Comparative analysis of lineaments extracted from Cartosat, SRTM and ASTER DEM: a study based on four watersheds in Konkan region, India

Das, Sumit; Pardeshi, Sudhakar D.

doi:10.1007/s41324-017-0155-x

Cited by 62 publications

(17 citation statements)

References 35 publications

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“…After the removal of man-made structural features, length and frequency of all the extracted lineaments were calculated in ArcGIS 10.4 software in order to compare the data sets [4] , [5] , [6] , [7] . Lineament density maps was prepared through ArcGIS 10.4 environment using ‘‘Line density’’ tool with the formerly extracted lineament data comparisons and analysis of lineaments was done in Rockware software for the preparation of rose diagrams [8] .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Data on comparative studies of lineaments extraction from ASTER DEM, SRTM, and Cartosat for Jilledubanderu River basin, Anantapur district, A.P, India by using remote sensing and GIS

Rajasekhar

Raju

et al. 2018

Data in Brief

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The data deals with the functions that automatically extracted lineaments from the Cartosat, ASTER and SRTM of Digital Elevation Model (DEM) of different spatial resolutions, in the software ArcGIS 10.4. The extracted lineaments result shows the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) DEM gives the lowest number of lineaments reflects Cartosat and SRTM (Shuttle Radar Topography Mission) DEM shows a medium number of lineaments. Cartosat DEM is most appropriate for extraction of contours precisely rather than ASTER and SRTM. This study reveals the Cartosat DEM data is best to use extraction of lineaments in the Indian provinces, offers at most comprehensive geological structural info amongst all the data sets. The extracted lineaments lengths and densities are determined by the statistical method. Based on the data generated lineament density and rose diagram. Cartosat DEM data are the best suited for studying very small areas as through geological and structural information can be mined by using this data.

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Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Data on comparative studies of lineaments extraction from ASTER DEM, SRTM, and Cartosat for Jilledubanderu River basin, Anantapur district, A.P, India by using remote sensing and GIS

Rajasekhar

Raju

et al. 2018

Data in Brief

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“…SRTM DEM was processed in ArcGIS under spatial analysis tool to prepare slope map. Das and Pardeshi (2018b) demonstrated that Cartosat DEM provides a highly detailed shaded relief map which provides higher surface information compared to the SRTM and ASTER DEM. Therefore, shaded relief maps of eight different azimuth angles were developed from Cartosat DEM in order to extract the lineaments of the Pravara basin (Das et al 2018).…”

Section: Geospatial Database Preparationmentioning

confidence: 99%

“…Lineaments are basically the weakness of topography such as joints, cracks, faults and shears. In hard-rock lithology due to lower porosity of terrain, groundwater potentiality generally depends on other structural features (Kumanan and Ramasamy 2003;Avtar et al 2011b;Singh et al 2011b;Das and Pardeshi 2018b;Das et al 2018). Hence, lineaments play a fundamental role in groundwater potential as the structural weakness increases the infiltration capacity.…”

Section: Lineament Densitymentioning

confidence: 99%

“…Therefore, researchers are highly interested towards mapping the different potential areas for groundwater. The availability of groundwater depends on several factors such as geology, geomorphology, slope, soil texture, drainage density, lineament concentration, rainfall and land use of an area (Sander et al 1996;Nag 2005;Sener et al 2005;Solomon and Quiel 2006;Ganapuram et al 2009;Singh et al 2011b;Magesh et al 2012;Mukherjee et al 2012;Das et al 2017Das et al , 2018Das and Pardeshi 2018b). Hence, a sincere assessment of these parameters can provide a clear understanding regarding the groundwater potential of a region.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, remote sensing and GIS techniques play a crucial role in the assessment of Earth's natural resources (Das et al 2018). These techniques are highly cost-effective and less time-consuming to understand the groundwater potential of a region (Murthy 2000;Leblanc et al 2003;Jha and Peiffer 2006;Prasad et al 2008;Pradhan 2009;Arkoprovo et al 2012;Manap et al 2013;Mallick et al 2015;Rahmati et al 2015;Das et al 2017); the main reason is the availability of satellite data which make the analysis easier (Ganapuram et al 2009) compared to the traditional techniques such as ground drilling, geophysical assessment of lineaments and field observations (Das et al 2017(Das et al , 2018Das and Pardeshi 2018b). Integration of different environmental factors to delineate groundwater potential zone gave satisfactory results of many previous works (Krishnamurthy et al 1996;Rao and Jugran 2003;Lokesha et al 2005;Khan et al 2006;Solomon and Quiel 2006;Avtar et al 2010;Dar et al 2010;Jha et al 2010;Elewa and Qaddah 2011;Magesh et al 2012;Bagyaraj et al 2013;Rahmati et al 2015;Kirubakaran et al 2016;Roy and Sahu 2016;Tahmassebipoor et al 2016;Das et al 2017).…”

