Remote Sensing (RS)
Keyvan Mokhtari; Hooshang Asadi Harouni; Mohammad Ali Aliabadi; Somayeh Beiranvand
Abstract
Extended Abstract 1- IntroductionAlteration is the simplest, cheapest and most suitable means of mineral exploration. The best way to find changes is to use satellite data processing.Asadi and Tabatabaei (2007) have used band ratio processing methods and false color images by using selected principal ...
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Extended Abstract 1- IntroductionAlteration is the simplest, cheapest and most suitable means of mineral exploration. The best way to find changes is to use satellite data processing.Asadi and Tabatabaei (2007) have used band ratio processing methods and false color images by using selected principal component processing (PCA) to identify the range of variations in different regions on Aster images. Gomez et al. (2005) visualized the lithological units of Namibian using the PCA algorithm on Aster data.The exposed rock units in Muteh mining area include a series of sedimentary, volcanic, and volcanic-clastic metamorphic rocks that extends from the green schist facies to the border of green schist and amphibolites along the northeast-southwest direction. These units have been repeatedly penetrated by alkaline intrusions, especially acid and granite (Rashidenjad, Omran et al., 2002).In general, the controlling elements of mineralization in Muteh area include structural factors (faults and fractures), alteration, and deformation. Field observations indicate the occurrence of vein mineralization and gold sulfide deposits in mylonite shear zones and fault zones in felsic to mafic metavolcanic host rocks.Gold mineralization is mainly concentrated in highly altered metariolites containing iron and copper sulfides and within fractures as veins and deposits. Alterations in silica, sericite, and carbonation are also observed along with these sediments, which are studied as exploration keys (Moritz et al., 2006).In this area, according to the lithology and distribution of alteration zones and the type of mineralization in Muteh gold mine, gold orogeny-type mineralizations are expected, which can be indirectly identified by recognizing the above alteration.2- Materials and methodsIn this study, Aster satellite images have been used to identify, discover and separate alteration zones in ENVI 5.3 software. Also, Landsat 8 satellite images have been utilized for general investigation and identification of hydrothermal alteration zones and expansion of iron oxide minerals, and Sentinel 2 satellite data due to better spatial and radiometric resolution than the above data has been applied to increase the spatial resolution of these data and the spatial accuracy of the map from the extracted changes.In order to validate between the field observations and spectral analysis, 24 rock samples were taken from the place of alteration, especially siliceous, argillic, and sercitic alteration around Senjedeh and Chah Khatoon deposits. 11 samples were sent to Zarazma laboratory for XRD analysis, and five samples were sent to Zarkavan Alborz Company’s laboratory for chemical analysis of 41 elements by ICP-MS method and gold element by Fire Assay method.3- ResultsConsidering the relationship between alteration zones and metal mineralization, it is very important to know and map these areas in the exploration of these deposits.The results and images show that the methods used in determining and separating the altered areas in Muteh exploratory area are acceptable and the optimal and effective methods in this research, SAM and MF, have been introduced.According to the field observations and surface sampling around Chah Khatoon and Senjedeh mineral deposits, as well as the investigation of changes, it was found that the most important changes in the region are: silicification, kaolinization, sericization, chlorination, alonation, pyrite, carbonation and so forth. This wide range shows the difference in intensity of alteration in different parts of the mineral reserve, which can be attributed to the system of joints, fractures and faults in the region.According to the available evidence, the metariolite rock is highly silicified in the tensile zones or in places with dense seams, and the pyrite particles in the context of these rocks have turned into iron hydroxide.4- DiscussionBy using satellite data processing, various data and information can be identified and extracted. Satellite data processing is done in two ways: visual and digital processing. By combining these two methods, the desired effects can be detected more accurately than the accuracy of satellite images. The visual method consists of preparing images of different color combinations by placing spectral bands in the red, green, and blue channels. Digital satellite image processing methods include band ratio, principal component analysis, least square regression method (Ls-Fit), spectral analysis, spectral angle mapping (SAM), and adaptive MF filter. The selection of the above methods was based on the type of information requested to extract data from images.Aster sensor images have no blue band (spectral range 0.4-0.5 µm) and the color composition of its VNIR bands is a standard RGB (1,2,3) false color composition. In this color combination, vegetation is seen in red. Since the study area is located in a relatively arid environment without vegetation, vegetation cover was avoided in the spectral analysis.The use and processing of Aster satellite data is one of the main features of this sensor; the use of unique spectral reflectance curves of alteration indicator minerals helped to identify and highlight these altered areas as well as finding the potential of areas prone to metal mineralization. Due to the high ability of Sentinel-2A images in identifying gossan and iron oxide ranges, the processing of these data was used to highlight these areas better.5- ConclusionAccording to the agreement of the results of geochemical and XRD studies with the distribution map of the alteration zones identified from the reference spectrum (USGS) and the spectral library (JPL), with the distribution map of lines and structural fractures of Muteh exploratory zone outside the pre-identified areas, new alteration zones were also introduced that require field research to confirm the results of stereo data processing.
