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Remote Sens. 2017, 9(9), 945; doi:10.3390/rs9090945

Applicability of Earth Observation for Identifying Small-Scale Mining Footprints in a Wet Tropical Region

1
Centre for Water in the Minerals Industry, Sustainable Minerals Institute, The University of Queensland, St. Lucia Campus, QLD 4072, Australia
2
School of Environmental and Geographical Sciences, Faculty of Science, University of Nottingham Malaysia Campus, Jalan Broga, Semenyih 43500, Malaysia
3
Rio Tinto Limited, 123 Albert St., Brisbane, QLD 4000, Australia
*
Author to whom correspondence should be addressed.
Received: 22 August 2017 / Revised: 2 September 2017 / Accepted: 8 September 2017 / Published: 12 September 2017
(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)
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Abstract

The unpredictable climate in wet tropical regions along with the spatial resolution limitations of some satellite imageries make detecting and mapping artisanal and small-scale mining (ASM) challenging. The objective of this study was to test the utility of Pleiades and SPOT imagery with an object-based support vector machine (OB-SVM) classifier for the multi-temporal remote sensing of ASM and other land cover including a large-scale mine in the Didipio catchment in the Philippines. Historical spatial data on location and type of ASM mines were collected from the field and were utilized as training data for the OB-SVM classifier. The classification had an overall accuracy between 87% and 89% for the three different images—Pleiades-1A for the 2013 and 2014 images and SPOT-6 for the 2016 image. The main land use features, particularly the Didipio large-scale mine, were well identified by the OB-SVM classifier, however there were greater commission errors for the mapping of small-scale mines. The lack of consistency in their shape and their small area relative to pixel sizes meant they were often not distinguished from other land clearance types (i.e., open land). To accurately estimate the total area of each land cover class, we calculated bias-adjusted surface areas based on misclassification values. The analysis showed an increase in small-scale mining areas from 91,000 m2—or 0.2% of the total catchment area—in March 2013 to 121,000 m2—or 0.3%—in May 2014, and then a decrease to 39,000 m2—or 0.1%—in January 2016. View Full-Text
Keywords: artisanal and small-scale mining; geographic-object-based image analysis; wet tropical region; Philippines; Didipio catchment; object-based support vector machine artisanal and small-scale mining; geographic-object-based image analysis; wet tropical region; Philippines; Didipio catchment; object-based support vector machine
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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

Isidro, C.M.; McIntyre, N.; Lechner, A.M.; Callow, I. Applicability of Earth Observation for Identifying Small-Scale Mining Footprints in a Wet Tropical Region. Remote Sens. 2017, 9, 945.

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