Estimating forest aboveground carbon sink based on Landsat time-series and its response to climate change

Abstract

Accurately estimating forest carbon sink and exploring their climate-driven mechanisms are essential for achieving carbon neutrality and sustainable development. Taking Pinus densata in Shangri-La as the research object, we established three Random Forest (RF) dynamic models based on Landsat time series and ground data with 5-year interval variation, 10-year interval variation, and annual average variation. Then, Genetic Algorithm (GA) was applied to optimize the parameters of RF to establish GA-RF dynamic models, and selected the optimal model to estimate the carbon sink intensity (CSI) of Pinus densata. Finally, climate-driven mechanisms were explored by correlation analysis. We found that 1) the GA-RF model based on the annual average variation had the highest accuracy with an R2 of 0.83. 2) The CSI of Pinus densata in Shangri-La was 7.84–12.35×104 t C·hm− 2 from 1987 to 2017. 3) Precipitation had the greatest effect on CSI. The joint weak drive of CSI by precipitation, temperature and surface solar radiation was the most dominant form of CSI drive for Pinus densata. These results suggest that the GA-RF model can be used for large-scale long-term estimation of above-ground carbon sinks in highland forests. In addition, the precipitation-led multifactorial synergistic driving mechanism will stabilize the carbon sink capacity of Pinus densata in the long term.