Abstract
Forest type mapping requires considerable manpower and resources. Therefore, using
remote sensing techniques to reduce resource requirements is common in forest
inventories. Remote sensing includes active radar and passive optical sensors which can
provide different kinds of information. Conventional remote sensing methods for forest type classification mainly use optical images, but they are affected by weather and day/night
with the consequence that results are not always accurate. Synthetic aperture radar (SAR)
relies on microwave radiation, and is not affected by the above restrictions. Some studies
combine SAR and optical images to increase the accuracy of forest type classification. In
this study, we used ALOS PALSAR L band images with a wavelength of 0.25 m. We used a combination of spectral features and roughness information to improve the classification
accuracy. For analysis and processing, radar images and multispectral satellite images
were combined using the intensity, hue and saturation (IHS) and wavelet transformation (WT).
We used IHS images, WT images, and SPOT images to classify the forest types by the
maximum likelihood method. Results showed that the overall accuracy was 83.86%, and
the kappa value was 0.81 for IHS, and the overall accuracy was 72% and kappa value was
0.68 for WT. These results were better than those based on SPOT images, the overall
accuracy of which was 65% and kappa value was 0.60. We found that combining SAR
and optical images improved the accuracy by approximately 18%, thereby improving forest
type classification.