The scientific community strongly recommends the adoption of indicators for the evaluation and monitoring of progress towards sustainable development. Furthermore, international organizations consider that indicators are powerful decision-making tools. Nevertheless, the quality and reliability of the indicators depends on the application of adequate and appropriate criteria to assess them. The general objective of this study was to evaluate how indicators related to water use and management perform against a set of sustainability criteria. Our research identified 170 indicators related to water use and management. These indicators were assessed by an international panel of experts that evaluated whether they fulfil the four sustainability criteria: social, economic, environmental, and institutional. We employed an evaluation matrix that classified all indicators according to the DPSIR (Driving Forces, Pressures, States, Impacts and Responses) framework. A pilot study served to test and approve the research methodology before carrying out the full implementation. The findings of the study show that 24 indicators comply with the majority of the sustainability criteria; 59 indicators are bi-dimensional (meaning that they comply with two sustainability criteria); 86 are one-dimensional indicators (fulfilling just one of the four sustainability criteria) and one indicator do not fulfil any of the sustainability criteria.

El cambio climático ha provocado amenazas a la producción agrícola; los cambios extremos de temperatura y humedad, y otros factores de estrés abióticos contribuyen a la aparición de enfermedades y plagas en los cultivos. En este sentido, recientes esfuerzos de investigación se han enfocado en la predicción de plagas y enfermedades en cultivos haciendo uso de algoritmos de aprendizaje supervisado. En este artículo es presentada una revisión bibliográfica de los algoritmos de aprendizaje supervisado más utilizados para la detección de plagas y enfermedades en cultivos como: maíz, arroz, café, mango, maní, tomate, entre otros, con el objetivo de seleccionar los algoritmos con mejor rendimiento para el sector agrícola.

Land Use and Land Cover (LULC) classification using remote sensing data is a challenging problem that has evolved with the update and launch of new satellites in orbit. As new satellites are launched with higher spatial and spectral resolution and shorter revisit times, LULC classification has evolved to take advantage of these improvements. However, these advancements also bring new challenges, such as the need for more sophisticated algorithms to process the increased volume and complexity of data. In recent years, deep learning techniques, such as convolutional neural networks (CNNs), have shown promising results in this area. Training deep learning models with complex architectures require cutting-edge hardware, which can be expensive and not accessible to everyone. In this study, a simple CNN based on the LeNet architecture is proposed to perform LULC classification over Sentinel-2 images. Simple CNNs such as LeNet require less computational resources compared to more-complex architectures. A total of 11 LULC classes were used for training and validating the model, which were then used for classifying the sub-basins. The analysis showed that the proposed CNN achieved an Overall Accuracy of 96.51% with a kappa coefficient of 0.962 in the validation data, outperforming traditional machine learning methods such as Random Forest, Support Vector Machine and Artificial Neural Networks, as well as state-of-the-art complex deep learning methods such as ResNet, DenseNet and EfficientNet. Moreover, despite being trained in over seven million images, it took five h to train, demonstrating that our simple CNN architecture is only effective but is also efficient.

Diffuse agricultural pollution is a leading contributor to surface water degradation, particularly in regions undergoing rapid land use change and agricultural intensification. In many developing countries, conventional assessment approaches fall short of capturing the spatial complexity and cumulative nature of multiple environmental drivers that influence surface water vulnerability. This study addresses this gap by introducing the Integral Index of Vulnerability to Diffuse Contamination (IIVDC), a spatially explicit, multi-criteria framework that combines the Analytical Hierarchy Process (AHP) with Geographic Information Systems (GIS). The IIVDC integrates six key indicators—slope, soil erodibility, land use, runoff potential, hydrological connectivity, and observed water quality—weighted through expert elicitation and mapped at high spatial resolution. The methodology was applied to the Guachal River watershed in Valle del Cauca, Colombia, where agricultural pressures are pronounced. Results indicate that 33.0% of the watershed exhibits high vulnerability and 4.3% very high vulnerability, with critical zones aligned with steep slopes, limited vegetation cover, and strong hydrological connectivity to cultivated areas. By accounting for both biophysical attributes and pollutant transport pathways, the IIVDC offers a replicable tool for prioritizing land management interventions. Beyond its technical application, the IIVDC contributes to sustainability by enabling evidence-based decision-making for water resource protection and land use planning. It supports integrated, spatially targeted actions that can reduce long-term contamination risks, guide sustainable agricultural practices, and improve institutional capacity for watershed governance. The approach is particularly suited for contexts where data are limited but spatial planning is essential. Future refinement should consider dynamic water quality monitoring and validation across contrasting hydro-climatic regions to enhance transferability.

The classification of land covers is one of the most relevant tasks carried on to understand the state of a certain region. Additional studies about the biodiversity, hydrology, human impact, modeling dynamics, and phenology in the study area, can be carried on. In these cases, a wide temporal series of images need to be considered in order to get the tendencies throughout the years. In some regions, such as the South-West part of Colombia (Andean region), studies over large areas are needed in order to obtain unified and coherent statistics that can be representative of the region. This means that different images, acquired by the same satellite and over different areas, or acquired by different sensors, or at different times, need to be classified. Standard classification methods do not work properly to perform this task, due to the heterogeneity in both land cover and orography. This paper presents a hybrid approach for the classification of multitemporal, multiregion, and multisensor images. Classification and regression trees (CART) decision tree and an SVM-based clustering were used in cascade in order to get the final classification maps. Experimental results carried over three Landsat Path/Rows, three sensors, and six different years, confirm the effectiveness of the proposed approach, where the overall accuracy was of 93% with a kappa factor of 0.92.