oai:archiv.ub.uni-heidelberg.de:28100
type: CreativeWork
name: Remote sensing analyses for open pit mine area computation. A comparative study on the implementation of multi-spectral classifications and crowdsourcing to compute the spatial extent of four open-pit mines in Indonesia, Australia, Canada and Brazil
description: 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…
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…
author: Bürck, Silvana
date published: 2019-06-21
date modified: 2026-07-24T02:31:02Z
provider: Universität Heidelberg ; Thes
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Universität Heidelberg