Climate change and increasing climatic variability have intensified droughts, rising temperatures, and rainfall uncertainty, placing growing pressure on water resources and spatial planning systems. In this context, governance structures play a central role in shaping how societies anticipate, respond to, and adapt to water scarcity. This study presents a qualitative comparative case analysis of drought governance in two climate-vulnerable regions: the lower Segura, Alicante basin (Spain) and the upper and middle Mira River basin (Ecuador–Colombia border). Although both regions experience recurrent water stress, they differ in institutional arrangements, governance capacities, and levels of stakeholder coordination. Using document-based qualitative analysis and interpretive comparison of policy and institutional frameworks, the study identifies structural and operational governance weaknesses conceptualized as systemic “glitches” that affect coordination, adaptive capacity, and participatory water management. Rather than measuring performance, the study focuses on how governance processes function in practice and how institutional configurations shape drought response. The analysis addresses three interconnected dimensions: (1) the structural factors shaping drought vulnerability, (2) the institutional configurations that condition climate risk governance, and (3) the governance patterns that influence adaptive capacity at the regional scale. The findings indicate that southern Alicante exhibits strong formal institutional capacity but limitations in operational coordination, while the Ecuadorian case shows more pronounced structural constraints affecting planning and implementation. By comparing these two governance contexts, the study contributes to a deeper understanding of how institutional design and policy coherence influence drought management outcomes. The results highlight the importance of flexible, integrated, and multi-level governance systems for improving resilience in water-scarce regions.
Water soil erosion is a significant threat to agricultural sustainability and a key process of land degradation. Climate change is altering precipitation patterns and increasing rainfall erosivity globally, leading to more extreme hydrological conditions, yet many agriculturally significant regions lack detailed long-term analysis. This study addresses this gap by quantifying 90-year trends (1934–2023) in key precipitation metrics—total depth, number of rainy days, heavy rainfall (% of rainfall ≥ 50 mm), and rainfall erosivity (R-factor)—in a critical cropland region of Argentina. Analysis of daily data from the Paraná Agrometeorological Station revealed significant breakpoints in the 1970s. Post-breakpoint increases were observed for annual rainfall depth (+16% since 1975), rainy days (+19% since 1972), heavy rainfall events (+21% since 1971), and rainfall erosivity (+24% since 1977). On a monthly basis, March exhibited the highest erosivity. Despite a recent decrease in total monthly precipitation for March, its erosive potential appears to be sustained by a higher concentration of rainfall in intense events. Conversely, the period from May to September was characterized by lower and more stable erosivity values. Positives and significant correlations were established between the El Niño-Southern Oscillation (ENSO), measured by the Oceanic Niño Index (ONI), and all rainfall variables, with the strongest influence observed during the spring-summer seasons for rainfall depth (r= 0.24 to 0.51). Furthermore, the frequency of quarters classified as El Niño conditions has increased about 91% since the mid-1970s breakpoint. This study demonstrates a clear climatic shift towards conditions of higher precipitation and greater erosivity in the region. These findings underscore the urgent need for enhanced soil and water conservation policies to mitigate the increasing risk of land degradation.
El cambio climático está induciendo modificaciones en los patrones de distribución de especies en regiones biodiversas como el Chocó Biogeográfico Ecuatoriano. Este estudio identifica áreas prioritarias para la conservación de tres especies de aves clave (Cephalopterus penduliger, Morphnarchus princeps y Bangsia edwardsi) mediante modelado de nicho ecológico y superposición difusa. Se utilizó el software MaxEnt para proyectar la idoneidad del hábitat actual y futuro bajo el escenario SSP585 (2021 - 2040), integrando variables bioclimáticas, topográficas y registros de presencia. La validación mediante TSS y AUC indicó un desempeño robusto de los modelos. Los resultados proyectan respuestas heterogéneas ante el cambio climático: una contracción crítica del nicho para Cephalopterus penduliger (pérdida del 31,4 % de hábitat idóneo) y una reducción moderada para Morphnarchus princeps (-10,9 %), impulsadas por restricciones térmicas y topográficas. En contraste, Bangsia edwardsi mostró una expansión potencial del 16,6 % hacia zonas de mayor altitud. La superposición difusa (Fuzzy Gamma) destacó las estribaciones montañosas al este de la región (800-1.800 m a.s.l.) como refugios climáticos prioritarios. Se concluye que, si bien existen áreas de refugio, la viabilidad de estas especies dependerá de la protección de corredores altitudinales frente a la fragmentación del paisaje.
