Ruecker, Gernot 
  
trunks when weather is wet. Forest canopy has a smoothing effect on relief — when it is removed during fire 
effects on small-scale topography affect the radar beam much stronger leading to an increase in image intensity 
variations in these areas (Siegert et al 1999). In large areas of the peat swamp forest, were most of the fire 
damage class 3 is located, fire causes vegetation death almost without altering vegetation canopy structure. 
Therefore, in many test areas, no change in mean backscatter nor in standard deviation of backscatter could be 
detected in the images acquired under moist conditions. This allowed for reliable mapping of fire damage and 
burn scars in these peat-swamp areas only under dry conditions. Discrimination of three damage classes based on 
mean backscatter is better for images taken under dry weather conditions, while under wet conditions, standard 
deviation of backscatter is a better indicator for fire damage. This can be interpreted as a consequence of a 
decrease in soil moisture during the drought leading to extremely dry soils when exposed after a fire 
(Holdsworth and Uhl 1997)), which in turn leads to a decrease in dielectric constant of the soil and subsequently 
in backscatter. During moist weather, in turn, the patchiness of vegetation cover and the interaction of the radar 
beam with the moist soil and the exposed underlying relief may need to a considerably higher image texture, 
manifested in an increase of standard deviation of backscatter for those areas. 
Mapping accuracy for fire scars is generally good, although according to the ground inventory for one 
concession, the high error makes discrimination of damage classes not feasible. However, according to the air 
survey-study, damage class mapping is possible with an overall accuracy greater than 60%. This discrepancy in 
results is readily explained by two facts: Firstly, for the area investigated for the block inventory, only images 
from the moist period in July were available. As indicated by results of the backscatter study, these are not suited 
for accurate discrimination of damage classes. Secondly, assessment from the ground produces results quite 
different than damage assessment from the air. Thus, an observer from the air can not identify damage by low 
intensity ground fires that leave tree crowns unaffected. An assessment from the air tends therefore to be more 
conservative. This may also explain the better agreement between air survey and radar map. However errors of 
omission for the slightest damage class are more than 40% for both surveys, indicating difficulties in dedecting 
this damage class correctly. Although the ground survey was more detailed, the dataset produced from the air 
survey is to be considered as being more reliable, since the area covered by the air survey was much larger, 
covering different vegetation and relief types which influence the radar image properties in a different way 
before and after fire impact. 
The high mapping accuracy for burn scars allows assessment of fire affected areas using standard ERS-2 satellite 
imagery become operational. With a lesser accuracy a fire damage estimation was possible. The total area to be 
assumed as fire affected is therefore to be considered much larger than was suggested by other investigations 
(Liew et al., 1998, MoFEC, 1999). It also has to be borne in mind that accuracy assessment of the radar map 
indicate that the estimate of the burned surface may be too conservative, since ground fires may have slipped 
detection. 
ACKNOWLEDGEMENTS 
Many thanks to Anja Hoffmann (GTZ/IFFM) for help and support during the field surveys and to Alexander 
Hinrichs (GTZ/SFMP) for providing the block inventories and to both projects for financing the major part of the 
study. 
REFERENCES 
Dwyer, E., Grégoire, J.-M., Malingreau, J.-P., 1998. A global analysis of vegetation fire using satellite images: 
spatial and temporal dynamics. Ambio, 27 (2), pp. 175-181. 
ESA (European Space Agency), 1997. SAR Toolbox Algorithmic Specifications, Workpackage 213, European 
Space Agency, Frascati, Italy. 
French, N. H.; Kasischke, E.S.; Bourgeau-Chavez, L.L. and Harrel, P:A., 1996. Sensitivity of ERS-1 SAR to 
variation in soil water in fire disturbed boreal forest ecosystems. International Journal of Remote Sensing 17, 
pp. 3037-3053. 
Fuller, D.O. and Fulk, M. Comparison of NOAA-AVHRR and DMSP-OLS for operational fire monitoring in 
Kalimantan, Indonesia. International Journal of Remote Sensing, 21 (1), pp. 181-189. 
Holdsworth, A.R., Uhl, C., 1997. Fire in Amazonian Selectively Logged Rain-Forest and the Potential for Fire 
Reduction. Ecological Applications, 7 (2), pp. 713-725. 
  
1292 International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000.