Application of <scp>ASTER</scp> and Landsat <scp>TM</scp> Data for Geological Mapping of Esfandagheh Ophiolite Complex, Southern <scp>I</scp>ran

Pournamdari, Mohsen; Hashim, Mazlan; Pour, Amin Beiranvand

doi:10.1111/rge.12038

Cited by 58 publications

(22 citation statements)

References 37 publications

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“…The Landsat 8 with the RGB composite of 567 (equal with RGB 457 in Landsat 7 ETM+) was used to distinguish the surface geological units. The RGB 567 is the best composite and efficiently composite to display the major lithological units including lineament at the regional scale [33][34][35][36]. Similar to the Landsat 7 ETM+, band 5 (near-infrared: 0.851-0.879 µm) is a useful tool for displaying water or land surface because most of the radiation in this wavelength range is absorbed by water.…”

Section: Outcrop Identification Geological Units and Lineament Intermentioning

confidence: 99%

Determining Earthquake Susceptible Areas Southeast of Yogyakarta, Indonesia—Outcrop Analysis from Structure from Motion (SfM) and Geographic Information System (GIS)

et al. 2018

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Located approximately a hundred kilometres north of Java Subduction Zone, Java Island has a complicated geology and geomorphology. The north zone is dominated by the folded area, the centre is dominated by the active volcanic arc and the south of Java including the study area (Southeast part of Yogyakarta City), is dominated by the uplifted southern mountain. In general, the study area is part of the Bantul's Graben. In the middle part of study area flows the Opak River, which is often associated with normal faults of Opak Fault. The Opak Fault is such a complex fault system which has a complex local fault which can cause worst local site effect when earthquakes occur. However, the geology map of Yogyakarta is the only data that gives the characteristics of Opak Fault roughly. Thus, the effort to identify unchartered fault system needs to be done. The aims of this study are to conduct the outcrop study, to identify the micro faults and to improve the understanding of faults system to support the earthquake hazard and risk assessment. The integrated method of remote sensing, structure from motion (SfM), geographic information system (GIS) and direct outcrop observation was conducted in the study area. Remote sensing was applied to recognize the outcrop location and to extract the nature lineament feature which can be used as fault indicator. The structure from motion was used to support characterising the outcrop in the field, to identify the fault evidence, and to measure the fault displacement on the outcrops. The direct outcrop observation is very useful to reveal the lithofacies characteristics and to reconstruct the lithostratigraphic correlation among the outcrops. Meanwhile, GIS was used to analyse all the data from remote sensing, SfM, and direct outcrop observation. The main findings of this study were as follows: the middle part of study area has the most complicated geologic structure. At least 56 faults evidence with the maximum displacement of 2.39 m was found on the study area. Administratively, the north part of Segoroyoso Village, the middle part of Wonolelo Village, and the middle part of Bawuran village are very unstable and vulnerable to the ground motion amplification due to their faults configuration. The further studies such as geo-electric survey, boreholes survey, and detail geological mapping still need to be conducted in the study area to get better understanding of Opak Fault. Additionally, the carbon testing of charcoal that found in the outcrop and identification of exact location of the ancient eruption source also need to be done.

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Section: Outcrop Identification Geological Units and Lineament Intermentioning

confidence: 99%

Determining Earthquake Susceptible Areas Southeast of Yogyakarta, Indonesia—Outcrop Analysis from Structure from Motion (SfM) and Geographic Information System (GIS)

et al. 2018

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“…The larger the value, the greater the amount of information contained in a spectral band, and values close to one contain mostly noise [36]. Overall, MNF transform is an effective technique for producing an output of increased accuracy [35,37]. The first 5 MNF bands were used in this study with MNF variances at 96%.…”

Section: Lulc Maps Production and Accuracy Assessmentmentioning

confidence: 99%

Modeling Urban Encroachment on the Agricultural Land of the Eastern Nile Delta Using Remote Sensing and a GIS-Based Markov Chain Model

