scholarly journals Band Ratios Matrix Transformation (BRMT): A Sedimentary Lithology Mapping Approach Using ASTER Satellite Sensor

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3213 ◽  
Author(s):  
Ghasem Askari ◽  
Amin Pour ◽  
Biswajeet Pradhan ◽  
Mehdi Sarfi ◽  
Fatemeh Nazemnejad
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Accuracy Assessment ◽  
Spectral Band ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Matrix Transformation ◽  
Band Ratio ◽  
Band Ratios ◽  
Sedimentary Succession

Remote sensing imagery has become an operative and applicable tool for the preparation of geological maps by reducing the costs and increasing the precision. In this study, ASTER satellite remote sensing data were used to extract lithological information of Deh-Molla sedimentary succession, which is located in the southwest of Shahrood city, Semnan Province, North Iran. A robust and effective approach named Band Ratio Matrix Transformation (BRMT) was developed to characterize and discriminate the boundary of sedimentary rock formations in Deh-Molla region. The analysis was based on the forward and continuous division of the visible-near infrared (VNIR) and the shortwave infrared (SWIR) spectral bands of ASTER with subsequent application of principal component analysis (PCA) for producing new transform datasets. The approach was implemented to ASTER spectral band ratios for mapping dominated mineral assemblages in the study area. Quartz, carbonate, and Al, Fe, Mg –OH bearing-altered minerals such as kaolinite, alunite, chlorite and mica were appropriately mapped using the BRMT approach. The results match well with geology map of the study area, fieldwork data and laboratory analysis. Accuracy assessment of the mapping result represents a reasonable kappa coefficient (0.70%) and appropriate overall accuracy (74.64%), which verified the robustness of the BRMT approach. This approach has great potential and capability for mapping sedimentary succession with diverse local–geological–physical characteristics around the world.

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 Mapping of Intrusive Complex on a Small Scale Using Multi-Source Remote Sensing Images

2020 ◽  
Vol 9 (9) ◽  
pp. 543
Author(s):  
Yuzhou Zhang ◽  
Dengrong Zhang ◽  
Jinwei Duan ◽  
Tangao Hu
Keyword(s):  
Remote Sensing ◽  
Thermal Emission ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Northwest China ◽  
Fusion Process ◽  
Small Scale ◽  
Intrusive Complex ◽  
Statistical Regression ◽  
Band Ratio

Multi-stage intrusive complex mapping plays an important role in regional mineralization research. The similarity of lithology characteristics between different stages of intrusions necessitates the use of richer spectral bands, while higher spatial resolution is also essential in small-scale research. In this paper, a multi-source remote sensing data application method was proposed. This method includes a spectral synergy process based on statistical regression and a fusion process using Gram–Schmidt (GS) spectral sharpening. We applied the method with Gaofen-2 (GF2), Sentinel-2, and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data to the mapping of the Mountain Sanfeng intrusive complex in northwest China in which Carboniferous intrusions have been proven to be directly related to the formation of Au deposits in the area. The band ratio (BR) and relative absorption band depth (RBD) were employed to enhance the spectral differences between two stage intrusions, and the Red-Green-Blue (RGB) false colour of the BR and RBD enhancement images performed well in the west and centre. Excellent enhancement results were obtained by making full use of all bands of the synergistic image and using the Band Ratio Matrix (BRM)-Principal Component Analysis (PCA) method in the northeast part of the study area. A crucial improvement in enhancement performance by the GS fusion process and spectral synergy process was thus shown. An accurate mapping result was obtained at the Mountain Sanfeng intrusive complex. This method could support small-scale regional geological survey and mineralization research in this region.

