基于多时SAR干扰测量 (MTInSAR) 的地表沉降及其对印度克里什纳戈达瓦里 (KG) 盆地的影响研究:支持矢量方法
Akshar Tripathi1, Kapil Malik2, Arjuman Rafiq Reshi3
1Department of Civil & Environmental Engineering, Indian Institute of Technology (IIT) Patna, Patna, India.
Environmental monitoring and assessment
|October 12, 2023
概括
印度克里斯纳戈达瓦里盆地的地表沉降正在加速,导致可耕地的大量损失. 使用多时SAR干扰测量 (MTInSAR) 及时监测对于管理对人口和有价值的农业区的影响至关重要.
科学领域:
- 地质科学 地质科学
- 遥感 遥感 遥感 遥感
- 环境监测 环境监测
背景情况:
- 自1983年以来,印度克里希纳戈达瓦里 (KG) 盆地因原油和天然气开采而面临越来越多的表面沉降.
- 私营部门参与勘探需要迅速监测土地沉降情况,以便重新安置人口和保护农业用地.
- 由于沉降,海水进入肥沃的土地,威胁到有价值的耕地.
研究的目的:
- 用遥感技术监测KG盆地的地表沉降和植被损失.
- 评估2017年至2022年期间的土地沉降率及其对耕地的影响.
- 根据沉降数据开发植被丧失的预测模型.
主要方法:
- 使用 Sentinel-1 C 波段 SAR 数据进行多时 SAR 干扰测量 (MTInSAR) 来测量表面沉降.
- 应用RADAR植被指数 (RVI) 来量化同一时期的植被损失.
- 采用支向量回归器 (SVR) 模型,使用沉降和反向散射参数预测植被损失.
主要成果:
- 在2020年4月至2022年6月期间,KG流域的平均表面沉降率为80毫米/年.
- 在2020年至2022年期间,共有3.21平方公里可耕地被损失.
- 在预测植被丧失方面,SVR模型取得了高准确性 (训练R2为0.89,测试R2为0.873).
结论:
- MTInSAR和RVI是监测KG盆地的土地沉降和植被变化的有效工具.
- 该研究强调了与持续的资源开采相关的重大环境和农业风险.
- 预测建模可以为沉降影响地区的积极土地管理和缓解策略提供帮助.
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