Geologic Interpretation Japanese ERDS-1 Simulation data merged with geographic information
H. Watanabe, M. Tsukada
Japex Geo science institute, Inc.
Introduction
The J-ERS-1 (Japanese Eearte Resources Satellite - 1) is at the final stage of launching sin early 1991. As is well known, JERS-1 has two sensors on its platform: SAR (Synthetic Aperture Radar) and OPS (Optical Sensor). Especially, the letter has two characteristics.
- Along-track stereo capability with B/H ratio 0.3
- Multi spectral property with emphasis on SWIR (Short Ware infra red)
Major parameters of J-ERS-1 are listed in Tab. 1 Both of these two characteristics have been attracting many geologists' attention. Since it is a new attempt for OPS to include 4 bands in SWIR region, we have been trying to conduct a simulation study using Airborne imaging Spectrometer (AIS) of Geophysical Environmental Research Inc. USA (1). And, we have verified the capability of spectral discrimination of rocks and soil, not only by original simulated J-ERS-1 data with larger bandwidth. Moreover, such topographic and geologic maps over the images. The experiment ahs clearly shown both the capability of the OPS J-ERS-1 and the usefulness of the merging of remote sensing data and the geographic information.
Brief discrimination of the site (2)
Target area is comb Ridge, which is located in western part of Paradox Basin extending over Uath, Colorado, Arizona and New Mexico; USA This basin is a large sedimentary basin of Paleozoic to Mesozoic era. Comb Ridge is a N-S trending monocline
Characterized by the flat iron composed of Navajo white sand stone Eastern, side of Comb Ridge is an almost horizontal Jurassic horizon, while at the Western side appear older and older horizon and near the top of the anticline called Raplee is Rico Formation composed of limestone of Pennsylvanina era. Between Rico and Navajo Formation, Cutler formation of Permian and Chinle Formations of Triassic come p in the belt form parallel to Comb ridge. Cutler formation can be subdivided into Haligato, Cedar Mesa, Organ Roct and De Cherry Members, successively from the left to the right. Within these members, Cedar Mesa member is known to contain gypsum, particularly in the neighborhood of Comb Ridge. The geologic map is digitized by our interactive system as shown in fig. 1
Figure.1 Digitzed geologif map(after O'Sullivan)
GER AIS data
GER AIS is, data this moment, one of the most advanced airborne remote sensing sensors. Major parameters of GER-AIS are listed in tab.2 As shown in this Table, AIS has 63-spectra channels, 32 of which are located in SWIR region (1.5 - 1.7
mm, 2.0-2.5
mm). In particular, the high spectral resolution for 2.0-2.5
mm is a powerful tool for rock and mineral as well known. In addition to this spectral characteristics, imaging capacity of this sensors allows us to locate easily specific pixel. We can see two typical examples of spectral features with its position, which are interactively searched on screen: one example is limestone of RCI formation and the other, gypsum of Cedar Mesa member. The spectral curves on the right hand side are calculated from 2.0-2.5
mm data of GER AIS by using normalizing method, and the associated spectral curves are the pointed of spectral measurement in the laboratory. It can be pointed out that both features express clearly spectral characteristics of limestone and gypsum, respectively. Here only the example of limestone is shown in Figs. 2 and 3.
Figure.2 Extracted Spectral curve of GER-AIS data(limestone of Rico formation)
Figure.3 Labouatory measurement of the sample from Rico formation
Generation of ERS-1 OPS Multispectral data
GER-AIS data with narrow bands are summed up to produce simulation data of ERS-1 OPS, taking into account band characteristics, spatial resolution and signal to noise ration. A sample image of ERS-1 OPS as shown in Fig. 4 where band combination is 1,4 and 5 and its quality is excellent. However, if we choose 3 bands in SWIR, color become much more monotonous, caused by the high correlation between bands. In such images, we can emphasis the difference of the colors keeping the original color as much as possible, by using decorrelated stretch. By this technique, we can find out many different colors caused by the lithology., and these color combination can be explained by the spectral characteristics as seen in the previous section. By stacking up such experience of color interpretation, we may be able to make a color table for spectral interpretation of rock and soil for J-ERS-1 OPS.
Figure.4 J-ERS-1 simulation image of bands1,4,and5
