Antonio María Cabrera Ariza

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.

Climate change has intensified drought stress, threatening global food security by affecting sensitive crops like maize (Zea mays). This study evaluated the potential of Antarctic fungal endophytes (Penicillium chrysogenum and P. brevicompactum) to enhance maize drought tolerance under field conditions with different irrigation regimes. Drought stress reduced soil moisture to 59% of field capacity. UAV-based multispectral imagery monitored plant physiological status using vegetation indices (NDVI, NDRE, SIPI, GNDVI). Inoculated plants showed up to two-fold higher index values under drought, indicating improved stress resilience. Physiological analysis revealed increased photochemical efficiency (0.775), higher chlorophyll and carotenoid contents (45.54 mg/mL), and nearly 80% lower lipid peroxidation in inoculated plants. Lower proline accumulation suggested better water status and reduced osmotic stress. Secondary metabolites such as phenolics, flavonoids, and anthocyanins were elevated, particularly under well-watered conditions. Antioxidant enzyme activity shifted: SOD, CAT, and APX were suppressed, while POD activity increased, indicating reprogrammed oxidative stress responses. Yield components, including cob weight and length, improved significantly with inoculation under drought. These findings demonstrate the potential of Antarctic endophytes to enhance drought resilience in maize and underscore the value of integrating microbial biotechnology with UAV-based remote sensing for sustainable crop management under climate-induced water scarcity.

Background: The influence of temperature on seed germination is crucial, and climate change effects on plant distribution are significant. To grasp climate change’s impact on terrestrial ecosystems, understanding plant adaptation to temperature shifts is vital. Nothofagus glauca, a vulnerable species endemic to Chile’s Mediterranean region, is the most representative species of South American Mediterranean forests. Methods: Seeds of N. glauca were collected from two Andean provenances during mast years (2017, 2022) and processed according to the standards of the International Seed Testing Association (ISTA). Germination experiments were conducted under controlled laboratory conditions in the absence of light, testing four temperature levels (18°C, 22°C, 26°C, and 30°C) and two provenances (El Colorado, located further north, and San Fabian, located further south). Seeds were pretreated with gibberellic acid to break physiological dormancy, and germination parameters were assessed over a 40-day period. Results: Temperature had a significant impact on germination process, although the effect varied by provenance. The optimal germination capacity temperature was 22°C for the northern origin and 26°C for the southern origin. In both provenances, germination capacity remained relatively high at temperatures of 18°C and 30°C, suggesting that these temperatures did not approach the minimum or base and maximum or ceiling temperature thresholds. Furthermore, no clear trend was observed in the germination start day for either provenance. Significant differences were observed in average germination speed and germination vigour between the two provenances. Considering all variables, the optimal temperature differs between them (22°C for El Colorado and 26°C for San Fabian). Conclusions: Temperature’s pivotal role in germination and diverse provenance responses highlight potential impacts on genetic distribution and conservation. Understanding provenance-specific adaptation to changing climates is essential for comprehending climate change’s effects on terrestrial ecosystems. Tailoring conservation approaches to distinct provenances, like N. glauca, is crucial. This approach can effectively tackle climate change challenges and protect vulnerable species.

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.

Background: Araucaria araucana is a mast species that presents a high variability in annual cone production. Researchers have recorded synchronization events in cone production in different populations, which allows the seed production to be concentrated, reducing the percentage of seeds consumed by different animal species. Methods: We sampled three populations located in the Andes Mountains, Araucanía Region, Chile. In 2004 we began the collection of data on cone production, for which we installed permanent plots (1200 m² each) at each location. We identified and labeled each female tree in each plot to monitor its cone production. In 2012 we selected a total of 30 trees near the plots to evaluate the number of seeds per cone. In each February from 2012 to 2014 we selected two mature cones and covered them with a porous mesh for subsequent collection and storage in March. At the beginning of June, we counted and weighed the seeds, determining the average weight, the number of seeds per cone, the germination capacity (GC), and the germination speed (GS). Results: Cone production was synchronous across the three locations. We observed significant differences among the locations and years evaluated. The cones had fewer seeds in 2013 (high production) compared to those in 2012 (low production), but their weights were similar. In 2014 the cones produced smaller seeds in fewer quantities. The difference between the years 2013 and 2014 resulted from the high-energy expenditure in 2013. Regarding GC and GS, there were significant differences among the three locations (GC: F = 45.41, p < 0.01; GS: F = 96.08, p < 0.01), where the highest values were observed in 2013. Conclusions: Both GC and GS are related to seed weight but not to the number of cones produced in a given year. These annual fluctuations in seed production are determining factors in the population dynamics of forest species. Our results allow a better understanding of the reproductive phenology of A. araucana and could help define sustainable use and conservation actions for this species.

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.

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.