Bratley

Ghoneim

2018

Land

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Historically, the Nile Delta has played an integral part in Egyptian civilization, as its fertile soils have been cultivated for centuries. The region offers a lush oasis among the expansive arid climate of Northern Africa; however, in recent decades, many anthropogenic changes to the environment have jeopardized Egypt’s agricultural productivity. Political instability and lack of sufficient regulations regarding urban growth and encroachment have put agricultural land in the area at risk. Advanced geospatial techniques were used to assess the rate at which urban areas are increasing within the region. A hybrid classification of Landsat satellite imagery for the eastern sector of the Nile Delta, between the years 1988 and 2017, was conducted to map major land-use and land-cover (LULC) classes. The statistical change analysis revealed that urban areas increased by 222.5% over the study period (29 years). Results indicated that urban areas are encroaching mainly on established agricultural lands within the Nile Delta. Most of the change has occurred within the past nine years, where approximately 235.60 km2 of the cultivated lands were transitioned to urban. Nonetheless, at the eastern delta flank, which is bordered by desert, analysis indicated that agricultural lands have experienced a considerable growth throughout the study period due to a major desert reclamation effort. Areas most at risk from future urban expansion were identified. A simulation of future urban expansion, using a Markov Chain algorithm, indicated that the extent to which urban area is simulated to grow in the region is 16.67% (277.3 km2) and 37.82% (843 km2) by the year 2026, and 2050, respectively. The methods used in this study are useful in assessing the rate of urban encroachment on agricultural lands and can be applied to similar at-risk areas in the regions if appropriate site-specific modifications are considered.

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“…Due to its lower cost and higher accessibility than hyperspectral data, multispectral imagery, such as Landsat-5 TM, Landsat-7 ETM+ (enhanced thematic mapper plus), Landsat-8 OLI (operational land imager), and ASTER, is broadly utilized to extract lithologic, mineral, and structural information in metallogenic provinces [6][7][8]. As one of the world's earliest and longest continuously acquired collection of spaceborne moderate-resolution land remote sensing data, the Landsat series has been applied in geology for decades [6,[9][10][11], for applications such as hydrothermal alteration (ferric iron and hydroxides) extraction, lithological discrimination, and geotectonic interpretation.…”

Section: Introductionmentioning

confidence: 99%

“…With a higher spectral resolution in the shortwave infrared (SWIR) and thermal infrared (TIR) range than other multispectral data, ASTER provides a higher potential to undertake semi-quantitative mineral mapping. It has become the most popular imagery in geological exploration, especially in hydrothermal alteration and lithological unit discrimination, since the Terra satellite was launched in 1999 [7,[12][13][14]. For example, Son et al [15] utilized ASTER data to map distribution patterns of hydrothermal alteration and igneous rocks in the southwestern Gobi in Mongolia.…”

Section: Introductionmentioning

confidence: 99%

Lithological Classification Using Sentinel-2A Data in the Shibanjing Ophiolite Complex in Inner Mongolia, China

Cheng

Tang

et al. 2018

Remote Sensing

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As a source of data continuity between Landsat and SPOT, Sentinel-2 is an Earth observation mission developed by the European Space Agency (ESA), which acquires 13 bands in the visible and near-infrared (VNIR) to shortwave infrared (SWIR) range. In this study, a Sentinel-2A imager was utilized to assess its ability to perform lithological classification in the Shibanjing ophiolite complex in Inner Mongolia, China. Five conventional machine learning methods, including artificial neural network (ANN), k-nearest neighbor (k-NN), maximum likelihood classification (MLC), random forest classifier (RFC), and support vector machine (SVM), were compared in order to find an optimal classifier for lithological mapping. The experiment revealed that the MLC method offered the highest overall accuracy. After that, Sentinel-2A image was compared with common multispectral data ASTER and Landsat-8 OLI (operational land imager) for lithological mapping using the MLC method. The comparison results showed that the Sentinel-2A imagery yielded a classification accuracy of 74.5%, which was 2.5% and 5.08% higher than those of the ASTER and OLI imagery, respectively, indicating that Sentinel-2A imagery is adequate for lithological discrimination, due to its high spectral resolution in the VNIR to SWIR range. Moreover, different data combinations of Sentinel-2A + ASTER + DEM (digital elevation model) and OLI + ASTER + DEM data were tested on lithological mapping using the MLC method. The best mapping result was obtained from Sentinel-2A + ASTER + DEM dataset, demonstrating that OLI can be replaced by Sentinel-2A, which, when combined with ASTER, can achieve sufficient bandpasses for lithological classification.

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Application of ASTER and Landsat TM Data for Geological Mapping of Esfandagheh Ophiolite Complex, Southern Iran

Cited by 58 publications

References 37 publications

Determining Earthquake Susceptible Areas Southeast of Yogyakarta, Indonesia—Outcrop Analysis from Structure from Motion (SfM) and Geographic Information System (GIS)

Determining Earthquake Susceptible Areas Southeast of Yogyakarta, Indonesia—Outcrop Analysis from Structure from Motion (SfM) and Geographic Information System (GIS)

Modeling Urban Encroachment on the Agricultural Land of the Eastern Nile Delta Using Remote Sensing and a GIS-Based Markov Chain Model

Lithological Classification Using Sentinel-2A Data in the Shibanjing Ophiolite Complex in Inner Mongolia, China

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