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

Kun Yang; Kai Luo; Jialong Zhang; Bo Qiu; Feiping Wang; Qinglin Xiao; Jun Cao; Yunrun He; Jian Yang

doi:10.1038/s41598-024-84258-7

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

Sci Rep. 2025 Jan 2;15(1):589. doi: 10.1038/s41598-024-84258-7.

Authors

Kun Yang¹, Kai Luo¹, Jialong Zhang², Bo Qiu¹, Feiping Wang³, Qinglin Xiao¹, Jun Cao¹, Yunrun He¹, Jian Yang⁴

Affiliations

¹ The Key Laboratory of Forest Resources Conservation and Utilization in The Southwest Mountains of China Ministry of Education, Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest, China, Yunnan Province Key Laboratory For Conservation and Utilization of In-forest Resource, Southwest Forestry University, Kunming, 650224, Yunnan, China.
² The Key Laboratory of Forest Resources Conservation and Utilization in The Southwest Mountains of China Ministry of Education, Key Laboratory of National Forestry and Grassland Administration on Biodiversity Conservation in Southwest, China, Yunnan Province Key Laboratory For Conservation and Utilization of In-forest Resource, Southwest Forestry University, Kunming, 650224, Yunnan, China. jialongzhang@swfu.edu.cn.
³ Guangxi State-owned Gaofeng Forest Farm, Nanning, 530001, Guangxi, China.
⁴ State-owned Jiaozuo Forest Farm, Jiaozuo, 454000, Henan, China.

Abstract

Accurately estimating forest carbon sink and exploring their climate-driven mechanisms are critical to achieving carbon neutrality and sustainable development. Fewer studies have used machine learning-based dynamic models to estimate forest carbon sink. The climate-driven mechanisms in Shangri-La have yet to be explored. In this study, a genetic algorithm (GA) was used to optimize the parameters of random forest (RF) to establish dynamic models to estimate the carbon sink intensity (CSI) of Pinus densata in Shangri-La and analyze the combined effects of multi-climatic factors on CSI. We found that (1) GA can effectively improve the estimation accuracy of RF, the R² can be improved by up to 34.8%, and the optimal GA-RF model R² is 0.83. (2) The CSI of Pinus densata in Shangri-La was 0.45-0.72 t C·hm^{- 2} from 1987 to 2017. (3) Precipitation has the most significant effect on CSI. The combined weak drive of precipitation, temperature, and surface solar radiation on CSI was the most dominant drive for Pinus densata CSI. These results indicate that dynamic models can be used for large-scale long-term estimation of carbon sink in highland forest, providing a feasible method. Clarifying the driving mechanism will provide a scientific basis for forest resource management.

Keywords: Climate change; Dynamic modeling; Forest aboveground carbon sink; Genetic algorithm; Time series.

Abstract

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