How can satellite imagery be used for mineral exploration in thick vegetation areas?. (15th February 2017)

• Record Type: Journal Article
• Authors: Hede, Arie Naftali Hawu; Koike, Katsuaki; Kashiwaya, Koki; Sakurai, Shigeki; Yamada, Ryoichi; Singer, Donald A.
• Is Part Of: Geochemistry, geophysics, geosystems. Volume 18:Number 2(2017)
• Page Start: 584
• Page End: 596
• Publication Date: 2017-02-15
• Subjects: geobotanical -- remote sensing -- vegetation index -- mineral exploration -- landsat; Geochemistry -- Periodicals; Geophysics -- Periodicals; Earth sciences -- Periodicals; 550.5
• DOI: 10.1002/2016GC006501
• Languages: English
• ISSNs: 1525-2027

Abstract

Abstract: The Hokuroku district, northern Japan, is globally recognized for rich ore deposits (kuroko and vein types), which have been thoroughly explored under thick vegetation cover. This situation is ideal to evaluate the effects of ore deposits on vegetation anomalies through geobotanical remote sensing. Here we present novel methods to detect vegetation anomalies caused by ore deposits and verify their usefulness by comparing the anomalies with a deposit potential map produced from multiple geological data. We use the reflectance spectra of Landsat ETM+ images acquired in summer and autumn to calculate a vegetation index for plant physiological activity. A key variable to detect the anomalies is a variation of vegetation index with time at each pixel. Difference in variation is enlarged by a sequence of image enhancement methods for the detection. We find that the vegetation anomalies, defined by the large ratios, correspond well to the high potential zones of ore deposits and known major deposits. Consequently, our methods can extend the applicability of remote sensing‐based mineral exploration to the areas covered by thick vegetation, in addition to traditional arid and semiarid areas. Key Points: Satellite image analysis to detect mineral occurrences in areas with thick vegetation cover Identification of vegetation anomaly zones using vegetation index and enhancement methods Correspondence of vegetation anomaly zones with geologic data‐based anomaly zones