El suelo es un elemento clave para sostener la vida y el equilibrio de los ecosistemas, pues provee nutrientes y participa en procesos como el ciclo del agua y la captura de carbono. Por ello, analizar su estado y promover su conservación es una prioridad ante el cambio climático. Uno de los principales retos en su gestión es la representación espacial, dado que la heterogeneidad de las propiedades edáficas y la variabilidad ambiental dificultan una caracterización precisa del territorio. Este desafío es especialmente relevante porque una representación precisa permite identificar patrones de distribución, áreas vulnerables y zonas de alta calidad ambiental, facilitando la toma de decisiones informadas. En este contexto, el objetivo principal de este estudio es caracterizar el suelo del Parque Natural Los Alcornocales (Andalucía, sur de España) desde una perspectiva física, orgánica e hídrica mediante técnicas avanzadas de modelización espacial. Para ello, se utilizó el algoritmo Random Forest (RF), basado en árboles de decisión. Este algoritmo permite integrar múltiples variables ambientales e incrementar la exactitud de los resultados. La modelización se fundamentó en el análisis de 461 muestras de suelo (0-10 cm de profundidad) y la validación se efectuó mediante un enfoque dual: una validación externa con un 10 % de los datos como conjunto independiente y la validación interna Out-of-Bag (OOB) del algoritmo RF. Los indicadores de error y precisión evidenciaron la solidez del modelo RF para la generación de cartografía predictiva en un parque natural con elevada heterogeneidad eco-geomorfológica (CC, R² = 0,937; Factor K, R2 0,935; COS, R² = 0,918). Los resultados evidenciaron que los factores bióticos, derivados de datos espectrales, tienen un papel determinante en la distribución espacial de los indicadores edáficos analizados (carbono orgánico, capacidad de retención hídrica y erodabilidad del suelo). Esta información constituye una herramienta clave para orientar la planificación y gestión sostenible del territorio, presentando un elevado potencial de aplicabilidad en otros contextos mediterráneos similares.
La fenología del hielo en lagos pequeños de alta montaña es un indicador sensible del cambio climático, pero su monitoreo sigue siendo limitado debido a la inaccesibilidad invernal y a las restricciones de resolución espacial y temporal de la teledetección satelital. Este estudio presenta la implementación y evaluación de LIMS-TL (Lake Ice Monitoring System – Time-Lapse), un sistema de adquisición automatizada de imágenes basado en cámaras programadas para el seguimiento de la cubierta de hielo en siete lagos naturales (6,5–24,5 ha) del Parque Nacional de Aigüestortes i Estany de Sant Maurici (Pirineos), durante tres temporadas invernales (2021–2024). LIMS-TL fue diseñado para operar en condiciones extremas y evaluado en términos de fiabilidad, autonomía energética y capacidad para registrar eventos clave de congelación y deshielo. Mediante la inspección visual experta de más de 17.500 imágenes, se identificó el 90 % de las fechas fenológicas clave (Inicio de la Congelación, Final de la Congelación, Inicio del Deshielo, Final del Deshielo) y se documentaron procesos dinámicos con alta resolución temporal. Además, se desarrolló una metodología de clasificación visual para evaluar la calidad de las imágenes, aplicando tres categorías según su utilidad para el análisis. En promedio, el 80,7 % de los días presentó al menos una imagen válida por lago durante la temporada 2021–2022. Este trabajo constituye la primera documentación sistemática de alta resolución temporal de la fenología del hielo en lagos pirenaicos, al lograr identificar las cuatro fechas clave del ciclo estacional. Las imágenes obtenidas representan un archivo visual valioso para el estudio detallado de la dinámica superficial y de los procesos hidrológicos asociados. A pesar de algunas limitaciones, como fallos técnicos puntuales o la acumulación de nieve en el objetivo, LIMS-TL demostró ser una herramienta eficaz, replicable y de bajo coste, aplicable tanto de forma autónoma como en combinación con otros métodos de observación. Esta metodología constituye una base sólida para ampliar el monitoreo de la dinámica del hielo lacustre en regiones de montaña y mejorar la comprensión de su evolución en un contexto de cambio climático.
