scholarly journals Lithological mapping using remote sensing techniques: A case study of Alagbayan area, Dornogobi province, Mongolia

2021 ◽  
Vol 26 (53) ◽  
pp. 37-54
Author(s):  
Badrakh Munkhsuren ◽  
Batkhuyag Enkhdalai ◽  
Tserendash Narantsetseg ◽  
Khurelchuluun Udaanjargal ◽  
Demberel Orolmaa ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Principal Component ◽  
Granite Gneiss ◽  
Support Vector ◽  
Landsat 8 ◽  
Landsat Images ◽  
Band Ratio ◽  
Lithological Mapping ◽  
Remote Sensing Techniques ◽  
Thin Section Analysis

This study investigated the multispectral remote sensing techniques including ASTER, Landsat 8 OLI, and Sentinel 2A data in order to distinguish different lithological units in the Alagbayan area of Dornogobi province, Mongolia. Therefore, Principal component analysis (PCA), Band ratio (BR), and Support Vector Machine (SVM), which are widely used image enhancement methods, have been applied to the satellite images for lithological mapping. The result of supervised classification shows that Landsat data gives a better classification with an overall accuracy of 93.43% and a kappa coefficient of 0.92 when the former geologic map and thin section analysis were chosen as a reference for training samples. Moreover, band ratios of ((band 7 + band 9)/band 8) obtained from ASTER corresponds well with carbonate rocks. According to PCs, PC4, PC3 and PC2 in the RGB of Landsat, PC3, PC2, PC6 for ASTER data are chosen as a good indicator for different lithological units where Silurian, Carboniferous, Jurassic, and Cretaceous formations are easily distinguished. In terms of Landsat images, the most efficient BR was a ratio where BRs of 5/4 for alluvium, 4/7 for schist and 7/6 to discriminate granite. In addition, as a result of BR as well as PCA, Precambrian Khutag-Uul metamorphic complex and Norovzeeg formation can be identified but granite-gneiss and schist have not given satisfactory results.

scholarly journals Mineral Prοpecting and Lithοlοgical Mapping Using Remοte Sensing Apprοaches in Between Yazihan-Heki̇mhan (Malatya) Turkey

2021 ◽  
Author(s):  
ULAŞ İNAN SEVİMLİ ◽  
MAMADOU TRAORE ◽  
YUSUF TOPAK ◽  
SENEM TEKİN
Keyword(s):  
Remote Sensing ◽  
Landsat 8 ◽  
Potential Zone ◽  
Landsat 8 Oli ◽  
Ophiolite Complex ◽  
Band Ratio ◽  
Lithological Mapping ◽  
Chromite Ore ◽  
Aster Data ◽  
Basic Rocks

Abstract The Remote Sensing processing analysis have become a directing and hopeful instrument for mineral investigation and lithological mapping. Mineral exploration in general and bearing chromites associated with ultrabasic and basic rocks of the ophiolite complex in particular has been successfully carried out in recent years using Remote Sensing techniques. Yazıhan-Hekimhan (Malatya) region of East Taurus mountain belt, ranks second in terms of iron mineralization in Turkey are accepted. The area is characterized by high grade iron ore deposits in use, development and exploration. Lithological mapping and chromite ore exploration of this area is challenging owing to difficult access (High Mountain 2243 m) using the traditional method of exploration. The main objective of this research is to evaluate the capacity of Landsat-8 OLI and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery to discriminate and detect the potential zone of chromites bearing mineralized in Malatya (Yazıhan). Several images processing techniques, Vegetation Mask, Band Ratio (BR), Band Ratio Color Composite (BRCC), Principal Component Analysis (PCA), Decorrelation Stretch, Minimum Noise Fraction and Supervised classification using Spectral Angle Mapper (SAM) exist in previous studies have been performed for lithological mapping. The obtained results show that, BR, PCA and Decorrelation Stretch methods applied on NVIR-SWIR bands of Landsat-8 and ASTER were clearly discriminate the ophiolite rocks at a regional scale. In Addition, SAM classification was applied on a spectral signature of differents ultrabasic and basic rocks extracted from ASTER data. The results are promising in identifying the potentials zones of chromite ore mineralization zones within the ophiolite region. Thus, the techniques used in this research are suitable to detect or identify the high-potential chromite bearing areas in the ophiolite complex rocks using Landsat-8 OLI and ASTER data.

scholarly journals Analysis of cloud condition on Sentinel-2 MSI and Landsat-8 OLI images of a public supply lake in Belém-Pará-Brazil

2019 ◽  
Vol 14 (4) ◽  
pp. 1 ◽  
Author(s):  
Vítor Abner Borges Dutra ◽  
Paulo Amador Tavares ◽  
Hebe Morganne Campos Ribeiro
Keyword(s):  
Remote Sensing ◽  
Principal Component ◽  
Economic Losses ◽  
Landsat 8 ◽  
Sensing Applications ◽  
Dissolved Organic Matters ◽  
Combined Use ◽  
Cloud Regime ◽  
Remote Sensing Techniques ◽  
Sentinel 2

