Multitemporal analysis of loss of coverage in 65 micro-basins of the Honduran dry corridor. Year 2010 - 2017

Authors

  • José Cáceres Coello National Autonomous University of Honduras

DOI:

https://doi.org/10.5377/ce.v13i1.11255

Keywords:

Remote sensing, Multitemporal Analysis, GEOMOD, Forest Fire, Deforestation

Abstract

Mapping and evaluating land cover is one of the central areas of remote sensing. The change in land cover is an important variable of global change that affects ecological systems with an impact on the environment, associated with climate change. Despite the role of information on land cover in monitoring and understanding the environment, we still lack knowledge about land cover and its dynamics, especially in rural Honduras. The objective of the study was to analyze the dynamics of change in land cover in 65 micro-basins in the Honduran dry corridor. Images derived from ETM + and OLI sensors from Landsat satellites, for the years 2010, 2015 and 2017 were classified into 13 categories. The coverage of pastures and crops was the predominant one during the three years analyzed (23%, 28% and 33%), showing an advance in time to the detriment of forest cover. Forest loss for the 2010-2015 period was 15% and 12% for the 2015-2017 period. The GEOMOD2 model was used to make a projection of deforestation to 2020, and it was obtained that the forest cover for the study area will represent 47% (being 58% for 2010). Finally, an analysis of the incidence of forest fires was carried out, obtaining that the most vulnerable micro-basins are those located in the MAMSURPAZ and MANCOSOL communities, with dense pine being the most affected.

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Author Biography

José Cáceres Coello, National Autonomous University of Honduras

Faculty of Space Sciences (FACES), National Autonomous University of Honduras (UNAH)

Published

2020-04-05

How to Cite

Cáceres Coello, J. (2020). Multitemporal analysis of loss of coverage in 65 micro-basins of the Honduran dry corridor. Year 2010 - 2017. Ciencias Espaciales, 13(1), 42–59. https://doi.org/10.5377/ce.v13i1.11255

Issue

Section

Science and Geographic Information Technologies