جستجوی مقالات مرتبط با کلیدواژه « Aster » در نشریات گروه « جغرافیا »

Corona and Hexagon satellite photographs with a spatial resolution of 1.8 to 9 meters are a good source for monitoring and evaluating changes in surface phenomena. The purpose of this study is to monitor the changes in Zaribar Lake in the period 1969-2019 using the data of Corona, Hexagon and Aster satellites and their ability to monitor and extract the coastline, lake boundary and water surface. In this study, to geometric correction the corona and hexagon data from Google Earth images, linear extraction algorithms, binary mask and mean shift segmentation were used to extract the coastline and lake boundary, detect lake changes and extract and monitor the water area of ​​Zaribar Lake. The results showed that in the first step: geometric correction of corona and hexagon images was obtained using Google Earth images with RMSE of 0.3 and 0.4 pixels. In the second stage, the linear extraction algorithm for extracting the lake boundary and coastline using corona and hexagon photographs has high accuracy and has a high correlation with the topographic map of 1.50000. In the third step, the unsupervised classification of binary mask method, in order to detect lake changes using corona and hexagon photographs, acceptablely identified the altered and unchanged pixels, so that 11 hectares of lake surface had the most changes. Finally, in the fourth step, it was found that the mean shift segmentation algorithm and threshold worked better by applying the corona, hexagon, and Aster events to extract the water surface, and in the meantime, the corona image performed better due to its higher resolution. The above results showed that Zaribar Lake decreased by 6.5% from 1969 to 2019 and the findings have a high correlation with the product of the ester aquifer. In general, the findings of this study show the potential of using digital image processing methods for corona and hexagon data to monitor and detect changes in lakes.

Earthquake is one of the most important natural disasters that annually causes a lot of damage to human and natural communities. These damages have always led researchers to look for a way to predict this phenomenon. These include using satellite images and analyzing them. Since technology has not yet been invented to accurately predict the location and time of an earthquake, it is very useful to examine the information and analysis of past earthquakes to better understand earthquake-related phenomena. In this study, thermal investigation of Bam fault, using ASTER and MODIS images in December 2003 earthquake Singel-channel algorithm and web coding in Google Earth engine, the heat trend was calculated for six months before and after the earthquake. This process was also monitored using hourly weather data from the Meteorological Organization near the study site to obtain a better view of the thermal status of the area. The results showed that contrary to existing beliefs, the earthquake had no detectable thermal effect with the ASTER and MODIS satellite images on the Earthchr('39')s surface.

  Wind erosion is affected by climate change and several droughts have caused environmental degradation in arid regions. This process causes sand and dust storm (SDS) phenomenon which is one of the most important challenges in fragile areas. This phenomenon is harmful for human health and causes socio-economic problems. Over the past two decades, SDS have been increasing in Iran, particularly in the south-east of the country. Therefore, in order to manage this environmental phenomenon in arid areas, it is an urgent need to identify the dust storm sources. The changes of factors such as vegetation, soil moisture, slope, land temperature, and geological units have effective roles in the occurrence of dust storms. So, investigating the changes in these factors is an effective solution to identify dust storm sources. In this regard, using remote sensing data is one of the effective methods for the detection and mapping of dust storms resource, which combined with field methods, provides a coherent approach to manage and control this phenomenon. This study aimed to identify dust sources and storms in the southeast of Iran with emphasis on the Sistan dust storm by using field work and remote sensing data.

The study area is located in the south east of Iran and contains some of the border areas of Iran and Afghanistan (including some parts of the south Khorasan and Sistan and Baluchestan province in Iran as well as Farah and Nimroz province in Afghanistan with coordinates of 60˚ 16′ - 61˚ 36′ east longitude and 29˚ 8′ - 32˚ 32′ north latitude). In north of the study area, the mountainous and low-altitude areas are intertwined but the southern area is mostly flat and lacks topography and natural features. The most important characteristics of this area is 120-day winds, which sometimes reach speeds of more than 120 kilometers per hour and is accompanied by high intensity of sand and dust storms. In this study, to identify dust storm sources, the combination of remote sensing, field study and wind analysis methods are used. To achieve this purpose, at first effective factors in the occurrence of dust storms such as vegetation, soil moisture, slope, land temperature, and geological units were prepared. In this regard, by using Modis satellite images, map of vegetation, soil moisture and land temperature were prepared by applying Soil Adjusted Vegetation Index (SAVI), integrating temperature vegetation dryness index (TVDI) and Land surface temperature (LST), respectively. Also slope map (using Aster satellite) and geological units were prepared. In the next stage, the fuzzy logic method was employed to combine layers and prepare the map of dust storms source, which is assessed by an error matrix and available dust source map. Detection of sand dust storms were performed by using BTD dust detection indicator. For winds analysis, we used WRplot view 8 Software. In the field study, several dust storms sources were determined and their characteristics were recorded.

