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Mapping Mining Areas in the Brazilian Amazon Using MSI/Sentinel-2 Imagery (2017)

Authors :
Felipe de Lucia Lobo
Pedro Walfir M. Souza-Filho
Evlyn Márcia Leão de Moraes Novo
Felipe Menino Carlos
Claudio Clemente Faria Barbosa
Source :
Remote Sensing, Vol 10, Iss 8, p 1178 (2018)
Publication Year :
2018
Publisher :
MDPI AG, 2018.

Abstract

Although mining plays an important role for the economy of the Amazon, little is known about its attributes such as area, type, scale, and current status as well as socio/environmental impacts. Therefore, we first propose a low time-consuming and high detection accuracy method for mapping the current mining areas within 13 regions of the Brazilian Amazon using Sentinel-2 images. Then, integrating the maps in a GIS (Geography Information System) environment, mining attributes for each region were further assessed with the aid of the DNPM (National Department for Mineral Production) database. Detection of the mining area was conducted in five main steps. (a) MSI (MultiSpectral Instrument)/Sentinel-2A (S2A) image selection; (b) definition of land-use classes and training samples; (c) supervised classification; (d) vector editing for quality control; and (e) validation with high-resolution RapidEye images (Kappa = 0.70). Mining areas derived from validated S2A classification totals 1084.7 km2 in the regions analyzed. Small-scale mining comprises up to 64% of total mining area detected comprises mostly gold (617.8 km2), followed by tin mining (73.0 km2). The remaining 36% is comprised by industrial mining such as iron (47.8), copper (55.5) and manganese (8.9 km2) in Carajás, bauxite in Trombetas (78.4) and Rio Capim (48.5 km2). Given recent events of mining impacts, the large extension of mining areas detected raises a concern regarding its socio-environmental impacts for the Amazonian ecosystems and for local communities.

Details

Language :
English
ISSN :
20724292 and 10081178
Volume :
10
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.7825de396ccc46529daff11c97c1f4cc
Document Type :
article
Full Text :
https://doi.org/10.3390/rs10081178