Assessment of foliar dust using Hyperion and Landsat satellite imagery for mine environmental monitoring in an open cast iron ore mining areas

The scope of this paper is to estimate foliar dust concentration using Hyperion (Narrow-bands data) and Landsat (Broad-bands data) images, with the aid of eight different vegetation indices (VIs) and fieldbased laboratory spectra. A PCE Instrument for measurement of dust accumulation on leaves and Spectroradiometer for spectral signatures, was also used to estimate foliar dust concentration. The result depicted a negative relationship between VIs (Hyperion and Landsat satellite imagery), and field based dust measurements. The Normalized Difference Vegetation Index (NDVI) shows an excellent negative correlation (R-2 = 0.89 for Hyperion and R-2 = 0.81 for Landsat) as it is not much affected by the variation in vegetation types and patterns. Amongst the eight VIs, NDVI was selected as an optimal VI (RMSE = 0.06 g/m(2) for Hyperion and 0.11 g/m(2) for Landsat) based on both, the field measurement and satellite data for estimation of foliar dust concentration. Furthermore, a positive relationship between the field-based measured dust concentration (g/m(2)) and satellite image (by VIs) based dust concentration (g/m(2)) was observed. Field-based measured foliar dust concentration taken for 20 samples was plotted against their estimated dust values using the NDVI (R = 0.90 for Hyperion and R = 0.81 for Landsat). Hyperion data is considered as the reliable one as it gave better results than the Landsat data. Finally, the Hyperion data based foliar dust map was analyzed by a High-resolution Google Earth image (Geo Eye) for different locations viz., mines, transport sites as well as forests and matched with the field-based measured dust concentration. The result shows that maximum foliar dust was concentrated near the ore transportation network, surrounding mining locations, tailing ponds, and mining dumps areas. For making the environmental management effective (in the mining and allied areas), Hyperspectral remote sensing aided by field-based methods, for estimating foliar dust concentration would be very helpful. (C) 2019 Elsevier Ltd. All rights reserved.