    The results showed that the ranges of SAVI index are + 0.6 to -1 and the highest amount of this index coordinate with highest soil moisture (TVDI) and the lowest land surface temperature (LST) area. This area is mostly related to rangeland, combination of wetland-rangeland, hand-planted forests and agricultural lands. The amount of SAVI index and soil moisture reaches its minimum value in the salt marsh lands, abandoned agricultural lands, desert areas and degraded rangeland. By merging the layers and preparing the map of dust storm sources, it was found that the critical dust storms resources coordinate with high land surface temperature and low vegetation and soil moisture areas. In addition, the highest dust source area is in the slopes of 0 to 4 % with geological units such as playa deposits, eolian deposits, alluvium sediment and wet playa deposit. According to the results, the incoming dust storms into the study area come from the east and southeast of south Khorasan province in Iran and the southwest of Farah province in Afghanistan.

  In this study by using effective factors including vegetation, soil moisture, land surface temperature, slope and geological units, the sources of dust production were identified. According to the results, poor vegetation and low soil moisture in flat areas with sensitive formations are the key factors to create dust storm resources in the study area. Also, the most important active hot-spot areas for dust storm is concentrated in the southwest of Afghanistan and north of sistan region (dry lake bed of Hamoon). Therefore, it can be concluded that using satellite images and the studied factors, the dust sources can be properly identified. Furthermore, by using Time series images of modis and the updated dust source maps, a coherent approach can be provided in order to manage and control this phenomenon.

Alteration 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.

In 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.

Considering 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.

By 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.

According 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.

The study area is located in the northeast of neyriz and near the village of Ghori in Fars province. Geologically, the units of the study area are located in the zone-Sanandaj-Sirjan and with the general northwest-southeastern trend. Most of these Units calcareous units, units sericitic - chlorite schist and amphibolite units up. In this research, ASTER sensor images and ground magnetometric data were used to explore and identify iron-rich regions in the study area. In this investigation, we applied methods of False Color Composite (FCC), Band Ratio (BR), Principle Component Analysis (PCA) using ASTER images and areas with severe alterations propellitic, phyllic and sericite. Using methods of ground magnetometric processing, many methods containes reduce to pole (RTP), upward continuation, Analytic Signal, Tilt Angle, Vertical Derivative were used to identify the sources and we were able to identify the edges of these anomalies. In the study area, we were able to identify four anomalies under the ground that it is very important. The results of both methods explored four anomalies. Aster imager process and magnetometric data led to primary potential mineral map of the area. For credibility of results, 52 samples were taken and analyzed by XRD methods. Five boreholes have been drilled to a depth of 140 meters and all results are consistent with each other. The methods used are important and valuable.

Today the world is experiencing an unprecedented flow of urban . population growth and urbanization is one of the factors influencing air temperature increase in urban areas , which causes the creation of heat island on these regions compared to the surroundings , and its effects can play a vital role in air quality and consequently public health . in this study , aster data are investigated in a case in shiraz , and the خوارزمیک of 5 bands are used to retrieve earth surface temperature and determine the local effects of heat island . in addition , the correlation between surface temperature and vegetation index , construction , barren land and water difference were obtained to determine the effects of green , bare , blue and residential areas on the heat island . the results showed that the effect of urban heat island in shiraz is mainly located in the industrial and residential areas of the west and south east . also , the negative correlation between the temperature of the ground and vegetation index of normal difference showed that green earth and water - covered areas could have weakened the effect of heat island while positive correlation between ground surface temperature and the normalized difference difference can increase the effect of heat island effect in the study area . nowadays , the most important problem in urban areas is to increase the temperature of the earth surface due to the at the natural levels ,

