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Use of LiDAR for abandoned mine mapping
Vyuziti distancnich dat LiDAR 5G pri evidenci starych dulnich del

Zpravy o Geologickych Vyzkumech v Roce, 2018, Vol.51(1), pp.17-24 [Peer Reviewed Journal]

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  • Use of LiDAR for abandoned mine mapping

  • Title:
    Vyuziti distancnich dat LiDAR 5G pri evidenci starych dulnich del
  • Author: Dvorak, Igor J. ; Krejca, Frantisek ; Sir, Pavel ; Strupl, Vit ; Raus, Miroslav
  • Contributor: Ceska Geologicka Sluzba
  • Found In: Zpravy o Geologickych Vyzkumech v Roce, 2018, Vol.51(1), pp.17-24 [Peer Reviewed Journal]
  • Subjects: Environmental Geology ; Applied Geophysics ; Abandoned Mines ; Arcgis ; Bohemia ; Central Europe ; Czech Erzgebirge ; Czech Republic ; Digital Terrain Models ; Erzgebirge ; Europe ; Geographic Information Systems ; Information Systems ; Laser Methods ; Lidar Methods ; Mapping ; Medenec Mining District ; Mines ; Mining ; Remote Sensing ; Underground Mining
  • Language: Czech
  • Description: This study is intended to demonstrate the great potential of detection and measuring of surface features using the LiDAR 5th generation (LiDAR 5G) method to obtain high resolution elevation data, and their further processing in QGIS open source software. Two localities were selected to confirm long and rich history of mining, specifically the Medenec ore mining area (Fe+Cu, Ag ores, Erzgebirge Mts.) and the Ratiborske Hory ore mining region (Ag-Pb-Zn ores, Blanice Furrow, Central Bohemia), where numerous signs of previous mining operations can be observed on the surface even today. The analysis of the remote sensing data (LiDAR 5G) was carried out in both areas. The objective was to identify sites with higher density of elevations and depressions which could indicate old and abandoned mine workings. The analysis of acquired data on elevations is able to identify geomorphic phenomena and landforms particularly in areas where past mining has been known and undermined sites are registered which is the case of the above two regions. The digital elevation model (DEM) with a resolution of 1X1 m was generated using the ArcGIS platform tools. Then the open source QGIS software (http://www.qgis.org/en/site/index.html) was employed for further data processing and analysis. The GRASS 7 plugin or morphometry toolkit from SAGA GIS software is available in QGIS environment. These toolkits can generate a large variety of raster layers from the initial DEM. After searching and assessing the optimal conditions three raster layers generated from DEM were selected: 1. slope, 2. maximal curvature, 3. minimal curvature. All these raster layers are useful for better understanding geomorphic features and spatial distribution of objects on the surface of the given area. A procedure of eight successive steps, consisting of raster calculator and functions from the GRASS 7 toolbox (r.fill.dir, r.reclass, r.to.vect, v.dissolve) was chosen in order to detect local depressions. The original DEM was then transformed using a simple mathematical operation (2 * value of median - DEM) to identify elevations that may represent mine waste piles. This procedure generates a new DEM where the original elevations turn into depressions. The same procedure of eight successive steps was applied to identify depressions. The results were constrained to sites with an area larger than 5 m2 and higher or deeper than 0.5 m. Sites with higher density of the searched objects were subsequently compared with the database of old mine workings. The remote sensing data from the Ratiborske Hory area were also confronted with the field observations. Despite some disadvantages resulting from local inaccuracies of LiDAR elevation data or constraints in the elevation definition, the selected procedure provides reliable results in identifying two major geomorphic features, i.e. elevations and depressions. This procedure may in the future upgrade the accuracy of registering the old mine workings or even assist in searching for new sites. It can also contribute to better understanding the impacts of historical human activities on the local environment.
  • Identifier: ISSN: 0514-8057 ; E-ISSN: 2336-5757 ; DOI: 10.3140/zpravy.geol.2018.03

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