فصلنامه سنجش از دور و GIS ایران
سال دهم شماره 4 (پیاپی 40، زمستان 1397)

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.

Land cover classification in remote sensing imagery is one of the most widely used spatial information extraction methods, which can facilitate generating object imagery classes of the ground surface in order to automate and accelerate meeting the basic needs of management, organization, and exploitation of the environment. Due to the similar behavior of pixels, remote-sensing image classification using merely the spectral and textural information would lead to inefficiency in the classification. In fact, in classification process, objects are commonly identified using spectral properties of image pixels. If the spatial and conceptual properties are also considered, it causes to a better distinction between image classes and closes the machine process to human interpretation and adds to the system's performance. The present research is mainly focused on the use of interactive segmentation and interpretation processes with respect to the geometry of the image classes. The accuracy of the results have improved by introducing the knowledge-based rules to control and regulate the interactive process, taking into account the geometric properties of target classes. To evaluate the efficiency of the proposed method, the results were evaluated and compared with some of the other methods on IRS satellite images in an urban area. The results showed that geometric and conceptual features as a complementary information source, improve classification results in the urban area with heterogeneous spectral effects. Overall, the proposed hybrid technique improved overall accuracy and Kappa coefficient by %8 and 11.18, respectively.

Evaluation of the image classification results is very important in the remote sensing projects. So far, many indices have been presented to assess the accuracy of image classification, though Kappa coefficient and Overall accuracy are the most famous ones. Some researchers have criticized these two parameters, and have presented new parameters for evaluation of the classification results. In this paper, the relation between two new accuracy assessment parameters (presented by Pontius & Millones) and traditional accuracy assessment parameters (Overall accuracy and kappa coefficient) is studied. These two new parameters are called “Quantity disagreement” and “Allocation disagreement” which report disagreement between ground truth and classification data. In order to apply the comparative study on the traditional and new disagreement measures, supervised maximum likelihood classification was applied on 31 satellite images with different spatial resolutions. Then, Kappa and Overall accuracy as traditional accuracy parameters and Quantity disagreement and Allocation disagreement as new measures were computed for each classified image and then the correlation coefficients of the both measures were calculated. The results show a high correlation between new parameters and traditional ones in negative direction irrespective the spatial resolution. In this way, the disagreement do not provide new information about the classification results to the user, and only if there is any request for classification error, the new disagreement parameters can be used along with the traditional ones.

The radative energy balance received and returned from Earth planet reflects the energy available in each part of the Earth-Atmosphere system. Also, net solar radiation is the most fundamental driving force for evaporation, and all actions and reactions between the Earth's surface and the atmosphere. These reactions significantly affect the climate and its transformations. Hence, the wide-scale cross-sectional estimation of pure net energy is important in terms of global and regional climate models. In this research, in order to study the trend of long-term monthly average changes of surface-Albedo, the Albedo products from the sensors of MODIS Satellite Terra named MCD34B4were used. The spatial resolution of the images taken was 1×1 km for a 11-year statistical period (2222-2213) for April, May, and June. After capturing images by NASA's land processes distributed active archive center, all 31 downloaded images. The next step was to convert the image format to ASCII format; each ASCII includes 42202 pixels. Finally, by using both statistical methods of Sen's slope estimator, and Classic Linear Regression the trends of long-term monthly average Albedo changes were analyzed on a pixel-based scale. The results of these two models showed that these two models did not differ in their estimation of the trends of Albedo's average changes, and acted precisely the same. Also, the results of this research showed that the center of the most slowly declining slope of Albedo changes is located in the northeast, where, due to the flow of the Hirmand River, in this part of the plain the agriculture is widespread. The incremental magnitude of the slope of the change process is also very limited, and there are small and large spots in the north, northeast, and center of the plain. This increasing trend in the values of Albedo's index in the north of the plain was exactly the same as the drying of the Hamoon triple lakes. The rest of the plain area, which has desert landscape and does not have any vegetation, as well as any human population, has not shown any particular trend. In this study, it was also clearly found that, the use of nonparametric method of Sen's slope estimator and parametric method of classic linear regression can be very effective in studying the trend of Albedo changes in the arid regions resulted from satellite products of MADIS sensors.

