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

Alex Lechner,Celso Isidro,Ian Callow,Neil Mcintyre

doi:10.3390/rs9090945

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.

Highlights

Small-scale mines are defined as a mine that operates using rudimentary mining and milling methods at the level of individuals, families or cooperatives, and temporarily makes a specific mining claim or in other cases may be illegal [1,2]
Plot 1B, 2B, 3B) in which soil is exposed have similar reflectance across all spectral channels. These classes include: open land made up of stripping and burning of vegetation; small-scale mines in which deeper soil layers were brought to the surface; roads mainly made up of compacted sand and gravel; and the mine pit benches
The application of NDVI maps helped separate vegetated areas from bare soil and the variation of reflectance in the near infrared region provides some information on the potential presence of patches of vegetation

Summary

Introduction

Small-scale mines are defined as a mine that operates using rudimentary mining and milling methods (minimal mechanization and minimal, or absence of, structures that control mine and mill spillages) at the level of individuals, families or cooperatives, and temporarily makes a specific mining claim or in other cases may be illegal [1,2]. Small-scale mines can be major contributors to local economies and livelihoods [3,4] but in many cases, this comes with significant environmental impacts due to unsuitable locations, rudimentary mining methods, and lack of effective regulation [5,6]. The diffuse and remote locations of the mine sites—and their relatively short and unpredictable life span—impede the systematic documentation of small-scale mine development and footprints [7]. This is challenging for accurately surveying land use, the accurate attribution of impacts and the targeting of regulatory resources. Remote sensing provides the opportunity to observe changes in LULC over inaccessible regions. Spectral, radiometric and temporal resolutions are permitting greater levels of information about the type, timing and extent of changes to be obtained

Objectives

Methods

Results

Conclusion