Accurate digital terrain models(DTMs)are essential for a wide range of geospatial and environmental applications,yet their derivation in forested regions remains a significant challenge.Existing global DTMs,typically ...Accurate digital terrain models(DTMs)are essential for a wide range of geospatial and environmental applications,yet their derivation in forested regions remains a significant challenge.Existing global DTMs,typically generated from satellite stereo photogrammetry or interferometric synthetic aperture radar(InSAR),fail to accurately capture understory terrain due to limited penetration capabilities,resulting in elevation overestimation in densely vegetated areas.While airborne light detection and ranging(LiDAR)can provide high-accuracy DTMs,its limited spatial coverage and high acquisition cost hinder large-scale applications.Thus,there is an urgent need for a scalable and cost-effective approach to extract DTMs directly from satellite-derived digital surface models(DSMs).In this study,we propose a simple,interpretable understory terrain extraction method that utilizes canopy height data from Global Ecosystem Dynamics Investigation(GEDI)and Ice,Cloud,and Land Elevation Satellite-2(ICESat-2)to construct a tree height surface model,which is then subtracted from the stereo-derived DSM to generate the final DTM.By directly incorporating LiDAR constraints,the method avoids error propagation from multiple heterogeneous datasets and reduces reliance on ancillary inputs,ensuring ease of implementation and broad applicability.In contrast to machine learning-based terrain modeling methods,which are often prone to overfitting and data bias,the proposed approach is simple,interpretable,and robust across diverse forested landscapes.The accuracy of the resulting DTM was validated against airborne LiDAR reference data and compared with both the Copernicus Digital Elevation Model(DEM)and the forest and buildings removed DEM(FABDEM),a global bare-earth elevation model corrected for vegetation bias.The results indicate that the proposed DTM consistently outperforms the Copernicus DEM(CopDEM)and achieves accuracy comparable to FABDEM.In addition,its finer spatial resolution of 1 m,compared to the 30 m resolution of FABDEM,allows for more detailed terrain representation and better capture of fine-scale variation.This advantage is most pronounced in gently to moderately sloped areas,where the proposed DTM shows clearly higher accuracy than both the CopDEM and FABDEM.The results confirm that high-resolution DTMs can be effectively extracted from DSMs using spaceborne LiDAR constraints,offering a scalable solution for terrain modeling in forested environments where airborne LiDAR is unavailable.To illustrate the potential utility of the proposed DTM,we applied it to a fire risk mapping application based on topographic parameters such as slope,aspect,and elevation.This case highlights how improved terrain representation can support geospatial hazard assessments.展开更多
The principal purpose of this paper is to extract entire sea surface wind＇s information from spaceborne lidar, and particularly to utilize a appropriate algorithm for removing the interference information due to whit...The principal purpose of this paper is to extract entire sea surface wind＇s information from spaceborne lidar, and particularly to utilize a appropriate algorithm for removing the interference information due to white caps and subsurface water. Wind speeds are obtained through empirical relationship with sea surface mean square slopes. Wind directions are derived from relationship between wind speeds and wind directions im plied in CMOD5n geophysical models function （GMF）. Whitecaps backscattering signals were distinguished with the help of lidar depolarization ratio measurements and rectified by whitecaps coverage equation. Subsurface water backscattering signals were corrected by means of inverse distance weighted （IDW） from neighborhood non-singular data with optimal subsurface water backscattering calibration parameters. To verify the algorithm reliably, it selected NDBC＇s TAO buoy-laying area as survey region in camparison with buoys＇ wind field data and METOP satellite ASCAT of 25 km single orbit wind field data after temporal-spa tial matching. Validation results showed that the retrieval algorithm works well in terms of root mean square error （RMSE） less than 2m/s and wind direction＇s RMSE less than 21 degree.展开更多
Accurate estimation of forest terrain and canopy height is crucial for timely understanding of forest growth.Gao Fen-7(GF-7)Satellite is China’s first sub-meter-level three-dimensional(3D)mapping satellite for civili...Accurate estimation of forest terrain and canopy height is crucial for timely understanding of forest growth.Gao Fen-7(GF-7)Satellite is China’s first sub-meter-level three-dimensional(3D)mapping satellite for civilian use,which was equipped with a two-line-array stereo mapping camera and a laser altimeter system that can provide stereo images and full waveform LiDAR data simultaneously.Most of the existing studies have concentrated on evaluating the accuracy of GF-7 for topographic survey in bare land,but few have in-depth studied its ability to measure forest terrain elevation and canopy height.The purpose of this study is to evaluate the potential of GF-7 LiDAR and stereo image for forest terrain and height measurement.The Airborne Laser Scanning(ALS)data were utilized to generate reference terrain and forest vertical information.The validation test was conducted in Pu’er City,Yunnan Province of China,and encouraging results have obtained.The GF-7 LiDAR data obtained the accuracy of forest terrain elevation with RMSE of 8.01 m when 21 available laser footprints were used for results verification;meanwhile,when it was used to calculate the forest height,R^(2)of 0.84 and RMSE of 3.2 m were obtained although only seven effective footprints were used for result verification.The canopy height values obtained from GF-7 stereo images have also been proven to have high accuracy with the resolution of 20 m×20 m compared with ALS data(R2=0.88,RMSE=2.98 m).When the results were verified at the forest sub-compartment scale that taking into account the forest types,further higher accuracy(R^(2)=0.96,RMSE=1.23 m)was obtained.These results show that GF-7 has considerable application potential in forest resources monitoring.展开更多
基金supported by the National Key Research and Development Program of China(Nos.SQ2022YFB3900026 and 2022YFB3903305)supported by the Leading Talents of Guangdong Pearl River Talent Program(No.2021CX02S024)the Guangdong S&T programme(No.2024B1212050011).