Section: Introductionmentioning

confidence: 99%

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Integration of different influencing factors in GIS to delineate groundwater potential areas using IF and FR techniques: a study of Pravara basin, Maharashtra, India

2018

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In the present days, remote sensing and geographic information system (GIS) techniques are comprehensive tools for the assessment of water resource, its management and conservation. In this study, remote sensing and GIS techniques are taken into consideration for zonation of different groundwater prospects of Pravara basin. Several contributing factors in which groundwater potential of an area entirely or partially depends such as lithology, geomorphology, slope, soil, lineament density, drainage density, land use and rainfall are assessed individually as well as together for making the different groundwater prospect maps through influencing factor (IF) and frequency ratio (FR) techniques. In ArcGIS software, all these thematic layers are prepared using different satellite imageries and conventional data sets, obtained from different sources. All these layers are transformed into high-resolution raster format and meshed together in GIS environment using IF and FR methods. Groundwater prospect maps are constructed as a result of overlay analysis. Based on the constructed map of groundwater prospect zones, as a result of this study, the study area is divided into five categories of different groundwater potential zones: very high, high, moderate, low and very low. Validation of the resultant maps has shown that frequency ratio technique is having higher accuracy (AUC = 73%) compared to the influencing factors (AUC = 69%). The present study of groundwater zonation provides a simple and less time-consuming technique; also, the results can directly be used for planning and sustainable management in Pravara basin.

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Evaluation of stream ordering systems in the context of topography and open‐source data

Pareta,

Pareta

2024

Earth Surf Processes Landf

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Satellite‐derived digital elevation models (DEMs) and geographic information systems (GIS) offer effective means for both qualitative and quantitative analysis of drainage networks within watersheds. An extensive review of various stream order systems reveals their outdated nature, given advancements in digital technology and the availability of fine to coarse resolutions data. Analysis of open‐source DEMs with resolutions ranging from 12.5 to 225 m, along with topographical maps at scales of 1:50 000 and 1:250 000, was conducted across four physiographically distinct micro‐watersheds of approximately equal size (~300 km2). The steepest descent algorithm (D8) was utilized to derive raster stream networks, applying a channel initiation threshold (CIT) of 900 m2 as a criterion. It was observed that stream order numbers are influenced by map scale, leading to variability in mainstream order extraction across different DEM datasets and topographic maps. To address this issue, an innovative stream order system was proposed to ensure consistent mainstream order regardless of the spatial resolution of DEM data or map scale. Correlation analysis highlighted the importance of considering both spatial resolution and topographic variability in stream order analysis, underscoring the significance of accurate DEM data and landscape characteristics in understanding stream network dynamics. This method of classifying stream orders is recognized by scientists in geography, geology, hydrology and geomorphology for providing crucial information about the size and strength of waterways within stream networks, contributing to effective water management strategies.

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Comparative analysis of lineaments extracted from Cartosat, SRTM and ASTER DEM: a study based on four watersheds in Konkan region, India

Cited by 62 publications

References 35 publications

Data on comparative studies of lineaments extraction from ASTER DEM, SRTM, and Cartosat for Jilledubanderu River basin, Anantapur district, A.P, India by using remote sensing and GIS

Data on comparative studies of lineaments extraction from ASTER DEM, SRTM, and Cartosat for Jilledubanderu River basin, Anantapur district, A.P, India by using remote sensing and GIS

Integration of different influencing factors in GIS to delineate groundwater potential areas using IF and FR techniques: a study of Pravara basin, Maharashtra, India

Evaluation of stream ordering systems in the context of topography and open‐source data

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