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1 July 2020 Mapping small and medium-sized water reservoirs using Sentinel-1A: a case study in Chiapas, Mexico
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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%.

© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2020/$28.00 © 2020 SPIE
Alejandra A. López-Caloca, Boris Escalante-Ramírez, and Pilar Henao "Mapping small and medium-sized water reservoirs using Sentinel-1A: a case study in Chiapas, Mexico," Journal of Applied Remote Sensing 14(3), 036503 (1 July 2020).
Received: 17 January 2020; Accepted: 19 June 2020; Published: 1 July 2020

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