The mountainous marly slopes of the Atlantic High Atlas host a unique, vulnerable ecosystem shaped by a complex interplay of biotic, abiotic, and human (anthropogenic) factors. Characterized by steep slopes, poor soils, and degraded Argan (Argania spinosa) forests, this landscape exhibits active and complex geomorphological processes, with the presence of diverse erosion forms (gullies and rills) indicating ongoing instability. The study focused on the dynamics of marly slopes in the lower valley of Wadi Tamri in the northern part of Agadir Ida Ou Tanane province. Using geomorphometric analysis, remote sensing, and field observations, we investigated the impact of various factors, including climate, geology, and human activities, on landscape evolution. Various applied geomorphometric indices were compared with the results of a hydrological evaluation model. The comparison was made using two digital elevation models with multiple dates (2007 and 2014). To assess land use changes within this sparsely forested, marly slope landscape unit, Landsat imagery from 1984, 2000 and 2022 was analyzed, supported by landscape point sampling and field validation to facilitate interpretation of the maximum likelihood classification algorithm. The results showed that the number, length, drainage and hydrographic density values of gullies decreased between 2007 and 2014. Bifurcation and length ratios vary according to stream order and demonstrate some evolution during this period. Evolutionary stages and scenarios are marked by the degradation of the forested landscape between 1984 and 2022. Conversely, the area of marly substrate within the landscape unit has increased by 891.30 ha between 2000 and 2022. Declining forest cover can expose marly surfaces to hydric erosion. This research provides a scientific foundation for further investigation of marly slopes and offers valuable insights that will serve as an example for regional soil and water conservation strategies.
Este estudio analizó los patrones de cambio de uso del suelo en Loja, Ecuador, entre 1990 y 2020 mediante matrices de transición y el marco metodológico de la intensidad de cambio. A partir de estas dinámicas espacio-temporales, se proyectó la expansión urbana hasta 2070 y se generaron escenarios alternativos. Para explicar las dinámicas actuales, el modelo Random Forest presentó el mejor desempeño (precisión: 90,72 %; Kappa: 0,852; desacuerdo en cantidad: 0,04; desacuerdo en ubicación: 0,17). En el escenario tendencial para 2070, la expansión urbana afectaría principalmente áreas agrícolas (93,6 %) y la vegetación natural (6,15 %), incluyendo aproximadamente el 17,2 % de las zonas de alto riesgo de deslizamientos. En escenarios climáticos futuros, esta expansión se concentraría en áreas con incrementos proyectados de temperatura y precipitación, lo que podría aumentar la vulnerabilidad a eventos extremos. Los resultados evidencian la utilidad de herramientas de acceso libre para modelar escenarios urbanos futuros en contextos con limitaciones de datos, así como la replicabilidad de la metodología en otras ciudades intermedias con desafíos similares, lo que contribuye al diseño de estrategias sostenibles de gestión territorial adaptadas a contextos locales.
