Thermal unmanned aerial vehicles for the identification of microclimatic refugia in topographically complex areas

Biodiversity loss is one of the most relevant consequences of climate change. Therefore, identifying areas and environmental features that allow certain organisms to be less exposed to the effects of the current global warming is priority for biodiversity conservation. In this study, we describe a novel approach for the identification of microclimatic refugia in rugged mountain areas, specifically for the detection of most thermally stable areas, using an unmanned aerial vehicle (UAV) capable of recording in the visible and thermal infrared spectral bands. We estimated land surface temperatures (LST) at very-high spatial resolution in six topographically complex sectors of the Pyrenees (NE Spain), across seasons with vegetative activity (summer 2020, autumn 2020, spring 2021, and summer 2021), and at two thermally contrasted times of the day (early in the morning: LSTmin, and in the afternoon: LSTmax). LST were validated with a network of miniaturized temperature sensors in the field. LSTmin and LSTmax allowed us to calculate the daily thermal range of each sector across the seasons, and thus the most thermally stable areas over the year. To reveal the importance of different variables on low and narrow thermal ranges we applied Gradient Boosted Models to seven terrain variables derived from ALS-LiDAR (slope, northness, eastness, heat load, wind exposure index, SAGA's topographic wetness index, and vector ruggedness measure) and a proxy of forest density through the three-dimensional point clouds of the UAV data. The northness was the variable that most promoted thermal stability, followed by the slope and forest density, so that microclimatic refugia resulted to be located in northern slopes, small sites under rocky cliffs, and forested areas. Our results demonstrate that thermal UAVs can become promising tools for the identification of microclimatic refugia in topographically complex areas, providing information at unprecedented spatial resolution, and thus of high interest for biodiversity conservation

Análisis de la peligrosidad y del riesgo de inundación del tramo medio del río Ega en Navarra

River floods constitute one of the most important and widespread natural risks in the world. For this reason, its study and analysis represent a great opportunity for the prevention and protection of the territory. In this study, the danger and risk of flooding of Ega river has been evaluated for its middle section through Navarre (Spain). The hydrological behavior of the river system has been analyzed and the information elaborated by PGRI has been used to determine the effects on territory under different flooding scenarios. These data have been checked from flow rates and satellite imageries related to the flood event of Ega river on December 2021. Results obtained are intended to help reduce the risk of flooding in this sector.Las crecidas e inundaciones fluviales constituyen uno de los riesgos naturales más importantes y extendidos del mundo. Por este motivo, su estudio y análisis representan una gran oportunidad de prevención y protección hacia el territorio. En este estudio se ha evaluado la peligrosidad y el riesgo de inundación del río Ega en su tramo medio en la Comunidad Foral de Navarra. Se ha analizado el comportamiento hidrológico del sistema y se ha utilizado información elaborada por el PGRI para conocer las afecciones en el territorio bajo distintos escenarios de inundabilidad que han permitido identificar las áreas más vulnerables y de mayor riesgo de inundación. Estos datos han sido contrastados mediante registros de caudal y satelitales relativos a la crecida del Ega de diciembre de 2021. Los resultados obtenidos pretenden servir de ayuda para reducir el riesgo de inundación en este sector

Identifying the Factors behind Climate Diversification and Refugial Capacity in Mountain Landscapes: The Key Role of Forests

Recent studies have shown the importance of small-scale climate diversification and climate microrefugia for organisms to escape or suffer less from the impact of current climate change. These situations are common in topographically complex terrains like mountains, where many climate-forcing factors vary at a fine spatial resolution. We investigated this effect in a high roughness area of a southern European range (the Pyrenees), with the aid of a network of miniaturized temperature and relative humidity sensors distributed across 2100 m of elevation difference. We modeled the minimum (Tn) and maximum (Tx) temperatures above- and below-ground, and maximum vapor pressure deficit (VPDmax), as a function of several topographic and vegetation variables derived from ALS-LiDAR data and Landsat series. Microclimatic models had a good fit, working better in soil than in air, and for Tn than for Tx. Topographic variables (including elevation) had a larger effect on above-ground Tn, and vegetation variables on Tx. Forest canopy had a significant effect not only on the spatial diversity of microclimatic metrics but also on their refugial capacity, either stabilizing thermal ranges or offsetting free-air extreme temperatures and VPDmax. Our integrative approach provided an overview of microclimatic differences between air and soil, forests and open areas, and highlighted the importance of preserving and managing forests to mitigate the impacts of climate change on biodiversity. Remote-sensing can provide essential tools to detect areas that accumulate different factors extensively promoting refugial capacity, which should be prioritized based on their high resilience

