Inland water is fundamental for the conservation of flora and fauna and is a source of drinking water for humans; therefore, monitoring its quality and ascertaining its status is essential for making decisions in water resources management. As traditional measuring methods present limitations in monitoring with high spatial and temporal coverage, using satellite images to have greater control over lake observation can be a handy tool and have satisfactory results. The study of chlorophyll-a (Chl-a) has been widely used to ascertain the quality of the inland aquatic environment using remote sensing, but in general, it depends on the local conditions of the water body. In this study, the suitability of the Sentinel-2 MSI sensor for Chl-a estimation in a lake in south-central Chile is tested. An empirical approach is proposed, applying multiple linear regressions, comparing the efficiency and performance with L1C and L2A products, separating the equations constructed with spring-summer and fall-winter data, and restricting Chl-a ranges to those measured in the field to generate these regressions. The algorithms combining spectral bans proved to have a good correlation with Chl-a measured in the field, generally resulting in R² greater than 0.87 and RMSE and MAE with errors less than 6 μg L⁻¹. The spatial distribution of Chl-a concentrations at the study site was obtained based on the proposed equations.

Although intermittent rivers account for over half of the global fluvial network and could increase in length and quantity in Mediterranean climates (in response to climate changes), there is little documentation of organic matter input to them. This study was made possible by the cooperation of the Concepción Chiguayante School community and the Explora project (Chile), with the support of citizen science. The aim was to compare coarse particulate organic matter quantities and types in the Lonquén basin. Samplings were performed in two perennial rivers and two intermittent rivers. First, the riparian vegetation of the streams was characterized through photointerpretation and subsequently the organic matter accumulated in the selected river beds was quantified and typified. Spearman’s correlation was used. The riparian vegetation was similar in both types of rivers, though significantly greater (p < 0.05) plant material accumulation was found in intermittent rivers compared to perennial rivers (1029 and 337 g m− 2, respectively). Likewise, there was a significant relationship among leaves, smaller organic matter, seeds, herbs and shrubs in intermittent rivers. The results reveal the importance of the intermittent rivers that were sampled as transitory reservoirs of organic matter with high productive potential, especially in the first flood pulses, when this material is transported downstream.

Wildfires pose a major environmental and societal challenge, due to their link with anthropogenic activities and changing climatic conditions. This study aimed to enhance our understanding of the drivers of wildfire occurrence across continental Chile by developing robust predictive models incorporating climatic, land cover, and anthropogenic variables. We leveraged geospatial data on historical fire events, infrastructure, fuels and weather, coupled with historical fire records through Random Forest binary models to ascertain the key drivers of ignition across four distinct ecological zones: North, Central Chile, South, and the Andes. Our analysis explored potential differences between arson and unintended fires within these regions. Model validation, assessed using the Area Under the Curve (AUC), revealed significant regional variations in predictive performance. The southern and northern zones exhibited higher predictive capacity, potentially due to less complex landscapes and fewer ignition sources compared to the densely populated and infrastructure- prone central zone, which showed the lowest AUC. The Andes region displayed intermediate performance. Our results indicated that anthropogenic factors, particularly the distance to power lines, roads, and the wildland-urban interface (WUI), were consistently among the most important predictors of wildfire ignition across the majority of the studied regions. This highlights the significant impact of human accessibility and infrastructure on fire incidence in Chile. In contrast, fuel-related and climatic variables, such as Dry Fuel Moisture Content (DFMC) and its anomaly, showed generally lower importance, although their influence increased notably in the southern zone. Partial dependence plots further elucidated the distinct ways in which these key variables influenced ignition probability across different regions and between arson and unintended fires. The findings emphasize the necessity of adopting region-specific approaches in wildfire modeling and prevention strategies, acknowledging the different interactions between natural and anthropogenic factors across Chile. This research provides a fundamental understanding for future advanced modeling and targeted risk management efforts. Future research should aim to incorporate more detailed socioeconomic data to further refine predictive models and inform effective risk mitigation strategies.

The vast latitudinal extent of continental Chile (approximately 17° to 55° South), combined with its contrasting anthropogenic land-use patterns and diverse altitudinal configurations, presents significant challenges for understanding fuel configurations. This study aims to define standard fuel models, based on the Scott and Burgan classification, to support stochastic wildfire spread simulations at the landscape scale. In terms of landscape regions associated with forested or woody vegetation areas, three main regional units can be broadly identified. In the north (17°-30°), forest cover is sporadic or absent. In the center (30°-41°), the landscape is dominated by monoculture plantations of Pinus radiata and Eucalyptus globulus. In the south (41°-55°), extensive native forests prevail, with minimal or no human intervention. The central Mediterranean zone presents the greatest challenge for defining fuel models, as this region has experienced the highest wildfire occurrence and damage levels in recent decades. Notably, two “firestorms” in 2017 and 2023 burned more than 900,000 hectares combined. In this area, forest monocultures undergo significant temporal changes due to both exploitation and wildfire impacts. Additionally, the lack of official data complicates the estimation of forest monoculture biomass and vertical structure, requiring the use of ancillary datasets to improve estimates of canopy structure and vegetation conditions. In this study, fuel models were mapped across continental Chile producing a 100-meter resolution raster dataset standardized according to the Scott and Burgan methodology. The Vegetation Resources Cadaster produced by CONAF served as the foundational dataset, adapted and updated for fuel class assignments, with further refinements made by incorporating remote-sensed products such as canopy height data.