Understanding the spatial configuration of local climate in mountainous agricultural areas is essential for assessing land suitability and adaptation strategies under global warming. This study aims to quantify fine-scale thermal variability and evaluate its implications for site-specific vineyard management under frost and heat stress conditions. The study area is a high-altitude vineyard (1430–1550 m a.s.l.), known as Monasterio (RAQUIS project), located in Gualtallary, Mendoza (Argentina). Air temperature was recorded every 5–15 minutes using twelve sensors and one automatic weather station over two growing seasons (October–March) and one dormancy period (May–September). Thermal and bioclimatic spatial patterns were examined through point-based measurements and regression-based modelling in SAGA-QGIS. Results show that variations in slope, aspect, and altitude produce strong microclimatic contrasts, delineating two distinct Winkler bioclimatic zones within the vineyard, ranging from cold–moderate to moderate thermal regimes. East-, northeast- and southeast-facing slopes exhibited higher heat accumulation, whereas lower sectors were prone to nocturnal cold-air pooling and frost formation. During the growing season, mean maximum and minimum temperatures differed by up to 3.5°C and 2.5°C, respectively, revealing marked thermal heterogeneity across short distances. This study highlights the role of fine-scale topography in shaping vineyard climates and demonstrates the value of high-resolution climatic monitoring and GIS-based spatial analysis for understanding topoclimatic dynamics in mountainous agricultural environments. These findings contribute to both viticultural adaptation and broader research in physical geography and local climatology.
La caracterización precisa y continua de las propiedades de los combustibles forestales es esencial para evaluar el riesgo de incendio y predecir el comportamiento del fuego. Este estudio aborda los desafíos asociados a la escasa disponibilidad temporal de datos lidar, integrando imágenes ópticas (Landsat) con adquisiciones lidar puntuales para estimar variables estructurales del bosque, como la fracción de cabida cubierta (FCC) y la altura del dosel (H), en dos regiones de la España peninsular: Madrid y el País Vasco. Estas variables constituyen indicadores indirectos (proxies) para la caracterización de las propiedades del combustible, ya que reflejan tanto la cantidad como la continuidad espacial de los combustibles. Se compararon modelos de aprendizaje automático (Random Forest y Extreme Gradient Boosting) con arquitecturas de aprendizaje profundo, incluyendo transformers con mecanismos de auto-atención (NeNeT). Los resultados muestran que los modelos de aprendizaje profundo mejoran significativamente la precisión, con una reducción promedio del error cuadrático medio (RMSE) del 30% respecto a los métodos tradicionales. En particular, NeNeT destacó por su capacidad para capturar relaciones espaciales complejas, mejorando las estimaciones de altura en bosques densos del País Vasco (RMSE reducido de 7,0 a 4,0 m). En contraste, en los bosques mediterráneos más abiertos de Madrid, las diferencias fueron menores, lo que sugiere que métodos menos costosos computacionalmente, como XGB, pueden ser adecuados en ciertos contextos. A pesar de sus ventajas, los modelos de aprendizaje profundo presentan limitaciones operativas, especialmente por sus altas demandas computacionales. Por ejemplo, la generación de mapas históricos en la España peninsular requeriría hasta cuatro años de procesamiento con NeNeT frente a siete meses con XGB, si no se dispone de paralelización. Además, los modelos DL tienden a aprender patrones espurios relacionados con la adquisición de imágenes (por ejemplo, número de observaciones o fechas), lo que puede introducir sesgos si no se controlan adecuadamente En conclusión, la combinación de sensores lidar y ópticos, junto con modelos avanzados de inteligencia artificial, permite estimar con alta precisión variables útiles en la gestión de incendios forestales. No obstante, la elección del modelo debe equilibrar la precisión obtenida con los recursos computacionales disponibles, adaptándose al tipo de ecosistema y a las necesidades de la aplicación.
