The diameter at breast height(DBH) of trees and stands is not only a widely used plant functional trait in ecology and biodiversity but also one of the most fundamental measurements in managing forests. However, syste...The diameter at breast height(DBH) of trees and stands is not only a widely used plant functional trait in ecology and biodiversity but also one of the most fundamental measurements in managing forests. However, systematically measuring the DBH of individual trees over large areas using conventional ground-based approaches is labour-intensive and costly. Here, we present an improved area-based approach to estimate plot-level tree DBH from airborne Li DAR data using the relationship between tree height and DBH, which is widely available for most forest types and many individual tree species. We first determined optimal functional forms for modelling heightDBH relationships using field-measured tree height and DBH. Then we estimated plot-level mean DBH by inverting the height-DBH relationships using the tree height predicted by Li DAR. Finally, we compared the predictive performance of our approach with a classical area-based method of DBH. The results showed that our approach significantly improved the prediction accuracy of tree DBH(R^(2)=0.85–0.90, rRMSE=9.57%–11.26%)compared to the classical area-based approach(R^(2)=0.80–0.83, rRMSE=11.98%–14.97%). Our study demonstrates the potential of using height-DBH relationships to improve the estimation of the plot-level DBH from airborne Li DAR data.展开更多
胸径(Diameter at Breast Height,DBH)是指树木主干离地表面胸高处的直径,根据无人机可见光影像估算单木DBH对林业资产管理与评估具有重要意义。以云南师范大学呈贡校区内的银杏为研究对象,首先,获取其无人机可见光影像,基于摄影测量原...胸径(Diameter at Breast Height,DBH)是指树木主干离地表面胸高处的直径,根据无人机可见光影像估算单木DBH对林业资产管理与评估具有重要意义。以云南师范大学呈贡校区内的银杏为研究对象,首先,获取其无人机可见光影像,基于摄影测量原理生成数字正射影像图;然后,在此基础上提取银杏单木的冠幅(Crown Width,CW);最后,建立CW与DBH的4个回归模型,通过该模型估算得到DBH值。将实际测量的DBH值与估算值进行精度验证,最终一元二次函数模型R 2为0.75,均方根误差为0.0129 m,平均误差率为3.22%,均小于其他3个模型,具有较高的精度。实验结果表明基于无人机可见光影像可以较为准确地估算单木DBH。展开更多
【目的】以人工落叶松为例,探索基于无人机激光雷达(Unmanned aerial vehicle LiDAR,UAVLiDAR)点云的单木探测提取树高的误差对胸径反演的影响并校准,实现单木参数(胸径、树高)的准确度量,为大尺度高效便捷估测单木参数提供新的思路。...【目的】以人工落叶松为例,探索基于无人机激光雷达(Unmanned aerial vehicle LiDAR,UAVLiDAR)点云的单木探测提取树高的误差对胸径反演的影响并校准,实现单木参数(胸径、树高)的准确度量,为大尺度高效便捷估测单木参数提供新的思路。【方法】以东北林业大学帽儿山实验林场13块4个龄组(幼龄林、中龄林、近熟林和成熟林)的落叶松人工林样地UAV-LiDAR数据及野外调查数据为数据源,基于UAVLiDAR点云的单木探测提取的树高,分别以普通最小二乘法(Ordinary least squares,OLS)和3种误差变量回归(标准主轴(Standard major axis,SMA)、远程主轴(Ranged major axis,RMA)和极大似然估计(Maximum likelihood estimate,MLE))构建胸径-树高模型,研究探测误差对各龄组人工落叶松胸径反演的影响并校准。【结果】利用UAV-LiDAR点云的单木探测提取4个龄组树高的相对均方根误差(rRMSE),误差范围为3.41%~5.14%;在胸径-树高模型预测方面,3种误差变量回归均优于OLS,RMA预测效果最好,4个龄组反演单木胸径的rRMSE降低了2.21%~3.58%。【结论】当满足模型假设时,误差变量回归比OLS在预测响应变量方面表现更好,是估计无偏的模型系数的理想方法,本研究中RMA方法表现最好;本研究所构建的人工落叶松胸径反演模型具有较高的预估精度,各项误差均保持在合理范围内,可实现应用UAV-LiDAR高效便捷地估测大尺度森林单木参数的目的,可在实践中推广。展开更多
