Author(s)

Abderrazak Bannari¹, Ali El-Battay¹, Ali Saquaque², Abdelhafid Miri²

¹Department of Geoinformatics, Arabian Gulf University, Manama, Kingdom of Bahrain.
²Managem Mineral ONA Group, Twin Center, Casablanca, Kingdom of Morocco.

Characterized by lithological diversity and rich mineral resources, Benshangul-Gumuz National Regional State located in Asosa Zones, Western Ethiopia has been investigated for geological mapping and morpho-structural lineaments extraction using PALSAR (Phased Array type L-band Synthetic Aperture Radar ) Fine Beam Single (FBS) L-HH polarization and Landsat-5 TM (Thematic Mapper ) datasets. These data were preprocessed to retrieve ground surface reflectance and backscatter coefficients. To overcome the geometry acquisition between the two sensors, they were geometrically and topographically rectified using ASTER-V2 DEM. Intensity-Hue-Saturation, directional filters and automatic lineaments extraction were applied on the datasets for lithological units’ discrimination and structural delimitation for potential mineral exploration. The obtained results showed good relationship among the topographic morphology, rock-substrate, structural variations properties, and drainage network. The spectral variations were easily associated with lithological units. Likewise, the morpho-structural information highlighted in the PALSAR image was visible without altering the radiometric integrity of the details in TM bands through the fusion process. Moreover, predominant lineaments directions trending NE-SW, NS, and NW-SE were identified. Results of this study highlighted the importance of the PALSAR FBS L-HH mode and TM data fusion to enhance geological features and lithological units for mineral exploration particularly in tropical zones.

Geology, Mineral Exploration, Lineaments Extraction, Data Fusion, Intensity-Hue-Saturation, Mapping, Landsat-TM, PALSAR-FBS, L-HH Polarization, ASTER DEM

Bannari, A. , El-Battay, A. , Saquaque, A. and Miri, A. (2016) PALSAR-FBS L-HH Mode and Landsat-TM Data Fusion for Geological Mapping. Advances in Remote Sensing, 5, 246-268. doi: 10.4236/ars.2016.54020.