使用遥感和机器学习评估干旱对农业LULC变化影响的技术
Musa Mustapha1, Mhamed Zineddine2
1School of Digital Engineering and Artificial Intelligence, Euromed University of Fes, UEMF, 30000, Fes, Morocco. m.musa@ueuromed.org.
Environmental monitoring and assessment
|May 6, 2024
概括
干旱严重影响半干旱地区的土地利用. 本研究使用人工智能和遥感来追踪土地覆盖变化和干旱影响,帮助未来的监测系统.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 干旱事件对水资源和农业构成重大风险,特别是在雨量不可预测的半干旱地区.
- 气候变化加剧了干旱的影响,需要先进的监测技术.
研究的目的:
- 评估一种用于评估干旱对土地利用/土地覆盖 (LULC) 变化在2018年至2022年之间的影响的新技术.
- 为了比较梯度树增强 (GTB) 和随机森林 (RF) 模型在LULC分类中的性能.
主要方法:
- 使用了Sentinel-2卫星图像进行LULC分类.
- 综合气候数据 (CHIRPS,AgERA5),地质形态数据 (ALOS) 和植物健康指数 (VHI) 来自MODIS.
- 训练并评估GTB和RF模型,使用整体准确性 (OA) 和卡帕系数 (K) 评估准确性.
主要成果:
- 该GTB模型的性能优于RF,达到OA> 90%和K> 0.9.9.
- 在费斯观察到显著的LULC变化:19.92%的建筑扩张,34.86%的裸露土地增加,17.86%的水体减少,37.30%的农业用地减少.
- 在农业LULC变化,降雨量和VHI之间发现了强烈的正相关性 (0.81,0.89). 2020年和2022年出现了轻度干旱情况.
结论:
- 人工智能和遥感对于有效的干旱和环境变化评估至关重要.
- 调查结果强调了评估技术对加强脆弱地区干旱监测系统的有用性.
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