Sarbisheh mineral occurrence is located in the vicinity of the Tourshab village about 50 km south of Birjand, the center of Southern Khorassan province, east Iran. The predominant geologic characteristics of the region are highly-tectonic deformation, and intrusive, granodiroritic-dioritic, to volcanic, dacitic-andsitic rocks associated with broad alteration. To the northern part of the studied area, extensive variety of alteration, kaolanitic, sillicified to choloritic, can be observed clearly in the filed and remotely sensed data. Concerning to previous geologic works, these alteration zones have been formed apparently NW-trending extensional trends. This is more obvious feature to the southeast part of the area where the main tectonic features with NW-SE trend delimited the southern border of Kaolinitic alteration in adjacent of the Torshab spring. Based on previous geologic surveying and preliminary mineral prospecting, the area apparently has a convincing mineral potential, especially for copper, gold etc., to encourage some governmental organization or any private company to launch more detailed exploration study. The main goal of the present study was to introduce an integrated method for mineral exploration for the copper-gold prospects using the remote sensing and field observations and to produce different alteration maps. To distinguish between different types of alteration developed within the studied area, ASTER satellite data were collected and analyzed using various methods. In this manner, argillaceous alteration haloes and iron-oxide zones representing possible high-mineral potential extents were extracted successfully by applying the Korsta method. Meantime, other remote sensing techniques, such as color composite maps, principle component analysis, supervised classification, least-squares fit, band rationing and MF methods. By applying the above methods, this study produced different accurate alteration maps of the area.

Desertification relates to the both the process and end state of drylands degradation. Salinization and alkalinization are two indicators of soil degradation in arid and semi-arid regions. The main objectives of this research is monitoring of soil salinity using high spectral and spatial resolution of remote sensing to assess desertification in the Marvast plain, Yazd province. Two images of Terra satellite, ASTER synchronous to 2003 and 2010 are used. After preprocessing and analyzing of the images, relationship between parameters of soil salinity (i.e. SAR and EC) and spectral reflections were determined and, both two satellite images were classified using maximum likelihood method. Then, the surface area of each class and the amount of its changes were calculated. Results showed that during the period of 7 years (2003-2010), area of non-saline lands has decreased while, the area of saline land has increased, which leads to the salinization of agricultural lands, reduction of its yield and also extent of desertification in this region. Accuracy of EC map classification for 2003 and 2010 images are 87.5% and, 82.5%, respectively. Kappa coefficients for both images are 0.83 and 0.76. Accuracy of SAR map classification for 2003 and 2010images are 87.5% and 87.5%, respectively. Kappa coefficients for these two images are 0.81 and 0.77, respectively. Generally, it can be conclude that using of remote sensing data, especially ASTER images has high efficiency for change detection analysis in soil salinity and natural resources management.

Recognition equal units and segregation them and upshot planning per units most basic method for management forest units. Aim this study presentation and comparison classification and regression tree (CART) and random forest (RF) algorithm for forest type mapping using ASTER satellite data in district one didactic and research forest's darabkola. In start using inventory network 500* 350 m, take number 150 sample plat in over district. After accomplish Geometric correction and reduce atmospheric effect on image processing bands rationing, create general vegetation indices, principal component analysis and tesslatcap index. After extraction spectrum values relevant by sample plats fabric and processing bands, classification values other pixel accomplish using investigating algorithms. Evaluation accuracy results classification accomplish by some sample plat that not participate in process classification. The result showed preparation map using RF with overall accuracy 66% and kappa coefficient 0.57 than classification and regression tree with overall accuracy 58% and kappa coefficient 0.49 has superior accuracy. Totality result showed using above algorithm may increased accuracy forest type map.

Image processing of remote sensing for separation hydrothermal alteration, in the case of missing initial spectra of pixels, can be a challenge for the researcher. The previous researches has shown that accurate separation of hydrothermal alteration zones using Conventional methods of image processing based on Spectral Properties of pixels, is not possible. Therefore, this research is trying to present a multi-stage algorithm that identify and discriminate the hydrothermal alteration zones in western part of Kerman province with high accuracy. To achieve this goal, the principal component analysis method, Fractal Concentration-Area Model and Full Index Kriging (FIK) geostatistical model are used in combination. The results show the high accuracy of the FIK model in identifying and separating each of the phyllic, argillic and propylitic alterations in the study area. Also, to evaluate the classification error, the Confusion matrix was investigated. The results of the Confusion matrix showed that the FIK model performs well in terms of image classification. Also, given the high number of training pixels in the phyllic zone, the FIK model has been able to identify this type of alteration very well.

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.

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.

