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AN EVALUATION OF A SI= IMAGE TO DETERMINE FOREST DENSITY UNDER 

CONDITIONS OF MODERATE TOPOGRAPHICAL VARIATION 

Milton Smith and John Adams 

Department of Geological Sciences 

University of Washington 

Final Report 

Jet Propulsion Laboratory 

Contract 956431 

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Many studies have shown that radar images have increased 

classification accurracy over spectral classifications using only 

Landsat MSS images (e.g. Ulaby et al. 1982). Our analysis of 

spectral mixture models to identify ultramafic rocks in the 

Klamath Mountains near Hayfork, California using only Landsat MSS 

images demonstrated an inability to accurrately separate units 

that were vegetated with more than 50% ground cover (Cooke et al. 

1985). In addition, we had difficulty separating pasture lands 

from xeric sparse forests on south slopes. 

to determine if a SIRA image taken over Hayfork when used alone 

or in conjunction with Landsat MSS data would increase separation 

of units not identified by Landsat spectral mixture models. Our 

hypothesis was that the longer wavelength of SIRA images could 

It was our objective 

contain a significant and direct response to the surface 

roughness created by the vegetation architecture. 

hypothesis we compared areas in the Landsat model of varying 

vegetation density (0-50%) that had proven to be accurrate by 

field surveys. These areas were primarily the less densely 

vegetated ultramafics characteristic of the area. 

To test this 

An analysis of the second order surface roughness effects 

(e.g. diaelectric and architecture effects) in SIRA images 

requires that the primary effects due to topography be removed. 

The SIRA image of Hayfork was initially rectified and registered 

to provide a projection and resolution equivalent to the digital 

terrain data. To remove that component of the radar return that 

was specifically due to topography we used a radar simulation 

developed by Mike Kobrick at JPL. The simulation computed the 

2 
incidence angle of the transmitted signal to surface topography 

using digital terrain data and other parameters related to the 

instrumentation and space shuttle orbit. 

image (Fig. 1) illustrates the relative coarseness of the digital 

terrain data by some of the flat valleys and ridges. 

qualitative comparison of Fig. 1 with the original SIRA image 

(Fig. 2) indicates that the primary feature highlighted in the 

image is the topography. 

The predicted radar 

A 

An empirical calibration of the SIRA image was performed to 

remove that component in the image due solely to the topography. 

The calibration consisted of determining the cosine response of 

the SIRA image as a function of computed incidence angle using 

digital terrain data, 

DN return for each pixel rounded to the nearest degree of 

incidence angle. 

when the data were regressed using the following equation: 

The plot of data in Fig, 3 is the average 

The points in Fig. 3 have a correlation of 0.98 

SIRA DN = 116.27 cos(incidence angle) + 73.98 

The observed cosine response exhibited by the data provides 

evidence of the validity of the digital terrain simulation as 

well as illustrating the a symmetrical distribution of surface 

angles in the area. Subtracting each pixel of the image using 

Equation (1) from the SIRA image of Fig. 2 results in a SIRA 

image without the primary response due to topography (Fig. 4). 

Although major topographical variations have been removed 

(Fig. 4) topographical features still remain a dominant component 

of the SIRA image. The only other features apparent in the image 

are bodies of water (e.g. a few lakes and a resevior). 

Fig. 5 is an image for comparison where density of 

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vegetation is proportional to the image brightness (brightest 

areas are 100% vegetation density - dark areas are 0% vegetation 

density). 

separating the spectral signature of vegetation from that of the 

shade and substrate. The visual lack of topographical 

information in Fig. 5 compared to Fig. 4 is an indication that 

the SIRA image is not significantly responding to changes in 

vegetation density or any other vegetation architectural changes. 

The vegetation densities illustrated by Fig. 5 closely coincide 

with field work and depict a wide range of density variation as 

shown by the spatial variation in brightness. 

clearcuts which appear as dark rectangular areas in Fig. 5 are 

not observed in the SIRA image (Fig. 2) or in the residual image 

(Fig. 4), indicating that SIRA is not sensitive to vegetation 

density in this scene. 

In summary, we find in the Hayfork area that SIRA does not 

This image was computed from Landsat MSS data by 

Note that the 

increase or help delineation of vegetation or ultramafic units 

over Landsat MSS. In general, these results suggest a need to 

isolate the physical causes resulting in increased classification 

accurracies found in other studies, Many studies in forest 

inventory have concluded that classification accurracy of forests 

using Landsat MSS can be increased significantly by adding 

digital terrain data alone (e.g. Sugarbaker et al, 1980). Our 

results suggest that SIRA images are predominately an expression 

of the topography and not of the small scale surface roughness 

associated with vegetation or diaelectic coefficients. 
REFERENCES 

Cooke, S., J. Adams, and M. Smith. The use of remote 

sensing in geobotanical mapping projects: mapping ultramafic 

rocks and assoicated vegetation communities in northern 

California. Submitted. 

Sugarbaker, L., T. Gregg, E. Barthmaier, and R. Scott. 

Forest cover type mapping in Eastern Washington with Landsat and 

digital terrain data. 

Regional Commission Grant X10890613. 

using airborne radar and Landsat data. IEEE Trans. Geoscience - and 

Remote Sensing 20:1,42-50, 1982. 

Final Report for Pacific Northwest 

Ulaby, F., R. Li, and K. Shanmugan. Crop classification 

5 
Figure 1. Simulated SIRA image using digital terrain of area 

south of Hayfork, California. 

6 
.- 

Figure 2. SIRA image of area south of Hayfork, California. 

7 
z 

0 

0, 

250 

200 

150 

100 

50 

I I I I 

0 10 20 30 40 50 60 70 80 

+ 

90 

INCIDENCE ANGLE 

Figure 3. The cosine relation between computed incidence angle 

from digital terrain data to the average SIRA DN for 

each pixel of the scene in Fig. 2 has a correlation of 

0.98. 
Figure 4. SIRA image with the primary expression of topography 

removed using the empirical cosine calibration of 

Equation 1. 

9 
. ,* 

Figure 5. Vegetation density resolved from spectral mixtures 

using a Landsat image of Hayfork, California. Image is 

scaled so that white is 100% vegetation density and 

black is 0% vegetation density. 

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