This paper presents a methodology to detect the coastal erosion and progradation effects in the ‘Atrato River’ delta, located in the Gulf of Urabá in Colombia, using SAR (Synthetic Aperture Radar) images. Erosion is the physical–mechanical loss of the soil that affects its functions and ecosystem services while producing a reduction in its productive capacity. Progradation is the deposition of layers in the basinward direction while moving coastward. Other studies have investigated these two phenomena using optical images, encountering difficulties due to the persistent presence of clouds in this region. In order to avoid the cloud effects, in this study, we used 16 Sentinel 1 SAR images with two different polarizations between 2016 and 2023. First, each image was rescaled from 0 to 255, then the image was despeckled by a deep learning (DL) model. Afterwards, a single RGB image was composed with the filtered polarizations. Next, a classifier with 99% accuracy based on Otsu’s method was used to determine whether each pixel was water or not. Then, the classified image was registered to a reference one using Oriented FAST and Rotated BRIEF (ORB) descriptor. Finally, a multitemporal analysis was performed by comparing every image to the previous one to identify the studied phenomena, calculating areas. Also, all images were integrated to obtain a heatmap that showed the overall changes across eight years (2016–2023) in a single image. The multitemporal analysis performed found that the newly created mouth is the most active area for these processes, coinciding with other studies. In addition, a comparison of these findings with the Oceanic Niño Index (ONI) showed a relative delayed coupling to the erosion process and a coupling of progradation with dry and wet seasons.

The Aburrá Valley, located in the northwestern region of Colombia, has undergone significant land cover changes and urban expansion in recent decades, driven by rapid population growth and infrastructure development. This region, known for its steep topography and dense urbanization, faces considerable environmental challenges. Monitoring these transformations is essential for informed territorial planning and sustainable development. This study leverages Synthetic Aperture Radar (SAR) imagery from the Sentinel-1 mission, covering 2017–2024, to propose a methodology for the multitemporal analysis of land cover dynamics and urban expansion in the valley. The novel proposed methodology comprises several steps: first, monthly SAR images were acquired for every year under study from 2017 to 2024, ensuring the capture of surface changes. These images were properly calibrated, rescaled, and co-registered. Then, various multitemporal fusions using statistics operations were proposed to detect and find different phenomena related to land cover and urban expansion. The methodology also involved statistical fusion techniques—median, mean, and standard deviation—to capture urbanization dynamics. The kurtosis calculations highlighted areas where infrequent but significant changes occurred, such as large-scale construction projects or sudden shifts in land use, providing a statistical measure of surface variability throughout the study period. An advanced clustering technique segmented images into distinctive classes, utilizing fuzzy logic and a kernel-based method, enhancing the analysis of changes. Additionally, Pearson correlation coefficients were calculated to explore the relationships between identified land cover change classes and their spatial distribution across nine distinct geographic zones in the Aburrá Valley. The results highlight a marked increase in urbanization, particularly along the valley’s periphery, where previously vegetated areas have been replaced by built environments. Additionally, the visual inspection analysis revealed areas of high variability near river courses and industrial zones, indicating ongoing infrastructure and construction projects. These findings emphasize the rapid and often unplanned nature of urban growth in the region, posing challenges to both natural resource management and environmental conservation efforts. The study underscores the need for the continuous monitoring of land cover changes using advanced remote sensing techniques like SAR, which can overcome the limitations posed by cloud cover and rugged terrain. The conclusions drawn suggest that SAR-based multitemporal analysis is a robust tool for detecting and understanding urbanization’s spatial and temporal dynamics in regions like the Aburrá Valley, providing vital data for policymakers and planners to promote sustainable urban development and mitigate environmental degradation.

Understanding and quantifying the coupled effects of land cover change and climate change on hydrological regimes is critical for sustainable water management in tropical mountainous regions. The Río Negro Basin in eastern Antioquia, Colombia, has undergone rapid urban expansion, agricultural intensification, and deforestation over recent decades, profoundly altering its hydrological dynamics. This study integrates advanced satellite image processing, AI-based land cover modeling, climate change projections, and distributed hydrological simulation to assess future streamflow responses. Multi-sensor satellite data (Landsat, Sentinel-1, Sentinel-2, ALOS) were processed using Random Forest classifiers, intelligent multisensor fusion, and probabilistic neural networks to generate high-resolution land cover maps and scenarios for 2060 (optimistic, trend, and pessimistic), with strict area constraints for urban growth and forest conservation. Future precipitation was derived from MPI-ESM CMIP6 outputs (SSP2-4.5, SSP3-7.0, SSP5-8.5) and statistically downscaled using Empirical Quantile Mapping (EQM) to match the basin scale and precipitation records from the national hydrometeorological service of the Colombia IDEAM (Instituto de Hidrología, Meteorología y Estudios Ambientales, Colombia). The TETIS hydrological model was calibrated and validated using observed streamflow records (1998–2023) and subsequently used to simulate hydrological responses under combined land cover and climate scenarios. Results indicate that urban expansion and forest loss significantly increase peak flows (Q90, Q95) and flood risk while decreasing baseflows (Q10, Q30), compromising water availability during dry seasons. Conversely, conservation-oriented scenarios mitigate these effects by enhancing flow regulation and groundwater recharge. The findings highlight that targeted land management can partially offset the negative impacts of climate change, underscoring the importance of integrated land–water planning in the Andes. This work provides a replicable framework for modeling hydrological futures in data-scarce mountainous basins, offering actionable insights for regional authorities, environmental agencies, and national institutions responsible for water security and disaster risk management.

