South-Central Section - 36th Annual Meeting (April 11-12, 2002)

Paper No. 0
Presentation Time: 11:20 AM

GEOLOGIC MAPPING IN ARID REGIONS WITH ASTER DATA: AN EXAMPLE FROM NW ARIZONA


LANG, Nicholas P., Deparment of Geological Sciences, Southern Methodist Univ, 3225 Daniel Avenue, Dallas, TX 75275 and ABDELSALAM, Mohamed G., Geoscience, Univ of Texas at Dallas, 2601 North Floyd Rd, PO Box 830688, Richardson, TX 75083-0688, nlang@mail.smu.edu

The 1999-present Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) on the Terra satellite is a multi-spectral optical system collecting moderate-spatial resolution remote sensing data of Earth that are useful for geologic mapping, especially in arid regions. ASTER has 14 bands; 3 in visible and near infrared (VNIR) (15 m spatial resolution), 6 in short wave infrared (SWIR) (30 m spatial resolution), and 5 in thermal infrared (TIR) (90 m resolution). ASTER data, supported by field studies, are used to map an area in the southern semi-arid Black Mountains of northwest Arizona. Common lithologies here include andesite, rhyolite, and felsic tuff, which have distinctive spectral characteristics in the VNIR and SWIR regions of the electromagnetic spectrum. Hence, using the first 9 ASTER bands for imaging the region yields best results in discriminating lithological units. Using bands 3-2-1, 5-3-2, 6-2-1, 7-3-1, and 7-4-3 in Red-Green-Blue (R-G-B) false-color overlay revealed andesites, rhyolites, and tuffs in sharply different colors while highlighting morphologically-defined structures. 3-2-1 and 6-2-1 ASTER images proved useful in identifying hydrothermal alteration zones that are surface expressions of potential mineral deposits such as massive sulfides. Implementation of ASTER for geologic mapping is especially effective when used in conjunction with in situ and ancillary data such as field studies and published geologic maps. Because of ASTER’s success in aiding mapping in the Black Mountains, we recommend its use for aiding field studies in similar environments, especially when interpretation of remote sensing data is based on knowledge of the terrain’s geology and morphology.