An empirical assessment of the potential of post-fire recovery of tree-forest communities in Mediterranean environments

The accumulation of fuel and the homogenization of the landscape in Mediterranean forests are leading to an increasingly hazardous behavior of wildfires, fostering larger, more intense, severe, and frequent wildfires. The onset of climate change is intensifying this behavior, fostering the occurrence of extreme forest fires threatening the persistence of forest communities. In this study we present an assessment of the post-fire recovery potential of the most representative tree-forest communities affected by fire in Spain: Pinus halepensis, Pinus nigra, Pinus pinaster and Quercus ilex. A large database of field data collected during specific campaigns -carried out 25 years after the fire- is used in combination with remote sensing, forest inventory and geospatial data to build an empirical model capable of predicting the chances of recovery. The model, calibrated using Random Forest, combines information on burn severity (remote sensing estimates of the Composite Burn Index), local topography (slope and terrain aspect) and climatic data (mean values and trends of temperature and precipitation) to provide information on the degree of similarity (vegetation height, horizontal cover of the vegetation layer along vertical strata, aboveground biomass and species diversity) between the plots burned in the summer of 1994 and the unburned control. Overall, only 33 out of the 131 burned plots could be considered as recovered, that is, reaching a similar state to unburned stands in neighboring areas. Our results suggest a primary role played by burn severity (the higher the severity the lower the probability of recovery), but strongly modulated by local topographic features (higher probability of recovery on steep north-facing slopes). In turn, increasingly warm and wetter conditions increased the chance of recovery

Modelado geoestadístico del combustible forestal en paisajes de Pinus halepensis Mill. mediante datos LiDAR y de campo

Los incendios forestales constituyen una de las perturbaciones más importantes de los paisajes mediterráneos, causando graves afecciones tanto en los ecosistemas como en las poblaciones. Por ello, es necesario conocer el comportamiento del fuego sobre una masa forestal. En este sentido, los modelos de combustible proporcionan una valiosa información sobre la propagación de un eventual incendio, así como sobre la cantidad de biomasa existente. El presente trabajo aborda una estimación de modelos de combustible Prometheus para tres áreas de la Península Ibérica afectadas por grandes incendios forestales en 1994, considerando la zona calcinada, en regeneración, y su entorno inmediato. Los métodos empleados se han apoyado en medios tradicionales, como la recopilación de datos en campo en parcelas experimentales de Pinus halepensis Mill., y en tecnologías de la información geográfica, mediante la creación e implementación de una base de datos para el almacenamiento de la información de campo, y el uso de los SIG y de datos de teledetección LiDAR. Ello ha permitido generar cartografías de combustibles forestales a través del modelado geoestadístico, considerando como variables independientes las procedentes del trabajo de campo y de los registros LiDAR; los mejores resultados se han obtenido mediante el modelo no paramétrico Support Vector Machine (Accuracy coef. 0,68). Tales resultados constituyen una herramienta poderosa para la gestión forestal y territorial

Fuel Type Classification Using Airborne Laser Scanning and Sentinel 2 Data in Mediterranean Forest Affected by Wildfires

