COMPARISON OF SPACEBORNE AND AIRBORNE HYPERSPECTRAL IMAGING 
SYSTEMS FOR ENVIRONMENTAL MAPPING 
Haluk Cetin 
Mid-America Remote Sensing Center, Murray State University, Murray KY 42071 USA - Haluk.Cetin@MurrayState.edu 
KEY WORDS: Remote Sensing, Hyperspectral, Comparison, Platforms, Land Cover Mapping, Performance, Hyperion, AVIRIS 
ABSTRACT: 
The main purpose of this study was to compare hyperspectral remotely sensed data collected by the Hyperion satellite, and the 
airborne Real-time Data Acquisition Camera System (RDACS-3) and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) 
for environmental mapping and vegetation species identification. Hyperion was NASA's first hyperspectral imager aboard NASA's 
Earth Observing-1 (EO-1) spacecraft. The EO-1 mission had three advanced land imaging instruments; Advanced Land Imager, 
Hyperion, and Atmospheric Corrector. AVIRIS collects 224 contiguous spectral bands with wavelengths from 0.4 to 2.5 jum, 
whereas RDACS-3 has many spectral modes (64, 128, 256, etc.). The study area, Land-Between-the-Lakes (LBL), is located in 
western Kentucky, USA. Most of LBL consists of forested areas, which are predominantly oak and hickory, and open land areas. 
Twenty-five percent (17200 hectares) of LBL falls within the Biosphere Reserve. AVIRIS was flown on the Twin Otter turboprop 
at approximately 4000m above the ground with 4m spatial resolution on November 11, 1999 and September 10, 2001. The 
Hyperion provided 242 spectral bands (from 0.4 to 2.5 um) with a 30 meter spatial resolution and covered 7.5km by 200km area on 
April 29, 2001. An RDACS-3 imagery with 120 spectral bands and 2x4m spatial resolution was collected at 2350m above the 
ground by the ITD Spectral Visions on September 7, 1999. During the overflights, ground spectra using an ASD FieldSpec-FR" 
spectroradiometer (0.35-2.5 pm) were collected for data calibration, spectral library construction, atmospheric correction and species 
identification. Moreover, multispectral satellite and aerial imagery at 1m resolution was collected for some of the test sites in the 
area Several hyperspectral and multispectral processing tools were utilized for atmospheric corrections, enhancements, and 
classifications. Best results were obtained using the AVIRIS and RDACS-3 data. The Hyperion data also provided very good 
results for the mapping; however, its spatial resolution was one of the limitations of the Hyperion sensor. The statistical difference 
among the classifications using the sensors proved to be mostly significant. 
1. INTRODUCTION multispectral imagery could not be used for very detailed 
mapping and identification of surface material, for which 
The primary goal of the NASA Earth Observation System hyperspectral and/or ultraspectral sensors have been utilized. 
(EOS) is to study the effects of climate on terrestrial Unlike the multispectral classifiers, hyperspectral classifiers 
vegetation (Huete ef al, 1994). The development of are used to identify objects using spectral endmembers in 
multispectral imaging spectrometers during the early 1970s spectral libraries. Many attempts have been made to classify 
allowed scientists for the first time to classify large areas of hyperspectral data using the traditional multispectral 
terrain (Marmo, 1996) This led to the advent of classifiers. Classification time has been very long and 
hyperspectral sensors with many bands and high spatial classification accuracy has not improved by the increased 
resolution, allowing for the classification of large areas with number of bands when the multi-spectral classifiers were 
finer spectral resolution (Cloutis, 1996). Current used (Lee and Landgrebe, 1993). Another approach using 
multispectral satellites that orbit the earth have their own hyperspectral data has been mapping of cover types based on 
limitations. The multispectral satellites such as Landsat and — their abundances by using spectral unmixing techniques 
SPOT as well as high spatial resolution sensors such as (Adams et al., 1986; Boardman 1990; Dwyer et al., 1995; 
[KONOS and QuickBird have broad spectral bands. These Mustard and Pieters, 1987). 
bands cover the visible, near and middle-infrared regions of 
the electromagnetic spectrum (Jakubauskas and Price, 1997). Ecologists are now only beginning to explore the potential 
This greatly reduces the ability of the multispectral sensor to uses of high spatial and high spectral resolution remote 
spectrally discriminate between two objects on the ground sensing. For example, Schlesinger and Gramenopoulos (1996) 
(Marmo, 1996). Multispecral sensors have been utilized for used high spatial resolution satellite imagery and aerial 
many purposes including regional mapping. However, photography to test for desertification in the Sahel by 
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