结合UAV-LiDAR和Sentinel-2的森林蓄积量估测  

Integrating UAV-LiDAR and Sentinel-2 for Growing Stock Volume Estimation

作　　者：雷宇宙 毛学刚[1] LEI Yuzhou;MAO Xuegang(School of Forestry,Northeast Forestry University,Harbin 150040,China)

机构地区：[1]东北林业大学林学院,哈尔滨150040

出　　处：《森林工程》2023年第6期46-54,共9页Forest Engineering

基　　金：黑龙江省自然科学基金(LH2021D001)。

摘　　要：遥感技术结合实测样地数据能够实现稳健和高效的森林蓄积量(growing stock volume)估测,可以大大减少调查时间和成本,同时便于定期监测。以内蒙古自治区赤峰市旺业甸林场为研究区,以典型温带针叶人工林为研究对象,通过两阶段外推法,利用随机森林算法结合实测样地数据、UAV-LiDAR数据和Sentinel-2多光谱数据估测整个研究区人工林的森林蓄积量。将实测样地数据外推至UAV-LiDAR样地,建立Field-UAV-LiDAR模型;将第一阶段生成的UAV-LiDAR估测的森林蓄积量外推至全覆盖的Sentinel-2卫星图像,建立Field-UAV-LiDAR-S-2模型。通过与实测样地数据和Sentinel-2提取的特征变量建立的直接预测模型(Field-S-2)进行对比。研究结果表明,基于Field-UAV-LiDAR模型的森林蓄积量估测精度最高(R^(2)=0.79、RMSE为42.34 m^(3)/hm^(2)、rRMSE为19.24%),其次是基于Field-UAV-LiDAR-S-2模型的森林蓄积量估测精度(R^(2)=0.68、RMSE为50.19 m^(3)/hm^(2)、rRMSE为29.43%),基于Field-S-2模型的森林蓄积量估测精度最低(R^(2)=0.49、RMSE为63.25 m^(3)/hm^(2)、rRMSE为33.08%)。研究结果验证了结合UAV-LiDAR和Sentinel-2数据估测森林蓄积量有效性,为大面积森林蓄积量估测提供参考思路。Remote sensing technology combined with sample plot data allows for robust and efficient estimation of growing stock volume,which greatly reduces survey time and costs and facilitates regular monitoring.Taking Wangyedian Forest Farm in Chifeng City,Inner Mongolia Autonomous Region as the study area and a typical temperate coniferous plantation forest as the research object,the forest stock volume of the plantation forest in the whole study area was estimated by the two-stage extrapolation method using the random forest algorithm in combination with the measured sample data,the UAV-LiDAR data and the Sentinel-2 multispectral data.The measured sample plot data were extrapolated to the UAV-LiDAR sample plots to build the Field-UAV-LiDAR model,and the UAV-LiDAR estimated forest stock volume generated in the first stage was extrapolated to the full-coverage Sentinel-2 satellite image to build the Field-UAV-LiDAR-S-2 model.The direct prediction model(Field-S-2)was compared by combining with the measured sample plot data and the feature variables extracted from Sentinel-2.The results showed that the highest accuracy of growing stock volume estimation(R^(2)=0.79,RMSE=42.34 m^(3)/hm^(2),rRMSE=19.24%)was based on the Field-UAV-LiDAR model,followed by the accuracy of growing stock volume estimation based on the Field-UAV-LiDAR-S-2 model(R^(2)=0.68,RMSE=50.19 m^(3)/hm^(2),rRMSE=29.43%),and the lowest accuracy of growing stock volume estimation based on Field-S-2 model(R^(2)=0.49,RMSE=63.25 m^(3)/hm^(2),rRMSE=33.08%).The results of the study verified the validity of combining UAV-LiDAR and Sentinel-2 data to estimate forest stock volume,which provides a reference idea for estimating forest stock volume in large areas.

关 键 词：森林蓄积量 Sentinel-2 UAV-LiDAR 两阶段外推 随机森林 

分 类 号：S758.5[农业科学—森林经理学]