An improved Carnegie-Ames-Stanford Approach model for estimating ecological carbon sequestration in mountain vegetation

Abstract

The vegetation in mountainous areas is abundant, and its ecological carbon sequestration ability is of great significance to maintain the sustainable and healthy development of the ecological environment. However, when estimating the carbon sequestration of mountain vegetation, the Carnegie-Ames-Stanford Approach (CASA) model assigns a uniform value to the maximum light energy utilization (εmax = 0.389 gC/MJ), ignoring the influence of vegetation types and topographic factors on εmax, resulting in the low accuracy of the CASA model in estimating the carbon sequestration of mountain vegetation. In this paper, the improved CASA model was combined with Landsat 8 Operational Land Imager (OLI) remote sensing image data to improve the estimation accuracy of carbon sequestration of mountain vegetation. The first was the establishment of a linear link between the terrain characteristics (slope and aspect), vegetation types, and εmax in mountainous locations. The second was the improvement of the CASA model’s calculation method for key parameters. The different distributions of the estimation results from the two techniques in 2015 and 2016 are then compared using Landsat 8 data as the data source, and the impact of the terrain factors in the improved CASA model on the estimation results is confirmed. Finally, the improved CASA model and the CASA model are used to estimate the Net Primary Productivity (NPP) of the study area from 2000 to 2020, and the estimated results of the two models are compared with the computation results of the MODIS data NPP product. The findings indicate that the improved CASA model’s estimation results have a higher degree of fit and a better correlation. The improved CASA model aids in precisely understanding the ecological carbon sequestration potential of mountain areas and increases the estimation accuracy of vegetation carbon sequestration in mountainous areas.