Home Articles Mapping surface cover types using ASTER data

Mapping surface cover types using ASTER data

Dr Abdullah Mah
Earth Resource Mapping Pty. Ltd., Australia

ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) is an imaging instrument that is flying on the NASA’s Terra satellite launched in December 1999. ASTER acquires 14 spectral bands and can be used to obtain detailed maps of land surface temperature, emissivity, reflectance and elevation. The specifications of the 14 ASTER spectral bands are mentioned in Table 1.

Table 1: 14 Spectral bands of ASTER

Spectral channel   Spectral range   Spatial resolution   Dynamic range   Comment
1 (Visible Green)   0.520 to 0.600um  15m   8 bit integer  Near Infrared has 2 
bands (3B and 3N) 
which are stereo pair 
images from which DEM 
can be generated. 

Orbit: 705km, 10:30am 
descending node, 

Swath=60x 60 km

2 (Visible Red)   0.630 to 0.690um  15m   8 bit integer
3 (Near Infrared)   0.760 to 0.860um  15m   8 bit integer
4 (SWIR)   1.60 to 1.70um  30m   8 bit integer
5 (SWIR)   2.145 to 2.185um  30m   8 bit integer
6 (SWIR)   2.185 to 2.225um  30m   8 bit integer
7 (SWIR)   2.235 to 2.285um  30m   8 bit integer
8 (SWIR)   2.295 to 2.365um  30m   8 bit integer
9 (SWIR)   2.36 to 2.43um  30m   8 bit integer
10 (TIR)   8.125 to 8.475um  90m   12 bit integer
11 (TIR)   8.475 to 8.825um  90m   12 bit integer
12 (TIR)   8.925 to 9.275um  90m   12 bit integer
13 (TIR)   10.25 to 10.95um  90m   12 bit integer
14 (TIR)   10.95 to 11.65um  90m   12 bit integer

Each scene covers 60 x 60km and is daily capturing about 600 scenes. The recorded data exceed the specified signal to noise ratios (Yamaguchi et al, 2001). ASTER data has been used to map silicate and carbonate rocks (Hewson, et al, 2001), and has been used to carry out volcanic studies, urban studies, lithologic mapping, monitoring of coastal environments (Yamaguchi, et al, 2001).

The present paper used 2 dates (22 March, 2001 and 7 April, 2001) ASTER imagery of an area in ASIA to map surface cover types and to demonstrate change detection of vegetated areas.

The 14 ASTER spectral bands are saved into three groups; 3 bands with 15m resolution in VNIR, 6 bands with 30m resolution in SWIR and 5 bands with 90m resolution in TIR They are co-registered to WGS84 and a common NUTM zone.

Mapping surface cover types:

Reflectance feature of water at Visible Green and absorption feature at NIR (Lillesand and Kieffer, 1994) are used to map surface water. A threshold of 0.4 from the normalised difference ((Gr-NIR)/(Gr+NIR)) image between Visible Green and NIR of the ASTER 22 March 2001 image is used to map surface water. Threshold to map water may vary from a scene to a scene due atmospheric affect and pollution in water. Surface water mapped is shown in blue color in the figure below with a background of the ratioed image in grey scale.

Normalised Difference Vegetation Index (NDVI) [(NIR-Gr)/(NIR+Gr)] ratio image is used to map vegetation. No atmospheric noise reduction nor decorrelation stretch was applied on the data in mapping the vegetation in image shown below. Threshold used is 0.00432 and may vary due to different type of vegetation from a scene to another. Vegetation is shown in green in the figure below with the ratioed grey scale image shown as background.


Decorrelation Strech:
Decorrelation stretch is applied to the ASTER data. The decorrelation stretched is to reduce the inter-channel correlation and stretch the dynamic range to the full extent which enhances the color variation and improve the visualisation for interpretation (Gillespie et al, 1986, Gillespie, 1992). Before and after decorrelation stretched VNIR, SWIR and TIR images are shown below.

Before and after decorrelation stretched RGB (NIR, VisRed, VisGreen) images of VNIR suggest that the D-Stretched image has reduced the mist/cloud, which is found above the dam at the bottom-left of the image. The D-Stretched images of SWIR (RGB ASTER 6,7,9) and TIR (RGB ASTER 13,12,10) images have substantially improved and enhanced the color variation of the bands combination for visual interpretation.

