This study explores the use of unmanned aerial vehicles (UAVs) and machine learning algorithms for the identification of Nothofagus alessandrii (ruil) species in the Mediterranean forests of Chile. The endangered nature of this species, coupled with habitat loss and environmental stressors, necessitates efficient monitoring and conservation efforts. UAVs equipped with high-resolution sensors capture orthophotos, enabling the development of classification models using supervised machine learning techniques. Three classification algorithms—Random Forest (RF), Support Vector Machine (SVM), and Maximum Likelihood (ML)—are evaluated, both at the Pixel- and Object-Based levels, across three study areas. The results reveal that RF consistently demonstrates strong classification performance, followed by SVM and ML. The choice of algorithm and training approach significantly impacts the outcomes, highlighting the importance of tailored selection based on project requirements. These findings contribute to enhancing species identification accuracy in remote sensing applications, supporting biodiversity conservation and ecological research efforts.

To achieve sustainable and profitable production of sorghum for energy purposes, it is crucial to ensure the efficient use of the nutrients necessary for its growth and development. This research investigates the influence of diverse management practices on biomass production, nutrient use efficiency, and nitrogen balance in two sorghum hybrids cultivated for bioenergy applications. A comprehensive field study was conducted over two growing seasons, evaluating the effects of fertilization methods and crop rotation strategies. Results indicate that high nitrogen (HN) fertilization increased dry biomass production (up to 20.7 Mg ha⁻¹) and nutrient removal (up to 343.5 kg K ha⁻¹) in both sorghum hybrids. The H128 hybrid showed higher nutrient use efficiency, especially for phosphorus, while the nitrogen balance was positive under HN but varied under low nitrogen (LN), with the H133 hybrid experiencing a net nitrogen loss at LN. These findings contribute valuable insights into sustainable sorghum cultivation for bioenergy production, highlighting the importance of tailored management practices in achieving optimal crop performance.

This study focuses on the natural regeneration of Nothofagus glauca, a critical component of temperate forests in the Mediterranean region of the Southern Hemisphere in Chile. Various harvesting and canopy opening methods were evaluated to understand their impact on the regeneration of this species. A “mast seeding” behavior was observed in seed production, with alternating years of high and low production. Seed viability was affected by lepidopteran infestation and extreme weather events. The results indicate variable seed quantity and quality, influencing regeneration. First-year seedling mortality was significant, reaching up to 95%, and regeneration decreased annually. Additionally, changes in canopy opening and solar radiation transmission were analyzed, revealing significant post-harvest modifications. This study provides valuable insights for the sustainable management of Mediterranean forests, emphasizing the importance of considering seed production variability and regeneration dynamics in decision-making processes.

The upper Mapocho River basin, located in central Chile, has been affected by numerous landslides in the past, which may become more frequent due to a projected increase in intense precipitation events in the context of climate change. Against this background, this study aimed to analyze the ground deformation associated with an active landslide area in the Yerba Loca basin using the SBAS–DInSAR technique with PAZ and Sentinel-1 images acquired during two time periods, 2019–2021 and 2018–2022, respectively. Using PAZ imagery, the estimated vertical displacement velocity (subsidence) was as high as 9.6 mm/year between 2019 and 2021 in the area affected by the Yerba Loca multirotational slide in August 2018. Analysis of Sentinel-1 images indicated a vertical displacement velocity reaching −94 mm/year between 2018 and 2022 in the Yerba Loca landslide, suggesting continued activity in this area. It, therefore, may collapse again soon, affecting tourism services and the local ecosystem. By focusing on a mountainous region, this study demonstrates the usefulness of radar imagery for investigating landslides in remote or hard-to-reach areas, such as the mountain sector of central Chile.

Wildfire monitoring using synthetic aperture radar (SAR) provides critical capabilities under challenging atmospheric conditions where optical sensors are limited by smoke and cloud cover. We evaluated Sentinel-1 C-band SAR interferometric coherence for Burned-area detection of the 2017 “Las Máquinas” megafire (Maule, Chile), comparing Ascending (Asc) and Descending (Dsc) orbital geometries processed with the AMSTer InSAR software. Multi-temporal RGB Coherent Change Detection composites were constructed using two interferometric pairs per orbit: the Normalised Differential Activity Index (NDAI, R channel), pre-fire coherence (G channel), and co-event coherence (B channel), clearly delineating the fire scar through red and orange signatures reflecting fire-induced vegetation loss and soil exposure. Seven machine-learning classifiers (Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbours (KNN), Gradient Boosting Classifier (GBC), and XGBoost) were trained on the three-band coherence feature space. For the Ascending orbit, XGBoost achieved the highest performance (OA = 0.9328; F1 = 0.9195) and mapped 143,950 ha (76.2%) as Burned. For the Descending orbit, XGBoost also performed best (OA = 0.9221; F1 = 0.9055) and mapped 144,475 ha (76.5%) as Burned. In this case study, the Ascending geometry performed marginally better than the Descending one; however, the leading classifiers were statistically indistinguishable, indicating that the Burned and Unburned classes are close to linearly separable in the coherence feature space. These results confirm the effectiveness of coherence-based SAR analysis for large-scale wildfire mapping under adverse atmospheric conditions.