Patricia Oliva

As the MODIS era approaches its operational end, maintaining the long-term consistency of global burned area (BA) records is essential for fire-climate research, emissions assessment and climate applications. Here, we present MRBA60, a harmonised global monthly BA dataset designed to ensure continuity between MODIS-based FireCCI51 and Sentinel-3-based FireCCIS311.MRBA60 should be used as a combined time series: MRBA60H provides the harmonised historical component for 2003-2018, while FireCCIS311 provides the continuation from 2019 onwards. The combination of both components is referred to as MRBA60, providing a continuous global BA record at 0.25°. For the historical period, biome- and month-specific Random Forest models were trained during the 2019-2024 overlap between FireCCI51 and FireCCIS311, using auxiliary predictors related to active fires, fire radiative power, climate, vegetation and spatial gradients. Model outputs were then adjusted through quantile mapping to better reproduce the FireCCIS311 BA distribution, while preserving historical extreme events not represented in the calibration period.During 2003-2024, MRBA60 estimates a mean global BA of approximately 5.5 Mkm² yr⁻¹, compared with 4.4 Mkm² yr⁻¹ from FireCCI51. This represents a cumulative increase of ~24.5 Mkm² relative to FireCCI51, equivalent to ~1.1 Mkm² additional BA per year and a relative increase of 25%. Differences are seasonally heterogeneous: the largest increases occur in Sep-Oct-Nov and Mar-Apr-May, while Jun-Jul-Aug shows the smallest differences. Despite this increase in absolute BA magnitude, MRBA60 preserves the long-term global decline in burned area, with a cumulative reduction of -25.1% between 2003 and 2024, compared with -33.3% estimated by FireCCI51. Overall, MRBA60 provides a traceable and physically consistent time series for analysing BA trends across the MODIS-Sentinel transition, and is designed to support studies of fire regimes, climate-model evaluation, fire-climate attribution, emissions estimation and ecological impact assessment. MRBA60H is available through CEDA: https://catalogue.ceda.ac.uk/uuid/db75c5f51ee240ae8743355dcebbb9b9/. FireCCIS311 is available through CEDA: https://catalogue.ceda.ac.uk/uuid/da8e669a74334c82a56e0b470bc4ef04/.Acknowledgements: This work was developed within the framework of the European Space Agency Fire Climate Change Initiative (FireCCI) project, contract Nº 4000126706/19/I-NB. We acknowledge CEDA for data archiving and distribution, and the providers of the satellite, climate and validation datasets used in the development and evaluation of MRBA60.

El Escaneo Láser Terrestre (TLS) permite estimar la distribución en el dosel de variables estructurales forestales como LAI y LAD. Sin embargo, la fusión de múltiples escaneos puede eliminar la perspectiva original de adquisición, limitando modelos basados en ángulos. Este trabajo propone un enfoque geométrico que reconstruye las posiciones de escaneo mediante el cono ciego del trípode, combinado con indexación angular adaptativa (AIX) y trazado angular con propagación kNN 3D. El método posicionó el escaneo con un RMSE medio de 2 cm y redujo el espacio de búsqueda al 0,055 ± 0,01% de la nube, .

En este trabajo se evalúan las estimaciones de biomasa consumida, aérea total y foliar, y del factor de eficiencia de quemado mediante el uso de datos LiDAR aeroportados y datos GEDI integrados con datos Sentinel-1 y Sentinel-2. Ambos enfoques mostraron patrones espaciales similares, aunque la estimación de biomasa consumida varió considerablemente debido a las distintas fracciones de biomasa consideradas. La estimación de biomasa aérea consumida con datos LiDAR aeroportados fue de 138 Gg y con un valor de eficiencia de quemado de 0.38. La estimación de biomasa foliar consumida con datos GEDI, Sentinel-1 y Sentinel-2, considerando la biomasa foliar como combustible disponible, fue de 14 Gg, y el valor de eficiencia de quemado obtenido fue de 0.32.

El aumento reciente de grandes incendios bajo condiciones de sequía intensa ha reforzado la necesidad de contar con indicadores espaciales y actualizables del estado hídrico del combustible. En este estudio se evaluó la utilidad del Índice Diferencial Normalizado de Agua (NDWI), derivado de Sentinel-3, para caracterizar las condiciones previas a incendios extremos, comparándolo con el Índice Estandarizado de Precipitación-Evapotranspiración a 3 meses (SPEI-3) como referencia de sequía meteorológica. El análisis se centró en diez grandes incendios (>10.000 ha) ocurridos en agosto de 2025 en el sector noroccidental de la península Ibérica. Se compararon tres ámbitos espaciales: el interior de los perímetros quemados, un buffer externo de 5 km y zonas alejadas utilizadas como control. Los resultados muestran un deterioro sostenido de las condiciones hídricas durante el verano. El SPEI-3 evolucionó hacia valores más negativos, alcanzando niveles severos a extremos (≤ -2) en las dos últimas semanas de agosto. El NDWI descendió gradualmente desde junio y presentó una caída abrupta al final de agosto dentro de los perímetros, más moderada en el buffer y menos marcada en las zonas alejadas. La relación entre NDWI y SPEI-3, evaluada mediante el coeficiente de correlación de Pearson, fue mayor en los perímetros (0,68) y en el buffer (0,64) que en las zonas alejadas (0,51).

