文献类型：期刊文献

英文题名：Estimation of Forest Aboveground Biomass and Leaf Area Index Based on Digital Aerial Photograph Data in Northeast China

作者：Li, Dan[1,2] Gu, Xingfa[1] Pang, Yong[3] Chen, Bowei[3] Liu, Luxia[4]

第一作者：Li, Dan

通信作者：Gu, XF[1]

机构：[1]Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China;[2]Univ Chinese Acad Sci, Beijing 100049, Peoples R China;[3]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[4]Anhui Agr Univ, Sch Forestry & Landscape Architecture, Hefei 230036, Anhui, Peoples R China

年份：2018

卷号：9

期号：5

外文期刊名：FORESTS

收录：;EI(收录号：20182105227086);Scopus(收录号：2-s2.0-85047113897);WOS:【SCI-EXPANDED(收录号:WOS:000435193700050)】；

基金：The study was funded by the China National Key Research and Development Program (2017YFD0600404); the China State Administration of Science, Technology and Industry for National Defense Program, "Major Special Project-the China High-Resolution Earth Observation System, Project of civil space pre-research of the 13th five-year plan" (Y7K00100KJ) and the Chinese Academy of Forestry Foundation (CAFYBB2016ZD004).

语种：英文

外文关键词：digital aerial photograph; aboveground biomass; leaf area index; photogrammetric point cloud; recursive feature elimination; machine-learning

摘要：Forest aboveground biomass (AGB) and leaf area index (LAI) are two important parameters for evaluating forest growth and health. It is of great significance to estimate AGB and LAI accurately using remote sensing technology. Considering the temporal resolution and data acquisition costs, digital aerial photographs (DAPs) from a digital camera mounted on an unmanned aerial vehicle or light, small aircraft have been widely used in forest inventory. In this study, the aerial photograph data was acquired on 5 and 9 June, 2017 by a Hasselblad60 digital camera of the CAF-LiCHy system in a Y-5 aircraft in the Mengjiagang forest farm of Northeast China, and the digital orthophoto mosaic (DOM) and photogrammetric point cloud (PPC) were generated from an aerial overlap photograph. Forest red-green-blue (RGB) vegetation indices and textural factors were extracted from the DOM. Forest vertical structure features and canopy cover were extracted from normalized PPC. Regression analysis was carried out considering only DOM data, only PPC data, and a combination of both. A recursive feature elimination (RFE) method using a random forest was used for variable selection. Four different machine-learning (ML) algorithms (random forest, k-nearest neighbor, Cubist and supporting vector machine) were used to build regression models. Experimental results showed that PPC data alone could estimate AGB, and DOM data alone could estimate LAI with relatively high accuracy. The combination of features from DOM and PPC data was the most effective, in all the experiments considered, for the estimation of AGB and LAI. The results showed that the height and coverage variables of PPC, texture mean value, and the visible differential vegetation index (VDVI) of the DOM are significantly related to the estimated AGB (R-2 = 0.73, RMSE = 20 t/ha). The results also showed that the canopy cover of PPC and green red ratio index (GRRI) of DOM are the most strongly related to the estimated LAI, and the height and coverage variables of PPC, the texture mean value and visible atmospherically resistant index (VARI), and the VDVI of DOM followed (R-2 = 0.79, RMSE = 0.48).