Unmanned aerial vehicle light detection and ranging(UAV–LiDAR)is a new method for collecting understory terrain data.The high estimation accuracy of understory terrain is crucial for accurate tree height measurement ...Unmanned aerial vehicle light detection and ranging(UAV–LiDAR)is a new method for collecting understory terrain data.The high estimation accuracy of understory terrain is crucial for accurate tree height measurement and forest resource surveys.The UAV–LiDAR flight altitude and forest canopy cover significantly impact the accuracy of understory terrain estimation.However,since no research examined their combined effects,we aimed to investigate this relationship.This will help optimize UAV–LiDAR flight parameters for understory terrain estimation and forest surveys across various canopy cover.This study analyzed the impacts of three flight altitudes and three canopy cover on the estimation accuracy of understory terrain.The results showed that when canopy cover exceeded a specific value,UAV–LiDAR flight altitudes significantly affected understory terrain estimation.Given a forest canopy cover,the reduction in ground point coverage increased significantly as the flight altitude increased;given a flight altitude,the higher the canopy cover,the more significant the reduction in ground point coverage.In forests with a canopy cover≥0.9,there were substantial differences in the accuracies of understory digital elevation models(DEMs)generated using UAV–LiDAR at different flight altitudes.For forests with a canopy cover<0.9,the mean absolute error(MAE)of understory DEMs from UAV–LiDAR at different flight altitudes was≤0.17 m and the root mean square error(RMSE)was≤0.24 m.However,for forests with a canopy cover≥0.9,the UAV–LiDAR flight altitude significantly affected the accuracy of understory DEMs.At the same flight altitude,the MAE and RMSE of the estimated elevation for forests with a canopy cover≥0.9 were approximately twice those of the estimated elevation for forests with a canopy cover<0.9.In forests with low canopy cover,it is possible to improve data collection efficiency by selecting a higher flight altitude.However,UAV–LiDAR flight altitudes significantly affected understory terrain estimation in forests with high canopy cover,it is essential to adopt terrain-following flight modes,reduce flight altitudes,and maintain a consistent flight altitude during longterm monitoring in high canopy cover forests.展开更多
Gaussian mixture algorithm （GMA） is an effective approach for off-road terrain estimation, but still suffers from some difficulties in practical applications, such as complex calculation and object abstraction. In t...Gaussian mixture algorithm （GMA） is an effective approach for off-road terrain estimation, but still suffers from some difficulties in practical applications, such as complex calculation and object abstraction. In this paper, GMA is modified to improve its real-time performance and to provide it with a potential ability of obstacle detection. First, a selection window is designed based on the dominant-ellipse-principle to limit the probability distribution area of each measurement point, therefore avoiding the calculation on the cells outside the dominant ellipse. Second, a clustering approach is proposed in order to distinguish objects efficiently and decrease the operation area of one laser scan. Third, a virtual point vector is introduced to further reduce the computational load of the mean square error matrix. The modified GMA is experimented on a tracked mobile robot, and its improved performance is shown in comparison to the original GMA.展开更多
The Ice,Cloud and Land Elevation Satellite-2(ICESat-2),a new spaceborne light detection and ranging(LiDAR)system,was successfully launched on September 15,2018.The ICESat-2 data increase the types of spaceborne LiDAR ...The Ice,Cloud and Land Elevation Satellite-2(ICESat-2),a new spaceborne light detection and ranging(LiDAR)system,was successfully launched on September 15,2018.The ICESat-2 data increase the types of spaceborne LiDAR data archive and provide new control point data for large-scale topographic mapping and geodetic surveying.However,the accuracy of the ATL 08 terrain estimates has not been fully evaluated on a large scale and in complex terrain conditions.This article aims to quantitatively assess the accuracy of ICESat-2 ATL 08 terrain estimates.Firstly,the ICESat-2 ATL 08 terrain estimates were compared with the high-precision airborne LiDAR digital terrain model(DTM),and impacts of acquisition time,vegetation cover type,terrain slope,and season change on the terrain estimation accuracy were analyzed.We get the following conclusions from the analysis:1)the mean and RMSE of the terrain estimates of day acquisitions are 0.22 m and 0.59 m higher than that of night acquisitions;2)the accuracy of the ATL 08 terrain estimates acquired in vegetated areas is lower than those in non-vegetated areas;3)the accuracy of the ATL 08 terrain estimates is inversely proportional to the slope,and the elevation error increases significantly when the terrain slope is larger than 30°;4)in the non-vegetation covered area,the accuracy of the ATL 08 terrain estimates of summer and winter acquisitions has no obvious discrepancy,but in vegetated area,the accuracy of winter acquisitions is significantly better than that of summer acquisitions.This research provides references for the selection and application of ICESat-2 data.展开更多
基金supported by the National Natural Science Foundation of China(No.32271876)the Research on Key Technologies of Intelligent Monitoring and Carbon Sink Metering of Forest Resources in Fujian Province(No.2022FKJ03)the Science and Technology Innovation Project of Fujian Agriculture and Forestry University(No.KFB23172A,KFB23173A).
