Department of Civil Engineering, Kangwon National University, Chuncheon 24341, Korea.
Institute of Forestry, Pokhara Campus, Tribhuvan University, Pokhara 33700, Nepal.
Sensors (Basel). 2018 Aug 7;18(8):2580. doi: 10.3390/s18082580.
Accurate and frequent updates of surface water have been made possible by remote sensing technology. Index methods are mostly used for surface water estimation which separates the water from the background based on a threshold value. Generally, the threshold is a fixed value, but can be challenging in the case of environmental noise, such as shadow, forest, built-up areas, snow, and clouds. One such challenging scene can be found in Nepal where no such evaluation has been done. Taking that in consideration, this study evaluates the performance of the most widely used water indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Automated Water Extraction Index (AWEI) in a Landsat 8 scene of Nepal. The scene, ranging from 60 m to 8848 m, contains various types of water bodies found in Nepal with different forms of environmental noise. The evaluation was conducted based on measures from a confusion matrix derived using validation points. Comparing visually and quantitatively, not a single method was able to extract surface water in the entire scene with better accuracy. Upon selecting optimum thresholds, the overall accuracy (OA) and kappa coefficient (kappa) was improved, but not satisfactory. NDVI and NDWI showed better results for only pure water pixels, whereas MNDWI and AWEI were unable to reject snow cover and shadows. Combining NDVI with NDWI and AWEI with shadow improved the accuracy but inherited the NDWI and AWEI characteristics. Segmenting the test scene with elevations above and below 665 m, and using NDVI and NDWI for detecting water, resulted in an OA of 0.9638 and kappa of 0.8979. The accuracy can be further improved with a smaller interval of categorical characteristics in one or multiple scenes.
遥感技术实现了地表水的精确和频繁更新。指数方法主要用于地表水估计，它基于阈值将水与背景分离。通常，阈值是一个固定值，但在环境噪声（如阴影、森林、建成区、雪和云）的情况下，阈值可能具有挑战性。尼泊尔就是一个具有挑战性的场景，在那里没有进行这样的评估。考虑到这一点，本研究评估了最广泛使用的水指数的性能：归一化差异植被指数（NDVI）、归一化差异水指数（NDWI）、改进的 NDWI（MNDWI）和自动水提取指数（AWEI）在尼泊尔的 Landsat 8 场景中的性能。该场景范围从 60 米到 8848 米，包含尼泊尔各种形式的水，以及不同类型的环境噪声。评估是基于使用验证点派生的混淆矩阵的措施进行的。从视觉和定量比较来看，没有一种方法能够以更高的精度提取整个场景中的地表水。在选择最佳阈值后，整体精度（OA）和kappa 系数（kappa）得到了提高，但并不令人满意。NDVI 和 NDWI 仅对纯水样本来表现出更好的结果，而 MNDWI 和 AWEI 无法拒绝雪覆盖和阴影。将 NDVI 与 NDWI 结合，将 AWEI 与阴影结合，可以提高精度，但继承了 NDWI 和 AWEI 的特征。将测试场景分割为海拔 665 米以上和以下的部分，并使用 NDVI 和 NDWI 检测水，得到的 OA 为 0.9638，kappa 为 0.8979。通过在一个或多个场景中使用较小的分类特征间隔，可以进一步提高精度。
