Abstract
Small-scale placer mining in Colombia takes place in rural areas and involves excavations resulting in large footprints of bare soil and water ponds. Such excavated areas comprise a mosaic of challenging terrains for cloud and cloud-shadow detection of Sentinel-2 (S2A and S2B) data used to identify, map, and monitor these highly dynamic activities. This paper uses an efficient two-step machine-learning approach using freely available tools to detect clouds and shadows in the context of mapping small-scale mining areas, one which places an emphasis on the reduction of misclassification of mining sites as clouds or shadows. The first step is comprised of a supervised support-vector-machine classification identifying clouds, cloud shadows, and clear pixels. The second step is a geometry-based improvement of cloud-shadow detection where solar-cloud-shadow-sensor geometry is used to exclude commission errors in cloud shadows. The geometry-based approach makes use of sun angles and sensor view angles available in Sentinel-2 metadata to identify potential directions of cloud shadow for each cloud projection. The approach does not require supplementary data on cloud-top or bottom heights nor cloud-top ruggedness. It assumes that the location of dense clouds is mainly impacted by meteorological conditions and that cloud-top and cloud-base heights vary in a predefined manner. The methodology has been tested over an intensively excavated and well-studied pilot site and shows 50% more detection of clouds and shadows than Sen2Cor. Furthermore, it has reached a Specificity of 1 in the correct detection of mining sites and water ponds, proving itself to be a reliable approach for further related studies on the mapping of small-scale mining in the area. Although the methodology was tailored to the context of small-scale mining in the region of Antioquia, it is a scalable approach and can be adapted to other areas and conditions.
Highlights
This paper addresses the important topic of cloud and cloud shadow detection over areas of Colombia where small-scale mining activities frequently occur
It presents a workflow of pixel-based classification followed by refinement of classes using solar-cloudshadow-sensor geometry
The geometry-based approach makes use of sun angles and sensor view angles available in Sentinel-2 metadata to identify potential directions of shadows for each pixel
Summary
Informal small-scale alluvial gold mining, known as placer mining, has major social and environmental impacts and has been at the heart of complicated armed conflicts in various parts of the world. It is distinct from subsistence mining as it utilizes large machinery to excavate soil and river sediment [1]. When carried out on the riverbanks, it leaves large footprints of bare soil along with ponds of water that are utilized for on-site processing [2,3].
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