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 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 Shan...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-024-84258-7 |
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| author | Kun Yang Kai Luo Jialong Zhang Bo Qiu Feiping Wang Qinglin Xiao Jun Cao Yunrun He Jian Yang |
| author_facet | Kun Yang Kai Luo Jialong Zhang Bo Qiu Feiping Wang Qinglin Xiao Jun Cao Yunrun He Jian Yang |
| author_sort | Kun Yang |
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| description | 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 R 2 can be improved by up to 34.8%, and the optimal GA-RF model R 2 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. |
| format | Article |
| id | doaj-art-e18450f5b28f4f148e7b53c5154311d7 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-e18450f5b28f4f148e7b53c5154311d72025-01-05T12:17:24ZengNature PortfolioScientific Reports2045-23222025-01-0115112010.1038/s41598-024-84258-7Estimating forest aboveground carbon sink based on landsat time series and its response to climate changeKun Yang0Kai Luo1Jialong Zhang2Bo Qiu3Feiping Wang4Qinglin Xiao5Jun Cao6Yunrun He7Jian Yang8The 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 UniversityThe 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 UniversityThe 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 UniversityThe 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 UniversityGuangxi State-owned Gaofeng Forest FarmThe 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 UniversityThe 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 UniversityThe 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 UniversityState-owned Jiaozuo Forest FarmAbstract 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 R 2 can be improved by up to 34.8%, and the optimal GA-RF model R 2 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.https://doi.org/10.1038/s41598-024-84258-7Forest aboveground carbon sinkTime seriesDynamic modelingGenetic algorithmClimate change |
| spellingShingle | Kun Yang Kai Luo Jialong Zhang Bo Qiu Feiping Wang Qinglin Xiao Jun Cao Yunrun He Jian Yang Estimating forest aboveground carbon sink based on landsat time series and its response to climate change Scientific Reports Forest aboveground carbon sink Time series Dynamic modeling Genetic algorithm Climate change |
| title | Estimating forest aboveground carbon sink based on landsat time series and its response to climate change |
| title_full | Estimating forest aboveground carbon sink based on landsat time series and its response to climate change |
| title_fullStr | Estimating forest aboveground carbon sink based on landsat time series and its response to climate change |
| title_full_unstemmed | Estimating forest aboveground carbon sink based on landsat time series and its response to climate change |
| title_short | Estimating forest aboveground carbon sink based on landsat time series and its response to climate change |
| title_sort | estimating forest aboveground carbon sink based on landsat time series and its response to climate change |
| topic | Forest aboveground carbon sink Time series Dynamic modeling Genetic algorithm Climate change |
| url | https://doi.org/10.1038/s41598-024-84258-7 |
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