The mangrove ecosystem stands out for the numerous environmental services it provides, in addition to being one of the most vulnerable ecosystems to climate change. Marismas Nacionales is located in northwestern Mexico and hosts one of the most extensive

areas of continuous mangrove ecosystem along the Pacific North coast of Mexico. Although vital, this mangrove ecosystem faces multiple pressures and is shifting inland in response to climate change —making ongoing monitoring of its size and health essential. Aiming for monitoring the health of mangrove and disturbed mangrove areas, a time series of monthly NDVI composites derived from Sentinel-2 imagery (10 m spatial resolution) was analyzed for the period 2019–2024. At the pixel level, the Mann-Kendall test was applied to determine significant trends. An inspection of the Z statistic was conducted to identify gradual and relevant changes; when possible, these changes were validated with field data and high-resolution imagery. The results revealed heterogeneity in the behavior of pixel time series, reflected in the values of the Z statistic. This heterogeneity is due to the fact that mangroves are subject to different change factors depending on their spatial location. Among the identified factors were: hurricane damage, land-use change, colonization of new inland areas, recovery through restoration actions, post-hurricane recovery, and loss due to coastal erosion. With an α = 0.05, 47.08% of the total studied area (80,959 ha) showed a significant negative trend, 20.19% a

non-significant negative trend, 18.07% a non-significant positive trend, and 14.66% a significant positive trend. The analyzed data revealed the dynamic nature of the mangrove ecosystem in response to various change factors, and the proposed method could serve as a foundation for integration into the national products generated by Mexico’s Mangrove Monitoring System.

Wildfires are a major threat in southern Europe, particularly in Galicia (Spain), where recurrence is among the highest and roadside vegetation contributes significantly to ignition and propagation risk. Nevertheless, manual inspections of roadside vegetation are still the most common method of checking compliance with current legislation, which is inefficient, limited in scope, and often outdated, highlighting the need for scalable, automated solutions. This study aims to assess the potential of Mobile Laser Scanning (MLS) LiDAR to automatically detect tree species, estimate their distance to roads, and evaluate compliance with Galician wildfire prevention legislation. Using MLS data, the methodology segments individual trees, extracts 3D structural features, classifies species via machine learning, and assesses proximity to roads against legal clearance thresholds. The approach enables accurate identification of non-compliant trees near roads, facilitating early detection of high-risk vegetation zones. Therefore, this method supports proactive wildfire prevention through targeted vegetation management, reinforcing risk-informed planning, climate adaptation strategies, and integrated disaster risk management using geospatial technologies.

Climate variability represents one of the main challenges for coastal regions, particularly in the Colombian Pacific, where phenomena such as El Niño and La Niña significantly alter environmental and socio-economic conditions. In this context, Sea Surface Temperature (SST) is a critical variable for understanding ocean–atmosphere interactions and anticipating impacts on water availability, agriculture, and territorial planning.

This study analyzes the spatio-temporal variability of SST in the department of Nariño during the 2018–2023 period by integrating Landsat 7, 8, and 9 imagery with hydrometeorological records from IDEAM. Satellite processing involved radiance-to-brightness temperature conversion, emissivity correction, and subsequent transformation to Celsius degrees. Auxiliary variables included humidity, precipitation, solar radiation, evapotranspiration, altitude, distance to the coast, latitude, longitude, and wind speed. Normality tests, anisotropy estimation, variogram construction, and spatial interpolation methods were applied using simple kriging (SK), ordinary kriging (OK), and radial basis functions (RBF), with performance evaluated through cross-validation.

The results showed that SST exhibits short-range spatial dependence, mainly influenced by latitude, longitude, solar radiation, and wind speed, while precipitation and altitude had a minor impact. Ordinary kriging with the spherical model achieved the best performance (R² = 0.84; RMSE = 0.49 °C), outperforming SK and RBF. Interpolated maps revealed well-defined coast–ocean gradients and successfully captured thermal anomalies associated with ENSO: warming during El Niño and cooling during La Niña. Kriging variance maps indicated higher uncertainties offshore, suggesting the need to reinforce monitoring networks in such areas.

From a practical perspective, the findings provide a robust tool for territorial planning and climate risk management, with direct applications in fisheries, agriculture, and water resource management. Methodologically, the research offers a replicable framework that integrates satellite and hydrometeorological data to generate reliable SST surfaces for coastal regions.

This work contributes to reducing the knowledge gap on oceanic variability in the Colombian Pacific, highlighting the local expression of large-scale climate drivers. Future research should extend the temporal scale to assess climate change trends, incorporate spatio-temporal modeling approaches to capture ENSO persistence, and employ multi-sensor comparisons (e.g., MODIS, Sentinel-3) to validate and complement Landsat-based SST estimates.