Lithologies / Minerals:
To be able to analyze spectral responses of surface cover types using SWIR ASTER data it is necessary to apply log residual algorithm, which reduces noises from topography, instrument and sun illumination (Green and Craig, 1985). The resultant data is assumed to be more representatives of the soils or lithologies of the exposed areas. Spectrum convolved from log residual applied data can be compared to the library spectrum. Two steps are taken in the log residual algorithm. Step one is generating an addition band to the bands of the ASTER data. For instance, to apply log residual to the 6-band SWIR ASTER data, an additional seventh band (AVG) is generated, which is the average of the all the input six bands (SWIR 6 bands). Step number two is applying the log residual formula to the SWIR bands. Instead of geometric means arithmetic means are used in the log residual algorithm. The formula used is as below: (i1 * mean(AVG))/(AVG * mean(i1)) where i1 = SWIR band 1 to 6 It is not recommended to apply log residual on VISNIR bands as there may be problem with additive atmospheric effects. It is also not recommended to apply log residual on TIR, as there may be non-linear emissivity temperature relationship of different lithologies (Dr Robert Hewson; Personal communication).

To reduce any bias from water, water is nullified prior to applying Log Residual on the SWIR 22 March, 2001 ASTER data. The classified image of the Log Residual applied ASTER data using Unsupervised classification is shown below:

There is no priori knowledge of the study area and soil/rocks are simply classified as Class1 to Class4. The spectrum of vegetation and the 4 soil/rock classes are displayed below. For comparison John Hopkin University (JHU) spectrum of grass, dolomite and calcite are also displayed. There are some similarity between vegetation and grass and between the 4 soil/rock classes and carbonates. Ground truth is necessary for verification.

Change detection:
A simple example of vegetation change detection is demonstrated. Decorrelation Stretch algorithm was applied to both dates imagery and resultant stretched images were rescaled back to unsigned 8 bit integer. NDVI formula ((NIR-Gr)/(NIR+Gr)) was applied to both dates imagery and a threshold of 0.3 was used to map vegetation.

Vegetation found only in the 22 March, 2001 imagery, vegetation common in both dates imagery and vegetation found only in 7 April, 2001 imagery are mapped using the following procedure:

  1. Map vegetation of ASTER 22 March, 2001 as veg1 and the remaining as non-veg1
  2. Map vegetation of ASTER 7 April, 2001 as veg2 and the remaining as non-veg2
  3. Combine the above 2 imagery as an integrated Virtual Dataset
  4. Map vegetation found only in ASTER 22 March, 2001 VNIR imagery using the formula (If veg1 and not veg2 then veg-22Mar-only else null)
    (NOTE: veg1, veg2, veg1-22Mar-only are variables)
  5. Map vegetation found only in ASTER 7 April, 2001 VNIR imagery using the formula
    (If veg2 and not veg1 then veg-7Apr-only else null)
  6. Map vegetation in both dates imagery using the formula
    (If veg1 and veg2 then common-veg else null)

Providing there is no or only minor cloud, surface water and vegetation can be easily mapped using ASTER data. Decorrelation Stretch algorithm can be applied to reduce inter-channel correlation and enhances VNIR, SWIR and TIR ASTER data to gain more spectral variation for visual interpretation. Log Residual algorithm can be applied on SWIR to reduce noises from the sun illumination, topography and instrument, after which 6 ASTER SWIR bands can be convolved for representative spectrum of surface cover types. If multi-temporal data are available of the same area, change detection can also be easily carried out.


  • Gillespie, A.R., Kahle, A.B. and Walker, R.E., (1986); “Color enhancement of highly correlated images. Decorrelation and HIS contrast stretches.” Remote Sens. Environ., v. 20, pp. 209-235.
  • Gillespie, A.R.,(1992); “Enhancement of multispectral thermal infrared images: decorrelation contrast stretching.” Remote Sens. Environ., v. 42, pp. 147-155.
  • Green, A.A and Craig, M.D.,(1985); “Analysis of aircraft spectrometer data with logarithmic residuals.” JPL Publ. 85-41, pp. 111-119
  • Hewson R.D., Cudahy T.J. and Huntington J.F. (2001); “Geologic and alteration mapping at Mt Fitton, South Australia, using ASTER satellite-borne data.”
  • Lillesand and Kieffer, 1994; “Remote Sensing and Image Interpretation”, 3rd Edition, 750 p, John Wiley & Sons, Inc Publisher
  • Yamaguchi, Y.; Fujisada, H.; Kahle, A.B.; Tsu, H.; Kato, M.; Watanabe, H.; Sato, I.; and Kudoh, M.; (2001); “ASTER Instrument Performance, Operational Status, and Application to Earth Sciences”, IEEE Trans. Geosci. Remote Sens., 2001