Comparison of different remote sensing methods for glacier mapping in Afghanistan

2020 ◽  
Author(s):  
Jamal Abdul Naser Shokory ◽  
Stuart Lane
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Accuracy Assessment ◽  
Thermal Infrared ◽  
Surface Model ◽  
Band Ratio ◽  
Manual Delineation ◽  
Ratio Image ◽  
Debris Cover ◽  
Glacier Mapping

<p>Glaciers are important sources of fresh water particularly in arid regions which have low summer precipitation. Moreover, retreating glaciers can cause serious hazards by destabilizing slopes or causing outbursts of glacial lakes. Therefore glacier monitoring is an essential task for water resources and risk management. Recently, efforts have been made to monitor glaciers using manual or semi-automated remote sensing techniques. However a particular challenge remains: as glaciers retreat they commonly develop a surface debris layer that optically is similar to zones that have not been glaciated or that are truly deglaciated: the debris cover on the glacier surface has a similar reflectance to surrounding moraines in the visible to near-infrared wavelength region. In other hand, where debris cover develops, it may insulate ice from solar radiation and diurnal temperature rises, and this will also reduce melt. Therefore, debris cover on glacier boundaries critically hinders the global inventory of glaciers. To overcome the challenges this study uses a multiple band ratio approach. The method was tested for delineating three glaciers in Afghanistan at different scales and locations to map both clean ice and debris-covered ice. We used Landsat Enhanced Thematic Mapper Plus, and a 5-meter resolution digital surface model DSM data to extract the morphological parameters. Since clean glacier ice has a high reflectivity in the visible to near-infrared wavelengths, at first we used NDIS to extract the clean ice area, but It was found that the NDSI method for glacier mapping is less sensitive to cast shadows and steep terrain. Similarly, a slope parameter has tested to map the debris cover ice area but it did not map areas with gentle slopes correctly.</p><p>Nonetheless, NIR and SWIR were identified as potential candidates for distinguishing between glaciers in shade and clean ice for the debris free case; and a combination of those bands in three different ratios and thresholds was applied successfully (Red/SWIR>= 1.5, Pan/SWIR>0.1, and NIR/SWIR>1). With regards to debris-covered ice the thermal infrared bands show potential in resolving such ambiguity, as considerable temperature differences are found to exist between debris covered ice and surrounding moraines. However, we found that thermal infrared bands have too coarse a resolution (60m) for valley glaciers. Hence, we developed a new band ratio image combining thermal infrared and panchromatic bands to better distinguish periglacial debris and supraglacial debris. This new band ratio image is given by (PAN-TIR)/(PAN+TIR), and is named as normalized supraglacial debris index (NSDI).</p><p>Accuracy assessment was carried out through comparisons of the classified maps with a manual delineation done using 1-meter high resolution RGB image with same temporal resolution. The accuracy assessment shows that the results from the proposed method are in good agreement with the manual delineation. The proposed synergistic approach therefore appears useful in the accurate mapping of debris-covered glaciers in Afghanistan.</p>

scholarly journals Snow dunes and glazed surfaces in Antarctica: new field and remote-sensing data

2002 ◽  
Vol 34 ◽  
pp. 81-88 ◽  
Author(s):  
Massimo Frezzotti ◽  
Stefano Gandolfi ◽  
Floriana La Marca ◽  
Stefano Urbini
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Accumulation Rate ◽  
Spectral Response ◽  
Remote Sensing Data ◽  
Snow Accumulation ◽  
Remotely Sensed Data ◽  
Surface Mass ◽  
Glazed Surface ◽  
Transverse Dunes

AbstractAs part of the International Trans-Antarctic Scientific Expedition project, the Italian Antarctic Programme undertook two traverses from the Terra Nova station to Talos Dome and to Dome C. Along the traverses, the party carried out several tasks (drilling, glaciological and geophysical exploration). The difference in spectral response between glazed surfaces and snow makes it simple to identify these areas on visible/near-infrared satellite images. Integration of field observation and remotely sensed data allows the description of different mega-morphologic features: wide glazed surfaces, sastrugi glazed surface fields, transverse dunes and megadunes. Topography global positioning system, ground penetrating radar and detailed snow-surface surveys have been carried out, providing new information about the formation and evolution of mega-morphologic features. The extensive presence, (up to 30%) of glazed surface caused by a long hiatus in accumulation, with an accumulation rate of nil or slightly negative, has a significant impact on the surface mass balance of a wide area of the interior part of East Antarctica. The aeolian processes creating these features have important implications for the selection of optimum sites for ice coring, because orographic variations of even a few metres per kilometre have a significant impact on the snow-accumulation process. Remote-sensing surveys of aeolian macro-morphology provide a proven, high-quality method for detailed mapping of the interior of the ice sheet’s prevalent wind direction and could provide a relative indication of wind intensity.

