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Watershed Planning within a Quantitative Scenario Analysis Framework
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使用Sentinel图像分析农业河流流域的水和土地分离能力
Jiatan Liang1, Guojian Liang2, Lina Sun3
1Australian Rivers Institute, Griffith University, Nathan, QLD, 4111, Australia.
Environmental monitoring and assessment
|October 13, 2023
概括
遥感精确地区分水和陆地,使用诸如NDWI和Sentinel-1 VV两极化等指数. 改进的方法使得达文河流域的水位测绘精度提高到96.94%.
科学领域:
- 水文和远程传感技术
- 对水资源的地理空间分析.
背景情况:
- 准确的水体识别对于管理影响农业的洪水和干旱至关重要.
- 遥感技术已经进步,改善了水和陆地特征之间的区别.
- 达文河流域需要精确的水位绘图,以有效地管理资源.
研究的目的:
- 提高使用遥感技术在达文河流域区分水体与陆地的准确性.
- 评估各种水感应方法的性能,以实现最佳的水陆分离.
- 识别和解决水体绘图中常见的错误分类问题.
主要方法:
- 将水体指数 (NDWI,MNDWI) 应用于Sentinel-2的图像.
- 分析 Sentinel-1 的 SAR 数据,比较 VV 和 VH 两极分化的水-土地差异.
- 使用VV极化图像上的直方图值进行分类.
- 实施形态扩展,发生角度分析,DEM和手动校正以改进分类.
主要成果:
- 规范化差异水指数 (NDWI) 对Sentinel-2数据的修改规范化差异水指数 (MNDWI) 显示出更高的性能.
- 卫星 Sentinel-1 的 VV 极化提供了比 VH 极化更好的水陆区分.
- 在VV极化数据上的直方图值在东平湖实现了97.58%的准确性,超过了奥图的方法 (97.36%).
- 在四个研究领域中,校正后的准确性平均为96.94%,比95.56%有所改善.
结论:
- 遥感,特别是Sentinel-1 VV偏振和NDWI,为水体绘制提供了一种可靠的方法.
- 解决像素混合,影子和值等问题对于提高分类准确性至关重要.
- 改进的方法显著提高了河流流域水土分离的精度.
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