Mostafa Khabazi; Ali Mehrabi; Javad Arabi
Abstract
Extended Abstract Introduction Digital elevation model (DEM) is the raster representation of the ground surface so that the information of each cell on the image has a value equal to the altitude from the sea level corresponding to the same spot on the ground. DEM is an appropriate tool for the generation ...
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Extended Abstract Introduction Digital elevation model (DEM) is the raster representation of the ground surface so that the information of each cell on the image has a value equal to the altitude from the sea level corresponding to the same spot on the ground. DEM is an appropriate tool for the generation of topographic maps and contour lines, access to the information of surface roughness, three dimensional vision, etc. (Jacobsen, 2004). The accuracy of the digital elevation model is effective on the accuracy of the information from which it is obtained. This is why researchers are always looking for a way to increase the accuracy of digital elevation models. Among the information resources that are used to generate this model are ground mapping, aerial photography, satellite images, radar data, and Lidar. Some of these data generate the digital elevation model with little accuracy due to the insufficiency of the elevation information. The aim of this paper is to investigate the accuracy of DEMs derived from ASTER satellite images and SRTM data with 30 and 90-meter pixel dimensions and the digital elevation model derived from the topographic 1:25000-scale maps with Differential Global Positioning System (DGPS) in different landforms including plains, hills and mountains. Materials and Methods The study area is a part of the project of dam and water transfer system from the Azad dam to the plain of Ghorve-Dehgolan (with the goal of transferring water from the catchments of Sirvan River into the country) in the province of Kurdistan and the city of Sanandaj. In this study, the Real-Time kinematic method (RTK) was used to locate the points. In this method, assuming that the coordinates of the reference station are known and comparing it with the location obtained from the GPS receiver, a correction value is obtained that is applied to the coordinates obtained for the Rover Station, which is known as the relative or differential method. In this method, the corrections are calculated asreal-time during the observations and are considered in the determination of the Rover location.The Leica GS10 GNSS receivers were used in this study. First, two reference stations were determined using the Fast Static method and then, the Real-Time kinematic (RTK) method was used. In order to investigate the extent of the data compliance and relation, the Pearson linear correlation analysis was used and the accuracy assessment of the extracted digital elevation models was carried out using the RMSE, mean error and standard deviation. Results & Discussion The statistical parameters such as root mean square error (RMSE), bias (µ) and standard deviation () were used to assess the accuracy of each one of the investigated digital models. By comparing different sources that create DEMs, it can be seen that the minimum error is first related to the digital elevation model extracted from the contour lines of the 1:25000-scale map (27/6 = RMSE) and then to the ASTER digital elevation model with the pixel size of 30 meters (RMSE=7.43). The 30-meter pixel size DEM has always led to better results than the 90- meter pixel size DEM. Based on the mean error standard, the minimum bias is related to ASTER30 m (bias of 2 m) and then to the 1: 25,000 DEM (2.17). The maximum bias was related to 30-and 90-meter models extracted from the SRTM data. The results of standard deviation error were in compliance with the RMSE results, which confirmed the superiority of 1:25000-scale map and ASTER30 m DEMs. The results showed that the determination coefficient of relationship between the ground data and digital elevation models is between 97 and 99. The maximum compliance is related to the digital elevation model extracted from the 1:25000-scale topographic data and the ASTER30 m DEM, while the minimum compliance is related to the SRTM90 m data. In general, the compliance of the digital elevation models with the ground data decreased as the field's conditions became more difficult, i.e. from plain to mountain. Conclusion The results of DEMs accuracy assessment showed that the minimum error was primarily related to 1:25000 contour lines DEM (RMSE=6.27) and then, to the ASTER30 m DEM (RMSE=7.43). The pixel size of 30 meters has always been better than the pixels size of 90 meters. Based on the mean error standard, the minimum bias is related to the ASTER 30 m (bias of 2 m) and then, to the 1: 25,000 DEM (2.17). The maximum bias was related to 30-and 90-meter models extracted from the SRTM data. The results of the standard deviation error were consistent with the RMSE results, which confirmed the superiority of the digital elevation models extracted from the topographic 1:25000-scale maps and the ASTER30 m DEM.