In this study, processing and interpretation methods in remote sensing such as visual and spectral analysis have been performed on the EO-1, ASTER and ETM+ data from Meshkinshahr North area, and as a result, the alteration zones in the area have been identified. Then result Aeromagnetic data, using geological information, alteration and mineralization from the area.  Development of advanced tools in remote sensing and geophysical exploration during recent decades indicates the necessity and importance of these tools in industry. For this purpose, a variety of image processing methods are used Aeromagnetic methods have an important role for exploration of metallic ore deposits. To achieve good results from these methods. In order to identify alteration zones, image processing methods such as PCA (principal component analysis), SAM (spectral angle mapping) and MTMF (Matched Filtering MF) using ENVI software were applied on the Hyperion EO-1, ASTER and ETM+ images from the study area. After removal of the noise from observed magnetic data, processing steps were considered, including IGRF subtraction for the proper years, reduction to pole, Signal Analytic, Tilt (TDR), THDR, and upward continuation 1000 meters. Identification of alteration zones in the study area using remote sensing and image processing methods, and interpretation of the geophysical Aeromagnetic results using geological and Mineralization and Hot Springs and Faults information in the area have been led to the identification of Alteration zone. Many Anomaly and Alterations Kaolinite and silica located in the Meshkinshahr north area (northwest Sabalan) and the other many situated in the northwest Sarab. For credibility of results, samples were taken and analyzed by XRD methods. Confirmed the results of remote sensing and aeromagnetic processes. Conclusions of this research revealed that applying concurrency both the remote sensing and aeromagnetic data could be led to improve the precision of the results.

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.  

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. 

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.

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.

The specific capabilities of satellite data in providing information from the Earth surface materials provide a possibility for producing the geological maps, and in this regard, the spatial and spectral resolutions of the utilized data are two fundamental characteristics in determining the precision and accuracy of the maps. In this research, the data sets of ASTER and Sentinel 2, due to their high spatial and spectral resolutions, were used to enhance the lithological units of the Sureyan complex, northeastern Fars. The metamorphosed sedimentary-volcanic complex of Sureyan is part of the Southern Sanandaj- Sirjan Belt, in Bavanat, Fars province. Investigating the spectral features of field samples, measured at the Shahid Chamran University of Ahvaz, and the spectra extracted from the imageries indicated that the main functional groups responsible for spectral features were Fe2+, Fe3+, OH, CO3, Al-OH, Mg-OH, and Fe-OH. Based on the mineralogical studies, these groups could be attributed to the occurrences of chlorite, muscovite, epidote, amphibole, calcite, and hematite, which were approved by studies of microscopic thin sections. The band ratios (6+8)/7, (7+5)/6, and (6+9)/(7+8) were conducted on 9 reflection bands of ASTER, and the principal components analysis, on 9 reflection bands of ASTER and Sentinel-2. These processing methods were successful in discriminating the chlorite-epidote schist, calk-schist, mica-schist, and the basalt and quartzite dykes as well. Comparing the results of this study to the field observations and the results obtained by laboratory investigations revealed that simultaneous use of ASTER and Sentinel-2 data and the applied processing methods could be successful in discriminating the lithological units of a metamorphic-sedimentary-volcanic complex.

Dust is one of the important processes of arid and semiarid regions that its occurrence has increased in recent years in Iran. Identifying the dust and sand sources, it is the first step in the management and control of this phenomenon. Because of the arid and semiarid climates where dust phenomenon takes place, always there are large areas to monitor and control that practically makes it impossible to manage them. Therefore, reduce the candidate regions to actual sources is one of the main concerns of the researchers. In this paper, identification of potential dust sources using remotely sensed data has been studied. Various spectral indices of moisture and vegetation were applied on the OLI sensor data and finally, wetness spectral index of Tasseled Cap Transformation and DVI vegetation index were selected based on their variation in the study area and was applied on satellite images from 2013 to 2015 and credibility potential maps of moisture and vegetation was produced. Roughness index was applied on the ASTER digital elevation model and credibility potential map of roughness was produced. Erosion sensitivity map of rocks was produced using geological maps. Potential dust sources map was prepared with a combination of credibility potential maps in multi- criteria evaluation model and validate using field based and these areas were visited based on stratified random sampling scheme. Results showed that as well can be identified potential dust sources using satellite images and determining to apply various indices.

