Osvaldo José Ribeiro Pereira

The Nhecolândia region, located in the southern portion of the Pantanal wetland area, is a unique lacustrine system where tens of thousands of saline-alkaline and freshwater lakes and ponds coexist in close proximity. These lakes are suspected to be a strong source of greenhouse gases (GHGs) to the atmosphere, the water pH being one of the key factors in controlling the biogeochemical functioning and, consequently, production and emission of GHGs in these lakes. Here, we present a new field-validated classification of the Nhecolândia lakes using water pH values estimated based on a cloud-based Landsat (5 TM, 7 ETM+, and 8 OLI) 2002–2017 time-series in the Google Earth Engine platform. Calibrated top-of-atmosphere (TOA) reflectance collections with the Fmask method were used to ensure the usage of only cloud-free pixels, resulting in a dataset of 2081 scenes. The pH values were predicted by applying linear multiple regression and symbolic regression based on genetic programming (GP). The regression model presented an R² value of 0.81 and pH values ranging from 4.69 to 11.64. A lake mask was used to extract the predicted pH band that was then classified into three lake classes according to their pH values: Freshwater (pH < 8), oligosaline (pH 8–8.9), and saline (≥9). Nearly 12,150 lakes were mapped with those with saline waters accounting for 7.25%. Finally, a trend surface map was created using the ALOS PRISM Digital Surface Model (DSM) to analyze the correlation between landscape features (topography, connection with the regional drainage system, size, and shape of lakes) and types of lakes. The analysis was in consonance with previous studies that pointed out that saline lakes tend to occur in lower positions compared to freshwater lakes. The results open a relevant perspective for the transfer of locally acquired experimental data to the regional balances of the Nhecolândia lakes.

After the publication of the research paper

Around 55% of all Brazilian cattle production is located in the Cerrado biome, which also contains the largest pasture area in Brazil. Previous studies indicated that about 60% of these pastures were degraded by 2010. However, up-to-date and more precise estimates are necessary to access the extent and degree of degradation of the Cerrado pastures, since these areas constitute strategic land reserves for both livestock intensification and soybean expansion. Therefore, in this study, we estimated the area of degraded pastures in the Cerrado by analyzing the trends of cumulative NDVI anomalies over time used as a proxy for pasture degradation. The generated slope surface was segmented into two classes, comprising non-degraded and degraded pastures, which were correlated with socio-economic and biophysical variables. According to our study, around 39% of the Cerrado pastures are currently degraded, encompassing 18.2 million hectares, mostly in areas with a cattle carrying capacity below 1.0 AU ha⁻¹. These areas, distributed in the northwest Cerrado, mostly within the Brazilian states of Maranhão, Piauí, and Bahia (i.e., Matopiba region), tend to be associated with decreasing rainfall patterns and low investments in soil conservation practices. The degraded areas also tend to be concentrated in municipalities with low human development indices (HDI).

The lower spatial resolution of thermal infrared (TIR) satellite images and derived land surface temperature (LST) is one of the biggest challenges in mapping temperature at a detailed map scale. An extensive range of scientific and environmental applications depend on the availability of fine spatial resolution temperature data. All satellite-based sensor systems that are equipped with a TIR detector depict a spatial resolution that is coarser than most of the multispectral bands of the same system. Certain studies may therefore be not feasible if applied in areas that depict a high spatial variation in temperature at small spatial scales, such as urban centers and flooded pristine areas. To solve this problem, this study applied an image downscaling method to enhance the spatial resolution of LST data by combining TIR, multispectral images, and derived data, such as Normalized Difference Vegetation Index (NDVI), according to the geographically weighted regression (GWRK) and area-to-point kriging of regressed residuals. The resulting LST images of the natural and anthropogenic urban areas of the Brazilian Pantanal are very highly correlated to the reference LST images. The approach, combining ASTER TIR with ASTER visible/infrared (VNIR) and Sentinel-2 images according to the GWRK method, performed better than all of the remaining state-of-the-art downscaling methods.