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. 2019 Nov 29;6:296. doi: 10.1038/s41597-019-0312-2

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — Diagram depicting the data mining process for each execution step. The first step is the creation of an R-tree with those fires that are considered active. Using the R-tree with the new burnt areas from MCD64A1 in vector format a distance sparse matrix compressed by rows is created considering the minimum distance between geometries and time stamps. Next, the compressed sparse matrix by rows is used as input for the DBSCAN, which will return the labels for the clusters. Finally, the fire array is updated with the labels, and those fires which are no longer considered as active are archived.