The eutrophication process leads to reduced water quality and economic losses worldwide. Furthermore, it is possible to apply remote sensing techniques for monitoring of aquatic environments. In this paper, we analysed the combined use of Sentinel-2 Multispectral Instrument and Landsat-8 Operational Land Imager data to monitor a eutrophic aquatic environment under adverse cloudy conditions, from July 2016 to July 2018. Data pre-selection was performed, and then the images were acquired for further investigation. After that, we created a key to the interpretation of cloud conditions for the study area and grouped each of 125 scenes in a Principal Component Analysis (PCA). The PCA grouped months with similarities in cloud conditions, highlighting their patterns in terms of the rainy and dry seasons for the study area. Another interesting result was that, even under the inherent adverse cloud regime of the Amazon, the combined use of both free satellite imagery data could be useful for further analyses, such as measuring of chlorophyll a, coloured dissolved organic matters, total suspended solids and turbidity. However, we highlight that, firstly, studies must be made to validate the data in situ, so that monitoring programs can be built through remote sensing applications.

scholarly journals EXPLORATION OF IOCG MINERALIZATIONS USING INTEGRATION OF SPACE-BORNE REMOTE SENSING DATA WITH AIRBORNE GEOPHYSICAL DATA

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 9-16
Author(s):  
M. Abdolmaleki ◽  
T. M. Rasmussen ◽  
M. K. Pal
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Support Vector ◽  
Mineral Potential Mapping ◽  
Band Ratio ◽  
Sensing Data ◽  
Mineral Potential ◽  
Potential Mapping ◽  
Sentinel 2

Abstract. Nowadays, remote sensing technologies are playing a significant role in mineral potential mapping. To optimize the exploration approach along with a cost-effective way, narrow down the target areas for a more detailed study for mineral exploration using suitable data selection and accurate data processing approaches are crucial. To establish optimum procedures by integrating space-borne remote sensing data with other earth sciences data (e.g., airborne magnetic and electromagnetic) for exploration of Iron Oxide Copper Gold (IOCG) mineralization is the objective of this study. Further, the project focus is to test the effectiveness of Copernicus Sentinel-2 data in mineral potential mapping from the high Arctic region. Thus, Inglefield Land from northwest Greenland has been chosen as a study area to evaluate the developed approach. The altered minerals, including irons and clays, were mapped utilizing Sentinel-2 data through band ratio and principal component analysis (PCA) methods. Lineaments of the study area were extracted from Sentinel-2 data using directional filters. Self-Organizing Maps (SOM) and Support Vector Machines (SVM) were used for classification and analysing the available data. Further, various thematic maps (e.g., geological, geophysical, geochemical) were prepared from the study area. Finally, a mineral prospectively map was generated by integrating the above mentioned information using the Fuzzy Analytic Hierarchy Process (FAHP). The prepared potential map for IOCG mineralization using the above approach of Inglefield Land shows a good agreement with the previous geological field studies.

scholarly journals A Satellite-based Remote Sensing Technique for Surface Water Quality Estimation

2019 ◽  
Vol 9 (2) ◽  
pp. 3965-3970 ◽  
Author(s):  
M. V. Japitana ◽  
M. E. C. Burce
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Temporal Trends ◽  
Oxygen Demand ◽  
Surface Water Quality ◽  
Principal Component ◽  
Input Image ◽  
Landsat 8 ◽  
Band Ratio ◽  
Remote Sensing Technique

Remote sensing provides a synoptic view of the earth surface that can provide spatial and temporal trends necessary for comprehensive water quality (WQ) monitoring and assessment. This study explores the applicability of Landsat 8 and regression analysis in developing models for estimating WQ parameters such as pH, dissolved oxygen (DO), total dissolved solids (TDS), total suspended solids (TSS), biological oxygen demand (BOD), turbidity, and conductivity. The input image was radiometrically-calibrated using fast line-of-sight atmospheric analysis (FLAASH) and then atmospherically corrected to obtain surface reflectance (SR) bands using FLAASH and dark object subtraction (DOS) for comparison. SR bands derived using FLAASH and DOS, water indices, band ratio, and principal component analysis (PCA) images were utilized as input data. Feature vectors were then collected from the input bands and subsequently regressed together with the WQ data. Forward regression results yielded significant high R2 values for all WQ parameters except TSS and conductivity which had only 60.1% and 67.7% respectively. Results also showed that the regression models of pH, BOD, TSS, TDS, DO, and conductivity are highly significant to SR bands derived using DOS. Furthermore, the results of this study showed the promising potential of using RS-based WQ models in performing periodic WQ monitoring and assessment.

