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Mapping small and medium-sized water reservoirs using Sentinel-1A: a case study in Chiapas, Mexico
Journal of Applied Remote Sensing ( IF 1.4 ) Pub Date : 2020-07-01 , DOI: 10.1117/1.jrs.14.036503
Alejandra A. López-Caloca 1 , Boris Escalante-Ramírez 2 , Pilar Henao 1
Affiliation  

Abstract. Using satellite data to study small water bodies (SWB) and medium-sized water bodies (MSWB) is extremely useful for understanding their status, how to conserve them as water reservoirs, and their vulnerability to climate variability. The images studied in our work correspond to different-sized lagoons located in areas with high and low topography in a tropical region of Chiapas, Mexico. Our research project delineates SWB and MSWB. For this analysis, we considered water bodies to be uniform regions in a synthetic aperture radar image. The robustness of the method was determined based on an analysis of the morphologies of 23 lagoons. Several methods, including Hermite transform, were analyzed and compared with other image denoising methods used to improve speckle reduction. To obtain additional spatial information for image classification, we analyzed texture using the gray-level co-occurrence matrix. The results indicate that the Hermite filter is the best method for identifying water bodies. The advantage of this filter is the identification of local patterns such as edges and lines. It also preserves and improves aspects related to the homogeneity of water bodies, using the Hermite coefficient selection criteria for local pattern feature selection/extraction. The lake water extent products demonstrate that Sentinel-1 is useful for identifying SWB in this study area. The results show very high detection of water bodies, with adequate detection for water bodies larger than 2 ha, and an area accuracy of 80%.

中文翻译:

使用 Sentinel-1A 绘制中小型水库图:墨西哥恰帕斯州的案例研究

摘要。使用卫星数据研究小型水体 (SWB) 和中型水体 (MSWB) 对于了解它们的状况、如何保护它们作为水库以及它们对气候变化的脆弱性非常有用。我们工作中研究的图像对应于位于墨西哥恰帕斯州热带地区高低地形区域的不同大小的泻湖。我们的研究项目描述了 SWB 和 MSWB。对于此分析,我们将水体视为合成孔径雷达图像中的均匀区域。该方法的稳健性是基于对 23 个泻湖形态的分析来确定的。分析了包括 Hermite 变换在内的几种方法,并将其与用于改善散斑减少的其他图像去噪方法进行了比较。为了获得用于图像分类的额外空间信息,我们使用灰度共生矩阵分析纹理。结果表明 Hermite 过滤器是识别水体的最佳方法。此过滤器的优点是识别局部模式,例如边缘和线条。它还使用 Hermite 系数选择标准进行局部模式特征选择/提取,从而保留和改进与水体同质性相关的方面。湖水范围产品表明 Sentinel-1 可用于识别该研究区域的 SWB。结果表明,对水体的检测率非常高,对大于2公顷的水体具有足够的检测能力,面积精度可达80%。此过滤器的优点是识别局部模式,例如边缘和线条。它还使用 Hermite 系数选择标准进行局部模式特征选择/提取,从而保留和改进与水体同质性相关的方面。湖水范围产品表明 Sentinel-1 可用于识别该研究区域的 SWB。结果表明,对水体的检测率非常高,对大于2公顷的水体具有足够的检测能力,面积精度可达80%。此过滤器的优点是识别局部模式,例如边缘和线条。它还使用 Hermite 系数选择标准进行局部模式特征选择/提取,从而保留和改进与水体同质性相关的方面。湖水范围产品表明 Sentinel-1 可用于识别该研究区域的 SWB。结果表明,对水体的检测率非常高,对大于2公顷的水体具有足够的检测能力,面积精度可达80%。湖水范围产品表明 Sentinel-1 可用于识别该研究区域的 SWB。结果表明,对水体的检测率非常高,对大于2公顷的水体具有足够的检测能力,面积精度达到80%。湖水范围产品表明 Sentinel-1 可用于识别该研究区域的 SWB。结果表明,对水体的检测率非常高,对大于2公顷的水体具有足够的检测能力,面积精度可达80%。
更新日期:2020-07-01
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