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Geosciences 2017, 7(2), 34; doi:10.3390/geosciences7020034

Land Cover Classification in an Ecuadorian Mountain Geosystem Using a Random Forest Classifier, Spectral Vegetation Indices, and Ancillary Geographic Data

1
Instituto de Ciencia, Tecnología, Investigación y Saberes, Universidad Nacional de Chimborazo, Riobamba, Provincia de Chimborazo 060150, Ecuador
2
Facultad de Ingeniería, Universidad Nacional de Chimborazo, Riobamba, Provincia de Chimborazo 060150, Ecuador
3
Facultad de Ciencias Forestales y Ambientales, Universidad de Los Andes, Mérida, Estado Mérida 5101, Venezuela
4
Facultad de Ciencias, Universidad de Los Andes, Mérida, Estado Mérida 5101, Venezuela
5
Facultad de Ciencias, Escuela Superior Politécnica de Chimborazo, Riobamba, Provincia de Chimborazo 060150, Ecuador
*
Author to whom correspondence should be addressed.
Academic Editors: Ruiliang Pu and Jesus Martinez-Frias
Received: 28 February 2017 / Revised: 19 April 2017 / Accepted: 21 April 2017 / Published: 3 May 2017
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Abstract

We presented a methodology to accurately classify mountainous regions in the tropics. These landscapes are complex in terms of their geology, ecosystems, climate and land use. Obtaining accurate maps to assess land cover change is essential. The objectives of this study were to (1) map vegetation using the Random Forest Classifier (RFC), spectral vegetation index (SVI), and ancillar geographic data (2) identify important variables that help differentiate vegetation cover, and (3) assess the accuracy of the vegetation cover classification in hard-to-reach Ecuadorian mountain region. We used Landsat 7 ETM+ satellite images of the entire scene, a RFC algorithm, and stratified random sampling. The altitude and the two band enhanced vegetation index (EVI2) provide more information on vegetation cover than the traditional and often use normalized difference vegetation index (NDVI) in other settings. We classified the vegetation cover of mountainous areas within the 1016 km2 area of study, at 30 m spatial resolution, using RFC that yielded a land cover map with an overall accuracy of 95%. The user´s accuracy and the half-width of the confidence interval for 95% of the basic map units, forest (FOR), páramo (PAR), crop (CRO) and pasture (PAS) were 95.85% ± 2.86%, 97.64% ± 1.24%, 91.53% ± 3.35% and 82.82% ± 7.74%, respectively. The overall disagreement was 4.47%, which results from adding 0.43% of quantity disagreement and 4.04% of allocation disagreement. The methodological framework presented in this paper and the combined use of SVIs, ancillary geographic data, and the RFC allowed the accurate mapping of hard-to-reach mountain landscapes as well as uncovering the underlying factors that help differentiate vegetation cover in the Ecuadorian mountain geosystem. View Full-Text
Keywords: land cover; random forest; vegetation cover; mountain geosystems; spectral vegetation index land cover; random forest; vegetation cover; mountain geosystems; spectral vegetation index
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ayala-Izurieta, J.E.; Márquez, C.O.; García, V.J.; Recalde-Moreno, C.G.; Rodríguez-Llerena, M.V.; Damián-Carrión, D.A. Land Cover Classification in an Ecuadorian Mountain Geosystem Using a Random Forest Classifier, Spectral Vegetation Indices, and Ancillary Geographic Data. Geosciences 2017, 7, 34.

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