In recent decades, problems associated with forest fires have intensified, particularly in the Mediterranean Basin, posing a serious threat to ecosystems and human populations. Although their frequency has decreased, wildfires have become more intense and severe, often exceeding suppression capacity, as evidenced by the emergence of sixth-generation fires on the Iberian Peninsula. Multiple factors contribute to this situation, including socio-economic dynamics (depopulation, land-use change), inadequate forest management, insufficient public policies, and climate change-further exacerbated by misinformation and media sensationalism. Within this socio-environmental context, and given the need for high-quality information, it is essential to strengthen the capacity of future specialists in prevention and restoration through a variety of strategies: on the one hand, improving forest management in order to create stands that hinder the spread of wildfires and enhance post-fire regeneration; and on the other hand, addressing ignition sources by raising public awareness of their underlying causes. The Plantando Cara al Fuego (PCF) initiative aims to improve environmental awareness and foster citizen participation in combating forest fires by involving the public in projects that apply academic knowledge to community needs. To this end, PCF develops educational innovation projects based on the Service-Learning (S-L) pedagogical approach, which integrates university education with forest-fire prevention and restoration efforts. The aim of this study is to compile experiences carried out between 2020 and 2025 in different Spanish regions, in which university students from various degree programmes have implemented S-L projects to address real community challenges related to forest fires through the design and development of activities in collaboration with public and private entities. A total of 35 projects is analyzed and classified into five categories (prevention, restoration, training, multidisciplinary and dissemination). These projects are examined alongside indicators of their educational and social impact, including the number of participating students, collaborating companies and institutions, outputs produced, bachelor's and master's theses completed, and the actual population reached. The results show that Service-Learning strengthens students' professional skills and environmental commitment, thereby contributing to sustainable land management.
. Forest fires are becoming increasingly frequent, leading to the loss of human lives, ecosystem fragmentation, and higher greenhouse gas (GHG) emissions. This study aims to identify, quantify and classify forest fire risk levels in the Headquarters District of the municipality of Cachoeiro de Itapemirim (Brazil/ES). To this end, the Analytic Hierarchy Process (AHP) was used to prioritise the factors contributing to fire risk. Three models were created: R1 included natural factors, R2 biological and socioeconomic factors and R3 the months without rain. By summing the areas classified as having moderate, high, and very high fire risk levels, we were able to identify (through zoning) those at greater risk of fire for each model, being 18.97% of the total area for R1, 59.81% for R2, and 71.41% for R3. These results highlight the significant influence of human intervention and climatic conditions on forest fire risk.
Accurate and continuous characterization of forest fuel properties is essential for assessing wildfire risk and predicting fire behavior. This study addresses the limitations posed by the scarce temporal availability of lidar data by integrating temporally consistent optical imagery (Landsat) with occasional lidar acquisitions to estimate structural forest variables such as canopy cover fraction (FCC) and canopy height (H) in two regions of peninsular Spain: Madrid and the Basque Country. These variables are widely recognized as proxies for fuel properties characterization, reflecting both the amount and spatial continuity of forest fuels. Machine Learning (ML) models (Random Forest and Extreme Gradient Boosting) were compared with Deep Learning architectures, (DL) including transformer-based models with self-attention mechanisms (NeNeT). The results show that DL models significantly improve accuracy, with an average reduction in root mean square error (RMSE) of 30% compared to traditional methods. NeNeT, in particular, demonstrated strong performance in capturing complex spatial relationships, improving height estimates in dense forests of the Basque Country (RMSE was reduced from 7.0 to 4.0 m). In contrast, differences were smaller in the more open Mediterranean forests of Madrid, suggesting that less computationally demanding methods like XGB may be suitable in certain contexts. Despite their advantages, DL models present operational limitations, particularly due to high computational demands. For instance, producing historical maps for the peninsular Spain would require up to four years of processing with NeNeT versus seven months with XGB, assuming no parallelization. Moreover, DL models tend to learn spurious patterns related to image acquisition (e.g., number of observations or dates), which can introduce biases if not properly controlled. In conclusion, combining lidar and optical sensors with advanced artificial intelligence models enables highly accurate estimation of key variables for wildfire management. However, model choice should balance achieved precision with available computational resources, taking into account the ecosystem type and specific application needs.
