Terrestrial laser scanning(TLS)accurately captures tree structural information and provides prerequisites for treescale estimations of forest biophysical attributes.Quantifying tree-scale attributes from TLS point clo...Terrestrial laser scanning(TLS)accurately captures tree structural information and provides prerequisites for treescale estimations of forest biophysical attributes.Quantifying tree-scale attributes from TLS point clouds requires segmentation,yet the occlusion effects severely affect the accuracy of automated individual tree segmentation.In this study,we proposed a novel method using ellipsoid directional searching and point compensation algorithms to alleviate occlusion effects.Firstly,region growing and point compensation algorithms are used to determine the location of tree roots.Secondly,the neighbor points are extracted within an ellipsoid neighborhood to mitigate occlusion effects compared with k-nearest neighbor(KNN).Thirdly,neighbor points are uniformly subsampled by the directional searching algorithm based on the Fibonacci principle in multiple spatial directions to reduce memory consumption.Finally,a graph describing connectivity between a point and its neighbors is constructed,and it is utilized to complete individual tree segmentation based on the shortest path algorithm.The proposed method was evaluated on a public TLS dataset comprising six forest plots with three complexity categories in Evo,Finland,and it reached the highest mean accuracy of 77.5%,higher than previous studies on tree detection.We also extracted and validated the tree structure attributes using manual segmentation reference values.The RMSE,RMSE%,bias,and bias%of tree height,crown base height,crown projection area,crown surface area,and crown volume were used to evaluate the segmentation accuracy,respectively.Overall,the proposed method avoids many inherent limitations of current methods and can accurately map canopy structures in occluded complex forest stands.展开更多
Forest ecosystems have been identified as major carbon stocks in terrestrial ecosystems;therefore,their monitoring is critical.Forests cover large areas,making it difficult to monitor and maintain up-to-date informati...Forest ecosystems have been identified as major carbon stocks in terrestrial ecosystems;therefore,their monitoring is critical.Forests cover large areas,making it difficult to monitor and maintain up-to-date information.Advances in remote sensing technologies provide opportunities for detailed small-scale monitoring to global monitoring of forest resources.Airborne laser scanning(ALS)data can provide precise forest structure measurements,but mainly for small-scale forest monitoring due to its expensive cost and limited spatial and temporal coverage.Spaceborne lidar(light detection and ranging)can cover extensive spatial scales,but its suitability as a replacement for ALS measurements remains uncertain.There are still relatively few studies on the performance of spaceborne lidar to estimate forest attributes with sufficient accuracy and precision.Therefore,this study aimed at assessing the performance of spaceborne lidar ICESat-2 canopy height metrics and understanding their uncertainties and utilities by evaluating their agreements with ALS-derived canopy height metrics in Mississippi,United States.We assessed their agreements for different forest types,physiographic regions,a range of canopy cover,and diverse disturbance histories using equivalence tests.Results suggest that ICESat-2 canopy height metrics collected using strong beam mode at night have higher agreement with ALS-derived ones.ICESat-2 showed great potential for estimating canopy heights in evergreen forests with high canopy cover.This study contributes to the scientific community’s understanding of the capabilities and limitations of ICESat-2 to measure canopy heights at regional to global scales.展开更多
基金supported by the National Natural Science Foundation of China(Nos.32171789,32211530031,12411530088)the National Key Research and Development Program of China(No.2023YFF1303901)+2 种基金the Joint Open Funded Project of State Key Laboratory of Geo-Information Engineering and Key Laboratory of the Ministry of Natural Resources for Surveying and Mapping Science and Geo-spatial Information Technology(2022-02-02)Background Resources Survey in Shennongjia National Park(SNJNP2022001)the Open Project Fund of Hubei Provincial Key Laboratory for Conservation Biology of Shennongjia Snub-nosed Monkeys(SNJGKL2022001).
文摘Terrestrial laser scanning(TLS)accurately captures tree structural information and provides prerequisites for treescale estimations of forest biophysical attributes.Quantifying tree-scale attributes from TLS point clouds requires segmentation,yet the occlusion effects severely affect the accuracy of automated individual tree segmentation.In this study,we proposed a novel method using ellipsoid directional searching and point compensation algorithms to alleviate occlusion effects.Firstly,region growing and point compensation algorithms are used to determine the location of tree roots.Secondly,the neighbor points are extracted within an ellipsoid neighborhood to mitigate occlusion effects compared with k-nearest neighbor(KNN).Thirdly,neighbor points are uniformly subsampled by the directional searching algorithm based on the Fibonacci principle in multiple spatial directions to reduce memory consumption.Finally,a graph describing connectivity between a point and its neighbors is constructed,and it is utilized to complete individual tree segmentation based on the shortest path algorithm.The proposed method was evaluated on a public TLS dataset comprising six forest plots with three complexity categories in Evo,Finland,and it reached the highest mean accuracy of 77.5%,higher than previous studies on tree detection.We also extracted and validated the tree structure attributes using manual segmentation reference values.The RMSE,RMSE%,bias,and bias%of tree height,crown base height,crown projection area,crown surface area,and crown volume were used to evaluate the segmentation accuracy,respectively.Overall,the proposed method avoids many inherent limitations of current methods and can accurately map canopy structures in occluded complex forest stands.
基金Funding for this project was provided in part by the McIntire-Stennis project accession number MISZ-700001support from the National Key Research and Development Program of China(2023YFB3907401)+1 种基金National Natural Science Foundation of China(grant no.42201366)Nanjing Normal University(grant no.184080H202B349).
文摘Forest ecosystems have been identified as major carbon stocks in terrestrial ecosystems;therefore,their monitoring is critical.Forests cover large areas,making it difficult to monitor and maintain up-to-date information.Advances in remote sensing technologies provide opportunities for detailed small-scale monitoring to global monitoring of forest resources.Airborne laser scanning(ALS)data can provide precise forest structure measurements,but mainly for small-scale forest monitoring due to its expensive cost and limited spatial and temporal coverage.Spaceborne lidar(light detection and ranging)can cover extensive spatial scales,but its suitability as a replacement for ALS measurements remains uncertain.There are still relatively few studies on the performance of spaceborne lidar to estimate forest attributes with sufficient accuracy and precision.Therefore,this study aimed at assessing the performance of spaceborne lidar ICESat-2 canopy height metrics and understanding their uncertainties and utilities by evaluating their agreements with ALS-derived canopy height metrics in Mississippi,United States.We assessed their agreements for different forest types,physiographic regions,a range of canopy cover,and diverse disturbance histories using equivalence tests.Results suggest that ICESat-2 canopy height metrics collected using strong beam mode at night have higher agreement with ALS-derived ones.ICESat-2 showed great potential for estimating canopy heights in evergreen forests with high canopy cover.This study contributes to the scientific community’s understanding of the capabilities and limitations of ICESat-2 to measure canopy heights at regional to global scales.
