Identifying Spatial Variation of Carbon Stock in a Warm Temperate Forest in Central Japan Using Sentinel-2 and Digital Elevation Model Data

Abstract

The accurate estimation of carbon stocks in natural and plantation forests is a prerequisite for the realization of carbon peaking and neutrality. In this study, the potential of optical Sentinel-2A data and a digital elevation model (DEM) to estimate the spatial variation of carbon stocks was investigated in a mountainous warm temperate region in central Japan. Four types of image preprocessing techniques and datasets were used: spectral reflectance, DEM-based topography indices, vegetation indices, and spectral band-based textures. A random forest model combined with 103 field plots as well as remote sensing image parameters was applied to predict and map the 2160 ha University of Tokyo Chiba Forest. Structural equation modeling was used to evaluate the factors driving the spatial distribution of forest carbon stocks. Our study shows that the Sentinel-2A data in combination with topography indices, vegetation indices, and shortwave-infrared (SWIR)-band-based textures resulted in the highest estimation accuracy. The spatial distribution of carbon stocks was successfully mapped, and stand-age- and forest-type-level variations were identified. The SWIR-2-band and topography indices were the most important variables for modeling, while the forest stand age and curvature were the most important determinants of the spatial distribution of carbon stock density. These findings will contribute to more accurate mapping of carbon stocks and improved quantification in different forest types and stand ages.