Separation and mapping of alteration zones in the exploration of porphyry copper types is of particular importance. Aster sensor of Terra satellite image is used to show these alteration zones. There are different alteration in the Sarkuh area,include potassic, propylitic, phyllic, argillic, siliceous veins, and secondary effects to iron oxide-hydroxides that are reflected in the development of Aster images. Using methods such as color composition, band ratio, false color composition from band bonding and spectral analysis methods(Matched Filtering ، Ls-fit), was used. In argillic alteration, iron oxides and propylitic processes, Matched Filtering ، Ls-fit And the bandwidth ratio method is used among these methods, the MF algorithm and bandy's ratio is well answered. Potassic alteration has a close connection with mineralization. propylititic alteration includes calcite + chlorite + epidote + actinolite + sericite + pyrite in the surroundings of Stock and also volcanic rocks around it.. Phyllic alteration contains sericite and quartz. The results obtained in this section are also consistent with the results of the XRD analysis. In survey field, the set of alteration zones shows a relatively regular zoning with the north-east-southwest process with the center of the porphyry-type Sarkuh porphyry mass.

Remote sensing technology is one of the efficient and innovative technologies for agricultural land use/cover mapping. In this regard, the object-based Image Analysis (OBIA) is new method of satellite image processing which integrates spatial and spectral information in satellite image process domain. This approach make use of spectral, environmental, physical and geometrical characteristics (texture, shape) context of images for modeling of land use/cover classes. Current study, aims to classify micro land use/cover of Meyandoab County by applying appropriate algorithms and parameters in the object based approach. For this goal, Quick Bird and Aster satellite images were used for processing and land use modeling. Accordingly, land use map was classified in 9 class based on spectral and spatial characteristic. The segmentation was performed in the scale of 10, shape parameter of 0.7 as well as the compactness of 0.3. In order to apply classification, fuzzy based algorithm and operators (AND, OR) was applied to detriment the membership functionality of segments for each class as well as classifying the related objects. We also applied textures, geometric, NDVI, GLCM, Braitnese algortims based on fuzzy operators. In order to validate results, the accuracy assessment step was performed and the finally overall accuracy of 93.6 was obtained for derived map. The Kappa coefficient was also detriment to be 0.92. The area under cultivation included respectively for lands of wheat and barley, prunes and plums, apples, vineyards and alfalfa hay2622.42, 4505, 4354.55, 4457.85, 14110.58 hectares.

