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基于机器学习和地理信息系统的框架,用于使用遥感和现场数据进行连续干旱影响的多维分析
Hıdır Serkendiz1, Hasan Tatli1, Emre Özelkan2
1Canakkale Onsekiz Mart University, Department of Geography, 17020, Canakkale, Turkiye.
The Science of the total environment
|September 17, 2025
概括
一个新的框架评估了干旱对农业的影响,揭示了土耳其的不可持续的水资源使用,尽管土地转型. 这突显了水和粮食安全的风险,为适应性治理策略提供了信息.
科学领域:
- 环境科学 环境科学
- 农业科学 农业科学
- 气候科学 气候科学
背景情况:
- 干旱对全球农业部门构成重大风险.
- 了解级联影响对于可持续的水和粮食安全至关重要.
- 土耳其的科尼亚封闭盆地是一个干旱敏感的农业地区.
研究的目的:
- 提出和应用一个多维的概念框架来评估干旱对农业的级联影响.
- 分析Konya封闭盆地的干旱动态和土地利用转变.
- 确定不适应的农业实践及其对水和粮食安全的影响.
主要方法:
- 远程探测 (NDVI,NDWI,LST,土地覆盖) 和地面数据 (降水量,温度,地下水) 的整合.
- 统计趋势分析 (曼 - 肯达尔) 的应用,用于干旱和土地利用特征.
- 利用机器学习算法 (SVM,ANN,RF) 基于干旱指标 (NDVI,NDWI,LST,PDSI) 建模土地使用变化.
主要成果:
- 灌土地的重大转型 (约. 在1990年至2018年期间,向非灌区增加了51万公的土地.
- 尽管水资源供应减少,但水资源密集型作物 (玉米,甜菜) 的生产持续增加.
- 确定了农业实践中的不适应性轨迹,表明不可持续的用水.
结论:
- 拟议的框架有效地评估了农业系统中连锁干旱的影响.
- 不可持续的用水和生产模式对水和粮食安全构成长期风险.
- 结果为气候脆弱地区的适应性农业和水资源治理提供了关键的见解.
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