Mediterranean forests are recurrently affected by fire. The recurrence of fire in such environments and the number and severity of previous fire events are directly related to fire risk. Fuel type classification is crucial for estimating ignition and fire propagation for sustainable forest management of these wildfire prone environments. The aim of this study is to classify fuel types according to Prometheus classification using low-density Airborne Laser Scanner (ALS) data, Sentinel 2 data, and 136 field plots used as ground-truth. The study encompassed three different Mediterranean forests dominated by pines (Pinus halepensis, P. pinaster y P. nigra), oaks (Quercus ilex) and quercus (Q. faginea) in areas affected by wildfires in 1994 and their surroundings. Two metric selection approaches and two non-parametric classification methods with variants were compared to classify fuel types. The best-fitted classification model was obtained using Support Vector Machine method with radial kernel. The model includes three ALS and one Sentinel-2 metrics: the 25th percentile of returns height, the percentage of all returns above mean, rumple structural diversity index and NDVI. The overall accuracy of the model after validation was 59%. The combination of data from active and passive remote sensing sensors as well as the use of adapted structural diversity indices derived from ALS data improved accuracy classification. This approach demonstrates its value for mapping fuel type spatial patterns at a regional scale under different heterogeneous and topographically complex Mediterranean forests

Identificación de micro-refugios mediante modelos topoclimáticos de alta resolución en el P.N. de Ordesa y Monte Perdido.

En el presente contexto de cambio climático resulta necesario conocer tanto las áreas más susceptibles como las más resistentes a la pérdida de biodiversidad, de ahí la importancia de identificar micro-refugios climáticos. Para ello es imprescindible disponer de una caracterización climática detallada del área de estudio. A partir de la programación de rutinas en entorno R, en este trabajo se han generado modelos topoclimáticos de alta resolución (5 m) en una de las áreas más complejas orográficamente y con mayor biodiversidad de Europa: el Parque Nacional de Ordesa y Monte Perdido. Se obtuvieron registros de temperatura durante tres años por 73 sensores de campo miniaturizados (iButton®) y se modelizaron las temperaturas máximas y mínimas medias mensuales de un año-tipo. Paralelamente, se generaron diversas variables topográficas y ambientales derivadas de LiDAR que permitieron predecir la variabilidad climática local. Mediante ajustes de modelos lineares, en todos los casos los modelos topoclimáticos fueron más explicativos que los que sólo incluyeron la altitud. La espacialización de los modelos sirvió para generar un “Refugia Index” (RI) para escenarios de calidez o frialdad extremas. Este índice permitió identificar los refugios en ambos tipos de escenarios, y se obtuvo además un índice combinado que destaca los refugios fríos y cálidos independientemente del escenario. El índice combinado resultó ser una variable explicativa muy significativa al analizar la proporción de plantas adaptatas al frío y al calor recogidas en más de 700 inventarios florísticos.<br /

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world\u27s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Assessing GEDI-NASA system for forest fuels classification using machine learning techniques

Identification of forest fuels is a key step for forest fire prevention since they provide valuable information of fire behavior. This study assesses NASA’s Global Ecosystem Dynamics Investigation (GEDI) system to classify fuel types in Mediterranean environments according to the Prometheus model in a forested area of NE Spain. We used 59,554 GEDI footprints and extracted variables related to height metrics, canopy profile metrics, and aboveground biomass density estimates from products L2A, L2B, and L4A, respectively. Four quality filters were applied to discard high uncertainty data, reducing the initial footprints to 9,703. Spectral indices from Landsat-8 OLI scenes were created to test the effect of their integration with GEDI variables on fuel types estimation. Ground-truth data were comprised of Prometheus fuel types estimated in two previous studies. Only the types that matched in each GEDI footprint in both studies were used, resulting in a final sample of 1,112 footprints. Spearman’s correlation coefficient, Kruskal-Wallis and Dunn’s tests determined the variables to be included in the classification models: the relative height at the 85th percentile, the Plant Area Index, and the Aboveground Biomass Density from GEDI, and the brightness from Landsat-8 OLI. Best performances were achieved with Random Forest (RF) and Support Vector Machine with radial kernel (SVM-R), which were lower including only GEDI variables (accuracies: RF and SVM-R = 61.54 %) than integrating the brightness from Landsat-8 OLI (accuracies: RF = 83.71 %, SVM-R = 81.90 %). These results allow validating GEDI for fuel type classification of Prometheus model, constituting a promising information for forest management over large areas