Mapping wildfire danger at regional scale with an index model integrating coarse spatial resolution remote sensing data

Abstract

[1] Wildfires are a prevalent natural hazard in the south of France. Planners need a permanent fire danger assessment valid for several years over a territory as large and heterogeneous as Midi-Pyrénées region. To this end, we developed an expert knowledgebased index model adapted to the specific features of the study area. The fire danger depends on two complementary elements: spatial occurrence and fire intensity. Among the GIS layers identified as input variables for modeling, vegetation fire susceptibility is one of the most influent. However, the main difficulty at this scale is the scarcity or the lack of exhaustiveness of the data. In this respect, remote sensing imagery is capable of providing relevant information. We proposed to calculate an annual relative greenness index (annual RGRE) that reflects vegetation dryness in summer. We processed times series of Normalized Difference Vegetation Index (NDVI) from SPOT-VEGETATION images over the last six available years (1998 to 2003). The first step was to verify that these images characterize vegetation types and highlight intraannual and interannual response variability. It is then possible to identify phenological stages corresponding to the maximum NDVI (and therefore to maximum photosynthetic activity) during the growing season, the minimum NDVI at the end of the growing season and the minimum NDVI during winter period. These phenology metrics ground the annual RGRE calculation. Values obtained for each observation year show significant correlation (r 2 = 0.70) with the De Martonne aridity index calculated for the same period. A synthesis of yearly index was integrated in the model as a variable that expresses fire susceptibility.Citation: Chéret, V., and J. P. Denux (2007), Mapping wildfire danger at regional scale with an index model integrating coarse spatial resolution remote sensing data,

Analysis of MODIS NDVI Time Series to Calculate Indicators of Mediterranean Forest Fire Susceptibility

“…All the study area was submitted to summer drought to various extents. The annual Relative Greenness Index (annual RGRE) was previously used to map vegetation status for fire danger assessment in a non-Mediterranean context (Chéret and Denux, 2007). For this study, we proposed an annual RGRE index adjusted to the vegetative growing season observed for this Mediterranean site.…”

“…As input data for static risk models, high-spatial-resolution images can be processed to map fuel and ignition parameters directly or by identifying vegetation types (Chuvieco et al, 2003b). Complementarily, coarse-spatial-resolution data can be useful in assessment of the fuel model (Burgan et al, 1998;Chéret and Denux, 2007). Dynamic approaches of fire risk can integrate frequent acquisition of images, usually at coarse spatial resolution, to map daily updates of vegetation status.…”

“…A yearly version of the RGRE was developed by the authors, and this annual RGRE is designed to map the drought intensity during summer (Chéret and Denux, 2007). A positive correlation was found between the sum of the NDVI and Canadian Forest Weather Indices (Leblon et al, 2001).…”

Remote sensing information for fire management and fire effects assessment

“…In this special section, several papers explore the use of satellite remote sensing data for mapping of vegetation cover for use in wildfire danger assessment. Cheret and Denux [2007] developed an approach for assessing wildfire danger using maps of vegetation cover derived from an analysis of coarse resolution SPOT‐Vegetation data. These data were combined with information on topography, fire ignitions, and climatic indices, derived from analysis of time series SPOT‐Vegetation data (see section 2.2.2 below).…”

Use of ENVISAT ASAR Global Monitoring Mode to complement optical data in the mapping of rapid broad-scale flooding in Pakistan

“…Even at such a time when technological advancement in data processing, storage and communication enables ever higher spatial resolutions from airborne and satellite sensors, the use of coarser resolution data still has a very firm place in the remote sensing field where broad-scale monitoring is required, such as the monitoring of algal blooms (Ahn and Shanmugam, 2006), assessing risks of fire (Chéret and Denux, 2007;A. and R., 2008) or drought (Rojas et al, 2011), the development of land surface models (Jarlan et al, 2005), the assessment of animal stocking rates (Hunt and Miyake, 2006) and the mapping of shorelines (M. -Muslim et al, 2007).…”