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Raster-based dataset for spatio-temporal analysis of forest fires in the Amazon rainforest from 2001 to 2020 - MaRDI portal

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Raster-based dataset for spatio-temporal analysis of forest fires in the Amazon rainforest from 2001 to 2020

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DOI10.5281/zenodo.7215402Zenodo7215402MaRDI QIDQ6679669

Dataset published at Zenodo repository.

Author name not available (Why is that?)

Publication date: 17 October 2022

Copyright license: No records found.



Description

Forest fire incidents are becoming increasingly common around the world, posing a threat to the environment, economy, and social life. These wildfires are further expected to rise in their frequency and intensity, considering the global climate change and human activities. A variety of attributes must be studied in order to analyse relationships between the probable causes of fire and the characteristics of wildfire incidents, and inform decision-making. Such attributes are available or easily collectable in various regions around the world, but they are not readily available in the South American Amazon. The Amazon rainforest covers such a large area that acquiring a useful dataset necessitates extensive effort and computer intensive pre-processing. The associatedstudy to this dataset investigates potential data sources for the Amazon, establishes a methodological baseline, and prepares a dataset of covariates thought to be contributing to the wildfire ignition process. The dataset is intended to be used for forest fire studies, specifically spatio-temporal and statistical analysis of wildfires. The study provides threesets of (i) raw data (acquired data with a global extent), (ii) pre-processed data (source data transformed to the same projection system and same file format), and (iii) working data (cropped to Amazon region extent with spatial resolution of 500 meters and monthly temporal resolution, to enable the scientific community to work with various possibilities of forest-fire analysis, and to further encourage research in study areas in the other parts of the world.






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