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[基于SRP模型的江西省生态脆弱性的变化和驱动机制]
Jin-Bo Qin1, Shu-Hua Qi1, Mei Hu2
1School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China.
Huan jing ke xue= Huanjing kexue
|February 9, 2026
概括
江西省的生态脆弱性呈现下降趋势,大多数地区的环境状况有所改善. 主要驱动因素包括土壤侵蚀和植被覆盖的变化,为生态保护战略提供了洞察力.
科学领域:
- 环境科学 环境科学
- 生态生态学 生态生态学
- 地理信息科学 地理信息科学
背景情况:
- 江西省是国家生态文明试点区,需要强有力的生态保护和可持续发展战略.
- 生态脆弱性评估对于指导该地区有效的保护和恢复工作至关重要.
研究的目的:
- 评估2000年至2020年江西省生态脆弱性的时空动态.
- 通过使用先进的机器学习技术,确定影响生态脆弱性变化的关键驱动因素.
主要方法:
- 使用基于生态敏感性-弹性-压力 (SRP) 模型的框架.
- 采用分水和分水单位进行评估,整合遥感,土地使用,土壤,气象和社会经济数据.
- 应用了一个可解释的机器学习模型 (XGBoost-SHAP) 来确定驱动因素.
主要成果:
- 较低的生态脆弱性集中在山区,而较高的脆弱性则出现在人类活动密集的地区,如波阳湖平原.
- 平均生态脆弱性指数显示下降趋势,从2000年的0.224%降至2020年的0.206%,其中75.75%的地区显示脆弱性下降.
- 土壤侵蚀强度,植被覆盖率 (FVC),土壤侵蚀比例高于中等,土地利用被确定为主要驱动因素.
结论:
- 2000年至2020年间,江西省的生态脆弱性普遍下降,这表明环境发生了积极的变化.
- 土壤侵蚀和植被覆盖的动态是影响生态脆弱性的重要因素,其贡献在空间上有所不同.
- 这些发现为江西的生态保护提供了科学基础,并证明了机器学习在脆弱性评估中的实用性.
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