Digital Elevation Models (DEMs) is one of the main geographical data models which form the basis of the different spatial analysis. DEM is known as fundamental data in for many modeling tasks. Nowadays, the results validation of GIS spatial analyzes, has become a major challenge in the world of GIS .The quality of a DEMis dependent upon a number of interrelated factors, including the methods of data acquisition, the nature of the input data, and the methods employed in generating the DEMs.Analysis of uncertainty in different fields, due to data quality and related issues such as error, uncertainty models, errors propagation, errors elimination and uncertainties in the data, are felt more than any other times. Of all these factors, data acquisition is the most critical one. Previous studies on DEM data acquisition have focused either on examination of generation method(s), or on case studies of accuracy testing. These studies are not adequate, however, for the purpose of understanding uncertainty (an indicator used to approximate the discrepancy between geographic data and the geographic reality that these data intend to represent) associated with DEM data and the propagation of this uncertainty through GIS based analyses. The development of strategies for identifying, quantifying, tracking, reducing, visualizing, and reporting uncertainty in DEM data are called for by the GIS community.
In order to apply uncertainty analysis on DEMs this study aimed to evaluate the error rate and uncertainty of elevation data obtained from SRTM and ASTER satellites. The objectives of this study are: (1) to understand the sources and reasons for uncertainty in DEMs produced by cartographic digitizing; (2) to develop methods for quantifying the uncertainty of DEMs using distributional measures and (3) to measure the uncertainty associated with DEMs and minimize the chances of error by manse of optimizing models. Quantifying uncertainty in DEMs requires comparison of the original elevations (e.g. elevations read from topographic maps) with the elevations in a DEM surface. Such a comparison results in height differences (or residuals) at the tested points to analysis the pattern of deviation between two sets of elevation data, conventional ways are to yield statistical expressions of the accuracy, such as the root mean square error, standard deviation, and mean. In fact, all statistical measures that are effective for describing a frequency distribution, including central tendency and dispersion measures, may be used, as long as various assumptions for specific methods are satisfied. Our research methodology includes several steps. The first step causing the statistical indices ME, STD and RMSE, the error rate of DTMs ​​ for obtaining the chances of error in ach model. It has to be mentioned that the main attraction of the RMSE lies in its easy computation and straightforward concept. However, this index is essentially a single global measure of deviations, thus incapable of accounting for spatial variation of errors over the interpolated surface. In order to obtain more accurate results, then uncertainty of data errors was also simulated by Monte Carlo method and error propagation pattern was extracted by interpolation of results.
The results of this step show that, the DEM derived from pair stereo ASTER despite having better spatial resolution, included more errors and practically lacking the details of DTM 30 meters. Finally, removing the error propagation pattern from DEMs, the secondary DEM was produced. By recalculating indicators describing the error and comparing these values with the initial values, the results indicate that, both DEMs show more accuracy after eliminating the error propagation pattern. TPI Index was used to determine the location of basin topography and the basin is divided into 6 classes and error rate in each class was calculated before and after the simulation. The results showed that, the error rates in all classes before and after the simulation in both DEMs were reduced. In terms of uncertainty analysis methods for DEMs, results of our research indicated that the RMSE methods alone is not sufficient for quantifying DEM uncertainty, because this measure rarely addresses the issue of distributional accuracy. To fully understand and quantify the DEM uncertainty, spatial accuracy measures, such accuracy surfaces, indices for spatial autocorrelation, and variograms, should be used results also indicated that Monet Carlo simulation is indeed sufficient methods for simulation error in DEMs. Results of this research are great of important for uncertainty analysis in domain of Geosciences and can be used for improving the accuracy of modeling in variety of applications.

Features extracted from single-date satellite images can not lead to high accuracy in crop classification. As a result, in this article use of multi-temporal images and textural information is assessed. This research evaluates discrimination of four crops i.e. alfalfa, wheat, potato and cucumber in Ghorveh county (Kordestan province) with single-date and multi-temporal images (SPOT5 & ASTER). Seven images of four different months were obtained and separability analyses determined optimal scene combination (i.e. July 2nd image for single-date image and July 2nd – October 21st for two-date image) for classification. GLCM method has been used for extracting textural information. Approximate window size was determined with variogram, then extracted features were stacked with single-date and two-date spectral bands. The best single-date image overall accuracy (classification without texture) was 24% higher than the worst single-date image accuracy (76.02% versus 52.28%). Accuracy in two-date image reached to the highest levels (89.61%) and only five-date image has higher accuracy than two-date image, but with adding texture features to five-date image, accuracy decreased 5% lower than two-date image with texture features. This can indicate that texture has more importance than multi-date images. The highest classification accuracy was reached with the best two-date image (97.48%). In single-date images, July 2nd image reached to highest classification accuracy (95.2%). Results indicated using multi-temporal images along with texture features has very higher accuracy in comparison to conventional methods (without texture features or multi-temporal images).

