文献类型：会议论文

英文题名：THE POTENTIAL OF FOREST BIOMASS INVERSION BASED ON CANOPY-INDEPENDENT STRUCTURE METRICS TESTED BY AIRBORNE LIDAR DATA

作者：Wang, Qiang[1,3] Ni-Meister, Wenge[2] Ni, Wenjian[3] Pang, Yong[4]

第一作者：Wang, Qiang

通信作者：Ni-Meister, W[1]

机构：[1]Heilongjiang Inst Technol, Dept Surveying Engn, Harbin 150040, Peoples R China;[2]CUNY Hunter Coll, Dept Geog, New York, NY 10021 USA;[3]Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China;[4]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China

会议论文集：IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

会议日期：JUL 28-AUG 02, 2019

会议地点：Yokohama, JAPAN

语种：英文

外文关键词：airborne lidar; forest biomass; canopy volume; DBH; rumple index

年份：2019

摘要：Forest biomass is an important for evaluating forest resources and optimizing efficiency in the forest industry. To improve our ability to estimate the structure parameter in the forest based on canopy-independent structure metrics, we used a suite of structural metrics that relate to three aspects of the forest biomass: DBH. tree height.forest density, and analyzed the relationships between structural metrics derived from airborne lidar scanner data and field measure data. The regression relationship between each structural metrics and mean diameter at breast height (DBH) was calculated for sites located at New York central park. The tree height had the weak correlations with mean DBH (R-2=0.482), and the two canopy-independent structure metrics (rumple index, canopy volume) had the stronger correlations with mean DBH than tree height, R-2 values were 0.516, 0.532 respectively. However, the correlations were significantly improved when the two canopy-independent metrics were introduced into regression. The canopy and trunk volume had the strongest correlations with mean DBH (R-2 =0.898), which included information such as tree height, canopy structure and forest density. Our results demonstrate that canopy-independent variables are useful explanatory variables for predicting forest biomass even if tree height can not be obtained.