A Comparison of AVIRIS and Landsat for Land Use Classification at the Urban Fringe

In this study we tested whether AVIRIS data allowed for improved land use classification over synthetic Landsat ETM+ data for a location on the urban-rural fringe of Colorado. After processing the AVIRIS image and creating a synthetic Landsat image, we used standard classification and post-classification procedures to compare the data sources for land use mapping. We found that, for this location, AVIRIS holds modest, but real, advantages over Landsat for the classification of heterogeneous and vegetated land uses. Furthermore, this advantage comes almost entirely from the large number of sensor spectral bands rather than the high Signal-to-Noise Ratio (SNR). This article is available at The Cupola: Scholarship at Gettysburg College: http://cupola.gettysburg.edu/esfac/8 Abstract In this study we tested whether AVIRIS data allowed for improved land use classification over synthetic Landsat ETM+ data for a location on the urban-rural fringe of Colorado. After processing the AVIRIS image and creating a synthetic Landsat image, we used standard classification and post-classification procedures to compare the data sources for land use mapping. We found that, for this location, AVIRIS holds modest, but real, advantages over Landsat for the classification of heterogeneous and vegetated land uses. Furthermore, this advantage comes almost entirely from the large number of sensor spectral bands rather than the high Signal-to-Noise Ratio (SNR).In this study we tested whether AVIRIS data allowed for improved land use classification over synthetic Landsat ETM+ data for a location on the urban-rural fringe of Colorado. After processing the AVIRIS image and creating a synthetic Landsat image, we used standard classification and post-classification procedures to compare the data sources for land use mapping. We found that, for this location, AVIRIS holds modest, but real, advantages over Landsat for the classification of heterogeneous and vegetated land uses. Furthermore, this advantage comes almost entirely from the large number of sensor spectral bands rather than the high Signal-to-Noise Ratio (SNR). Introduction In rapidly urbanizing areas, such as the Front Range of Colorado, maps fast lose their validity. Large areas of prairie or farmland land can be overrun by residential development in a matter of months. Remotely sensed data allows land use and land cover to be mapped quickly, relatively cheaply, and frequently. With improved mapping of rapidly changing areas, planners will be able to better address issues associated with urban sprawl. However, the choice of sensor can significantly influence the accuracy of the classification. While it is commonly thought that smaller Ground Sampling Distance (GSD), also called pixel size, is the key to better land use classification, the number of spectral bands and the Signal-to-Noise Ratio (SNR) may influence classification accuracy as well. Commonly, researchers use sensors such as those on Landsat or SPOT satellites for mapping land use and land cover (Table 1). Of these, the Landsat sensors have more spectral bands and a longer time series, while SPOT provides smaller GSD. Less traditional sensors may provide additional information that can improve mapping accuracy. The Airborne Visible Infrared Imaging Spectrometer (AVIRIS), for example, produces images with 224 spectral bands between 0.4 and 2.45 m, compared to six bands for Landsat (not including the thermal band) and three for SPOT’s Multispectral Imager (XS). Sensors with a large number of continuous spectral bands, such as AVIRIS, are called hyperspectral imagers (Green et al., 1998). Though hyperspectral imagers have been used in studies of mineralogical mapping and ecology, they have rarely been employed for land use mapping. A small number of studies have explored the integration of hyperspectral and Synthetic Aperture Radar (SAR) for urban mapping (Gamba and HoushA Comparison of AVIRIS and Landsat for Land Use Classification at the Urban Fringe Rutherford V. Platt and Alexander F.H. Goetz mand, 2001; Hepner et al., 1998). Other studies have used hyperspectral imagery to map a narrow range of urban materials and processes (Ben-Dor et al., 2001; Ridd et al., 1992; Salu, 1995). One study used an iterative spectral un-mixing procedure to delineate urban materials (Roessner et al., 2001). To date, however, no studies have tested whether hyperspectral imagery improves land use classification accuracy over and above multispectral imagery such as from Landsat. In this study, we tested whether AVIRIS data allowed for improved land use classification over synthetic Landsat ETM+ data for a location on the urban-rural fringe of Colorado. We expected that the large number of bands and high SNR provided by AVIRIS would help distinguish land cover types that are easily confused (irrigated urban areas and irrigated crops, for example). After processing the AVIRIS image and creating a synthetic Landsat image, we used standard classification and post-classification procedures to compare the data sources for land use mapping. Sensor Specifications and Classification Accuracy: The Case of the Urban Fringe Among the factors that may influence classification accuracy are the Ground Sampling Distance (GSD), number of spectral bands, and Signal-to-Noise Ratio (SNR) of a sensor. Generally, it is thought that GSD is the most important factor for classification accuracy of built environments (Forster, 1985). For example, a study in Indonesia found that SPOT Multispectral (XS) images are superior to Landsat Multispectral Scanner (MSS) images for mapping of heterogeneous, near-urban land cover because of SPOT’s smaller pixel size (Gastellu-Etchegorry, 1990). The link between GSD and classification accuracy, however, is sometimes tenuous. In heterogeneous areas, such as residential areas, it has been shown that classification accuracies may actually improve by up to 20 percent as GSD is increased (Cushnie, 1987). This occurs when the reflectance spectra of a variety of cover types in an urban environment blend to form an overall urban signal that can be easily distinguished from other land covers. SNR, which varies sensor-by-sensor and band-by-band and pixel-by-pixel, may also influence classification accuracy. The greater the SNR, the more usable information is available in the data. Overall, AVIRIS has much higher SNR than Landsat sensors. Within the Landsat family, the ETM+ in Landsat 7 has a higher SNR than the Thematic Mapper (TM) in Landsat 4 and 5. SNR may vary depending not only on sensor characteristics but also on the signal strength; summer images will have a higher SNR than winter images for the same time and place. While the advantages of high SNR are well documented in domains such as mineralogical mapping (Chabrillat et al., 2002; P H OTO G R A M M E T R I C E N G I N E E R I N G & R E M OT E S E N S I N G J u l y 2004 8 1 3 R.V. Platt was formerly with the Department of Geography, UCB 260, University of Colorado at Boulder, Boulder, CO 80309. He is presently with the Department of Environmental Studies, Gettysburg College, Gettysburg, PA 17325 ([email protected]). A.F.H. Goetz is with the Center for the Study of Earth from Space/CIRES/Department of Geological Sciences, UCB 216, University of Colorado, Boulder, CO 80309 ([email protected]). Photogrammetric Engineering & Remote Sensing Vol. 70, No. 7, July 2004, pp. 813–819. 0099-1112/04/7007–0813/$3.00/0 © 2004 American Society for Photogrammetry and Remote Sensing 99-018.qxd 6/9/04 10:51 AM Page 813