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Remote Sens. 2018, 10(5), 782; https://doi.org/10.3390/rs10050782

Natural Forest Mapping in the Andes (Peru): A Comparison of the Performance of Machine-Learning Algorithms

1
Forestry and Forest Products Research Institute, Matsunosato 1, Ibaraki Prefecture, Tsukuba City 305-8687, Japan
2
Dirección de Catastro Zonificación y Ordenamiento, Servicio Nacional Forestal y de Fauna Silvestre, Avenida 7 No. 229, Rinconada Baja, La Molina, Lima LIMA 12, Peru
*
Authors to whom correspondence should be addressed.
Received: 15 March 2018 / Revised: 10 May 2018 / Accepted: 13 May 2018 / Published: 18 May 2018
(This article belongs to the Special Issue Mountain Remote Sensing)
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Abstract

The Andes mountain forests are sparse relict populations of tree species that grow in association with local native shrubland species. The identification of forest conditions for conservation in areas such as these is based on remote sensing techniques and classification methods. However, the classification of Andes mountain forests is difficult because of noise in the reflectance data within land cover classes. The noise is the result of variations in terrain illumination resulting from complex topography and the mixture of different land cover types occurring at the sub-pixel level. Considering these issues, the selection of an optimum classification method to obtain accurate results is very important to support conservation activities. We carried out comparative non-parametric statistical analyses on the performance of several classifiers produced by three supervised machine-learning algorithms: Random Forest (RF), Support Vector Machine (SVM), and k-Nearest Neighbor (kNN). The SVM and RF methods were not significantly different in their ability to separate Andes mountain forest and shrubland land cover classes, and their best classifiers showed a significantly better classification accuracy (AUC values 0.81 and 0.79 respectively) than the one produced by the kNN method (AUC value 0.75) because the latter was more sensitive to noisy training data. View Full-Text
Keywords: Andes; mountain forest; remote sensing; machine learning; comparison analysis; accuracy analysis Andes; mountain forest; remote sensing; machine learning; comparison analysis; accuracy analysis
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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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Vega Isuhuaylas, L.A.; Hirata, Y.; Ventura Santos, L.C.; Serrudo Torobeo, N. Natural Forest Mapping in the Andes (Peru): A Comparison of the Performance of Machine-Learning Algorithms. Remote Sens. 2018, 10, 782.

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