Nowadays, evaluation and monitoring of change in environment and climate at the regional to global scale is an important issue for nowcasting and forecasting purposes. Surface Albedo is a key parameter in climate studies. Land surface Albedo is a product of Moderate Resolution Imaging Spector radiometer (MODIS) in global scale but in low resolution where the access to these products is free. However, Climate model outputs could be affected by errors in MODIS Albedo products. In this paper, the accuracy of Short wave Broadband Albedo estimated from MODIS global Albedo products (MCD43A3) is evaluated. The field used for this purpose is a plain homogeneous, semi-desert and non-vegetated region near the Qom-Tehran highway. Since MODIS and ASTER sensors are both on board of the same platform and due to the higher spatial and radiometric resolution of ASTER sensor, ASTER Albedo products are used as ground truth for evaluating MODIS Albedo Products.
The data used in this work consist of MODIS Albedo products, ASTER images, reflectance data, MODIS optical depth data, climatological data and land cover maps. To extract albedo from ASTER image, the geometric, atmospheric and radiometric corrections were implemented first. Then using the surface reflectance of ASTER, the shortwave broadband albedo was calculated. To calculate the MODIS actual albedo, the sun zenith angle image was co-registered with respect to ASTERs. Then after calculating fraction of diffused skylight, the MODIS actual albedo was calculated. Then to compare albedo images of ASTER and MODIS, the ASTER albedo products were resampled to 500m pixels. This was done by averaging all ASTER albedo values locating in a MODIS pixel.
In this work, using ASTER shortwave albedo, MODIS shortwave albedo was evaluated. Comparison between these two products showed that the ASTER albedo was more sensitive to the daily atmospheric conditions as well as surface cover than the MODIS albedo. However it is seen that the 16 days average of MODIS albedo reduces these effects. Also it is seen that except on Oct23, 2004, the MODIS shortwave albedos were always smaller than those of ASTERs. However, precipitation and increase of soil moisture reduces albedo values where this for ASTER albedos were more pronounced. Also it was seen that the RMSD between ASTER and MODIS shortwave albedo values on Sep14, 2003 and Oct23, 2004 were greater. The reason for this may come from the fact that on Oct23, 2004 we had raining and on Sep14, 2003 we had strong wind from nearby salt lake.
On the other hand, on Sep29, 2004 and Jul28, 2001, the atmospheric condition for ASTER acquisition day and for 16 days MODIS passage was the same where the difference between these two products minimized. Based on this, if the weather condition for the ASTER acquisition date and 16 days period of MODIS were the same, a correction of 4% increase to MODIS albedo values is suggested otherwise based on the severity of the weather conditions, this difference could be enormous and the MODIS albedo values are less reliable.
In this work, the actual MODIS shortwave albedo products were compared to those of ASTER shortwave albedos. The result of this investigation in years 2001 to 2004 showed that whenever the weather conditions in the ASTER acquisition date and 16 days period of MODIS were the same, a correction of 4% increase to MODIS albedo values is needed. Otherwise if the weather conditions of ASTER acquisition day were different from MODIS 16 day’s period, the difference between these two albedo products may reach to 9%, this difference could be enormous and the MODIS albedo values are less reliable. Due to the importance of surface albedo values in meteorological and climatological models, the error in estimation of albedo may cause erroneous conclusion in the environmental studies.

Surface Emissivity mapping is a need in thermal remote sensing. Having accurate values of emissivity, one can determine surface temperature more accurate where this temperature can be used in environmental and climatic studies as well as in weather forecasting models. Due to the importance of surface emissivity and the accuracy in their estimation, assessment of the accuracy of MODIS and ASTER emissivity products have been considered in this work. For this, insitu measured values as well as laboratory values of emissivity from 6 different regions in United States of America, whom were extracted from the results of other researchers have used. In this work, TES method was deployed for calculation of ASTER emissivity values where MODIS emissivity values were extracted from 041 and 005 versions. Then, their differences with insitu measured values were calculated and their accuracies were compared with values extracted from bands 8.5 and 11m for concurrent images of these two sensors. The results of concurrent values of two sensors show that ASTER can produce 4.6% more accurate emissivity values in 8.5m band compared to MODIS’s. Also in 11m band, for all regions on the average ASTER has 0.7% accuracy compared to ground values where this for MODIS is 1.2%. It is found that the error for MODIS extracted emissivity values of low emissivity surface covers increases. on the overall, ASTER emissivity values are more accurate compared to MODIS values. It is recommended that this results be taken in to account when using MODIS emissivity products as input in meteorological models as well as those application where accurate emissivity values is needed.

The Motrabad epithermal system 30 km southwest of Bajestan is located in an assemblage of intermediate to silicic volcanic rocks. The mineralization occurs as irregular veins, veinlets and hydrothermal breccias. Hydrothermal alteration is developed around the veins and consists of silicic (

