| 基于InVEST-Ridge Regression-PLUS模型的云贵高原碳储量时空演变及多情景预测 |
| 摘要点击 1697 全文点击 44 投稿时间：2025-01-09 修订日期：2025-04-19 |
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| 中文关键词 云贵高原 碳储量 InVEST模型 岭回归 PLUS模型 |
| 英文关键词 Yunnan-Guizhou Plateau carbon storage InVEST model ridge regression PLUS model |
| DOI 10.13227/j.hjkx.202501105 |
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| 中文摘要 |
| 碳储量作为衡量生态系统碳汇能力的关键指标，对全球气候变化的缓解具有重要意义. 结合机器学习和生态系统服务模型的优势，构建基于InVEST-Ridge Regression-PLUS模型的综合分析框架，对云贵高原2000~2020年碳储量的时空演变特征及其驱动机制进行定量分析，并据此设计未来情景，预测不同土地利用路径下区域碳储量的变化趋势. 结果表明：①云贵高原2000~2020年间碳储量总体呈缓慢增长的趋势，且增长速率不断下降，表现为“南高北低”的分布特征. ②植被覆盖度是影响该区域碳储量的决定性因素，不同土地利用类型之间的转化会影响碳储量空间分布及变化趋势. ③在未来情景模拟中，碳汇提升情景下的碳储量表现最优，有效验证了退耕还林和草地恢复等生态工程的作用，为云贵高原及同类型喀斯特地区的碳储量动态评估与优化提供科学依据. |
| 英文摘要 |
| As an important indicator for measuring the carbon sequestration capacity of ecosystems，carbon storage is of great significance for alleviating global climate change. By taking advantage of machine learning and ecosystem service models，an integrated analysis framework based on the InVEST-Ridge Regression-PLUS model was constructed to conduct a quantitative analysis of the spatio-temporal evolution characteristics and driving mechanisms of carbon storage in the Yunnan-Guizhou Plateau from 2000 to 2020，and future scenarios were designed to predict the changing trends of regional carbon storage under different land use paths. The results show that：Firstly，from 2000 to 2020，the carbon storage in the Yunnan-Guizhou Plateau generally presented a slow growth trend，and the growth rate continued to decline，showing a distribution pattern of "higher in the south and lower in the north." Secondly，vegetation coverage was a crucial determining factor for carbon storage in this area，and the conversion between different land use types affected the spatial distribution of carbon storage. Thirdly，in the future scenario simulation，the carbon storage under the carbon sink enhancement scenario performed best，effectively verifying the effects of ecological projects such as the conversion of farmland to forest and grassland restoration，providing a scientific basis for the dynamic assessment and optimization of carbon storage in the Yunnan-Guizhou Plateau and similar karst areas. |
