Mapping and analyzing the spatiotemporal dynamics of forest aboveground biomass in the ChangZhuTan urban agglomeration using a time series of Landsat images and meteorological data from 2010 to 2020

In the context of global warming, there is a substantial demand for accurate and cost-effective assessment and comprehensive understanding of forest above-ground biomass (AGB) dynamics. The timeliness and low cost of optical remote sensing data enable the mapping of large-scale forest AGB dynamics. However, mapping forest AGB with optical remote sensing data presents challenges primarily due to data uncertainty and the complex nature of the forest environment. Previous studies have demonstrated the potential of meteorological data in enhancing forest AGB mapping. To accurately capture the dynamics of forest AGB, we initially acquired Landsat datasets, digital elevation model (DEM), and meteorological datasets (temperature, humidity, and precipitation) from 2010 to 2020 in Changsha-Zhuzhou-Xiangtan urban agglomeration (CZT) located in Hunan Province, China. Spectral variables (SVs), including spectral bands and vegetation indices, were extracted from Landsat images, while meteorological variables (MVs) were derived from the monthly meteorological data using the Savitzky-Golay (S-G) filtering algorithm. Additionally, terrain variables (TVs) were also extracted from the DEM data. Three modelling models, multiple linear regression (MLR), K nearest neighbor (KNN) and random forest (RF), were developed for mapping the dynamics of forest AGB in CZT. The result revealed that MVs have the potential to improve forest AGB mapping. Integration of MVs into the models resulted in a significant reduction in root mean square error (RMSE) ranging from 32.85 % to 19.25 % compared to utilizing only SVs. However, minimal improvement was observed with the inclusion of TVs due to negligible topographic relief within the study area. An upward trend of forest AGB in CZT was observed during this period, which can be attributed to the effective implementation of government environmental protection policies. It is confirmed that the meteorological data has significant contribution to forest AGB mapping, thereby endorsing advancements in forest resource monitoring and management programs.