文献类型：期刊文献

英文题名：Estimating effective leaf area index using li-strahler geometricoptical model, landsat 7 ETM+, and airborne lidar in the greater khingan mountains of China

作者：Gu, Chengyan[1] Wang, Chongyang[2] Tian, Xin[2] Li, Zengyuan[2] Sun, Shanshan[2] Gao, Zhihai[2]

第一作者：Gu, Chengyan

通信作者：Tian, Xin

机构：[1] Planning and Design Institute of Forestry Product Industry, National Forestry and Grassland Administration, No.130, Chaoyangmennei Street, Beijing, 100010, China; [2] Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Dongxiaofu No.1, Xiangshan Rd., Beijing, 100091, China

年份：2019

卷号：2019-January

起止页码：6562-6565

外文期刊名：International Geoscience and Remote Sensing Symposium (IGARSS)

收录：EI(收录号：20213510842374)

语种：英文

外文关键词：Optical radar - Remote sensing - Geometry - Mean square error

摘要：Accurate estimation of forest effective leaf area index (LAIe) is of great significance for regional carbon sequestration studies, forest management and monitoring. In this study, an advanced method was developed to predict LAIe based on Li-Strahler geometric-optical model, airborne LiDAR and Landsat 7 ETM+ data. More specifically, based on the Li- Strahler geometric-optical model, a reliable method was developed to solve the mixed pixel problem, and further to realize the prediction of regional LAIe from airborne LiDAR and multispectral data. First, based on the airborne LiDAR-derived canopy height product, the LAIe was estimated over airborne LiDAR coverage. Second, the sunlit background component was calculated based on the simplified relationship with canopy gap, and LAIe. Then, the reflectance of sunlit background was calculated based on the linear decomposition model. Finally, the forest LAIe was estimated by using Li-Strahler geometric-optical model over the study area. Results showed that the retrieval method proposed in this study could be used effectively in the inversion of regional LAIe, with the significant coefficient of determination (R2) was 0.81 and root mean square error (RMSE) was 0.23, as compared with field measurements. ? 2019 IEEE.