Nowadays، remote sensing is considered as an important subject in many field studies، like agronomy، hydrogeology، mineral exploration، geography and geology. This knowledge has been applied in most of the geological fields. The significant application of remote sensing is preparation of lineament maps that is an important issue in the geological studies. In the remote sensing study، geological structures (e. g. cracks، fractures، faults، shear zones and foliations) are illustrated as a lineaments (Kamali et al.; 1390،2). Therefore، tectonic and structures of different areas can be investigated with use of remote sensing techniques and providing these maps. Lack of access to some areas as well as necessity to spend a lot of money and times for studying large areas have been forced researchers to find a solution for these problems. The first remote sensing studies related to geological applications dates back to first aerial photo studies in the early 20thcentury (Koç; 2005، 6). The lithology، structures and geology units had been identified with using aerial photographs، and finally geological maps had been drawn. After the Landsat images were published in 1972، the use of remote sensing was increased in the earth studying. Recently these images are largely applied in the geological researches. Since satellite images are obtained from varying wavelength intervals of the electromagnetic spectrum، they are considered to be a better tool to discriminate the lineaments and to produce better information than conventional aerial photographs (Casas et al.; 2000، 2011). The purpose of this research is to apply the remote sensing techniques and ASTER images for lineament extraction (faults) using both visual (manual) and automated methods in the central Alborz، map faults preparation، and then comparison of these maps with geology maps and with together. Study Area: Iran is located within the Alpine–Himalayan folded belt in a zone of continental convergence between Eurasia and Arabia platforms which is mostly accommodated by shortening and strike-slip faulting in the mountain belts such as the Great Caucasus، Zagros، Alborz، KopehDagh and also the active Makransubduction zone (Javidfakhr et al.; 2011، 290). The Alborz range is an active mountain belt that surrounds the South Caspian Basin. The deformation of the Alborz is due to the northward shortening between the central Iranian block and the Eurasian plate (Vernant et al.; 2004، 179). The researches show that the present-day deformation in Alborz is characterized by range-parallel left-lateral strike-slip and thrust faults (Djamour et al.; 2010، 1). The study area is located in the northern part of the Central Alborz Mountain range between longitude of 51. 19 -52. 13 E and latitude of 36. 01 -36. 68 N. The main structures of the study area are the North Alborz and Khazar faults، which are only separate structures east of 51°30''E. The North Alborz Fault is mapped as a south-dipping thrust (Geological Survey of Iran 1991a، b، Allen et al.; 2003، 662). It changes strike at 53° E from ENE to WNW. The Khazar Faultwith a southward dip direction is the longest fault in the northern edge of the Alborz Mountains. This structure separates the Caspian Sea to the north from the Alborz Mountains in the south (Motaghi et al.; 2010، 790).

In this study، three data sets have been used: 1) ASTER Digital Elevation Models (DEMs) of central Alborz (between longitudes of 51. 19 -52. 13 E and latitudes of 36. 01 -36. 68 N). 2) 1:25000 contour line maps of central Alborz. 3) 1:100000 geological maps of Baladeh and Marzan AbadBefore the lineament extraction، a set of image processing techniques should be done that are including radiometric and geometric images correction and image enhancement techniques. Since the radiometric correction had been performed in the ground stations on the satellite images، this correction wasn’t necessary for our ASTER images. The geometry correction and geo-referencing have been done only on VNIR images due to be high spatial resolution. The image enhancement techniques that applied on VNIR band are including filtering (directional)، PCA (principle component analysis) and band rationing (OSAVI (Rondeaux et al.; 1996، 102)). Then، lineaments have been extracted using both visual (manual) and automated digital interpretation method.

After lineament extraction the fault maps، rose diagram، density and length-density maps have been drawn. The comparison analysis of obtained faults maps by both methods (visual and automated) with faults maps that obtained from geology maps was done in points of estimating frequency، length، direction and density of line aments. The frequency of automatically extracted lineaments (3886) are greater than the manually ones (1912). The first reason for this results is that short lineaments have been identified by the program (LINE module of PCI Geomatica)، and the second one is that this program can’t distinguish faults from other lineaments (roads، rivers، etc.). The amount of identified lineaments with filtering، PCA and band rationing are respectively 1440،411 and 57. As you see، the frequency of identified lineaments with filtering and PCA are greater than the band rationing ones. The reason is due to further lineaments enhancement using directional filters as well as 80% information in the first component of the PCA. With using band rationing some of the lineaments in the southern part of the study area which could not be recognized by directional filters and PCA have been identified. The average length of the manually extracted lineaments (2. 19 km) that are close to the geology map lineaments (1. 93) are far from the average length in the automatically extracted ones (0. 18). The maximum length of lineaments for the manually extracted (25 km) which is as long as fault lengths in the central Alborz (e. g. 150 km for Mosha fault) is 0. 36 km in the automatically ones. Orientations of lineaments for both maps are illustrated with rose diagrams. The dominant orientations for the manually extracted lineaments are in the east-west and northwest-southeast directions. These orientations are as a result of deformation in the region. Dominant deformation in Alborz is characterized by range-parallel left-lateral strike-slip and thrust faults. Deformation is due to the north–south Arabia–Eurasia convergence، and westward motion of the adjacent South Caspian relative to Iran. Roughly north–south shortening occurs on thrusts that dip inwards from the range margins (Allen et al.; 2003، 659). The density (Zakir et. al.; 1999، 1073) and length-density maps indicate increasing of lineament density in the northern parts and in east-west direction (in the direction of Khazar and North Alborz faults). This means that there is a fracture zone in the area (Sarp; 2005، 58).