基金funded by the National Key Research and Development Program(No.2017YFD0600904)the National Natural Science Foundation of China(No.31922055)+3 种基金the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX21_0913)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)funded by the China Scholarship Council(Grant No.202108320285)partially supported by the Horizon 2020 Research and Innovation Programme—European Commission‘BIOSPACE Monitoring Biodiversity from Space’project(Grant Agreement ID 834709,H2020-EU.1.1)。
文摘The diameter at breast height(DBH) of trees and stands is not only a widely used plant functional trait in ecology and biodiversity but also one of the most fundamental measurements in managing forests. However, systematically measuring the DBH of individual trees over large areas using conventional ground-based approaches is labour-intensive and costly. Here, we present an improved area-based approach to estimate plot-level tree DBH from airborne Li DAR data using the relationship between tree height and DBH, which is widely available for most forest types and many individual tree species. We first determined optimal functional forms for modelling heightDBH relationships using field-measured tree height and DBH. Then we estimated plot-level mean DBH by inverting the height-DBH relationships using the tree height predicted by Li DAR. Finally, we compared the predictive performance of our approach with a classical area-based method of DBH. The results showed that our approach significantly improved the prediction accuracy of tree DBH(R^(2)=0.85–0.90, rRMSE=9.57%–11.26%)compared to the classical area-based approach(R^(2)=0.80–0.83, rRMSE=11.98%–14.97%). Our study demonstrates the potential of using height-DBH relationships to improve the estimation of the plot-level DBH from airborne Li DAR data.
文摘胸径(Diameter at Breast Height,DBH)是指树木主干离地表面胸高处的直径,根据无人机可见光影像估算单木DBH对林业资产管理与评估具有重要意义。以云南师范大学呈贡校区内的银杏为研究对象,首先,获取其无人机可见光影像,基于摄影测量原理生成数字正射影像图;然后,在此基础上提取银杏单木的冠幅(Crown Width,CW);最后,建立CW与DBH的4个回归模型,通过该模型估算得到DBH值。将实际测量的DBH值与估算值进行精度验证,最终一元二次函数模型R 2为0.75,均方根误差为0.0129 m,平均误差率为3.22%,均小于其他3个模型,具有较高的精度。实验结果表明基于无人机可见光影像可以较为准确地估算单木DBH。
文摘【目的】以人工落叶松为例,探索基于无人机激光雷达(Unmanned aerial vehicle LiDAR,UAVLiDAR)点云的单木探测提取树高的误差对胸径反演的影响并校准,实现单木参数(胸径、树高)的准确度量,为大尺度高效便捷估测单木参数提供新的思路。【方法】以东北林业大学帽儿山实验林场13块4个龄组(幼龄林、中龄林、近熟林和成熟林)的落叶松人工林样地UAV-LiDAR数据及野外调查数据为数据源,基于UAVLiDAR点云的单木探测提取的树高,分别以普通最小二乘法(Ordinary least squares,OLS)和3种误差变量回归(标准主轴(Standard major axis,SMA)、远程主轴(Ranged major axis,RMA)和极大似然估计(Maximum likelihood estimate,MLE))构建胸径-树高模型,研究探测误差对各龄组人工落叶松胸径反演的影响并校准。【结果】利用UAV-LiDAR点云的单木探测提取4个龄组树高的相对均方根误差(rRMSE),误差范围为3.41%~5.14%;在胸径-树高模型预测方面,3种误差变量回归均优于OLS,RMA预测效果最好,4个龄组反演单木胸径的rRMSE降低了2.21%~3.58%。【结论】当满足模型假设时,误差变量回归比OLS在预测响应变量方面表现更好,是估计无偏的模型系数的理想方法,本研究中RMA方法表现最好;本研究所构建的人工落叶松胸径反演模型具有较高的预估精度,各项误差均保持在合理范围内,可实现应用UAV-LiDAR高效便捷地估测大尺度森林单木参数的目的,可在实践中推广。
