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Vegetation Index Differencing for Estimating Foliar Dust in an Ultra-Low-Grade Magnetite Mining Area Using Landsat Imagery
- Source :
- IEEE Access, Vol 5, Pp 8825-8834 (2017)
- Publication Year :
- 2017
- Publisher :
- IEEE, 2017.
-
Abstract
- A supply of minerals is critical to socioeconomic development. However, such a supply also induces negative impacts on environment and ecology, e.g., leading to dust emission and deposition. An ultra-low-grade magnetite has been exploited as a new iron type since 2001 in China. In this paper, two Landsat images were used for monitoring foliar dust in Changhe River Mining Area, China. First, models were established to estimate foliar dust using vegetation indices (VIs) differences according to laboratory spectral measurements; normalized differenced VI was selected as an optimal VI for estimating foliar dust amount based on both field and laboratory spectral measurements (RMSE = 6.58 g/m2), and finally, the spatial patterns of foliar dust were analyzed by using ancillary high-resolution data. The result showed that most foliar dust distributed near ore transportation roads and around mining sites and tailings ponds, which was related to ultra-low-grade characteristics of the iron ore due to large-area extraction and tailings occupation, and large-amount dust emission released from ore transportation. The remote sensing method for estimating foliar dust may be beneficial for environmental management in mining areas.
- Subjects :
- 010504 meteorology & atmospheric sciences
General Computer Science
Soil science
010501 environmental sciences
engineering.material
01 natural sciences
chemistry.chemical_compound
vegetation change
General Materials Science
Extraction (military)
ultra-low-grade magnetite
0105 earth and related environmental sciences
Magnetite
VI difference
General Engineering
Vegetation
Tailings
landsat image
Deposition (aerosol physics)
Dust estimation
Iron ore
chemistry
engineering
Spatial ecology
Environmental science
lcsh:Electrical engineering. Electronics. Nuclear engineering
Vegetation Index
lcsh:TK1-9971
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 5
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....55e91531c08e8764c0622c780b7c0ec7