Abstract
Lineaments mapping is an authentic issue for deciphering the tectonic setting, geological history, mineral prospecting, and other several applications. Consequently, the study objective is to examine various remote sensing datasets at wide various spatial resolutions (10, 12.5, 15, 20, 30 m), to recommend the best in lineaments elicitation, for usage in the geological scientific community. Toward this aim, nine various remote sensing datasets including optical sensors (Landsat OLI, ASTER, Earth Observing-1 Advanced Land Imager, Sentinel 2A), radar data (Sentinel 1), and digital elevation models (ALOS PALSAR, SRTM, NASA, ASTER V3), are tackled. In this scope, we created an entirely automatic lineament derivation environment through the integration of edge detection and line-linking algorithms. Results show that the used optical sensors are less efficient than DEMs having the same spatial resolution. Sentinel 1 radar data is more competent than optical data sources. ALOS PALSAR DEM (12.5m) is more eligible than any other utilized data type even sentinel 1 data (10 m). Wholly, DEMs built from radar data (e.g., PALSAR DEM) proved their leverage in lineament extraction to a limit that can deviate from the well-known relationship between the number of extracted lineaments and pixel size.
Highlights
Lineaments are considered essential features for most surficial studies and represent the key for solving various issues in several dis ciplines (Pirasteh et al, 2013)
For singleband analysis of L8, the superiority of VNIR b8 is evident over all other bands by extracting 2951 lines and this undoubtedly is interpreted by its higher spatial resolution (15m) compared to the other bands (30m)
For ASTER data, single VNIR band 3 gives 2861 lines compared to PC1 (15m) obtained 2756 lines and this could be inter preted by higher data variance in VNIR band 3
Summary
Lineaments are considered essential features for most surficial studies and represent the key for solving various issues in several dis ciplines (Pirasteh et al, 2013). The iden tification of lineaments in an automatic way is more efficient and much faster than the manual (visual) process, which is influenced by subjec tive parameters like quality analysis and experience (Muhammad and Awdal, 2012). The availability of a various and wide range of remote sensing datasets and their potency to supply steady obvious data across large areas compared to ground-based assessments, offer an easier approach compared to the manual methods for lineaments extraction. The automatic methods have resulted in a more efficient lineament extrac tion process (Masoud and Koike, 2006, 2011; Tripathi et al, 2000). A lineament extraction process comprises two main steps, namely edge detection, and line-linking or line extraction, utilizing digital data like satellite images, determining algorithms, and certain software like the frequently used LINE module of PCI Geomatica
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