scholarly journals Optimized Lithological Mapping from Multispectral and Hyperspectral Remote Sensing Images Using Fused Multi-Classifiers

2020 ◽  
Vol 12 (1) ◽  
pp. 177 ◽  
Author(s):  
Mahendra Pal ◽  
Thorkild Rasmussen ◽  
Alok Porwal
Keyword(s):  
Remote Sensing ◽  
Kappa Coefficient ◽  
Hyperspectral Remote Sensing ◽  
Support Vector ◽  
Landsat 8 ◽  
Remote Sensing Images ◽  
Spectral Angle Mapper ◽  
Lithological Mapping ◽  
Accuracy Index ◽  
Hyperspectral Remote Sensing Images

Most available studies in lithological mapping using spaceborne multispectral and hyperspectral remote sensing images employ different classification and spectral matching algorithms for performing this task; however, our experiment reveals that no single algorithm renders satisfactory results. Therefore, a new approach based on an ensemble of classifiers is presented for lithological mapping using remote sensing images in this paper, which returns enhanced accuracy. The proposed method uses a weighted pooling approach for lithological mapping at each pixel level using the agreement of the class accuracy, overall accuracy and kappa coefficient from the multi-classifiers of an image. The technique is implemented in four steps; (1) classification images are generated using a variety of classifiers; (2) accuracy assessments are performed for each class, overall classification and estimation of kappa coefficient for every classifier; (3) an overall within-class accuracy index is estimated by weighting class accuracy, overall accuracy and kappa coefficient for each class and every classifier; (4) finally each pixel is assigned to a class for which it has the highest overall within-class accuracy index amongst all classes in all classifiers. To demonstrate the strength of the developed approach, four supervised classifiers (minimum distance (MD), spectral angle mapper (SAM), spectral information divergence (SID), support vector machine (SVM)) are used on one hyperspectral image (Hyperion) and two multispectral images (ASTER, Landsat 8-OLI) for mapping lithological units of the Udaipur area, Rajasthan, western India. The method is found significantly effective in increasing the accuracy in lithological mapping.

scholarly journals Remote sensing - a new tool in exploration geology

1986 ◽  
Vol 128 ◽  
pp. 135-146
Author(s):  
T Thyrsted
Keyword(s):  
Remote Sensing ◽  
Supervised Classification ◽  
Mineral Exploration ◽  
Principal Component ◽  
Landsat Images ◽  
Data Set ◽  
Remote Sensing Techniques ◽  
Lineament Analysis ◽  
Definition Of ◽  
Geophysical Measurements

Remote sensing techniques have been applied to mineral exploration in areas of South and East Greenland. The data consist of airborne and satellite-borne (Landsat) multispectral scanner images and geochemical and geophysical measurements interpolated into grid format and registered on the Landsat images. The main image processing methods applied include ratioing, principal component transformation/factor analysis and classification. In addition, visual and subsequent statistical analyses of lineaments were carried out on images from South Greenland. The results of the work include mapping of several hundred spectral anomalies which represent oxidation zones on the ground. The lineament analysis resulted in definition of major linear zones with increased lineament intensities; some of these zones may have geological significance. Supervised classification was carried out on an integrated data set consisting of images and geochemical/geophysical data. The training areas mainly included uranium showings, and the classified image depicts both previously known occurrences and a new area which is statistically similar to the training areas.

scholarly journals LITHOLOGICAL MAPPING USING LANDSAT 8 OLI AND ASTER MULTISPECTRAL DATA IN IMINI-OUNILLA DISTRICT SOUTH HIGH ATLAS OF MARRAKECH

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1255-1262 ◽  
Author(s):  
Z. Ourhzif ◽  
A. Algouti ◽  
A. Algouti ◽  
F. Hadach
Keyword(s):  
Classification Accuracy ◽  
Thermal Emission ◽  
Principal Component ◽  
Kappa Coefficient ◽  
Support Vector ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
High Atlas ◽  
Multispectral Data ◽  
Lithological Mapping

<p><strong>Abstract.</strong> This study exploited the multispectral Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat 8 Operational Land Imager (OLI) data in order to map lithological units and structural map in the south High Atlas of Marrakech. The method of analysis was used by principal component analysis (PCA), band ratios (BR), Minimum noise fraction (MNF) transformation. We performed a Support Vector Machine (SVM) classification method to allow the joint use of geomorphic features, textures and multispectral data of the Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite. SVM based on ground truth in addition to the results of PCA and BR show an excellent correlation with the existing geological map of the study area. Consequently, the methodology proposed demonstrates a high potential of ASTER and Landsat 8 OLI data in lithological units discrimination. The application of the SVM methods on ASTER and Landsat satellite data show that these can be used as a powerful tool to explore and improve lithological mapping in mountainous semi-arid, the overall classification accuracy of Landsat8 OLI data is 97.28% and the Kappa Coefficient is 0.97. The overall classification accuracy of ASTER using nine bands (VNIR-SWIR) is 74.88% and the Kappa Coefficient is 0.71.</p>

scholarly journals Utilization of Space-Borne Imagery for Lithologic Mapping: A Case Study From Um Had Area, Central Eastern Desert, Egypt

