Sentinel-1 Satellite Data as a Tool for Monitoring Inundation Areas near Urban Areas in the Mexican Tropical Wet

This work shows advances in the field of water body monitoring with radar images. Particularly, a monitoring procedure is developed to define the extension and frequency of inundation for continental waters of the Grijalva-Usumacinta basin, in the state of Tabasco, Mexico. This is a region located in the Mexican tropical wet and under its meteorological conditions, radar technology can be used to characterize monthly inundation frequency. The identification of water bodies were obtained by processing images at a monthly intervals captured by Sentinel-1A during 2015 having kappa indices and overall accuracy higher than 0.9. The chapter describes the seasonal variability of these water bodies, and at the same time, the relationship with human settlements located in their neighborhood. To do this, a proximity analysis was carried out to emphasize the importance of spatial-temporal studies of superficial water bodies, linked to an urban and a rural area. This information is useful to investigate changes in the ecosystem, as well as risks to human settlements, and as a contribution for a comprehensive management of hydric resources

Sentinel-1 observation for shoreline delineation applied to Mexico’s Coast

Coastal mapping with satellite imagery is broadly used to calculate shoreline positions due to its high ecological and socioeconomic value in the context of coastal conservation and management strategies. We show the applicability of the Sentinel-1 to monitor large-scale shoreline at a country level. The present study develops a novel shoreline extraction method based on C band from SAR missions, that improves coastal ocean/land discrimination. The method considers an automated processing chain using the incorporation of GLCM-mean texture information to increase improvements in image binarization by Sauvola thresholding. Results show that the proposed method may be used for shoreline monitoring of different types of geomorphology along the Mexican coastline, thus guaranteeing its applicability in different geographic surroundings. For the six specific areas of validation, the overall agreement between binarization ranges is from 90% to 100% and Sentinel-2 images are used to evaluate the VV and VH shorelines from Sentinel-1

Analyzing short term spatial and temporal dynamics of water presence at a basin-scale in Mexico using SAR data

The dynamic nature of continental water body covers is a main indicator of the status of this ecological capital and its trends on the land surface. With synthetic aperture radar (SAR), various water coverage processes can be identified using satellite data. This study proposes the use of a basin-level analysis with SAR images at a medium spatial resolution to perform a regional spatial-temporal analysis for detecting water presence. It develops a methodology to improve the separation of water bodies from other land covers by using a texture analysis, a support-vector machine classification, and the gray level co-occurrence matrix (GLCM). GLCM-mean texture parameter was found to offer sufficient texture information for separating the water class from the land class by providing a more homogeneous measurement of water bodies. The case under study is the Grijalva River Basin, Mexico, a region that requires more in-depth research so that the management of surface water coverage can be integrated with ecosystems, particularly those that are subject to intense human activity. The entire basin was mapped using a series of observations from Sentinel-1. This study produced a total of 36 water body maps with an acceptable classification accuracy of over 90% for each year studied (2016, 2017, and 2018). Water presence was also quantified, resulting in a set of maps containing regional detail, from which temporal data can be obtained on areas with water presence year-round and areas with seasonal flooding. Frequency maps with a 10-km unit cell as the spatial unit were used to detect trends and thereby identify water distribution patterns. The results show the importance of ongoing, basin-level quantifications of zones with dynamic water presence and those with stable water presence