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ZENODO
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Statistically Determined Global Fire Regimes (GFRs) Empirically Characterized Using Historical MODIS Hotspots

Authors: Hargrove, William W.; Kumar, Jitendra; Norman, Steven P.; Hoffman, Forrest M.;

Statistically Determined Global Fire Regimes (GFRs) Empirically Characterized Using Historical MODIS Hotspots

Abstract

Statistically Determined Global Fire Regimes (GFRs) Empirically Characterized Using Historical MODIS Hotspots Fire regimes are areas having similar fire characteristics, and show the spatial pattern, frequency and intensity of fires that prevail in that area over long periods of time. Fire regimes are created and maintained by multivariate interactions between climate, vegetation/fuels, and ignitions. Like ecoregions, fire regimes indicate the extent and overlap of particular vegetative/fuel communities and climatic conditions, and are important for understanding, monitoring, predicting and managing fire. More than 83M MODIS “hotspot” thermal detections from 2002-2019 were grouped into 10km cells, and 21 derived variables describing fire characteristics of fire intensity, return frequency, and seasonality within each cell were developed and subjected to unsupervised Multivariate Geographic Clustering to produce world maps of Global Fire Regimes (GFRs), each having similar fire intensity and timing characteristics. Methodology behind these datasets are described in manuscript currently in review. W. W. Hargrove, Jitendra Kumar, Steven P. Norman, Forrest M. Hoffman (2022), "Empirical Characterization of Global Fire Regimes Show Shared Fire Relationships" 2022 (in review) This data collection includes: 1. Multivariate Geographic Clustering Global Fire Regimes at 3000, 1000, 500, 100, 50, 20, 10 levels of divisions in form of geospatial raster in IMG formats, and associated color tables. 2. Characteristics of GFRs 3. Location groups 4. Geospatial maps of global fire frequency modes, global seasonality strength, and 12 types of global fires. 5. PNG maps for all data products 6. Description and script for global date transform algorithm.

Keywords

remote sensing, climate change, MODIS, wildfires, fire regimes

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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