Exploring image fusion of ALOS/PALSAR data and LANDSAT data to differentiate forest area

Remote sensing data utilize valuable information via various satellite sensors that have different specifications. Image fusion allows the user to combine different spatial and spectral resolutions to improve the information for purposes such as forest monitoring and land cover mapping. In this study, I assessed the contribution of dual-polarized Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar data to multispectral Landsat imagery. The research investigated the separability of forested areas using different image fusion techniques. Quality analysis of the fused images was conducted using qualitative and quantitative analyses. I applied the support vector machine image classification method for land cover mapping. Among all methods examined, the à trous wavelet transform method best differentiated the forested area with an overall accuracy (OA) of 94.316%, while Landsat had an OA of 92.626%. The findings of this study indicated that optical-SAR-fused images improve land cover classification, which results in higher quality forest inventory data and mapping.