In recent years, there has been increasing evidence of changes in fire frequency, with varying intensities and magnitudes across ecosystems worldwide. La Pampa, located in central Argentina, is affected by significant annual wildfire activity. This study aimed to analyse variations in climatic and spectral indices over a 29-year period (1995-2023) to identify patterns that might enhance our understanding of the fire regime in xerophytic shrublands. Additionally, it evaluated trends in climatic variables within the context of global climate change. Meteorological data and records of burned area were analysed; the Standardised Precipitation and Evapotranspiration Index (SPEI) was calculated at four timescales; and monthly Normalised Difference Vegetation Index (NDVI) data for shrubs and grasslands were obtained. The Theil-Sen estimator and the non-parametric Mann-Kendall test were used to detect any trends or correlations between variables, and a SARIMA model was used to explore lagged correlations between selected variables. The SPEI values typically ranged between-2 and 2, with SPEI-12 showing the highest correlation with large, severe fire events. The NDVI for shrubland and grassland exhibited positive correlations with SPEI-24 and SPEI-6/SPEI-12, respectively. SPEI-12 and burned area displayed a significant negative correlation. Monitoring climatic and spectral indices over time helps identify periods of phytomass accumulation and ignition thresholds. In the context of climate change, the observed increasing trends in precipitation, mean temperature, and maximum temperature suggest a future with heightened fire frequency in xerophytic shrublands. This study underscores the importance of integrating climatic and vegetation indices to improve our understanding and management of fire regimes in fire-prone ecosystems.
The Ait Herbil oases (Timoulay Oumaloukt and Timoulay n'Tozomte) represent an ancient center of human settlement and civilization. They feature emblematic landscapes that reflect a profound interaction between humans and the natural environment. These landscapes possess both heritage and spatial identities that distinguish them from neighboring oases, due to their historical adaptation to the region's challenging environmental conditions. A preliminary examination of the Timoulay Oumaloukt and Timoulay n'Tozomte oases reveals distinct functions and land-use patterns. These are agrarian environments shaped by a traditional subsistence economy. Over generations, farmers have molded these highly anthropized landscapes by making use of the available natural resources. However, recent environmental and socio-spatial transformations have triggered an unprecedented crisis, resulting in major imbalances within the landscape systems. The objective of this study is to characterize the anthropogenic landscapes of the Timoulay Oumaloukt and Timoulay n'Tozomte oases, located in the territorial commune of Aday, by analyzing their spatial organization, functioning, and dynamics. The methodology consists, first, of a general diagnosis and typology of the region's anthropized landscapes. It then focuses on the current state of four specific types of anthropized features: agricultural terraces, threshing floors, collective granaries (locally known as Agadir), and agrarian landscapes. The study is primarily based on field visits and surveys, semi-structured interviews, climate data (1981-2022), and data derived from Geographic Information Systems (GIS), remote sensing, and analysis of photographic documentation collected between 2017 and 2022. NDVI imagery for 1987, 2001, and 2023 reveals a marked decline in oasis vegetation: from 61.35 ha in 1987, to 53.21 ha in 2001, and 47 ha in 2023, mainly due to recurrent droughts between 1981 and 2022. This differential dynamic reflects two contrasting trends: agricultural abandonment in some areas, and increased human pressure in others-both shaped by natural, socio-economic, and technological factors. Persistent droughts have accelerated agricultural decline, contributing to the socio-spatial exodus of the population. This research provides a scientific foundation for further investigation into oasis landscapes, offering critical insights to inform regional strategies for resource management and heritage conservation.
