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Related Experiment Video

Updated: Sep 3, 2025

Wind Tunnel Experiments to Study Chaparral Crown Fires
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Country-level fire perimeter datasets (2001-2021).

Adam L Mahood1,2, Estelle J Lindrooth3,4, Maxwell C Cook3,5

  • 1Earth Lab, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, USA. admahood@gmail.com.

Scientific Data
|July 30, 2022
PubMed
Summary
This summary is machine-generated.

Accessible global fire data is now available. We created global fire event perimeter datasets using the open-source FIREDpy software, simplifying access to crucial fire history information.

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Area of Science:

  • Environmental science
  • Geospatial analysis
  • Remote sensing

Background:

  • Global fire activity is changing, necessitating research into social and ecological system responses.
  • Many countries lack accessible historical fire data, hindering research and management.
  • Existing satellite-based fire products are often difficult to access, process, and computationally intensive.

Purpose of the Study:

  • To develop an automated, open-source software package for processing satellite-based fire data.
  • To create a comprehensive collection of global fire event perimeter datasets.
  • To improve accessibility and usability of fire history data for research and conservation.

Main Methods:

  • Developed the open-source software package, Fire Event Delineation for python (FIREDpy).
  • FIREDpy automates downloading and processing of MODIS burned area product data.
  • Employed a spatiotemporal flooding algorithm to convert gridded data into fire perimeter shapefiles.

Main Results:

  • Generated global fire event perimeter datasets for every country using FIREDpy.
  • Successfully converted hundreds of gridded satellite data files into single, usable shapefiles.
  • Established a foundational dataset for global fire research and analysis.

Conclusions:

  • FIREDpy provides an efficient and accessible method for generating fire perimeter data.
  • The created global datasets offer a valuable resource for understanding fire impacts.
  • Future work will focus on enhancing algorithm efficiency and annually updating datasets.