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Abstract

Increasing awareness is dedicated to environmental impacts of mining activities around the world. In this context, Environmental Hazard Potential (EHP) is considered to be an appropriate way to assess all environmental impacts related to mining activities. A holistic EHP requires detailed knowledge about the spatial extent of the mining area. Even though several studies have already been conducted in the domain of mine detection and mapping, no comparative study has yet been carried out that investigates several remote sensing analyses, which are transferable to other geographic regions. The aim of this study is thus to compare remote sensing analyses that can be applied in order to determine the area that is subject to open-pit mining in different geographic regions. Therefore, this study examines strengths and weaknesses of remote sensing analyses, among them index-based, pixel-based and object-based multi-spectral classifications on single- and multi-source level, as well as crowdsourcing. Data sets comprise freely available Sentinel-2 optical imagery, Aster GDEM V2 elevation model and Sentinel-1 synthetic aperture radar imagery. Four copper or iron ore open-pit mines in Indonesia (Grasberg-Ertsberg Gold Copper Mine), Australia (Hamersley Iron Ore Mines), Canada (Highland Valley Copper Mine) and Brazil (Mariana Iron Ore Complex) constitute the study sites. Index-based, pixel-based and object-based classifications are applied on the datasets for each study site, whereby index-based classifications are conducted on single-source level, pixel-based and object-based classifications on multi-source level. Simultaneously, a crowdsourcing project is launched, where volunteers are asked to digitize the delineation of the four open-pit mines. First, classifications and crowdsourcing are investigated individually by visual interpretation, accuracy assessment and area computation. Secondly, both methods are compared by Intersection over Union (IoU), by area values, accuracy values and visual interpretation. Acquired new findings regarding the implementation of the methods and the achieved results support the final derivation of strengths and weaknesses of classifications and crowdsourcing. Classifications and crowdsourcing can both be applied in order to detect, classify and digitize open-pit mines in different geographic regions with an overall accuracy ≥ 77.41 % and to compute their spatial extent. Overall accuracy ranges for all methods from 77.41 % up to 97.73 %. The comparison of these methods reveals that classification and crowdsourcing results are not congruent, indicated by a mean IoU of 0.49 for all conducted comparisons. Classifications and crowdsourcing results differ among their respective area values, accuracy values and visual impression. Regarding area and accuracy values, crowdsourcing results have an intermediate position between the three considered classifications. Final derivation of strengths and weaknesses, as well as opportunities and threats shows that classifications and crowdsourcing differ further regarding effort, transferability, completeness, implementation, quality and credibility as well as their potential for automatization and further development. This study strongly supports decision making regarding method selection by providing strengths and weaknesses of remote sensing analyses for mine area computation. It contributes thus to the development of a holistic EHP of open-pit mines in different geographic regions. Future research recommendations are primarily related to the detection of unknown mines with classification approaches, to the development of a crowdsourcing project for global mine mapping and to the investigation of the potential of a combined application of classifications and crowdsourcing.