针对森林资源精准监测的需求,探索背包激光雷达(Light Detection and Ranging,LiDAR)在生产实践中的森林结构参数提取能力,以浙江建德林场为研究区,基于野外调查采集的8块样地背包LiDAR数据,提出一种改进的K-means分层聚类算法进行单木...针对森林资源精准监测的需求,探索背包激光雷达(Light Detection and Ranging,LiDAR)在生产实践中的森林结构参数提取能力,以浙江建德林场为研究区,基于野外调查采集的8块样地背包LiDAR数据,提出一种改进的K-means分层聚类算法进行单木分割,从分割后的单木点云中分别提取胸径、树高、冠幅、树冠投影面积、树冠体积、间隙率等6个单木结构参数,并计算56个点云分层高度特征,利用随机森林方法,构建单木材积估测模型并估测样地蓄积量。结果表明:改进的K-means分层聚类算法综合分割精度F的平均值为0.87,胸径的提取精度为91.26%,树高的提取精度为85.77%;仅用6个单木结构参数作为输入特征变量的单木材积估测模型,模型拟合结果的决定系数(R^(2))为0.89,均方根误差(RMSE)为0.053 m^(3);采用Person相关系数和随机森林特征重要性筛选单木结构参数和分层高度特征后,得到最终的单木材积估测模型,模型拟合结果的R^(2)为0.93,RMSE为0.041 m^(3);利用最优估测模型估算每个样地的蓄积量,平均精度为94.20%。研究结果表明,提出的改进的K-means分层聚类算法能够有效分割单木点云,随机森林方法可以较好地估测单木材积和样地蓄积量,为背包激光雷达在森林资源参数提取方面提供重要的参考价值。展开更多
Reliable and prompt information on forest above-ground biomass(AGB)and tree diameter at breast height(DBH)are crucial for sustainable forest management.Remote sensing technology,especially the Light Detection and Rang...Reliable and prompt information on forest above-ground biomass(AGB)and tree diameter at breast height(DBH)are crucial for sustainable forest management.Remote sensing technology,especially the Light Detection and Ranging(LiDAR)technology,has been proven to estimate important tree variables effectively.This study proposes predicting DBH and AGB from tree height and other LiDAR data extracted metrics.In the suggested DBH prediction,we developed a nonlinear estimation equation using the total tree height.As for the AGB prediction approach,we used regression methods such as multiple linear regression(MLR),random forest(RF)and support vector machine for regression(SVR).We conducted the study for the Gudao forest area dominated by Robinia Pseudoacacia trees,located in the Yellow River Delta(YRD),China.For our developed approaches,we used Unmanned Aerial Vehicle(UAV)and Backpack LiDAR point cloud datasets obtained in June 2017,and three field data measurements gathered in June 2017 and 2019 and October 2019,all from the same study area.The results demonstrate that:①The LiDAR data individual tree segmentation(ITS)from which we extracted individual tree information like tree location and tree height,was carried out with an overall accuracy F=0.91;②We used the ITS height data from the field stand in 2019 as a fit and developed a nonlinear DBH estimation equation with Root Mean Square Error(RMSE)=3.61 cm,later validated by the 2017 dataset;③Forest AGB at stand level was estimated with the MLR,RF and also SVR regression methods,and results show that the SVR method gave higher accuracy with R2=0.82 compared to the R2=0.72 of RF and the R2=0.70 of the MLR.Calculated AGB at plot level using the 2017 LiDAR data was used to validate both models’accuracy.Combining the UAV LiDAR data and the Backpack LiDAR significantly improved the overall ITS.The UAV LiDAR ability to provide high accuracy tree height abstraction,the DBH of the regression equation and other extracted LiDAR metrics showed high accuracy in estimating the forest AGB.This study shows that being cost-free is not the only advantage of free available software.In the performance of ITS and the LiDAR,metrics extraction proved to be as good as the commercially available software.展开更多
文摘Reliable and prompt information on forest above-ground biomass(AGB)and tree diameter at breast height(DBH)are crucial for sustainable forest management.Remote sensing technology,especially the Light Detection and Ranging(LiDAR)technology,has been proven to estimate important tree variables effectively.This study proposes predicting DBH and AGB from tree height and other LiDAR data extracted metrics.In the suggested DBH prediction,we developed a nonlinear estimation equation using the total tree height.As for the AGB prediction approach,we used regression methods such as multiple linear regression(MLR),random forest(RF)and support vector machine for regression(SVR).We conducted the study for the Gudao forest area dominated by Robinia Pseudoacacia trees,located in the Yellow River Delta(YRD),China.For our developed approaches,we used Unmanned Aerial Vehicle(UAV)and Backpack LiDAR point cloud datasets obtained in June 2017,and three field data measurements gathered in June 2017 and 2019 and October 2019,all from the same study area.The results demonstrate that:①The LiDAR data individual tree segmentation(ITS)from which we extracted individual tree information like tree location and tree height,was carried out with an overall accuracy F=0.91;②We used the ITS height data from the field stand in 2019 as a fit and developed a nonlinear DBH estimation equation with Root Mean Square Error(RMSE)=3.61 cm,later validated by the 2017 dataset;③Forest AGB at stand level was estimated with the MLR,RF and also SVR regression methods,and results show that the SVR method gave higher accuracy with R2=0.82 compared to the R2=0.72 of RF and the R2=0.70 of the MLR.Calculated AGB at plot level using the 2017 LiDAR data was used to validate both models’accuracy.Combining the UAV LiDAR data and the Backpack LiDAR significantly improved the overall ITS.The UAV LiDAR ability to provide high accuracy tree height abstraction,the DBH of the regression equation and other extracted LiDAR metrics showed high accuracy in estimating the forest AGB.This study shows that being cost-free is not the only advantage of free available software.In the performance of ITS and the LiDAR,metrics extraction proved to be as good as the commercially available software.
