No abstract

No abstract

Fires are a major contributor to atmospheric budgets of greenhouse gases and aerosols, affect soils and vegetation properties, and are a key driver of land use change. Since the 1990s, global burned area (BA) estimates based on satellite observations have provided critical insights into patterns and trends of fire occurrence. However, these global BA products are based on coarse spatial-resolution sensors, which are unsuitable for detecting small fires that burn only a fraction of a satellite pixel. We estimated the relevance of those small fires by comparing a BA product generated from Sentinel-2 MSI (Multispectral Instrument) images (20-m spatial resolution) with a widely used global BA product based on Moderate Resolution Imaging Spectroradiometer (MODIS) images (500 m) focusing on sub-Saharan Africa. For the year 2016, we detected 80% more BA with Sentinel-2 images than with the MODIS product. This difference was predominately related to small fires: we observed that 2.02 Mkm2 (out of a total of 4.89 Mkm2) was burned by fires smaller than 100 ha, whereas the MODIS product only detected 0.13 million km2 BA in that fire-size class. This increase in BA subsequently resulted in increased estimates of fire emissions; we computed 31 to 101% more fire carbon emissions than current estimates based on MODIS products. We conclude that small fires are a critical driver of BA in sub-Saharan Africa and that including those small fires in emission estimates raises the contribution of biomass burning to global burdens of (greenhouse) gases and aerosols.

Fire risk assessment should take into account the most relevant components associated to fire occurrence. To estimate when and where the fire will produce undesired effects, we need to model both (a) fire ignition and propagation potential and (b) fire vulnerability. Following these ideas, a comprehensive fire risk assessment system is proposed in this paper, which makes extensive use of geographic information technologies to offer a spatially explicit evaluation of fire risk conditions. The paper first describes the conceptual model, then the methods to generate the different input variables, the approaches to merge those variables into synthetic risk indices and finally the validation of the outputs. The model has been applied at a national level for the whole Spanish Iberian territory at 1-km2 spatial resolution. Fire danger included human factors, lightning probability, fuel moisture content of both dead and live fuels and propagation potential. Fire vulnerability was assessed by analysing values-at-risk and landscape resilience. Each input variable included a particular accuracy assessment, whereas the synthetic indices were validated using the most recent fire statistics available. Significant relations (P < 0.001) with fire occurrence were found for the main synthetic danger indices, particularly for those associated to fuel moisture content conditions.

This paper presents methods to generate fuel type maps from remote sensing data at a spatial and temporal scale adequate for operational fire management applications. Fuel type maps account for structural characteristics of vegetation related to fire behaviour and fire propagation. A fuel type classification system adapted to the ecological characteristics of the European Mediterranean basin was adopted for this study. The Cabañeros National Park (in central Spain) area was selected for testing and validating the methods. Fuel type maps were derived from two Landsat TM satellite images and digital elevation data. Atmospheric and topographic corrections of the satellite images were performed to reduce spectral variability. A sensitivity analysis was carried out to determine the most appropriate bands for fuel type mapping. The final classification was checked by an intense field survey, the final classification accuracy being estimated at 83%. The main problem was discriminating among those fuel types that differ only in vegetation height or composition of the understory layer. The mean mapping accuracy was 15 m (0.6 pixels), and no areal discrepancy or boundary displacement with vegetation maps was apparent.Résumé : Cet article présente des méthodes pour générer des cartes de types de combustible à partir de données de té-lédétection à des échelles spatiale et temporelle adéquates pour être utilisées dans la gestion opérationnelle des feux. Les cartes de types de combustible tiennent compte des caractéristiques structurales de la végétation en lien avec le comportement et la propagation du feu. Un système de classification des types de combustible adapté aux caractéristi-ques écologiques du bassin de la Méditérannée européenne a été adopté pour cette étude. La région du parc national Cabañeros (dans le centre de l'Espagne) a été choisie pour tester et valider les méthodes. Les cartes de types de combustible ont été produites à partir de deux images satellitaires Landstat capteur TM et de données digitales d'altitude. Les images satellitaires ont été corrigées pour tenir compte des conditions atmosphériques et topographiques afin de ré-duire la variabilité spectrale. Une analyse de sensibilité a été effectuée pour déterminer quelles bandes sont les plus appropriées pour cartographier les types de combustible. La classification finale a été vérifiée par un inventaire intensif sur le terrain et sa précision a été estimée à 83 %. La principale difficulté consiste à distinguer les types de combustible qui diffèrent seulement par la hauteur ou la composition de la végétation de sous-étage. La précision moyenne de la cartographie est de 15 m (0,6 pixels) et aucune divergence dans la superficie ni modification des contours ne sont apparentes comparativement aux cartes de végétation.[Traduit par la Rédaction] Riaño et al. 1315

