Mapeo semiautomático de áreas quemadas en Chimborazo-Ecuador utilizando medias compuestas de dNBR con umbrales ajustados

César Cisneros-Vaca; Julia Calahorrano; María Abarca; Mery Manzano

doi:10.4995/raet.2023.19428

Authors

César Cisneros-Vaca Universidad Nacional de Chimborazo https://orcid.org/0000-0001-9661-4581
Julia Calahorrano Universidad Nacional de Chimborazo https://orcid.org/0000-0003-1472-0319
María Abarca Universidad Nacional de Chimborazo https://orcid.org/0000-0002-3107-5174
Mery Manzano Universidad Nacional de Chimborazo https://orcid.org/0000-0001-9262-9671

DOI:

https://doi.org/10.4995/raet.2023.19428

Keywords:

Landsat, fire, paramo, dNBR, threshold

Abstract

A semi-automatic methodology was implemented for the delimitation of burning areas in the province of Chimborazo in Ecuador, during the period 2018-2021, by using the database of forest fires provided by the “Amazonia sin fuego” program of the “Ministerio de Ambiente Agua y Trnacisión Ecológica” (MAATE). The collections of atmospherically corrected Landsat 7 and Landsat 8 images available on the Google Earth Engine (GEE) platform were used. To delimit the burning areas, mean composite images of normalized burning indices (NBR) were calculated in GEE and the most appropriate thresholds of the difference of normalized burning indices (dNBR) were evaluated above which the burning for paramo ecosystem is delimited. The results show: (a) the dNBR threshold value, based on Landsat 7 and Landsat 8 composite mean images, that best fits to identify burning areas in the study area is 0.15; (b) nine events with areas equal to or greater than 100 ha were found, but only seven could be located; (c) most of the burned areas recorded in the MAATE database were overestimated from 25.2% to 84.9% compared to the burn areas digitized on satellite images. These findings provide information that contributes to the strengthening of national fire statistics, useful for the construction of public policies for monitoring and managing forest fires in Ecuador.

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