Application of hyperspectral remote sensing for environment monitoring in mining areas

Abstract

Environmental problems caused by extraction of minerals have long been a focus on environmental earth sciences. Vegetation growing conditions are an indirect indicator of the environmental problem in mining areas. A growing number of studies in recent years made substantial efforts to better utilize remote sensing for dynamic monitoring of vegetation growth conditions and the environment in mining areas. In this article, airborne and satellite hypersectral remote sensing data-HyMap and Hyperion images are used in the Mount Lyell mining area in Australia and Dexing copper mining area in China, respectively. Based on the analyses of biogeochemical effect of dominant minerals, the vegetation spectrum and vegetation indices, two hyperspectral indices: vegetation inferiority index (VII) and water absorption disrelated index (WDI) are employed to monitor the environment in the mining area. Experimental results indicate that VII can effectively distinguish the stressed and unstressed vegetation growth situation in mining areas. The sensitivity of VII to the vegetation growth condition is shown to be superior to the traditional vegetation index-NDVI. The other index, WDI, is capable of informing whether the target vegetation is affected by a certain mineral. It is an important index that can effectively distinguish the hematite areas that are covered with sparse vegetation. The successful applications of VII and WDI show that hyperspectral remote sensing provides a good method to effectively monitor and evaluate the vegetation and its ecological environment in mining areas.

A Review of Fine-Scale Land Use and Land Cover Classification in Open-Pit Mining Areas by Remote Sensing Techniques

“…Remote sensing data sources have evolved from low resolution to high resolution. For example, some researchers have focused on identifying contamination in tailings and mine areas using hyperspectral data [10][11][12][13]. However, the swath width of a hyperspectral image is usually narrow, and there are fewer satellite data sources.…”

“…References opencast stope, mineral processing land, dumping site 3 [35] reclaimed herbaceous vegetation, reclaimed woody vegetation, barren (including haul roads, active quarries, land disturbed by mining) 3 [22][23][24] mine-reclaimed grassland (including reclaimed land within mine sites and valley fills dominated by herbaceous vegetation) 1 [25] open stope, stripping area, waste-dump area, mine industrial area 4 [26] active, disturbed vegetation and pasture, rehabilitation, remnant, spoil/waste, and water management 6 [27] coal deposit, over burden dump, mine dump 3 [28] tree cover, dense grass, sparse grass, bare ground 4 [29] coal mining excavation cities, coal dump areas 2 [30] active mine, reclaimed mine (including grass, woodland, forest) 2 [16] opencast mining (coal), overburden dump 2 [12] bare tailings, exposed lime, water/wet tailings 3 [31] tailings zone, dry vegetation zone, transition zone, vegetated islands 4 [32] mine, dump, coal stockpile 3 [33,34] …”

Monitoring the ecological environment of open-pit coalfields in cold zone of Northeast China using Landsat time series images of 2000-2015

“…Townsend et al [17] examined the land use changes of central Appalachian Mountain region in the eastern United States using decision trees and Landsat time series data. Zhang et al [18] based their research on HyMap and Hyperion image data used in Vegetation Inferiority Index and adopted Water Absorption Disrelated Index and NDVI; they also investigated and evaluated the effect of mining activities on the environment. Vasileiou et al [9] analyzed the ecological environment changes of coalfields using NDVI after the mine is closed.…”

The EnMAP-Box—A Toolbox and Application Programming Interface for EnMAP Data Processing

“…Successful applications with imaging spectroscopy data cover all environments, including the analysis of managed forests [5,6], agricultural areas [7][8][9], mining sites [10,11], urban areas [12][13][14], of unmanaged forests [15,16] and (quasi-)natural ecosystems [17][18][19], including deserts [20,21] or snow and ice [22,23], as well as inland waters and oceans [24,25]. Given the broad range of applications, a great variety of analysis approaches is used with imaging spectroscopy data, e.g., mapping and monitoring [26,27], empirical modeling [5,28,29] or physical-based modeling, especially with radiative transfer models [6,30,31].…”