文献类型： 外文期刊

作者： Qian, Chunhua ¹ ; Qiang, Hequn ¹ ; Li, Mingyang ³ ;

作者机构： 1.Suzhou Polytech Inst Agr, Suzhou 215008, Peoples R China

2.Jiangsu Acad Agr Sci, Agr Informat Res Inst, Nanjing 210014, Peoples R China

3.Nanjing Forestry Univ, Coll forest & grassland, Nanjing 210037, Peoples R China

关键词： Biological system modeling; Spatiotemporal phenomena; Analytical models; Forestry; Desertification; Economic indicators; Biomass; Vegetation mapping; Sun; Spatial resolution; Driving forces; forest aboveground biomass (AGB); geographically weighted model; karst region; spatiotemporal pattern

期刊名称：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING （ 影响因子：8.6； 五年影响因子：8.8 ）

ISSN： 0196-2892

年卷期： 2025 年 63 卷

页码：

收录情况： SCI

摘要： The spatiotemporal patterns and driving forces of forest aboveground biomass (AGB) in subtropical karst region provide key information for forest carbon sink management and regional ecological environment governance planning. In this article, a novel multiscale geographically and temporally gravity-weighted regression (G-MGTWR) model was proposed to assess the spatiotemporal patterns of AGB in the Guizhou province, China. First, the spatial bandwidths of the G-MGTWR model are optimized. Then, all the AGB data from four periods are put into the model. Finally, the center of gravity of AGB at a county scale is calculated to build the G-MGTWR model. It performed the best with an R-2 of 0.933, against 0.725, 0.847, 0.856, and 0.913 for the ordinary least squares (OLSs), geographically weighted regression (GWR), multiscale GWR (MGWR), and geographically temporally weighted regression (GTWR), respectively. It also has a corrected Akaike information criterion (AICc) index of 63.924, a Bayesian information criterion (BIC) of 26.272, and a residual sum of squares (RSSs) of 5.518 Mg/ha, and all the metrics of the G-MGTWR are superior to the other models. Compared to a fixed bandwidth used in the GTWR for all factors, the G-MGTWR significantly improves accuracy and makes the better interpretation of each factor more convincing by seeking the optimal bandwidth size for each factor and using the varying biomass centers over different periods instead of a fixed geographic center. The driving forces analysis results show that rocky desertification (RD) and population are the main negative factors causing AGB change while sunshine and precipitation were the main positive factors.