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2 results for “Categorization of Publications”
Dataset related to publication: Landcover-categorized fires respond distinctly to precipitation anomalies in the South-Central United States
<p>Landcover-categorized fires respond distinctly to precipitation anomalies in the South-Central United States</p> <p>Kátia Fernandes and Sen g. Young</p> <p>doi: 10.3389/fenvs.2024.1433920</p> <p>Abstract</p> <p>Satellite detection of active fires have contributed to advance our understanding of fire ecology, fire and climate dynamics, fire emissions and how to better manage the use of fires as a tool. In this study we use 12 years (2012-2023) of active fire data combined with landcover information in the South-Central United States to derive a monthly, <strong>open access dataset of categorized fires.</strong> This is done by calculating a fire predominance index used to rank fire prone land covers, which are then grouped into four main landscapes: grassland, forest, wildland and crop fires. County level aggregated analyses reveal spatial distributions, climatologies, and peak fire months that are particular to each fire type. Using the Standardized Precipitation Index (SPI), it is found that during climatological fire peak-month, SPI and fires exhibit an inverse relationship in forests and crops, whereas grassland and wildland fires show less consistent inverse or even direct relationship with SPI. This varied behavior is discussed in the context of landscapes’ responses to anomalies in precipitation, and fire management practices, such as prescribed fires and crop residue burning. In a case study of Osage County (OK) we find that large wildfires, known to be closely related to climate anomalies, occur where forest fires are located in the county and absent in areas of grassland fires. Weaker grassland fires response to precipitation anomalies can be attributed to the use of prescribed burning, which are normally planned under environmental conditions that facilitate control and thus avoided during droughts. Crop fires on the other hand, are set to efficiently burn residue and practiced more intensely in drier years than in wetter, explaining the consistently strong inverse correlation between fires and precipitation anomalies. In our increasingly volatile climate, understanding how fires, vegetation, and precipitation interact has become imperative to prevent hazardous fire conflagrations and to better manage ecosystems.</p>
supporting data for 'categorical colour metric' publication
<p>sampledMT.txt are metric tensors for the 'categorical colour metric' describe in the paper of the same name (to be) published in PLOS One.</p> <p>The data is stored as a nested list of dimension 21*21*21*3*3; with the outer dimension being the R coordinate of where the tensor lies running from 0.00, 0.05,...,0.95,1.00; next dimension G; then Bl then the tensors themselves.</p> <p>It can be directly read into Mathematica using <<</p>
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