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Article

Cloud and Cloud-Shadow Detection for Applications in Mapping Small-Scale Mining in Colombia Using Sentinel-2 Imagery

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Minerals Engineering, Materials & Environment (GeMMe), University of Liège, 4000 Liège, Belgium
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The College of Forestry, Beijing Forestry University, Beijing 100083, China
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United Nations Environment Programme, Bogota Cl. 82 #10-62, Colombia
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Institute for Environmental Sciences, University of Geneva, GRID-Geneva, Bd Carl-Vogt 66, CH-1211 Geneva, Switzerland
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Institute for Environmental Sciences, University of Geneva, EnviroSPACE Lab., Bd Carl-Vogt 66, CH-1211 Geneva, Switzerland
*
Author to whom correspondence should be addressed.
Academic Editor: Stefano Dietrich
Remote Sens. 2021, 13(4), 736; https://doi.org/10.3390/rs13040736
Received: 8 January 2021 / Revised: 10 February 2021 / Accepted: 13 February 2021 / Published: 17 February 2021
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. View Full-Text
Keywords: cloud; cloud shadow; classification; multispectral; small-scale mining cloud; cloud shadow; classification; multispectral; small-scale mining
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MDPI and ACS Style

Ibrahim, E.; Jiang, J.; Lema, L.; Barnabé, P.; Giuliani, G.; Lacroix, P.; Pirard, E. Cloud and Cloud-Shadow Detection for Applications in Mapping Small-Scale Mining in Colombia Using Sentinel-2 Imagery. Remote Sens. 2021, 13, 736. https://doi.org/10.3390/rs13040736

AMA Style

Ibrahim E, Jiang J, Lema L, Barnabé P, Giuliani G, Lacroix P, Pirard E. Cloud and Cloud-Shadow Detection for Applications in Mapping Small-Scale Mining in Colombia Using Sentinel-2 Imagery. Remote Sensing. 2021; 13(4):736. https://doi.org/10.3390/rs13040736

Chicago/Turabian Style

Ibrahim, Elsy, Jingyi Jiang, Luisa Lema, Pierre Barnabé, Gregory Giuliani, Pierre Lacroix, and Eric Pirard. 2021. "Cloud and Cloud-Shadow Detection for Applications in Mapping Small-Scale Mining in Colombia Using Sentinel-2 Imagery" Remote Sensing 13, no. 4: 736. https://doi.org/10.3390/rs13040736

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