Department of Civil Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune, Maharashtra, 412115, India.
F1000Res. 2022 Jul 11;11:774. doi: 10.12688/f1000research.121740.1. eCollection 2022.
Recent developments in optical satellite remote sensing have led to a new era in the detection of surface water with its changing dynamics. This study presents the creation of surface water inventory for a part of Pune district (an administrative area), in India using the Landsat 8 Operational Land Imager (OLI) and a multi spectral water indices method. A total of 13 Landsat 8 OLI cloud free images were analyzed for surface water detection. Modified Normalized Difference Water Index (MNDWI) spectral index method was employed to enhance the water pixels in the image. Water and non-water areas in the map were discriminated using the threshold slicing method with a trial and error approach. The accuracy analysis based on kappa coefficient and percentage of the correctly classified pixels was presented by comparing MNDWI maps with corresponding Joint Research Centre (JRC) Global Surface Water Explorer (GSWE) images. The changes in the surface area of eight freshwater reservoirs within the study area (Bhama Askhed, Bhatghar, Chaskaman, Khadakwasala, Mulashi, Panshet, Shivrata, and Varasgaon) for the year 2016 were analyzed and compared to GSWE time series water databases for accuracy assessment. The annual water occurrence map with percentage water occurrence on a yearly basis was also prepared. The kappa coefficient agreement between MNDWI images and GSWE images is in the range of 0.56 to 0.96 with an average agreement of 0.82 indicating a strong level of agreement. MNDWI is easy to implement and is a sufficiently accurate method to separate water bodies from satellite images. The accuracy of the result depends on the clarity of image and selection of an optimum threshold method. The resulting accuracy and performance of the proposed algorithm will improve with implementation of automatic threshold selection methods and comparative studies for other spectral indices methods.
光学卫星遥感的最新发展使我们进入了一个利用多光谱水指数法，通过检测地表水及其动态变化来获取地表水信息的新时代。本研究使用陆地卫星 8 操作陆地成像仪（OLI）和多光谱水指数法，为印度浦那地区（行政区域）的一部分创建了地表水清单。总共分析了 13 张陆地卫星 8 OLI 无云图像，以进行地表水检测。采用改进的归一化差异水指数（MNDWI）光谱指数法增强图像中的水像素。使用阈值切片法，通过反复试验的方法，区分地图中的水和非水区域。通过将 MNDWI 图与相应的联合研究中心（JRC）全球地表水探索者（GSWE）图像进行比较，基于kappa 系数和正确分类像素的百分比，提出了准确性分析。分析了研究区域内（巴马 Askhed、巴特加尔、查斯卡曼、卡达克瓦萨拉、穆拉希、潘谢特、希瓦拉塔和瓦尔萨贡）八个淡水水库的地表面积变化，并与 GSWE 时间序列水数据库进行了比较，以进行准确性评估。还根据每年的百分比水出现情况，准备了年度水出现图。MNDWI 图像与 GSWE 图像之间的 kappa 系数一致性在 0.56 到 0.96 之间，平均一致性为 0.82，表明具有很强的一致性。MNDWI 易于实现，是一种从卫星图像中分离水体的足够准确的方法。结果的准确性取决于图像的清晰度和最佳阈值方法的选择。随着自动阈值选择方法的实施和对其他光谱指数方法的比较研究，该算法的准确性和性能将得到提高。