Abstract:A new supervised burned area mapping software named BAMS (Burned Area Mapping Software) is presented in this paper. The tool was built from standard ArcGIS TM libraries. It computes several of the spectral indexes most commonly used in burned area detection and implements a two-phase supervised strategy to map areas burned between two Landsat multitemporal images. The only input required from the user is the visual delimitation of a few burned areas, from which burned perimeters are extracted. After the discrimination of burned patches, the user can visually assess the results, and iteratively select additional sampling burned areas to improve the extent of the burned patches. The final result of the BAMS program is a polygon vector layer containing three categories: (a) burned perimeters, (b) unburned areas, and (c) non-observed areas. The latter refer to clouds or sensor observation OPEN ACCESSRemote Sens. 2014, 6 12361 errors. Outputs of the BAMS code meet the requirements of file formats and structure of standard validation protocols. This paper presents the tool's structure and technical basis. The program has been tested in six areas located in the United States, for various ecosystems and land covers, and then compared against the National Monitoring Trends in Burn Severity (MTBS) Burned Area Boundaries Dataset.

This paper presents results of the AQL2004 project, which has been develope within the GOFC-GOLD Latin American network of remote sensing and forest fires (RedLatif). The project intended to obtain monthly burned-land maps of the entire region, from Mexico to Patagonia, using MODIS (moderate-resolution imaging spectroradiometer) reflectance data. The project has been organized in three different phases: acquisition and preprocessing of satellite data; discrimination of burned pixels; and validation of results. In the first phase, input data consisting of 32-day composites of MODIS 500-m reflectance data generated by the Global Land Cover Facility (GLCF) of the University of Maryland (College Park, Maryland, U.S.A.) were collected and processed. The discrimination of burned areas was addressed in two steps: searching for "burned core" pixels using postfire spectral indices and multitemporal change detection and mapping of burned scars using contextual techniques. The validation phase was based on visual analysis of Landsat and CBERS (China-Brazil Earth Resources Satellite) images. Validation of the burned-land category showed an agreement ranging from 30% to 60%, depending on the ecosystem and vegetation species present. The total burned area for the entire year was estimated to be 153 215 km2. The most affected countries in relation to their territory were Cuba, Colombia, Bolivia, and Venezuela. Burned areas were found in most land covers; herbaceous vegetation (savannas and grasslands) presented the highest proportions of burned area, while perennial forest had the lowest proportions. The importance of croplands in the total burned area should be taken with reserve, since this cover presented the highest commission errors. The importance of generating systematic products of burned land areas for different ecological processes is emphasized.

Four burned area tools were implemented in Google Earth Engine (GEE), to obtain regular processes related to burned area (BA) mapping, using medium spatial resolution sensors (Landsat and Sentinel-2). The four tools are (i) the BA Cartography tool for supervised burned area over the user-selected extent and period, (ii) two tools implementing a BA stratified random sampling to select the scenes and dates for validation, and (iii) the BA Reference Perimeter tool to obtain highly accurate BA maps that focus on validating coarser BA products. Burned Area Mapping Tools (BAMTs) go beyond the previously implemented Burned Area Mapping Software (BAMS) because of GEE parallel processing capabilities and preloaded geospatial datasets. BAMT also allows temporal image composites to be exploited in order to obtain BA maps over a larger extent and longer temporal periods. The tools consist of four scripts executable from the GEE Code Editor. The tools’ performance was discussed in two case studies: in the 2019/2020 fire season in Southeast Australia, where the BA cartography detected more than 50,000 km2, using Landsat data with commission and omission errors below 12% when compared to Sentinel-2 imagery; and in the 2018 summer wildfires in Canada, where it was found that around 16,000 km2 had burned.