2014 ◽  
Vol 6 (2) ◽  
pp. 113 ◽  
Author(s):  
Nedal Qaoud
Keyword(s):  
Remote Sensing Data ◽  
Principal Component ◽  
Eastern Desert ◽  
Granitic Rocks ◽  
Gray Scale ◽  
Band Ratio ◽  
Lithological Mapping ◽  
Central Eastern Desert ◽  
Dokhan Volcanics ◽  
Central Eastern

Remote sensing data are used to discriminate between the different lithologies covering the Um Had area, Central Eastern Desert of Egypt. Image processing techniques applied to the Enhanced Thematic Mapper (ETM+) data are used for mapping and discriminating the different basement lithologies of Um Had area. Principal component analysis (PCA), minimum noise fraction (MNF) transform and band rationing techniques provide efficient data for lithological mapping. The study area is underlain by gneisses, ophiolitic melange assemblage (talc-serpentinite, metagabbro, metabasalt), granitic rocks, Dokhan volcanics, Hammamat sediments and felsites. The resulting gray-scale PC2, PC3 and PC4 images are best to clearly discriminate the Hammamat sediments, amphibolites and talc-serpentinites, respectively. The gray-scale MNF3 and MNF4 images easily discriminate the felsites and talc-serpentinites, respectively. The band ratio 5/7 and 4/5 images are able to delineate the talc-serpentinites and Hammamat sediments, respectively. Information collected from gray-scale and false color composite images led to generation of detailed lithologic map of Um Had area.

scholarly journals Possibilities of milpa identification in Yucatan through remote sensing techniques and Sentinel-2 data

10.29007/hbs2 ◽  
2019 ◽  
Author(s):  
Juan Carlos Valdiviezo-Navarro ◽  
Adan Salazar-Garibay ◽  
Karla Juliana Rodríguez-Robayo ◽  
Lilián Juárez ◽  
María Elena Méndez-López ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Food Sovereignty ◽  
Region Of Interest ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Multispectral Images ◽  
Crop Fields ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Maya milpa is one of the most important agrifood systems in Mesoamerica, not only because its ancient origin but also due to lead an increase in landscape diversity and to be a relevant source of families food security and food sovereignty. Nowadays, satellite remote sensing data, as the multispectral images of Sentinel-2 platforms, permit us the monitor- ing of different kinds of structures such as water bodies, urban areas, and particularly agricultural fields. Through its multispectral signatures, mono-crop fields or homogeneous vegetation zones like corn fields, barley fields, or other ones, have been successfully detected by using classification techniques with multispectral images. However, Maya milpa is a complex field which is conformed by different kinds of vegetables species and fragments of natural vegetation that in conjunction cannot be considered as a mono-crop field. In this work, we show some preliminary studies on the availability of monitoring this complex system in a region of interest in Yucatan, through a support vector machine (SVM) approach.

scholarly journals Water stress coefficient determined by orbital remote sensing techniques

2020 ◽  
Vol 24 (12) ◽  
pp. 847-853
Author(s):  
Élvis da S. Alves ◽  
Roberto Filgueiras ◽  
Lineu N. Rodrigues ◽  
Fernando F. da Cunha ◽  
Catariny C. Aleman
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Irrigation Management ◽  
Simple Algorithm ◽  
Landsat 8 ◽  
Stress Coefficient ◽  
Use Efficiency ◽  
Remote Sensing Techniques ◽  
Water Stress Coefficient ◽  
The Relationship

ABSTRACT In regions where the irrigated area is increasing and water availability is reduced, such as the West of the Bahia state, Brazil, the use of techniques that contribute to improving water use efficiency is paramount. One of the ways to improve irrigation is by improving the calculation of actual evapotranspiration (ETa), which among other factors is influenced by soil drying, so it is important to understand this relationship, which is usually accounted for in irrigation management models through the water stress coefficient (Ks). This study aimed to estimate the water stress coefficient (Ks) through information obtained via remote sensing, combined with field data. For this, a study was carried out in the municipality of São Desidério, an area located in western Bahia, using images of the Landsat-8 satellite. Ks was calculated by the relationship between crop evapotranspiration and ETa, calculated by the Simple Algorithm for Evapotranspiration Retrieving (SAFER). The Ks estimated by remote sensing showed, for the development and medium stages, average errors on the order of 5.50%. In the final stage of maize development, the errors obtained were of 23.2%.

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