文摘Unmanned aerial vehicle light detection and ranging(UAV–LiDAR)is a new method for collecting understory terrain data.The high estimation accuracy of understory terrain is crucial for accurate tree height measurement and forest resource surveys.The UAV–LiDAR flight altitude and forest canopy cover significantly impact the accuracy of understory terrain estimation.However,since no research examined their combined effects,we aimed to investigate this relationship.This will help optimize UAV–LiDAR flight parameters for understory terrain estimation and forest surveys across various canopy cover.This study analyzed the impacts of three flight altitudes and three canopy cover on the estimation accuracy of understory terrain.The results showed that when canopy cover exceeded a specific value,UAV–LiDAR flight altitudes significantly affected understory terrain estimation.Given a forest canopy cover,the reduction in ground point coverage increased significantly as the flight altitude increased;given a flight altitude,the higher the canopy cover,the more significant the reduction in ground point coverage.In forests with a canopy cover≥0.9,there were substantial differences in the accuracies of understory digital elevation models(DEMs)generated using UAV–LiDAR at different flight altitudes.For forests with a canopy cover<0.9,the mean absolute error(MAE)of understory DEMs from UAV–LiDAR at different flight altitudes was≤0.17 m and the root mean square error(RMSE)was≤0.24 m.However,for forests with a canopy cover≥0.9,the UAV–LiDAR flight altitude significantly affected the accuracy of understory DEMs.At the same flight altitude,the MAE and RMSE of the estimated elevation for forests with a canopy cover≥0.9 were approximately twice those of the estimated elevation for forests with a canopy cover<0.9.In forests with low canopy cover,it is possible to improve data collection efficiency by selecting a higher flight altitude.However,UAV–LiDAR flight altitudes significantly affected understory terrain estimation in forests with high canopy cover,it is essential to adopt terrain-following flight modes,reduce flight altitudes,and maintain a consistent flight altitude during longterm monitoring in high canopy cover forests.
基金the National Natural Science Foundation of China (Grant Nos. 60775056, 60705028)
文摘Gaussian mixture algorithm （GMA） is an effective approach for off-road terrain estimation, but still suffers from some difficulties in practical applications, such as complex calculation and object abstraction. In this paper, GMA is modified to improve its real-time performance and to provide it with a potential ability of obstacle detection. First, a selection window is designed based on the dominant-ellipse-principle to limit the probability distribution area of each measurement point, therefore avoiding the calculation on the cells outside the dominant ellipse. Second, a clustering approach is proposed in order to distinguish objects efficiently and decrease the operation area of one laser scan. Third, a virtual point vector is introduced to further reduce the computational load of the mean square error matrix. The modified GMA is experimented on a tracked mobile robot, and its improved performance is shown in comparison to the original GMA.
基金Projects(41820104005,41904004,42030112)supported by the National Natural Science Foundation of China。
文摘The Ice,Cloud and Land Elevation Satellite-2(ICESat-2),a new spaceborne light detection and ranging(LiDAR)system,was successfully launched on September 15,2018.The ICESat-2 data increase the types of spaceborne LiDAR data archive and provide new control point data for large-scale topographic mapping and geodetic surveying.However,the accuracy of the ATL 08 terrain estimates has not been fully evaluated on a large scale and in complex terrain conditions.This article aims to quantitatively assess the accuracy of ICESat-2 ATL 08 terrain estimates.Firstly,the ICESat-2 ATL 08 terrain estimates were compared with the high-precision airborne LiDAR digital terrain model(DTM),and impacts of acquisition time,vegetation cover type,terrain slope,and season change on the terrain estimation accuracy were analyzed.We get the following conclusions from the analysis:1)the mean and RMSE of the terrain estimates of day acquisitions are 0.22 m and 0.59 m higher than that of night acquisitions;2)the accuracy of the ATL 08 terrain estimates acquired in vegetated areas is lower than those in non-vegetated areas;3)the accuracy of the ATL 08 terrain estimates is inversely proportional to the slope,and the elevation error increases significantly when the terrain slope is larger than 30°;4)in the non-vegetation covered area,the accuracy of the ATL 08 terrain estimates of summer and winter acquisitions has no obvious discrepancy,but in vegetated area,the accuracy of winter acquisitions is significantly better than that of summer acquisitions.This research provides references for the selection and application of ICESat-2 data.
