Structural interpretation of lineaments using satellite image processing: A case study in the vicinity of the Charvak reservoir

Abstract

This work presents the results of lineaments interpretation using the automated method of the satellite images in the territory of the Charvak water reservoir in Uzbekistan. Tectonic and local (water impoundment in Charvak reservoir) features of the region deformation were determined on base LINE algorithm in software PCI Geomatica. The thematic map with the geospatial arrangement of lineaments was constructed on base of satellite images LANDSAT-8 processing. We concluded that water level fluctuations have a greater influence on the appearance of the lineaments structure than periods of water filling and downstream in the reservoir. Lineament density maps showed dominantly increased density towards the north-southern direction is due to tectonic features of the region and the west-eastern direction is due to water level fluctuations in the reservoir. The lineaments density maps for summer-autumn periods showed the faults arising from water level fluctuations only. Winter-spring period affected with high influence of the seasonal (snow pack, rainfall) processes as well.

Statistical Analysis Of Lineaments Using Landsat 8 Data: A Case Study Of The Fergana Valley (East Uzbekistan)

“…To verify the results obtained, the extracted lineament structures are compared with the lineaments obtained by hand digitizing from geological maps [2]. The work presents a processing method using multi-temporal Landsat 8 imageries were determined on base LINE algorithm in software PCI Geomatica in the territory of the water reservoir [3]. The result showed that water level fluctuations have a greater influence on the appearance of the lineaments structure than periods of water filling and downstream in the reservoir.…”

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“…Plastic waste can be most clearly distinguished from the surrounding sea in the near-infrared (NIR) and short-wave infrared (SWIR) bands, in which is particularly sensitive to the wavelength range around 1215 nm and 1732 nm [Biermann et al, 2020;Ciappa, 2021]. Themistocleous et al (2020) analyzed the spectral reflectance characteristics of plastic waste on Sentinel 2 satellite images, in which the plastic fragments have a much higher reflectivity than the surrounding seawater, especially from the red to NIR wavelength [Themistocleous et al, 2020]. Kikaki et al (2020) used multiple remote sensing data sources, including 400 Planet high spatial resolution optical satellite images (spatial resolution from 3 to 5 m), 340 Sentinel 2 images (spatial resolution up to 10 m), 125 Landsat 8 images (30 m spatial resolution) and in situ data to create a plastic pollution map in the Caribbean and Motagua estuary (Honduras) [Kikaki et al, 2020].…”

“…Since the difference in spectral reflectance values between floating plastic waste and seawater is not large, it is very difficult to detect and classify marine plastic waste from optical satellite image bands. To overcome this limitation, Biermann et al (2020) proposed a Floating Debris Index (FDI) using red (band 4), NIR (band 8), Red Edge 2 (band 6) and SWIR1 (band 11) of Sentinel2 imagery. To improve the accuracy in classifying ocean plastic waste, the authors used a combination of FDI and NDVI, NDWI indices to eliminate the effects of seawater and plants on plastic waste [Biermann et al, 2020].…”