文摘Accurate digital terrain models(DTMs)are essential for a wide range of geospatial and environmental applications,yet their derivation in forested regions remains a significant challenge.Existing global DTMs,typically generated from satellite stereo photogrammetry or interferometric synthetic aperture radar(InSAR),fail to accurately capture understory terrain due to limited penetration capabilities,resulting in elevation overestimation in densely vegetated areas.While airborne light detection and ranging(LiDAR)can provide high-accuracy DTMs,its limited spatial coverage and high acquisition cost hinder large-scale applications.Thus,there is an urgent need for a scalable and cost-effective approach to extract DTMs directly from satellite-derived digital surface models(DSMs).In this study,we propose a simple,interpretable understory terrain extraction method that utilizes canopy height data from Global Ecosystem Dynamics Investigation(GEDI)and Ice,Cloud,and Land Elevation Satellite-2(ICESat-2)to construct a tree height surface model,which is then subtracted from the stereo-derived DSM to generate the final DTM.By directly incorporating LiDAR constraints,the method avoids error propagation from multiple heterogeneous datasets and reduces reliance on ancillary inputs,ensuring ease of implementation and broad applicability.In contrast to machine learning-based terrain modeling methods,which are often prone to overfitting and data bias,the proposed approach is simple,interpretable,and robust across diverse forested landscapes.The accuracy of the resulting DTM was validated against airborne LiDAR reference data and compared with both the Copernicus Digital Elevation Model(DEM)and the forest and buildings removed DEM(FABDEM),a global bare-earth elevation model corrected for vegetation bias.The results indicate that the proposed DTM consistently outperforms the Copernicus DEM(CopDEM)and achieves accuracy comparable to FABDEM.In addition,its finer spatial resolution of 1 m,compared to the 30 m resolution of FABDEM,allows for more detailed terrain representation and better capture of fine-scale variation.This advantage is most pronounced in gently to moderately sloped areas,where the proposed DTM shows clearly higher accuracy than both the CopDEM and FABDEM.The results confirm that high-resolution DTMs can be effectively extracted from DSMs using spaceborne LiDAR constraints,offering a scalable solution for terrain modeling in forested environments where airborne LiDAR is unavailable.To illustrate the potential utility of the proposed DTM,we applied it to a fire risk mapping application based on topographic parameters such as slope,aspect,and elevation.This case highlights how improved terrain representation can support geospatial hazard assessments.
文摘The principal purpose of this paper is to extract entire sea surface wind＇s information from spaceborne lidar, and particularly to utilize a appropriate algorithm for removing the interference information due to white caps and subsurface water. Wind speeds are obtained through empirical relationship with sea surface mean square slopes. Wind directions are derived from relationship between wind speeds and wind directions im plied in CMOD5n geophysical models function （GMF）. Whitecaps backscattering signals were distinguished with the help of lidar depolarization ratio measurements and rectified by whitecaps coverage equation. Subsurface water backscattering signals were corrected by means of inverse distance weighted （IDW） from neighborhood non-singular data with optimal subsurface water backscattering calibration parameters. To verify the algorithm reliably, it selected NDBC＇s TAO buoy-laying area as survey region in camparison with buoys＇ wind field data and METOP satellite ASCAT of 25 km single orbit wind field data after temporal-spa tial matching. Validation results showed that the retrieval algorithm works well in terms of root mean square error （RMSE） less than 2m/s and wind direction＇s RMSE less than 21 degree.
基金supported by the National Key Research and Development Program of China[grant numbers 2021YFE0117700 and 2022YFF1302100]the ESA-MOST China Dragon 5 Cooperation[grant number 59313]National Science and Technology Major Project of China's High Resolution Earth Observation System[grant numbers 30-Y30A02-9001-20/22-7 and 21-Y20B01-9001-19/22].
文摘Accurate estimation of forest terrain and canopy height is crucial for timely understanding of forest growth.Gao Fen-7(GF-7)Satellite is China’s first sub-meter-level three-dimensional(3D)mapping satellite for civilian use,which was equipped with a two-line-array stereo mapping camera and a laser altimeter system that can provide stereo images and full waveform LiDAR data simultaneously.Most of the existing studies have concentrated on evaluating the accuracy of GF-7 for topographic survey in bare land,but few have in-depth studied its ability to measure forest terrain elevation and canopy height.The purpose of this study is to evaluate the potential of GF-7 LiDAR and stereo image for forest terrain and height measurement.The Airborne Laser Scanning(ALS)data were utilized to generate reference terrain and forest vertical information.The validation test was conducted in Pu’er City,Yunnan Province of China,and encouraging results have obtained.The GF-7 LiDAR data obtained the accuracy of forest terrain elevation with RMSE of 8.01 m when 21 available laser footprints were used for results verification;meanwhile,when it was used to calculate the forest height,R^(2)of 0.84 and RMSE of 3.2 m were obtained although only seven effective footprints were used for result verification.The canopy height values obtained from GF-7 stereo images have also been proven to have high accuracy with the resolution of 20 m×20 m compared with ALS data(R2=0.88,RMSE=2.98 m).When the results were verified at the forest sub-compartment scale that taking into account the forest types,further higher accuracy(R^(2)=0.96,RMSE=1.23 m)was obtained.These results show that GF-7 has considerable application potential in forest resources monitoring.