Estimation of forest structural parameters is one of major basic information in sustainable management and planning in forest stands. In this study, relationship between ASTER satellite data and three forest structure factors including stand volume, basal area and number of trees per hectare were investigated in Darabkola forest, northern Iran. A multivariate linear regression approach was used to analyze and evaluate relationship between mentioned characteristics and ASTER satellite data. Relevant preprocessing and methods were conducted on spectral data. After gathering  terrestrial information, stand volume, basal area and number per hectare were calculated for sample plots. Using some plots, performance of the best models examined by relevant evaluation criterions. The results showed that a combination of MSAVI2, NDVI and Green bands could predict stand volume characteristics better with R2 adj=59.2; and RMSE=116.5 m3/h-1 in comparison with other indices and band combinations. For basal area, the best results were obtained using combination of MSAVI2, NDVI and simple ratio of SWIR12 with R2adj=73.5 and RMSE=5.14 m2/h-1. In addition, combination of MSAVI2, SWIR1 and SWIR2, was a better predictor for number per hectare rather than the other combinations by R2adj equal to 0.85 and RMSE about 50.95 number per hectare. Generally, this research showed that using linear regression approach by the ASTER data presents only general status of forest structure attributes in the study area and having more precise estimation of these attribute needs investigating other approaches such as nonlinear or nonparametric and learning machines approaches.

Introduction Modeling mineral potential based on the precise collection and processing of geological, geophysical, and satellite data enables us to predict the potential presence of mineral substances in a specific area. This process involves constructing complex mathematical models that, utilizing machine learning algorithms and thorough data analysis, assist authorities and decision-makers in the mining sector. It helps identify mineral-rich zones for optimal extraction and manage land resources in the best possible way. Given the diverse geological units present in the Saqez sheet, this sheet is considered one of the most promising areas for the formation of metallic deposits. The host rock for most Pb-Zn deposits in Iran is sedimentary, known as Sedimentary-Hosted Pb-Zn deposits. According to conducted surveys, it has been determined that the Pb-Zn mineralization occurring in the Saqez sheet is also of this type. Typically, calcite, dolomite, shale, sandstone, and igneous rocks serve as hosts for these deposits.   Materials and Methods In this research, the exploratory layers of lithology, dolomite alteration, and geochemical of Pb-Zn were used to prepare a map of the prediction of Pb-Zn mineralization in the turpentine sheet. Utilizing the singularity technique on sediments stream, using various exploratory layers performing laboratory spectroscopy, and applying the spectral behavior curves obtained on Sentinel-2A images in this innovative and creative research. It shows its existence compared to another similar research. After fuzzification of exploration layers in GIS software, the prediction map of Pb-Zn mineralization was obtained by Fuzzy-Gamma function and gamma value of 0.85. The results of XRF and ICP-MS analysis on the discovered Pb-Zn samples showed a grade between 3 and 7%, which indicates the correct selection of the studied area and the exploratory layers and their correct integration. Further, by conducting laboratory spectroscopy in a dark room with a halogen lamp and obtaining the spectral behavior curve of the sphalerite mineral related to the samples of the study area, the SAM matching algorithm was applied to the Sentinel-2A satellite images. Results and Discussion According to the Pb-Zn mineralization prediction map of the Saqez sheet, this map is classified into four categories of low, moderate, high, and extreme mineralization potential. It is evident from this map that the northern, central, and southeastern regions have the highest potential for Pb-Zn mineralization. Upon examining the topography and road identification of the Saqez sheet, six areas were selected for exploratory drilling of Pb-Zn. Samples were collected for analysis and validation of identified points. XRF and ICP-MS analysis results indicated that the total Pb-Zn content ranged from 2 to 7% and 70,000 to 20,000 ppm. Finally, high-grade Pb-Zn samples were selected for petrographic examination. Petrographic studies revealed that minerals such as sphalerite, galena, and pyrite were predominant in the collected samples, with their texture filling the pore spaces. Specifically, sphalerite replaced galena, and galena replaced pyrite. The results obtained from laboratory spectral analysis and the application of spectral behavior curves for sphalerite minerals on Sentinel-2A satellite images were utilized to assess the accuracy of the work and identify promising new mineralized areas. By comparing the corrected spectra from the laboratory experiments with the USGS spectral library, it was determined that the obtained spectral behavior is similar to the USGS spectral library. Therefore, the predictive map of Pb-Zn mineralization resulting from the application of spectral behavior curves for sphalerite on Sentinel-2A satellite images indicates the correct selection of imagery, appropriate spectral analysis, and all processing steps.   Conclusion Due to the fact that the host rocks of most Pb-Zn deposits in Iran are of sedimentary origin, the first step in modeling the mineral potential of these deposits is to accurately recognize the ore deposit type. Based on the evidence and samples observed in the study area, the ore deposit type in the study area can be considered as the Irish type. Therefore, based on this, modeling and prediction of Pb-Zn mineralization on the one hundred thousand scale map of Saqez were carried out according to the Irish type Pb-Zn mineralization. After evaluating the Pb-Zn mineral potential map, seven final areas were selected for exploration. Based on the exploration conducted in six areas (S1, S2, P, Q-Pb-Zn, Polygon12, and Polygon13), samples containing Pb-Zn mineralization were discovered, while Polygon5 was identified as lacking Pb-Zn mineralization, indicating the reliability of the exploration layers and integration method. XRF and Aqua Regia analysis results showed that the discovered Pb-Zn samples had grades ranging from two to seven percent, indicating economic-grade content for these metals.

