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.

No abstract

Aim This paper presents a map of global fire vulnerability, estimating the potential damage of wildland fires to global ecosystems. Location Global scale at 0.5° grid resolution. Methods Three vulnerability factors were considered: ecological richness and fragility, provision of ecosystem services and value of houses in the wildland–urban interface. Each of these factors was estimated from existing global databases. Ecological values were estimated from biodiversity relevance, conservation status and fragmentation based on Olson's ecoregions. The ecological regeneration delay was estimated from adaptation to fires and soil erosion potential. The former was assessed by comparing actual land cover with fire‐off simulations based on a dynamic global vegetation model (ORCHIDEE). The annual loss of ecosystem services was estimated with values transferred from other studies and loss coefficients. This was integrated throughout time by considering the regeneration delay. Value of houses was estimated at country level according to the market prices of real‐estate and land, the level of economic development and the population density. Economic and ecological evaluations were merged through cross‐tabulation logic to obtain qualitative ranks of fire vulnerability. Results The most vulnerable areas were found to be the rain forest of the Amazon Basin, Central Africa and Southeast Asia, the temperate forest of Europe, South America and north‐east America, and the ecological corridors of Central America and Southeast Asia. The lowest vulnerability was observed in boreal regions, particularly those already affected by fires or having low biodiversity, agricultural regions of Australia, India, Latin America and Central Asia. Main conclusions This is the first attempt to produce a map of global fire vulnerability, based on a wide variety of factors related to the impacts of fire on ecological and socio‐economic values. This product will help current efforts to model future scenarios of the impacts of biomass burning for different climate and land‐use scenarios.

Wildfires cause disturbances in ecosystems and generate environmental, economic, and social costs. Studies focused on vegetation regeneration in burned areas acquire interest because of the need to understand the species dynamics and to apply an adequate restoration policy. In this work we intend to study the variables that condition short-term regeneration (5 years) of three species of the genus Pinus in the Mediterranean region of the Iberian Peninsula. Regeneration modelling has been performed through multiple regressions, using Ordinary Least Squares (OLS) and Geographic Weight Regression (GWR). The variables used were fire severity, measured through the Composite Burn Index (CBI), and a set of environmental variables (topography, post-fire climate, vegetation type, and state after fire). The regeneration dynamics were measured through the Normalized Difference Vegetation Index (NDVI) obtained from Landsat images. The relationship between fire severity and regeneration dynamics showed consistent results. Short-term regeneration was slowed down when severity was higher. The models generated by GWR showed better results in comparison with OLS (adjusted R 2 = 0.77 for Pinus nigra and Pinus pinaster; adjusted R 2 = 0.80 for Pinus halepensis). Further studies should focus on obtaining more precise variables and considering new factors which help to better explain post-fire vegetation recovery.

Abstract:The "land use" concept has evolved during recent decades and it is now considered as the socioeconomic function of land. Land use representation and land use change assessment through remote sensing still remains one of the major challenges for the remote sensing scientific community. In this paper we present a methodological approach based on remote sensing techniques to assess land use in accordance with the requirements of the United Nations Framework Climate Change Convention, UNFCCC (1995). The methodology is based mainly on the recognition of the land key elements and their function and on the adoption of the "predominant land use" criteria in the classification scheme settled by rules. The concept that underpins these rules is that the land use function of land can be expressed through hierarchical relationships among key land elements, and that these functional relationships are based on thresholds reflecting the relevance and predominance of key land elements in the observed area. When analyses are supported by high (10-30 m) or very high (<10 m) spatial resolution remote sensing data, the methodology provides a systematic approach for the representation of land use that is consistent with the concepts and methodologies developed by the International Panel on Climate Change(IPCC) to fulfill UNFCCC commitments. In particular, data with high and very high spatial resolution provide good results, with overall accuracies above 87% in the identification of key land elements that characterize land use classes. The methodology could be used to assess land use in any context (e.g., for any land use category or in any country and region) as it is OPEN ACCESS Remote Sens. 2012, 4 1025 based on the definition of user/project rules that should be tailored on the land use function of any territory.