In this research، lineaments (faults) of central Alborz are extracted from the ASTER images by using remote sensing techniques and both visual (manual) and automated methods. The results show that directional filters and PCA had a main role in faults identification and extraction. The reason is due to further lineaments enhancement using directional filters as well as 80% information in the first component of the PCA. Some of the lineaments in the southern part have been identified by using band rationing. The extracted lineaments are consistent with the geology maps’ faults (Saidi; andGhassemi; 1381; VahdatiDaneshmand، 1379). These images processing techniques and lineaments identification indicate that directional filters (Sarp; 2005، 43. Koçal; 2004، 15) are the most important factors for lineaments recognition in the vegetated areas. Frequency، length and orientation of the automatically extracted lineaments have no consistency with the geology maps’ faults. Since the program (PCI geomatica) can’t distinguish fault lineaments from the others، the Khazar and North Alborz faults which are the most important structure in the Alborz region have not been recognized. So، for geological researches that always have been based on field studies and require high precision، the automated method because of the low accuracy isn’t recommended. In the visual (manually) method، length، orientation، density and length density are consistent with fault’s status in central Alborz. Consequently، the best way for geological and structural studies on large areas or areas that are difficult to access، is the visual method for lineament extraction.

The study of relationship between various soil parameters and satellite data is an effective step in the identification and separation of desert facies. To do so, this study aim to study two-variable regression methods based on different relationships between the various soil components data and ASTER satellite data in order to detect Abarkoo playa facies. To achieve this purpose, at first, 30 topsoil samples were collected from the study area and analyzed in laboratory. Various pedological components (Anion, Cations, Soil moisture, Texture and PH) were also measured. After performing the necessary processing on the satellite images, the value of pixels in each band were extracted by overlaying ground points over satellite image. In the next step, the correlation between satellite data and laboratory values were evaluated by using various two-variable regression methods. The accuracy of the models was assessed using Relative Error, Root Mean Square Error, and Correlation Coefficient of Efficiency. Results indicated that the minimum correlation coefficient is 45%, the maximum relative error of estimation and confirmed are respectively 247.4 and 2489.7 percent, root mean square error is low and the minimum Coefficient of Efficiency is 19 percent. Furthermore, the results of this study showed that there is no significant relationship between PH and soil moisture and satellite data in the study area.

Usefulness and authenticity of satellite data are strongly related to weather conditions. Dust storm, atmospheric gases and especially the presence of clouds can considerably affect on the reflected energy from surface and encounter the reading of optical sensors with error. Cloud contaminated pixels usually increase the reflection of land covers and show their temperature less than the real one. The clouds smaller than pixels are not observable and cause an increase in pixel reflection and error. Considering that different clouds have diverse interactions with each other, it is possible to determine the amount of cloudiness of pixel by combining the analysis of different spectral bands of MODIS. By using this method, it is possible to determine the the cloud polluted pixels faster. For the first time, MODIS Cloud Mask algorithm was declared by Ackerman and his colleagues in 2006. Firstly the cloudy pixels were identified in MODIS image by the help of the presented five- step model in MODIS Cloud. Than for upgrading the model and determining the present percentage cloud in one pixel for those clouds smaller than pixel dimensions, brightness temperature of the pixels in band 14 of ASTER and band 31 of MODIS was compared and the cloud coverage percentage of every pixel was calculated. Model evaluation demonstrates more than 93% correlation between the real amounts of distracted cloud coverage in ASTER and the measured amount by the model, which is promising regarding local resolution.