Surface Mining Signal Discrimination Using Landsat TM Sensor: An Empirical Approach

Abstract

In Chotanagpur plateau of Jharkhand State in India, mining is a prominent activity. Sample sites of three such ores, viz. Bauxite, Hematite and Uranium were taken up for the present study wherein the first two are extracted through surface mining, leaving their signatures on the earth’s surface, while the third one, extracted through underground mining process, leaves its trail in tailing-pond, after its beneficiation, because of the higher degree of its radioactive property (half-life of Uranium is around 4,500 million years [1]). The Study attempts to statistically discriminate mining signals that were picked up as DN (digital number) values of the first four spectral bands of TM (Thematic Mapper) sensor, displayed on the graphic screen as the additive colour composite, using three primary colours namely red, green and blue (RGB) as standard FCC (false colour composite) of Landsat satellite-image. The said discrimination were based on application of two independent statistical algorithm on these values, one being paired-sample Student’s t-Test (at 95% confidence level and 3°of freedom) through SPSS (ver. 19) software and the second, subsequently, ANOVA (Analysis of Variance) test (at 95% confidence level), in order to further discriminate the signals based on parameters like spectral bands and nature of mineral being mined. According to the first algorithm Bauxite was found to be clearly discriminated, both from Uranium as well as Hematite, while Hematite could only be distinguished from Bauxite but not from Uranium. Performance of ANOVA test on the DN values discriminated the surface mining signals pertaining to these three different ores and it showed a high variance between the spectral bands both within the same ore group emphasising that different bands of the satellite sensors specifically identify features and also between the different ore groups.