Digital elevation model (DEM) reconditioning methods are being used to improve its quality to be used in simulation and cellular hydrological modeling. In this paper , methods of improving DEM and creating “Hydrologic DEM” with using the ArcHydro1302 extention were studied and also evaluated in 40 subbasins of Karkheh river basin(KRB) and one of its applications in “accumulation runoff map” was implemented .Streamflow information is available at hydrometric stations as points but for determining the amount of river discharge continually at any point along the river and branches, it is needed to accumulate the runoff map. The 9 direction method (D9) is the basic model for river network delineation from DEM accumulated cells. In this algorithm, the flow of each cell pours to one of the eight adjacent cells in the "hydrologic DEM ". In hydrologic DEM the accumulated cells in the river line forward to the downstream increase continuously. With "Agree algorithm" and with trial and error the DEM improved and the upstream area of accumulation cells, have been compared with a vector area in each subbasin which correlation coefficient was 308894. After preparing runoff map of the basin, accumulation flow(accumulated runoff map) using a weighted cumulative flow function was created with 233m cell size then the continuous flow map was presented. The results indicate that the error between estimated discharge from accumulated flow map with observed discharge in 40 subbasins varies between 3029 to 001 percent.

The accurate estimation of crop biomass using satellite data is one of the important challenges in environmental remote sensing. Traditionally, spectral vegetation indices (VIs) derived from spectral reflectances in red (R) and near infrared (NIR) bands have been employed to statistically estimate the crop biomass; however, most of these VIs saturate at some level of LAI. Therefore, most of the recent studies have been investigated on using the reflectance spectra in the red-edge region to overcome the saturation limitation. In order to evaluate the performance of different VIs for the estimation of crop biomass, we conducted five sampling campaigns during the growing season of silage maize in Magsal, Qazvin and we totally collected 182 silage maize biomass samples. Then, ten spectral indices from the time series of Sentinel-2 images of 2117 which were simultaneous with our campaigns were computed and employed to statistically estimate the silage maize biomass. The silage maize biomasses were evaluated with the field measurements. The results showed that 𝐶𝐼𝑟𝑒𝑑𝑒𝑑𝑔𝑒 index with 𝑅2 = 1.55 and the lowest root mean square error (𝑅𝑀𝑆𝐸= 2.874 𝑘𝑔𝑚2 ⁄) was the best index to estimate silage maize biomass. Moreover, this work also showed that Sentinel-2 satellite which delivers high spatial resolution images of the red-edge band can be employed to accurately estimate the silage maize biomasses.

Polarimetric Interferometric SAR (POLINSAR) data by providing wealth of information containing intensity, polarimetric and interferometric measurements, have shown many capability of mentioned data in the land cover classification. These three componentes of POLINSAR data could be found independently in the Shannon entropy of POLINSAR data. These components play a complementary role in the classification where the presence of interferometric information improves the classification results. As well as the data acquired form the real world has spatial connectivity so considering the neighboring and spatial connectivity in the classification process is essential and useful. So in this paper Markov Random Field segmentation algorithm has been used for classification of Shannon Entropies of POLINSAR data. In order to provide a Markovian field for the MRF classification, an initialization method has been proposed where classifies the image into 16 classes according to the polarimetric and interferometric entropy and anisotropy and merges the clusters obtained to 4 clusters using equality test of coherency matrices. . The purity indices (PI) of the clusters obtained over the POLINSAR data acquired by DLR (German Aerospace center) E-SAR have been used to evaluate the effectiveness of the Entropy based MRF classification. The proposed method has been compared with the 𝐻/𝐴/𝛼 –Wishart (𝑇3), 𝐻𝑃𝑜𝑙−𝐴𝑃𝑜𝑙,𝐻𝐼𝑛𝑡−𝐴𝐼𝑛𝑡-Wishart (𝑇6 ), 𝐻𝑃𝑜𝑙−𝐴𝑃𝑜𝑙,𝐻𝐼𝑛𝑡−𝐴𝐼𝑛𝑡-FCM (𝑇6 ) and FCM clustering using Shannon Entropy parameters where this comparisons show approximately 242, 112, 172 and 202 increasing in the Purity Indices respectively.