This study aimed to create a spatiotemporal model of the Palmer Drought Severity Index in the state of Ceará, Brazil, using cloud computing and integrating a Random Forest machine learning model. Using the Google Earth Engine platform, the dataset selected for this work was TerraClimate, and the selected time interval was from 1994 to 2024. The model achieved a coefficient of determination (R²) of 0.72 and a Root Mean Squared Error of 1.27, demonstrating its ability to capture the non-linear dynamics of the region. The importance analysis of variables indicated 12-month accumulated precipitation as the main predictor (26.08%), followed by soil moisture (14.75%). Furthermore, wind speed (13.04%) stands out as a factor with greater influence than maximum temperature (8.19%), highlighting the relevance of the aerodynamic factor in the drought dynamics of the region. Spatially, the results estimate that approximately 8.7% of the state’s territory, particularly in the Inhamuns region and in the desertification nuclei of the municipality of Irauçuba, face extreme or severe drought vulnerability conditions. These findings suggest that data-driven models offer a robust and scalable alternative for assessing semi-arid areas with scarce data.
This study presents an analysis of Vertical Seismic Profiling (VSP) data and Pseudo-Acoustic Logging (PAL) inversion for well H1856 in the Govsany hydrocarbon field on the Absheron Peninsula, Azerbaijan. The study focuses on the analysis and prediction of seismic velocities below the well bottom in order to establish a reliable depth–time relationship, enabling the prediction of the depths of key seismic reflecting horizons located beneath the Kalin Suite, the main productive formation of the field. Multicomponent VSP observations demonstrated good data quality and showed a strong correlation with acoustic logging results, which allowed the construction of stable one-dimensional and two-dimensional velocity models for characterizing the acoustic properties of the subsurface section. PAL inversion, performed within the dominant frequency range of 5–70 Hz, produced stable velocity profiles that indicate a noticeable decrease in velocities below the well bottom. The resulting depth–time model enabled the prediction of the depths of major reflecting horizons and served as a basis for drilling the deep production well 1870. As a result, productive reservoirs were encountered within a zone of abnormal high formation pressure (AHPZ) with production rates of approximately 100 tons per day. The well was operated for a certain period and was later abandoned for technical reasons. The study demonstrates the usefulness of integrating VSP data with acoustic logging for improving seismic interpretation and reservoir characterization. It also discusses the limitations of conventional inversion approaches when applied to deviated wells such as H1856 and the associated uncertainties. For future seismic acquisition, the use of vibrational sources is recommended because they provide a more stable frequency spectrum at different depths and improved resolution compared to impulsive sources such as airguns. Overall, the study provides insights into seismic velocity modeling and depth prediction of reflecting horizons in complex geological settings, which may support improved well placement and resource development strategies.
To reduce the atmospheric effects on remote sensing imagery, there are methods to correct them, called atmospheric correction, which is an important stage of satellite image preprocessing. Thus, the present work aims to evaluate three different atmospheric correction techniques: Dark Object Subtraction, Sentinel Atmospheric Correction, and Atmospheric Correction, using images from the MSI sensor of the Sentinel satellite covering the Boa Esperança River basin, located in the city of Nova Friburgo, RJ. In this work, it was possible to conclude that two methods fulfilled the expectations of correcting images (Sen2cor and ATCOR with topographic correction). They are methods based on atmospheric modeling and present the expected behavior according to the literature. The Sen2Cor method, in the visible region, removed the interference of the atmospheric scattering, mainly in the blue band, with an average reduction of 75% of the reflectance values, while in the other visible bands, the average reduction was in the range of 43%. In the near infrared region, it had an increase of reflectance values in the order of 15%, due to the electromagnetic absorption of the radiation, according to the literature. On the other hand, the ATCOR method with topographic correction obtained an average decrease of about 70% in the visible range, while in the near infrared, the increase is around 8%.
This contribution estimates the Surface Urban Heat Island (SUHI) intensity during the period 2001-2016 for the Metropolitan Areas of Asunción (AMA) and Montevideo (AMM), located in Paraguay and Uruguay respectively. To quantify the SUHI, the quantile method was used at 0.05° resolution, obtained from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor on board the TERRA and AQUA satellites. The daytime SUHI intensity, obtained with the quantile method in the AMA and AMM, shows the maximum value in December, 4.5 ºC and 0.5 ºC respectively. The quantile method proves to be a robust approach for estimating SUHI intensity, as it is independent of terrain geometry and topography. The analysis defined urban, transitional, and rural areas using the International Geosphere Biosphere Programme (IGBP) method, and was conducted at a scale temporal monthly. Results indicate a consistent increase in urban surface temperatures, particularly in rapidly growing cities, intensifying thermal stress. This study contributes by analyzing the increase in surface temperature, providing a reliable framework for SUHI assessment and supporting targeted urban planning strategies.
Mountain ecosystems are highly sensitive to climate change due to pronounced environmental gradients and strong climatic variability. Understanding vegetation responses to ongoing warming is therefore essential for assessing ecosystem resilience and developing effective conservation strategies. This study investigates long-term vegetation dynamics and their relationship with climatic warming in Arevik National Park, located in the Syunik Province of southern Armenia. The analysis was conducted using Normalized Difference Vegetation Index (NDVI) data derived from Landsat 5 TM, 7 ETM+, 8 OLI, and 9 OLI-2 imagery for the period 1985–2025 at five-year intervals. Elevation data derived from a Digital Elevation Model (DEM) and August minimum and maximum temperature data obtained from the WorldClim database were integrated within a GIS environment to evaluate elevation-dependent vegetation responses. August was selected because it represents the driest period in the study area, minimizing the influence of seasonal precipitation variability and providing more reliable NDVI-based assessments of forest vegetation dynamics. The results revealed a clear warming trend across the study area, with both minimum and maximum August temperatures showing a gradual increase during the study period. NDVI analysis indicated an overall decline in vegetation greenness and productivity, although significant spatial heterogeneity was observed. Elevation-based analysis demonstrated contrasting vegetation responses along the altitudinal gradient. Negative NDVI trends were identified in elevation zones between 450 and 2400 m a.s.l., where vegetation greenness declined by up to 16.05%. In contrast, positive NDVI trends were recorded above 2400 m a.s.l., with the strongest increase (+25.77%) observed in the 2800–3200 m elevation range. Spatial change-detection analysis further confirmed the coexistence of areas exhibiting vegetation decline and vegetation expansion throughout the study area. The findings indicate that vegetation dynamics in Arevik National Park are strongly elevation-dependent and suggest that ongoing climatic warming affects mountain ecosystems differently across altitude zones. The integration of remote sensing, GIS analysis, and climatic datasets provides valuable insights into climate–vegetation relationships in the South Caucasus and supports evidence-based conservation and management of protected mountain landscapes under changing climatic conditions.
