Constructing a Comprehensive National Wildfire Database from Incomplete Sources: Israel as a Case Study

Abstract

In many regions, the frequency and extent of wildfires has increased in recent years, a trend which is expected to continue. Hence, there is a need for effective fire management strategies. Such strategies need to be based on accurate and complete data on vegetation condition and post-fire effects, collected in the field as well as by remote sensing approaches. Unfortunately, wildfire databases are often incomplete in terms of their spatial and temporal coverage, as well as the documentation of fire outcomes. The aim of this study was to devise a methodology to construct a comprehensive national wildfire database. We developed and tested this methodology for Israel, as a case study. The methodology combines data on wildfires in Israel from two sources: remote sensing and field data collected by governmental agencies, representing the period between 2015 and 2022. The resulting database contains 2276 wildfires larger than 10 ha with information (where and when available) on their occurrence date, fire duration, ignition cause, and more. The methodology presented here provides a blueprint for developing large-scale and comprehensive fire databases elsewhere, and facilitates future studies on wildfire risk by providing a robust, unified database of the fire history of Israel from 2015 onwards.

A geographically flexible approach for mapping the Wildland-Urban Interface integrating fire activity data

“…We repeated this workflow nine times: for three types of fuel [Herbaceous and Shrubs (HS), Conifer and Maquis (CM), and all Natural Vegetation (NV)] at three radii: 100, 250, and 500 m. The filtered database exhibited a similar temporal trend to the original database (Supplementary Figure S1B), with most fires taking place during the dry season (May to August with 61% of the dataset and October to November with 18% of the dataset). This trend is also known from the literature and was found in different fire databases, from different periods, from 1987 to date (Levin and Saaroni, 1999;Levin and Heimowitz, 2012;Turco et al, 2017;Guk et al, 2023), which confirms that the removal of those active fires was mainly of false positive detections.…”

“…Using ignition points as the fire data will produce WUI maps that are biased toward areas exposed to small fires, as most ignition events result in small burned areas. Additionally, precise data on ignition locations is unavailable in many countries, or, collected inconsistently by different land management agencies (Guk et al, 2023). A different approach would be to use data on burned areas (or fire perimeters) from available remote sensing products, e.g., MODIS (Roy et al, 2008).…”

“…Israel has a new unified wildfire database which contains all fire incidents greater than 10 ha (for the years 2015-2022; Guk et al, 2023). Yet in order to allow the replication of our approach elsewhere (where such data do not exist), we used remote sensing data to reconstruct the fire history of our study area.…”

Downward counterfactual analysis of multi-risk cascades

“…The human factor associated with this wildfire is arson, according to forensic evidence (Staff, 2021). Remote sensing and field data are the principal sources of Israel wildfire information (Guk et al, 2023). The first report of fire came from a resident of Beit Meir, a small village in the pine forests west of Jerusalem.…”