In recent decades, wildfires have become one of the main disturbances affecting Mediterranean forest ecosystems. Understanding how fire-affected formations recover is crucial for assessing their resilience and effectively managing potential hydrological-forest restoration measures. This study analyzes vegetation regeneration in burned areas representative of the landscape diversity of Arag & oacute;n (NE Iberian Peninsula) considering (i) the type of colonizing vegetation in relation to the pre-existing one and (ii) the impact of the colonizing vegetation type on the spatial distribution of the Leaf Area Index (LAI), which is used as a proxy for the eco-physiological functionality of the affected formations. High-spatial-resolution GeoSAT-2 images and Sentinel-2 L2A collections were used to generate maps of current vegetation distribution and multitemporal LAI composites, respectively. Contingency tables derived from diachronic comparisons of dominant vegetation type (before the fire and at present) and Random Forest (RF) predictive models were employed. The RF models also determined the importance of different natural factors in the spatial distribution of colonizing vegetation formations. The results highlighted the strong dependence between pre-fire and colonizing vegetation formations (chi(2) = 10.067) and the role of regenerative trajectories in the spatial distribution of LAI (p < 0.05). Greater regeneration was observed in areas dominated by species with active reproductive strategies (resprouting and serotiny). Additionally, in the Random Forest modeling (OOB = 21%), pre-existing vegetation emerged as the most determining factor (MDG = 600) in predicting current vegetation, surpassing fire severity and the regenerative trend of the Normalized Difference Vegetation Index (MDG approximate to 250), whose effects vary depending on the type of vegetation formation.
The abandonment of rural land has led to some rural areas falling into disuse or being taken over by forests. In this context, prescribed burning is a widely used forest management tool, but few studies have analysed the influence of pre-burn land use on post-burn soil recovery. This study seeks to determine the impact of prescribed burning on soil chemical properties and to examine any differences in these parameters based on prior land use. The study was conducted on two plots-one, a forest plot (TV2); the other, an abandoned agricultural terrace (TV3)-located in Tivissa (southern Catalonia), both dominated by Pinus halepensis Mill. and Quercus ilex L., and situated on Lithic Calcixerept soils. The plots, located 3 km apart, share a similar topography, exposure, and vegetation structure. Low-intensity prescribed burns were conducted in 2001, and soil samples (0-5 cm depth) were collected in five campaigns: just before the fire (BPF), immediately after the fire (APF), and at 1-, 3-, and 13-years post-burn (1YAPF, 3YAPF, and 13YAPF, respectively). In each sampling period, 30 samples were collected from an experimental plot of 72 m2 (4 x 18 m). The soil properties analysed included total carbon (TC), total nitrogen (TN), pH, electrical conductivity (EC), extractable calcium (Ca), magnesium (Mg), potassium (K), and available phosphorus (P) concentrations. In the TV2 soil, TC and TN increased in the short and medium term; soil pH increased after the fire and then decreased gradually; EC decreased in the short and medium term and increased in the long term; and extractable major cations increased until the medium term and then decreased in the long term. In the TV3 soil, TC decreased and TN increased gradually over time; pH increased in the short term and decreased in the long term; EC decreased in the short to long term, with a slight increase in the medium-term; and extractable major cations (except P, which decreased over time) increased until the long term. Changes caused by different pre-fire land uses and the consequent differences in vegetation cover reduce over time. Despite differences in soil properties in the short and medium term due to land use prior to prescribed fire, after 13 years, soil conditions had largely stabilised, and there was no evidence of horizontal or vertical continuity in plant fuel. These findings suggest that prescribed burning does not result in long-term soil degradation and, thus, remains a viable tool for the sustainable management of forests exposed to different land uses.
