文献类型：期刊文献

英文题名：Effects of Forest Canopy Structure on Forest Aboveground Biomass Estimation Using Landsat Imagery

作者：Li, Chao[1] Li, Mingyang[1] Iizuka, Kotaro[2] Liu, Jie[3] Chen, Keyi[4] Li, Yingchang[1]

第一作者：Li, Chao

通信作者：Li, MY[1]

机构：[1]Nanjing Forestry Univ, Coll Forestry, Coinnovat Ctr Sustainable Forestry Southern China, Nanjing 210037, Peoples R China;[2]Univ Tokyo, Ctr Spatial Informat Sci, Kashiwa, Chiba 2778568, Japan;[3]Nanjing Forestry Univ, Coll Landscape Architecture, Nanjing 210037, Peoples R China;[4]Chinese Acad Forestry, Res Inst Forestry Policy & Informat, Beijing 100091, Peoples R China

年份：2021

卷号：9

起止页码：5285-5295

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20210209761407);Scopus(收录号：2-s2.0-85099101724);WOS:【SCI-EXPANDED(收录号:WOS:000608197100001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 31770679, in part by the Priority Academic Program Development of Jiangsu Higher Education Institution (PAPD), and in part by the Top-notch Academic Programs Project (TAPP) of Jiangsu Higher Education Institutions, China, under Grant PPZY2015A062.

语种：英文

外文关键词：Forest inventory data; forest aboveground biomass; Landsat 8 image; canopy structure; piecewise model; subtropical forest

摘要：Accurate remote sensing-based forest aboveground biomass (AGB) estimation is important for accurate understanding of carbon accounting and climate change at a large scale. However, over- and underestimation are common in the process, resulting in inaccurate AGB estimations. Here, the AGB was estimated and mapped by combining Landsat 8 images and forest inventory data in western Hunan Province, China. We used forest canopy density (FCD) mapper to quantify the forest canopy structure. The linear model (LR) and piecewise model with FCD gradients (classified by k-means clustering; sparse, medium, and dense) were developed to estimate AGB for each forest type (coniferous, broadleaf, mixed, and total forests). The piecewise model considered the following scenarios: piecewise model using the variables of LR model (PM), and piecewise model using the variables selected for different FCD gradients (PMV). The PM (R-2:0.45-0.56) and PMV (R-2:0.63-0.75) models showed better agreement between observed and predicted AGB than the LR (R-2:0.18-0.27) models, and the PMV model was the most accurate for each forest type. The PM and PMV models performed better than LR models at different FCD gradients. The PM and PMV models can better alleviate the over- and underestimations of the LR models. At different FCD gradients, the PMV models had different variables, indicating that the correlation between the AGB and spectral variables was different. Overall, FCD is an important forest parameter that influences AGB estimation, and the piecewise model has potential to improve remote sensing-based AGB estimation.