Forest fires in Galicia have become a serious environmental problem over the years. This is especially the case in the Pontevedra region, where in October 2017 large fires (>500 hectares) burned more than 15,000 Ha. In addition to the area burned being of relevance, it is also very important to know quickly and accurately the different severity degrees that soil has suffered in order to carry out an optimal restoration campaign. In this sense, the use of remote sensing with the Sentinel-2 and Landsat-8 satellites becomes a very useful resource due to the variations that appear in soil after a forest fire (changes in soil cover are noticeably appreciated with spectral information). To calculate these variations, the spectral indices NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) are used, both before and after the fire and their differences (dNBR and dNDVI, respectively). In addition, as a reference for a correct discrimination between severity degrees, severity data measured in situ after the fire are used to classified at 5 levels of severity and 6 levels of severity. Therefore, this study aims to establish a methodology, which relates remote-sensing data (spectral indices) and severity degrees measured in situ. The R² statistic and the pixel classification accuracy results show the existing synergy of the Sentinel-2 dNBR index with the 5 severity degrees classification (R² = 0.74 and 81% of global accuracy) and, for this case, the good applicability of remote sensing in the forest fire field.

The characterization of aboveground biomass is important in forest management planning, with various objectives ranging from prevention of forest fires to restoration of burned areas, especially in fire-prone regions such as NW Spain. Although remotely sensed data have often been used to assess the recovery of standing aboveground biomass after perturbations, the data have seldom been validated in the field, and different shrub fractions have not been modelled. The main objective of the present study was to assess different vegetation parameters (cover, height, standing AGB and their fractions) in field plots established in five areas affected by wildfires between 2009 and 2016 by using Sentinel-2 spectral indices and LiDAR metrics. For this purpose, 22 sampling plots were established in 2019, and vegetation variables were measured by a combination of non-destructive measurement (cover and height) and destructive sampling (total biomass and fine samples of live and dead fractions of biomass).The structural characterization of gorse shrublands was addressed, and models of shrub cover—height, total biomass, and biomass by fraction and physiological condition—were constructed, with adjusted coefficients of determination ranging from 0.6 to 0.9. The addition of LiDAR data to optical remote sensing images improved the models. Further research should be conducted to calibrate the models in other vegetation communities.

The objective of this article is to create soil burn severity maps to serve as field support for erosion tasks after forest fire occurrence in Spain (2017–2022). The Analytical Spectral Device (ASD) FieldSpec and the CIMEL CE-312 radiometers (optical and thermal, respectively) were used as input data to establish relationships between soil burn severity and reflectance or emissivity, respectively. Spectral indices related to popular forest fire studies and soil assessment were calculated by Sentinel-2 convolved reflectance. All the spectral indices that achieve the separability index algorithm (SI) were validated using specificity, sensitivity, accuracy (ACC), balanced accuracy (BACC), F1-score (F1), and Cohen’s kappa index (k), with 503 field plots. The results displayed the highest overall accuracy results using the Iron Oxide ratio (IOR) index: ACC = 0.71, BACC = 0.76, F1 = 0.63 and k = 0.50, respectively. In addition, IOR was the only spectral index with an acceptable k value (k = 0.50). It is demonstrated that, together with NIR and SWIR spectral bands, the use of blue spectral band reduces atmospheric interferences and improves the accuracy of soil burn severity mapping. The maps obtained in this study could be highly valuable to forest agents for soil erosion restoration tasks.

Con la llegada del sensor MTG-FCI (Meteosat de Tercera Generación - Flexible Combined Imager), nuestro sistema de recepción y procesado automático de datos se ha quedado obsoleto. Considerando la resolución espacial (1-2km en nadir) y temporal (10 minutos) de los nuevos datos, este procesado ha de ser eficiente y rápido, sin dejar de lado el rigor científico de los productos obtenidos. En este trabajo, presentamos rápidamente estos nuevos datos y el sistema de recepción actualizado. Además, presentamos la metodología diseñada para poder llevar a cabo la estimación de varios parámetros biofísicos, como índice de vegetación, temperatura de la superficie de tierra y de mar, así como la visualización (RGB) de los datos y la detección de incendios en un plazo inferior a 5 minutos después de la adquisición de la imagen. La producción en tiempo casi real de estos parámetros permitirá dar continuidad a la larga serie de productos MSG-SEVIRI (Meteosat Segunda Generación – Spinning Enhanced Visible and Infra-Red Imager) estimados de manera continua desde 2007.

SIRIUS es una misión candidata al programa SCOUT de la ESA concebida para avanzar en la comprensión de los procesos de la Isla de Calor Urbana (UHI) mediante adquisiciones nocturnas repetibles sobre ciudades seleccionadas y observaciones multiangulares con geometría de visión controlada. La misión se basa en un satélite pequeño y ágil equipado con un sensor compacto no refrigerado (Smart-TIRI) con tres bandas en el infrarrojo térmico centradas en 8.85, 10.6 y 12.0 µm, lo que permite la estimación conjunta de la temperatura y la emisividad de la superficie (LST/LSE) mediante el algoritmo de separación temperatura–emisividad (TES). Con un muestreo espacial de 64 m y un ancho de barrido de aproximadamente 64 km, SIRIUS generará productos específicamente orientados al análisis de la UHI desde el Nivel L1 hasta el Nivel L3 (LST/LSE, SUHI, UTFVI, detección de puntos calientes e índice de Disconfort térmico), en complementariedad con las misiones térmicas LSTM, TRISHNA y SBG.