Monitoring and understanding vegetation responses to fire in Amazonian savanna ecosystems remains a very important scientific challenge to improve the landscape management practices of these areas. In this sense, the present study analyzes the dynamics of spectral separability as well as the postfire vegetation recovery process related to fire experiments carried out in open savanna ecosystems of the Campos Amaz & ocirc;nicos National Park (Brazil). For this purpose, a harmonized Landsat and Sentinel-2 dataset was processed and analyzed. The time series of the Normalized Difference Vegetation Index (NDVI) and the Normalized Burned Ratio 2 (NBR2) spectral indices were also generated from this same dataset for the period from 2019 to 2023 and evaluated in combination with fine fuel load in-situ measurements. M-Statistics and mean absolute difference were calculated comparing data from burned and unburned plots, considering different treatments of fire seasonality (Early-Dry Season-EDS; Middle-Dry Season-MDS fires) and time since last fire (2-year-old fuel age; 3-year-old fuel age; and 10-year-old or older fuel age fires). The combined use of Sentinel-2 and Landsat resulted in an availability of cloud-free or partially cloud-free images approximate to 0.6 times greater than that obtained when using Landsat images exclusively. The potential of the NBR2 stood out, generating statistically significant mean absolute difference values when comparing EDS and MDS fires, and also when comparing 2-year-old fuel age areas with 3-year-old or 10-year-old or older fuel age areas. Satellite and field information converged in the detection of a rapid response of vegetation to fire in these ecosystems, demonstrating that conditions similar to those observed before the fire were reached after three rainy seasons. The results reinforce the potential of Landsat and Sentinel-2 harmonized remote sensing datasets to assess and monitor fire-affected areas over Amazonian savanna ecosystems, providing ecological meaning and establishing connections between remote sensing and field datasets.
En las últimas décadas, la problemática relacionada con los incendios forestales se ha intensificado, especialmente en la cuenca Mediterránea, lo que genera una grave amenaza para los ecosistemas y las poblaciones humanas. Aunque han disminuido en número, los incendios son de mayor intensidad y severidad, superando la capacidad de extinción (incendios de sexta generación). Las causas de este aumento son diversas: factores socioeconómicos (despoblación, cambio de uso de suelo), gestión forestal deficiente, políticas inadecuadas y el cambio climático, agravados por la desinformación y sensacionalismo mediático. En este contexto socioambiental y de necesidad de información es necesario reforzar la capacidad de los nuevos especialistas en las actividades de prevención y restauración mediante diferentes estrategias. Por un lado, mediante mejoras en la gestión forestal, lo que conlleva la creación de masas forestales más resistentes a la propagación de los incendios forestales y con mayor capacidad de regeneración natural; por otro lado, actuando sobre las causas que originan la ignición, es decir, desarrollando concienciación social sobre las mismas. El proyecto Plantando Cara al Fuego (PCF) es una iniciativa que busca mejorar la concienciación ambiental y la participación ciudadana en la lucha frente a los incendios forestales, mediante su implicación en proyectos donde se aplican los conocimientos académicos a necesidades de la comunidad. Para ello, PCF desarrolla proyectos de innovación educativa basados en Aprendizaje-Servicio (ApS), metodología que integra la formación universitaria con la prevención y la restauración forestal. El objetivo de este trabajo es recopilar experiencias desarrolladas entre 2020 y 2025 en distintas comunidades autónomas españolas en las que el estudiantado universitario de titulaciones diversas ha desarrollado proyectos de ApS, contribuyendo a resolver problemáticas reales de sus comunidades ligadas a los incendios forestales mediante el diseño y desarrollo de actividades en colaboración con entidades públicas y privadas. Así pues, se analizan 35 proyectos, clasificados en 5 categorías (prevención, restauración, formación, multidisciplinar y divulgación), junto con indicadores de impacto educativo y social de la iniciativa: número de estudiantes implicados, empresas e instituciones colaboradoras, productos generados, TFGs o TFMs defendidos o alcance real de estos proyectos en términos poblacionales. Los resultados evidencian que el ApS fortalece la competencia profesional y el compromiso ambiental del alumnado, contribuyendo a la gestión sostenible del territorio.
