Effect of point cloud density on forest remote sensing retrieval index extraction based on Unmanned Aerial Vehicle Lidar Data
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摘要: 点云密度是激光雷达技术的重要参数，点云密度对森林遥感反演指数的提取有重要影响。以1600m×1450m大小的无人机激光雷达数据为实验数据，采用分级随机抽稀法对实验数据进行抽稀，获取不同点云密度数据集，利用不同密度数据集提取郁闭度、间隙率、叶面积指数、点云高度和密度分位数等森林遥感反演指数，通过与原始数据提取的森林遥感反演指数进行差值比较。（1）点云密度越低，提取的郁闭度略微偏低，而间隙率略微增加，点云密度对郁闭度、间隙率的影响极小。（2）当点云密度较高时，对叶面积指数的影响不大，但点云密度较小时，对叶面积指数的影响较大，个别区域可能出现叶面积指数突变。（3）在点云密度较大时，点云密度对高度、密度分位数的影响不明显，但当点云密度降至3.6p/m2时，可能会出现个别区域密度、高度密度分位数突变的情况。点云密度对森林遥感反演指数有重要影响，合适的点云密度有利于更准确的描述森林结构形态，过低的点云密度影响森林遥感反演指数的提取。本研究对无人机激光雷达林业应用中森林遥感反演指数估算点云密度的选择具有一定指导和借鉴意义。Abstract: 【Objective】 Point cloud density is an important parameter of lidar technology. Point cloud density has an important impact on the extraction of remote sensing retrieval index for forest.【Methods】 The experimental data, sized by1600m*1450m, had been obtained by UAV Lidar and thinned by the graded random thinning method in order to simulate different point cloud density during actual operation, which was used to extract the remote sensing retrieval index for forest such as Canopy Closure(CC), Gap Fraction (GF), Leaf Area Index（LAI）, height quantile variables and density quantile variables. Then these parameters were used to make difference comparison with the indexes extracted through raw data. 【Results】（1） The lower the point cloud density is, the lower the extracted Canopy Closure is slightly, while the extracted Gap Fraction is slightly increased. The point cloud density has little influence on the extracted Canopy Closure and Gap Fraction. (2) When the point cloud density is high, it has little impact on Leaf Area Index, but when the point cloud density is small, it has a great impact on Leaf Area Index, and some areas may have sudden changes on Leaf Area Index. (3) When the point cloud density is large, the effect of point cloud density on height and density quantile variables is not obvious, but when the point cloud density drops to 3.6 p/m2, there may be sudden changes in density and height density quantile variables in some areas. 【Conclusions】 In short, the point cloud density has an important impact on the description of forest structural characteristics. The appropriate point cloud density is conducive to describe the forest structure morphology more accurately, but the low point cloud density affects the extraction of remote sensing retrieval index for forest. This study has certain guidance and reference for selection of point cloud density to estimate the remote sensing retrieval index with UAV Lidar on forestry.
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[1] Kandare K, Ørka H O, Chan J C W, et al.Effects of Forest Structure and Airborne Laser Scanning Point Cloud Density on 3D Delineation of Individual Tree Crowns[J].European Journal of Remote Sensing, 2016, 49(1):337-359 [2] White J C, Wulder M A, Varhola A, et al.A Best Practices Guide for Generating Forest Inventory Attributes from Airborne Laser Scanning Data Using an Area-Based Approach[J].The Forestry Chronicle, 2013, 89(6):722-723 [3] Magnusson M, Fransson J E S, Holmgren J.Effects on Estimation Accuracy of Forest Variables Using Different Pulse Density of Laser Data[J].Forest Science, 2007, 53(6):619-626 [4] Guimarães N, Pádua L, Marques P, et al.Forestry Remote Sensing from Unmanned Aerial Vehicles:A Review Focusing on the Data, Processing and Potentialities[J].Remote Sensing, 2020, 12(6):1046 [5] Jennings S, Brown N, Sheil D.Assessing Forest Canopies and Understorey Illumination:Canopy Closure, Canopy Cover and other Measures[J].Forestry:an International Journal of Forest Research, 1999, 72(1):59-74 [6] Ma Q, Su Y J, Guo Q H.Comparison of Canopy Cover Estimations from Airborne LiDAR, Aerial Imagery, and Satellite Imagery[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(9):4225-4236 [7] Danson F M, Hetherington D, Morsdorf F, et al.Forest Canopy Gap Fraction from Terrestrial Laser Scanning[J].IEEE Geoscience and Remote Sensing Letters, 2007, 4(1):157-160 [8] Chen J M, Black T A.Measuring Leaf Area Index of Plant Canopies with Branch Architecture[J].Agricultural and Forest Meteorology, 1991, 57(1/2/3):1-12 [9] Richardson J J, Moskal L M, Kim S H.Modeling Approaches to Estimate Effective Leaf Area Index from Aerial Discrete-Return LIDAR[J].Agricultural and Forest Meteorology, 2009, 149(6/7):1152-1160 [10] Zhao X Q, Guo Q H, Su Y J, et al.Improved Progressive TIN Densification Filtering Algorithm for Airborne LiDAR Data in Forested Areas[J].ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 117:79-91 [11] Graham A, Coops N, Wilcox M, et al.Evaluation of Ground Surface Models Derived from Unmanned Aerial Systems with Digital Aerial Photogrammetry in a Disturbed Conifer Forest[J].Remote Sensing, 2019, 11(1):84 -
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