Introduction: Remote sensing science is one of the most powerful tools for the mineral explorations and mineral resource estimation. With regard to this science, any type of rocks with structural characteristics and mineral constituents has a special spectral signature, thus, using remote sensing techniques, different types of rocks in a particular area can be recognizable based on their reflective characteristics. Remote sensing techniques are considered as one of the standard methods in geological studies due to the identification of spatial patterns of rocks as well as their speed and economic price. Pervious geological studies indicate that the study area mostly contains basalt, limestone and marble, which has resulted in physical and chemical degradation of basalt stones under the influence of some geological events. Some parts containing basalt have lost their qualities due to these degradations. Therefore, the classification and separation of high-quality basalt zones from low-quality zones is the main objective of this paper. Materials and method: The main objective of this study is to identify high-quality basalt zones in the Dir-o-Morreh mine located 50 kilometers from Tehran city near the lake of Hoz-e-Soltan. Basalt is a dark-colored and fine-grained igneous rock composed mainly of plagioclase and pyroxene minerals. Typically, this type of rock is formed externally or in the presence of air, such as the flow of lava, and these rocks can also take form intrusively like igneous dikes or narrow pillars. The basalt in the Dir-o-morreh mine is of igneous dike basalt type. In this study, the ASTER satellite multi-spectral images were used. These images allow us to have a good spatial and spectral resolution with regard to the objectives. However, reflectance conversion and atmospheric corrections were carried out on these images before using them, in order to enhance the accuracy of the project. Aerosols contained in the atmosphere are liquid or solid particles suspended in the air, which are very important in the evaluation of satellite imagery for remote sensing. After applying pre-processing, Basalt Exploration Index (BEI) was introduced and used to identify the basalt. The BEI index has been extracted using various sources, including the basalt spectral signature provided by the department of applied mathematics and statistics of Johns Hopkins University, ASTER satellite behavior (defined by the space team of NASA and Japan) and the Earth’ s data which were collected to validate the results. This index has been able to identify different basalt zones, including major extraction zones and other potentially possible zones. Moreover, this index is able to completely separate the basalt zones from the surrounding areas (mainly limestone, marble and clay rocks). At the next step, convolution and morphology filters have been applied to separate high-quality Basalt zones from the low-quality. The amount of the brightness of an output pixel from the Convolution filters is a function of weighted average of the brightness of its surrounding pixels. Using convolution with the selected kernel in satellite imagery returns a new filtered spatial image. High-pass Standard convolution filter was used in this study, which eliminates low frequencies of an image by retaining the high frequencies. The morphological nuclei used in this study are only the structural elements of this project and should not be confused with convolution kernels. In order to control the obtained results, the classified zones were double-checked on the field. Results: The results obtained from the field studies and the identified zones are appropriately consistent with each other using the proposed index. Supervised classification was applied to improve the level of assurance and accuracy. Supervised classification is based on the idea that the user can select sample pixels in an image representing certain classes and then use image processing software using these educational samples as the referral for the classification of all other pixels in the image. This classification algorithm can be very effective and accurate and classifies satellite images in pixel-based or object-oriented form. Supervised classification can result in the preparation of two maps in two different classifications, which is has been done by using the Maximum Likelihood Algorithm. MaxVer or Maximum Likelihood is a statistical classification method that takes the weight of average value of the distance between the classes into consideration, using statistical parameters. To achieve sufficient accuracy, this algorithm requires a number of educational samples or pixels (more than 30). The primary classification includes 5 types of rocks or classes: high-quality basalt, low-quality basalt, limestone, marble stone, and clay which are designated on the map. In order to increase accuracy of the proposed method, the second map was prepared with 3 different classes (low-quality basalt, High-quality basalt, and surrounding rocks) in the second stage. Conclusion: These maps help us in preparing a new BEI which is more accurate and more capable. It was also able to prove its capability in the latest ground operations and determining the most zones with high-quality basalt.