| 基于SRP模型的江西省生态脆弱性变化及驱动机制 |
| 摘要点击 444 全文点击 15 投稿时间：2024-12-28 修订日期：2025-04-03 |
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| 中文关键词 SRP模型 生态脆弱性 驱动机制 XGBoost-SHAP模型 江西省 |
| 英文关键词 SRP model ecological vulnerability driving mechanisms XGBoost-SHAP model Jiangxi Province |
| DOI 10.13227/j.hjkx.20260228 |
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| 中文摘要 |
| 江西省作为首批国家生态文明试验区之一，在生态保护与可持续发展方面肩负着重要使命，生态脆弱性评估对江西省的生态保护与修复具有重要指导价值. 基于小流域和栅格评价单元，结合遥感影像数据、土地利用数据、土壤数据、气象数据和社会经济数据等，采用生态敏感性-恢复力-压力度（SRP）模型构建生态脆弱性评价指标体系，评价2000~2020年江西省生态脆弱性时空变化特征，并利用可解释的机器学习模型（XGBoost-SHAP）揭示其生态脆弱性变化的驱动因素. 结果表明：①江西省生态脆弱性较低的区域主要分布在东北部、西北部以及南部山区，而脆弱性较高的区域则集中在人类活动密集的平原及河流沿岸，如鄱阳湖平原区. 整体呈现微度和轻度脆弱为主的格局. ②2000年、2010年和2020年的生态脆弱性指数平均值分别为0.224、0.219和0.206，表明生态脆弱性指数呈下降趋势. 其中，生态脆弱性指数降低的区域占总面积的75.75%. ③土壤侵蚀强度、植被覆盖度、中度以上土壤侵蚀强度占比和土地利用变化是导致生态脆弱性变化的关键因子，其重要性占比分别为34.66%、25.99%、10.83%和10.63%，且不同因子的贡献度存在显著的空间差异. 研究结果可为江西省生态环境保护提供理论支持，同时为将机器学习方法应用于生态脆弱性研究中提供重要的参考和借鉴. |
| 英文摘要 |
| Jiangxi Province， as one of the first national ecological civilization pilot zones in China， holds a significant responsibility in ecological protection and sustainable development. Ecological vulnerability assessment is of great guiding value for ecological protection and restoration in Jiangxi Province. Based on the sub-watershed and raster evaluation units， combined with remote sensing image data， land use data， soil data， meteorological data， socio-economic data， etc.， an ecological vulnerability assessment framework was established using the ecological sensitivity-resilience-pressure （SRP） model to evaluate the spatiotemporal dynamics of ecological vulnerability in Jiangxi Province from 2000 to 2020， and the driving factors of ecological vulnerability changes were revealed using an interpretable machine learning model （XGBoost-SHAP）. The results indicate that： ① The areas with relatively low ecological vulnerability of Jiangxi Province were primarily distributed in the northeastern， northwestern， and southern mountainous regions， while areas with higher vulnerability were concentrated in the plains and riverbanks where human activities are intensive， such as the Poyang Lake plain area. The overall distribution was primarily characterized by mild and light vulnerability. ② In the years 2000， 2010， and 2020， the average ecological vulnerability index values were 0.224， 0.219， and 0.206， respectively， indicating a downward trend in the ecological vulnerability index. Among these， the areas where the ecological vulnerability index decreased accounted for 75.75% of the total area. ③ The changes in soil erosion intensity， FVC， percentage of soil erosion above moderate， and land use were key factors driving ecological vulnerability changes， with relative importance weights of 34.66%， 25.99%， 10.83%， and 10.63%， respectively. Moreover， the contributions of these factors exhibited significant spatial variation. The research findings can provide theoretical support for ecological environment protection in Jiangxi Province， while also offering important references and insights for the application of machine learning methods in the study of ecological vulnerability. |
