Desertification is considered one of the most serious threats to arid, semiarid, and sub-humid environments. In this study, several remote sensing techniques were integrated for monitoring desertification in a semiarid region of the central Mexican plateau (state of Querétaro). Landsat TM images were used to compute the Normalized Difference Vegetation Index (NDVI), Bare-Soil Index (BSI), and albedo (α). A change vector analysis (CVA) examined changes in the direction and magnitude of indicators during the 1993–2011 period in order to detect degradations or improvements in land conditions over a period of time. In addition, a desertification degree index (DDI) was applied based on the NDVI–α relationship. The results indicated that in the semiarid zone of the state of Querétaro, 48.3% of land use corresponds with agricultural areas; 2.7% of the area does not present any degree of desertification, while 49% presents the following degrees of desertification: 5.5% extreme, 10.9% severe, 18.9% moderate, and 13.7% low. The DDI applied in this approach for evaluating this phenomenon at the regional and municipal level is an effective tool for identifying areas at risk of desertification, in addition to monitoring changes in the degree or direction of desertification.

Remote sensing plays a crucial role in modeling surface water quality parameters (WQPs), which aids spatial and temporal variation assessment. However, existing models are often developed independently, leading to uncertainty regarding their applicability. This study focused on two primary objectives. First, it aimed to evaluate different models for chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), and total suspended solids (TSS) in a surface water body, the J. A. Alzate dam, in the Mexican highland region (R² ≥ 0.78 and RMSE ≤ 16.1 mg/L). The models were estimated using multivariate regressions, with a focus on identifying dilution and dragging effects in inter-annual flow rate estimations, including runoff from precipitation and municipal discharges. Second, the study sought to analyze the potential scope of application for these models in other water bodies by comparing mean WQP values. Several models exhibited similarities, with minimal differences in mean values (ranging from −9.5 to 0.57 mg/L) for TSS, TN, and TP. These findings suggest that certain water bodies may be compatible enough to warrant the exploration of joint modeling in future research endeavors. By addressing these objectives, this research contributes to a better understanding of the suitability of remote sensing-based models for characterizing surface water quality, both within specific locations and across different water bodies.

The aim of this work was to study, by remote sensing and numerical modeling, the thermal dispersion of a plume discharged into the sea by a nuclear power plant. The case study is the thermal discharge of the Laguna Verde nuclear power plant, located on the coast of the Gulf of Mexico. First, the thermal plume dispersion was characterized by applying remote sensing for different scenarios. Afterwards, Delft3D-FLOW numerical simulations were performed to expand the analysis of the thermal processes for a case in which the thermal plume tends towards the intake of the power plant. This thermal analysis was carried out by comparing the behavior of different dimensionless parameters. Moreover, the results of the numerical simulations were used to investigate the performance of the AEM and the k-L and k-ε turbulence models, available in the Delft3D-FLOW model. An LES turbulence model contribution was also analyzed. The results show that forced convection is predominant near the plume discharge area and at the vicinity of the intake structure. According to the metrics calculated, all turbulence models produced good agreement with the remote sensing data, except when the LES scheme was considered. Finally, the use of remote sensing and numerical simulations is helpful to better understand thermal plume dispersion.