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 Integrating Landsat 7 and 8 data to improve basalt formation classification: A case study at Buon Ma Thuot region, Central Highland, Vietnam

2019 ◽  
Vol 11 (1) ◽  
pp. 901-917
Author(s):  
Ngo Van Liem ◽  
Dang Van Bao ◽  
Dang Kinh Bac ◽  
Nguyen Hieu ◽  
Do Trung Hieu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spectral Band ◽  
Landsat 8 ◽  
Central Highland ◽  
Band Ratios ◽  
Landsat 7 ◽  
Cenozoic Basalt ◽  
Geological Map ◽  
Geological Formations

Abstract Cenozoic basalt regions contain various natural resources that can be used for socio-economic development. Different quantitative and qualitative methods have been applied to understand the geological and geomorphological characteristics of basalt formations. Nowadays the integration of remote sensing and geographic information systems (GIS) has become a powerful method to distinguish geological formations. In this paper, authors combined satellite and fieldwork data to analyze the structure and morphology of highland geological formations in order to distinguish two main volcanic eruption episodes. Based on remote sensing analysis in this study, different spectral band ratios were generated to select the best one for basalt classification. Lastly, two spectral combinations (including band ratios 4/3, 6/2, 7/4 in Landsat 8 and 3/2, 5/1, 7/3 in Landsat 7) were chosen for the Maximum Likelihood classification. The final geological map based on the integration of Landsat 7 and 8 outcomes shows precisely the boundary of the basalt formations with the accuracy up to 93.7%. This outcome contributed significantly to the correction of geological maps. In further studies, authors suggest the integration of Landsat 7 and 8 data in geological studies and natural resource and environmental management at both local and regional scales.

scholarly journals Measurement of nitrogen content in rice by inversion of hyperspectral reflectance data from an unmanned aerial vehicle

2018 ◽  
Vol 48 (6) ◽  
Author(s):  
Du Wen ◽  
Xu Tongyu ◽  
Yu Fenghua ◽  
Chen Chunling
Keyword(s):  
Remote Sensing ◽  
Nitrogen Content ◽  
Unmanned Aerial Vehicle ◽  
Gaussian Process Regression ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Ground Truth ◽  
Hyperspectral Remote Sensing ◽  
Inversion Algorithm ◽  
Aerial Vehicle

ABSTRACT: The Nitrogen content of rice leaves has a significant effect on growth quality and crop yield. We proposed and demonstrated a non-invasive method for the quantitative inversion of rice nitrogen content based on hyperspectral remote sensing data collected by an unmanned aerial vehicle (UAV). Rice canopy albedo images were acquired by a hyperspectral imager onboard an M600-UAV platform. The radiation calibration method was then used to process these data and the reflectance of canopy leaves was acquired. Experimental validation was conducted using the rice field of Shenyang Agricultural University, which was classified into 4 fertilizer levels: zero nitrogen, low nitrogen, normal nitrogen, and high nitrogen. Gaussian process regression (GPR) was then used to train the inversion algorithm to identify specific spectral bands with the highest contribution. This led to a reduction in noise and a higher inversion accuracy. Principal component analysis (PCA) was also used for dimensionality reduction, thereby reducing redundant information and significantly increasing efficiency. A comparison with ground truth measurements demonstrated that the proposed technique was successful in establishing a nitrogen inversion model, the accuracy of which was quantified using a linear fit (R2=0.8525) and the root mean square error (RMSE=0.9507). These results support the use of GPR and provide a theoretical basis for the inversion of rice nitrogen by UAV hyperspectral remote sensing.

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