Bakhtiar Feizizadeh; Salimeh Abdolah Abadei; Khalil Valizadeh Kamran
Abstract
Extended Abstract DigitalElevation Model (DEM) is one of the main geographical datamodels which forms the basis of the different spatial analysis. DEM is known as fundamental data for many modelingtasks. Nowadays, the result validation of GIS spatial analysis, hasbecome a major challenge in the ...
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Extended Abstract DigitalElevation Model (DEM) is one of the main geographical datamodels which forms the basis of the different spatial analysis. DEM is known as fundamental data for many modelingtasks. Nowadays, the result validation of GIS spatial analysis, hasbecome a major challenge in the world of GIS.Thequality of a DEM is dependent upon a number of interrelatedfactors, including the methods of data acquisition, the nature ofthe input data, and the methods employed in generating the DEMs.Analysis of uncertainty in different fields, due to data qualityand related issues such as error, uncertainty models, error propagation, error elimination and uncertainties in the data, are felt morethan any other times. Of all these factors, data acquisitionis the most critical one. Previous studies on DEM dataacquisition have focused either on examination of generation method(s), oron case studies of accuracy testing. These studies are not adequate,however, for the purpose of understanding uncertainty (an indicator used toapproximate the discrepancy between geographic data and the geographic reality thatthese data intend to represent) associated with DEM data and thepropagation of this uncertainty through GIS based analyses. The developmentof strategies for identifying, quantifying, tracking, reducing, visualizing, and reportinguncertainty in DEM data are called for by the GIS community. In order to apply uncertainty analysis on DEMs, this studyaimed to evaluate the error rate and uncertainty of elevationdata obtained from SRTM and ASTER satellites. The objectives ofthis study are: (1) to understand the sources and reasonsfor uncertainty in DEMs produced by cartographic digitizing; (2) to develop methodsfor quantifying the uncertainty of DEMs using distributional measures and (3) to measure the uncertainty associated with DEMs and minimizethe chances of error by means of optimizing models. Quantifying uncertaintyin DEMs requires comparison of the original elevations (e.g. elevations read from topographic maps) with the elevations in aDEM surface. Such a comparison results in height differences (orresiduals) at the tested points. To analyze the pattern ofdeviation between two sets of elevation data, conventional ways areto yield statistical expressions of the accuracy, such as the rootmean square error, standard deviation, and mean. In fact, allstatistical measures that are effective for describing a frequency distribution, including centraltendency and dispersion measures, may be used, as long asvarious assumptions for specific methods are satisfied. Our research methodology includesseveral steps. The first step was, using the statistical indices ME, STD and RMSE, the error rate of DTMsforobtaining the chances of error in ach model. It hasto be mentioned that the main attraction of the RMSElies in its easy computation and straightforward concept. However, this indexis essentially a single global measure of deviations, thus incapable ofaccounting for spatial variation of errors over the interpolated surface. Inorder to obtain more accurate results, then uncertainty of dataerrors was also simulated by Monte Carlo method and errorpropagation pattern was extracted by interpolation of results. The resultsof this step show that, the DEM derived from pairstereo ASTER despite having better spatial resolution, included more errorsand practically lacking the details of DTM 30 meters. Finally,removing the error propagation pattern from DEMs, the secondary DEMwas produced. By recalculating indicators describing the error and comparingthese values with the initial values, the results indicate that,both DEMs show more accuracy after eliminating the error propagationpattern. TPI Index was used to determine the location ofbasin topography and the basin is divided into 6 classesand error rate in each class was calculated before andafter the simulation. The results showed that, the error ratesin all classes before and after the simulation in bothDEMs were reduced. In terms of uncertainty analysis methods forDEMs, results of our research indicated that the RMSE methodsalone is not sufficient for quantifying DEM uncertainty, because this measurerarely addresses the issue of distributional accuracy. To fully understand andquantify the DEM uncertainty, spatial accuracy measures, such as accuracy surfaces, indices for spatial autocorrelation, and variograms, should be used. Results alsoindicated that Monet Carlo simulation is indeed sufficient methods forsimulation error in DEMs. Results of this research are of great importance for uncertainty analysis in domain of Geosciences andcan be used for improving the accuracy of modeling in avariety of applications.
