Estimation of Forest Aboveground Biomass in Karst Areas Using Multi-Source Remote Sensing Data and the K-DBN Algorithm

被引:14
作者
Qian, Chunhua [1 ,2 ]
Qiang, Hequn [2 ,3 ]
Wang, Feng [2 ]
Li, Mingyang [1 ]
机构
[1] Nanjing Forestry Univ, Sch Forestry, Nanjing 210037, Peoples R China
[2] Suzhou Polytech Inst Agr, Sch Smart Agr, Suzhou 215008, Peoples R China
[3] Soochow Univ, Sch Comp Sci & Technol, Suzhou 215301, Peoples R China
基金
中国国家自然科学基金;
关键词
aboveground biomass; Landsat; 8; OLI; Sentinel-1A; forest canopy density; DBN; SEASON NITROGEN REQUIREMENTS; COLOR INFRARED PHOTOGRAPHY; LEAF CHLOROPHYLL CONTENT; VEGETATION INDEX; CARBON STORAGE; LIDAR DATA; COMBINATIONS; PERFORMANCE; IMPACTS; DENSITY;
D O I
10.3390/rs13245030
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Accurate estimation of forest biomass is the basis for monitoring forest productivity and carbon sink function, which is of great significance for the formulation of forest carbon neutralization strategy and forest quality improvement measures. Taking Guizhou, a typical karst region in China, as the research area, this study used Landsat 8 OLI, Sentinel-1A, and China national forest resources continuous inventory data (NFCI) in 2015 to build a deep belief network (DBN) model for aboveground biomass (AGB) estimation. Based on the introduction of forest canopy density (FCD), we improved the DBN model to design the K-DBN model with the highest estimation accuracy is selected for AGB inversion and spatial mapping. The results showed that: (1) The determination coefficients R-2 of DBN is 0.602, which are 0.208, 0.101 higher than that of linear regression (LR) and random forest (RF) model. (2) The K-DBN algorithm was designed based on FCD to optimize the DBN model, which can alleviate the common problems of low-value overestimation and high-value underestimation in AGB estimation to a certain extent to improve the estimation accuracy. The maximum R-2 of the model reached 0.848, and we mapped the forest AGB using the K-DBN model in the study area in 2015. The conclusion of this study: Based on multi-source optical and radar data, the retrieval accuracy of forest AGB can be improved by considering the FCD, and the deep learning algorithm K-DBN is excellent in forest AGB remote sensing estimation. These research results provide a new method and data support for the spatio-temporal dynamic remote sensing monitoring of forest AGB in karst areas.
引用
收藏
页数:28
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