Se aplicó la noción de hipótesis abductiva al icono de los canales ancestrales zenúes en la Depresión Momposina colombiana. La hipótesis abductiva se desarrolló desde la concepción del semiólogo Pearce, quien afirma que solo un pensamiento abductivo lleva a la creación de algo genuinamente nuevo. El proceso abductivo parte del hecho sorprendente y llega hasta plantear una hipótesis, pasando por la interpretación semiológica de signos en tanto iconos, enigmas, indicios, sospechas, conjeturas y finalmente la enunciación de la hipótesis abductiva. Se tomó como base del hecho sorprendente que revela que la comunidad zenú de épocas prehispánicas, con su gran poder de control hidráulico, floreció en una zona de humedales de Colombia, pero luego desapareció, dejando una huella centenaria en el territorio. Este hecho sorprendente se plasma en el icono que representan los canales zenúes, una gran obra de ingeniería hidráulica cuyo abandono supuso la pérdida de intervención de las aguas de la Depresión Momposina, provocando inundaciones y sequías; problemas del cambio climático en nuestros tiempos. En el camino encontramos enigmas: como la relación hombre-hábitat; indicios: zona propicia para establecimiento de grandes culturas; sospechas: alto conocimiento y adaptación al territorio, la zona como gran templo espiritual, los canales zenúes como alternativa de manejo en la actualidad; conjeturas: conocimiento hidráulico de los zenúes, zona más manejable con canales que sin ellos, estrategia de ingeniería para controlar el ritmo actual de inundaciones y sequías; y con ellos se llegó al planteamiento- hipótesis -abductiva: ¿cómo la comprensión del sistema tradicional de canales y terraplenes de los zenúes, su recuperación y conservación, se traduciría en una estrategia de adaptación al cambio climático al permitir el control de inundaciones y sequías?

This study evaluates the use of synthetic aperture radar imagery for detecting and monitoring periodic flooding in Nechí, Colombia, where persistent cloud cover limits the use of optical images. A classification method combining autoencoder-based despeckling and Otsu thresholding was applied, achieving 91.16% overall accuracy, 95.33% specificity, 86.80% recall, and an F1-score of 90.58%. Flood mapping from 2017 to 2024 revealed strong interannual variability, with the largest event in 2017 (average extent 3.59 million m2; peak 12.95 million) and the second largest in 2020 (average 124,442 m2; peak 746,100 m2). Seasonal patterns were bimodal, with peaks in June (190,557 m2) and November (186,171 m2), coinciding with maximum river discharges above 3,500 m3/s. Wet conditions associated with La Niña increased flood extent, while dry El Niño years reduced it. Spatial analysis showed that embankments influence flooding patterns and that urban expansion between 2017 and 2025, with an approximate increase of 0.33 km2 (26%), invaded flood-prone areas. The findings demonstrate the value of radar imagery for continuous flood monitoring and its potential to strengthen early warning and risk management strategies in tropical river basins.

This study presents a novel methodology for the detection and monitoring of changes in surface water bodies, with a particular emphasis on the near-eastern region of Antioquia, Colombia. The proposed approach integrates remote sensing and artificial intelligence techniques through the fusion of multi-source imagery, specifically Synthetic Aperture Radar (SAR) and optical data. The framework is structured in several stages. First, radar imagery is pre-processed using an autoencoder-based despeckling model, which leverages deep learning to reduce noise while preserving structural information critical for environmental monitoring. Concurrently, optical imagery is processed through the computation of normalized spectral indices, including NDVI, NDWI, and NDBI, capturing essential characteristics related to vegetation, water presence, and surrounding built-up areas. These complementary sources are subsequently fused into synthetic RGB composite representations, ensuring spatial and spectral consistency between radar and optical domains. To operationalize this methodology, a standardized and reproducible workflow was implemented for automated image acquisition, preprocessing, fusion, and segmentation. The Segment Anything Model (SAM) was integrated into the process to generate semantically interpretable classes, enabling more precise delineation of hydrological features, flood-prone areas, and urban expansion near waterways. This automated system was embedded in a software prototype, allowing local users to manage large volumes of satellite data efficiently and consistently. The results demonstrate that the combination of SAR and optical datasets provides a robust solution for monitoring dynamic hydrological environments, particularly in tropical mountainous regions with persistent cloud cover. The fused products enhanced the detection of small streams and complex hydrological patterns that are typically challenging to monitor using optical imagery alone. By integrating these technical advancements, the methodology supports improved environmental monitoring and provides actionable insights for decision-makers. At the local scale, municipal governments can use these outputs for urban planning and flood risk mitigation; at the regional level, environmental and territorial authorities can strengthen water resource management and conservation strategies; and at the national level, risk management institutions can incorporate this information into early warning systems and disaster preparedness programs. Overall, this research delivers a scalable and automated tool for surface water monitoring, bridging the gap between scientific innovation and operational decision-making to support sustainable watershed management under increasing pressures from climate change and urbanization.