The Synthetic Aperture Radar Interferometry (InSAR) technique enables researchers to generate Digital Elevation Models (DEMs) from SAR data, which researchers widely apply in multi-temporal analyses, including ground deformation monitoring, susceptibility mapping, and analysis of spatial changes in erosion basins. In this study, we generated two interferometric DEMs from Sentinel-1 (S1, VV polarization) and TerraSAR-X (TSX, HH polarization, ascending orbit) data, processed in SNAP, over a mountainous sector of the central Andes in Chile. We assessed the accuracy of the DEMs against two reference datasets: the SRTM DEM and a high-resolution LiDAR-derived DEM. We selected 150 randomly distributed points across different slope classes to compute statistical metrics, including RMSE and MedAE. Relative to the LiDAR DEM, both sensors yielded rMSE values of approximately 20 m, increasing to 23–24 m when compared with the SRTM DEM. The MedAE, a metric less sensitive to outliers, was 3.97 m for S1 and 3.26 m for TSX with respect to LiDAR, and 7.07 m for S1 and 7.49 m for TSX relative to SRTM. We observed a clear positive correlation between elevation error and terrain slope. In areas with slopes greater than 45°, the MedAE exceeded 14 m relative to the LiDAR DEM and reached ~15 m relative to the SRTM for both S1 and TSX.

The Northwest of the Iberian Peninsula is one of the European regions with the highest frequency of forest fires. However, in the last decade fires in this region have burned larger areas and later in the fire season. Assessing the damage caused by fire and the pollutants released in the burning process is important to understand the effects on ecosystems and the carbon cycle, the recurrence of fires, and the effect on human health. In this work, we performed the estimation of emissions released in Galicia (Northwest Spain) in the last six years combining existing ESA CCI products. To quantify the area burned, we used the products from the Burned Area Algorithm developed within the Fire Climate Change Initiative (FireCCI) project. Then, the characterization and quantification of the total biomass were obtained from the Biomass CCI project at 100 m resolution by extracting the mean biomass by vegetation type from CORINE Land cover 2018. The burning efficiency factor was fitted using burn severity estimates from the dNBR calculation on the Sentinel-2 data. The emissions factors were selected from the literature. Our results show that during the last few years, there is a positive trend of annual emissions in Galicia. The sporadic maximums were registered in the years 2017 and 2022 when the climatic conditions aggravated the fire behaviour. In addition, Galicia is the region of Spain that registers the highest average estimates of emissions from fires since a high percentage of the affected area is occupied by pine and eucalyptus forests. These emissions contribute to a drastic decrease in air quality influencing the climate and affecting public health. Finally, we verified that adapting the burning efficiency factors to the specific conditions of the affected ecosystem generates more precise emission estimates.

In January 2017, 114 active fires burned throughout Chile at the same time. These fires spread quickly due to high temperatures, fast dry winds, and low vegetation water content. The fire events burned more than 570,000 ha, from which 20% of the area was endangered native forest. Timely and accurate burned area mapping is crucial for the evaluation of damages and management of the affected areas. As Chile is a diverse country with many types of ecosystems and vegetation, the use of novel spectral indices may improve the accuracy of satellite data-based burned area mapping algorithms. In this study, we explored the contribution of band angle indices (BAnI) to burned area mapping. The BAnI are based on trigonometric equations that proved to be sensitive to moisture conditions. Then, we aimed to test their sensitivity to the burned area spectral signature. We used Sentinel-2 data at 20 m resolution to calibrate and implement a random forest (RF) classifier in Google Earth Engine (GEE) computing platform. We ran the RF classifier with and without BAnI to evaluate their potential to identify burned areas and performed two accuracy assessments comparing the results with visually digitized fire perimeters from (1) WorldView 3 (WV3) images, and (2) Sentinel at 10 m resolution. We determined that both BA classifications were more accurate than the perimeters created by the Chilean National Forest Corporation (CONAF), which overestimates the area burnt. The overestimation of CONAF perimeters is produced by considering as burned the inner unburned areas and omitting some small, burned areas. The first assessment showed no significant differences between the two RF classifications. However, the second validation showed lower omission and commission errors for the RF classifier with the BAnI (5 and 17.8%, respectively). On the other hand, comparing both BA classifications with and without BAnI, we observed differences in the spatial distribution of the errors. However, the RF classification with BAnI offered fewer commission errors located in agricultural areas. The burned area algorithms developed in GEE showed their potential to map the fire-affected area quickly, efficiently, and accurately, accounting for all the areas burned in the season, including the small and agricultural fires the official perimeters did not consider.