A global wildfire dataset for the analysis of fire regimes and fire behaviour

Abstract

Global fire monitoring systems are crucial to study fire behaviour, fire regimes and their impact at the global scale. Although global fire products based on the use of Earth Observation satellites exist, most remote sensing products only partially cover the requirements for these analyses. These data do not provide information like fire size, fire spread speed, how fires may evolve and joint into single event, or the number of fire events for a given area. This high level of abstraction is very valuable; it makes it possible to characterize fires by types (either size, spread, behaviour, etc.). Here, we present and test a data mining work flow to create a global database of single fires that allows for the characterization of fire types and fire regimes worldwide. This work describes the data produced by a data mining process using MODIS burnt area product Collection 6 (MCD64A1). The entire product has been computed until the present and is available under the umbrella of the Global Wildfire Information System (GWIS).

Fire Activity in Mediterranean Forests (The Algerian Case)

“…The mountainous part of the northern coastline ( Figure 1 and Figure S1) has a humid and subhumid climate and it consists mostly of forests, infrastructures, and inhabitants. Fires lead to the fragmentation or disappearance of forest habitats, with an annual decrease rate of 0.5% of the Algerian forest area being recorded from 1990 to 2010 [18]. The central area is subarid, and the southernmost arid area displays intense degradation of vegetation (steppes), and desertification [13] and it is very unsuitable for wildfire.…”

“…Therefore, we used two MODIS fire products at 1-km and 500-m spatial resolution for the 2000-2019 period (Table S1). First, we used the GlobFire dataset [18] based on the fire hotspots MCD14ML series processed in the Collection 5 temporal thermal analysis active fire dataset with data from both the TERRA and AQUA satellite (NASA-Fire Information for Resource Management System (FIRMS), 2015). We downloaded this daily set ranging from 2000 to 2019 using the FIRMS website (Fire Information for Resource Management System: Archiving and Distributing MODIS Active Fire Data, Collection 5).…”

“…For that purpose, we used MODIS (Moderate-Resolution Imagery Spectrometer) fire hotspots and burned areas (MCD64A1v006). Remote-sensing products are increasingly abundant and accurate, and this provides valuable spatial-temporal information [18][19][20] on the fire regimes across Northern Africa, in a context where field-based fire data is often biased [5,6].…”

Assessing the accuracy of remotely-sensed fire datasets across the Southwestern Mediterranean basin

“…The Fire Atlas (Andela et al, 2019a(Andela et al, , 2019b, FRY (Laurent et al, 2018a(Laurent et al, , 2018b and GlobFire (Artés et al, 2019;Artés Vivancos and San-Miguel-Ayanz, 2018) represent the most recent RS individualized fire datasets. These datasets were built from specific algorithms to reconstruct fire patches from MCD64A1 pixel-based BA.…”

“…Previous studies indicated that rigorous evaluation of satellite estimates with ground-based data is needed to assess the reliability of the RS information at regional scale (Turco et al, 2019). Most validation procedures of these RS datasets were based on comparisons between different satellite products (Andela et al, 2019b;Laurent et al, 2018a), with however scarce attention to independent ground-based observations (Artés et al, 2019). In this work, we compared the three most recent RS datasets of individual fires (Fire Atlas, FRY and GlobFire) with highquality fire databases compiled by regional agencies across the most active fire region in Europe (i.e.…”