The San Luis Potosi metropolitan area has suffered considerable damage from land subsidence over the past decades, which has become visible since 1990. This paper seeks to evaluate the effects of groundwater withdrawal on land subsidence in the San Luis Potosi Valley and the development of surface faults due to the differential compaction of sediments. For this purpose, we applied the Coherent Pixels Technique (CPT), a Persistent Scatterer Interferometry (PSI) technique, using 112 Sentinel-1 acquisitions from October 2014 to November 2019 to estimate the deformation rate. The results revealed that the deformation areas in the municipality of Soledad de Graciano Sánchez mostly exhibit subsidence values between −1.5 and −3.5 cm/year; whereas in San Luis Potosi these values are between −1.8 and −4.2 cm/year. The PSI results were validated by five Global Navigation Satellite System (GNSS) benchmarks available, providing a data correlation between the results obtained with both techniques of 0.986. This validation suggests that interferometric derived deformations agree well with results obtained from GNSS data. The strong relationship between trace fault, land subsidence,e and groundwater extraction suggests that groundwater withdrawal is resulting in subsidence induced faulting, which follows the pattern of structural faults buried by sediments.
Changes in Land Use/Land Cover (LULC) generate several impacts which affect the energy balance of the Earth and, consequently, modifying the climate of a region. Accordingly, one of the most important indicators of this modification is the Land Surface Temperature (LST). The present work aims to analyze the relationship between LULC and LST, determining the influence of LULC on LST using Geographical Information Systems (GIS) and Remote Sensing (RS) techniques. The selected study area was the San Luis Potosí Basin, México (SLPB). A temporal analysis has been developed for 2007 and 2020. Satellite images from Landsat 5 TM and 8 OLI/TIRS has been used to calculate LST through a single-channel algorithm for winter and spring. LULC has been determined from a supervised classification with neural network algorithm. Finally, change rates for LULC and LST were assessed. The results indicate that an LST increase of 11 °C from 2007 to 2020 has been detected in the region. Also, results showed that covers with spare vegetation or without vegetation have the highest temperatures (29°C to 32°C). In comparison, the covers with dense vegetation and water showed the lowest temperatures (23°C to 25°C). This type of research allows addressing the LULC effects on LST, as well as prove its importance in improving land use planning systems.
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