the scope of resident trip distance decreases with the increase of distance, which conforms to the experience cognition and geographic spatial attenuation law. The results show that using kernel density estimation and head/tail breaks to analyze POI data and taxi trajectory data and identify the spatial distribution of DFR is reasonable and effective.
With the rapid development of economy, urban internal space structure has been optimized significantly. It is of great significance to identify the spatial distribution and interaction rules of diffe-rent functional regions (DFR) for urban structure analysis and rational planning. We identify the spatial distribution of DFR by analyzing points of interest(POI) data based on kernel density estimation and head/tail breaks. On this basis, we analyze the spatio-temporal discipline of attraction and mutual relationship between typical DFR based on taxi trajectory data. Inside the 5th ring road of urban region of Beijing, the study reveals that:①Typical DFR Xidan, Guomao, Zhongguancun are business-oriented districts, Wangjing is a residential district with a significantly commuting characteristic. ②Guomao has the robust gravity (39.4%) on itself, which indicates that Guomao has more comprehensive urban functions. ③The attraction of DFR within the scope of resident trip distance decreases with the increase of distance, which conforms to the experience cognition and geographic spatial attenuation law. The results show that using kernel density estimation and head/tail breaks to analyze POI data and taxi trajectory data and identify the spatial distribution of DFR is reasonable and effective.
18
2015, 40(4): 427-435.
DOI: 10.13203/j.whugis20140982
19
2019, 44(7): 996-1007.
DOI: 10.13203/j.whugis20190072
Abstract:
The interferometric synthetic aperture radar (InSAR) technique is used over Heifangtai loess terrace, Gansu province of China to map the distribution of potential loess landslides, the evolution of landslide deformation and the failure mode. Firstly, the archived synthetic aperture radar (SAR) datasets with different spatial resolutions and wavelengths from December 2006 to November 2017 are used to identify the potential landslides. Tens potential landslide areas are identified from December 2006 to March 2011 and from January 2016 to November 2016. Field investigation and optical remote sensing images validate the reliability and accuracy of the identified landslides. Then, the TerraSAR-X data with high spatial and temporal resolution are used to monitor the time series deformation of the typical unstable slopes. Results demonstrate that the landslides with the large accumulative deformation all occur in the following time, and the acceleration dates of failed landslides are successfully captured by InSAR time series results. Finally, two-dimensional deformation monitoring of loess landslide is conducted by combining with ascending and descending SAR datasets. The landslide failure mode are analyzed in depth according to the obtained two-dimensional deformation results, topographic map and remote sensing images. The accuracy of the obtained result is verified by field investigation.
The interferometric synthetic aperture radar (InSAR) technique is used over Heifangtai loess terrace, Gansu province of China to map the distribution of potential loess landslides, the evolution of landslide deformation and the failure mode. Firstly, the archived synthetic aperture radar (SAR) datasets with different spatial resolutions and wavelengths from December 2006 to November 2017 are used to identify the potential landslides. Tens potential landslide areas are identified from December 2006 to March 2011 and from January 2016 to November 2016. Field investigation and optical remote sensing images validate the reliability and accuracy of the identified landslides. Then, the TerraSAR-X data with high spatial and temporal resolution are used to monitor the time series deformation of the typical unstable slopes. Results demonstrate that the landslides with the large accumulative deformation all occur in the following time, and the acceleration dates of failed landslides are successfully captured by InSAR time series results. Finally, two-dimensional deformation monitoring of loess landslide is conducted by combining with ascending and descending SAR datasets. The landslide failure mode are analyzed in depth according to the obtained two-dimensional deformation results, topographic map and remote sensing images. The accuracy of the obtained result is verified by field investigation.
20
2020, 45(11): 1651-1659.
DOI: 10.13203/j.whugis20200043
Abstract:
Objectives In China, geohazards are wide-ranging. Traditional artificial investigations have found nearly three hundred thousand locations of potential geohazards. However, the recent occurred catastrophic geohazards are not within these determined locations. Widely identification of potential geohazards become one of the most important jobs for geohazard prevention and mitigation. Methods We propose some suggestions to promote early identification for potential geohazards. Results (1) Recently, various remote sensing techniques play an significant role in geohazard identification, but each technique has its limitation to recognize geohazards with different types and characteristics. Only integrated technologies, mutual complementation and verification, can effectively solve the problem. (2) Combination between traditional geological surveys and modern technologies (LiDAR, aerial and semi-aerial geophysical exploration, etc.) can improve the efficiency and accuracy for identification of the most difficult and unstable slops.(3) The deep machine learning is expected to realize the intelligent automatic identification of geohazards. Currently, it shows good performance in new geohazards with significant spectral and texture characteristics, while the accuracy of automatic identification for other types, such as ancient landslides and normal potential geohazards, is still not enough. Conclusions More efforts are in urgent need for further research in related fields.
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