فصلنامه سنجش از دور و GIS ایران
سال پنجم شماره 4 (پیاپی 20، زمستان 1392)

Mineral potential mapping process is introduced as a complex process due to the presence of multiple factors, variety of both quantitative and qualitative data to explore mineral deposits as well as the expertise and different interests. In this process, considering the fuzzy nature of mineral exploration in the process of modeling exploratory data, applying expert knowledge and flexibility for all types of mineral deposits in form of an integrated system play essential roles. To verify this, in the study, a new method has been presented using fuzzy inference systems and relations, and the mentioned method has been implemented in the ChahFiroozeh copper deposit.This study was conducted in two stages. First, the primary factor maps using asymmetric fuzzy relation analysis are weighted and using multilevel fuzzy comprehensive evaluation are combined together. And second, the factor maps which have been obtained in pervious stage, were integrated using a fuzzy inference system and then the mineral potential map has been generated.In the resulted mineral potential map, Porphyry copper mineralization prone area is located in central regions with north-south extension. For evaluation exploration boreholes in the area have been complying with the mineral potential map. The highest compliance rate in mineral potential map is calculated as 83.33%.

New generation of space radar sensors with high spatial resolution, heralds the possibility of these images usage for automatic feature extraction, specially road ones related. This study compares road extraction in high resolution radar and optical images. IKONOS and TerraSAR-X images are used for this purpose. In order to make possible the comparison, the images have been coregistered. Then, texture features have been extracted and classified using a back propagation neural network. By comparing the results with reference road network, provided by the operator, for TerraSAR-X and IKONOS images respectively, 46.10 and 57.72 percent for RCC, 46.58 and 93.27 percent for BCC and 0.61 and 0.31 for RMSE were obtained. Comparison of the outputs of two algorithms showed that each of optics and radar images have some defects in road extraction. For example, optical algorithms are sensitive to image parts which have the same texture and spectral features similar to roads such as parkings and roofs. In one hand, these areas have visible appearance and different texture in radar images, so radar images in urban areas especially with narrow roads and alleys, are suitable. On the other hand in areas with dense vegetation, Radar images do not perform well, while the optical images are able to distinguish the mentioned areas. In order to eliminate defects and increase the accuracy of the present algorithm, because of high functionality of these images, fusion of them seems to be an efficient wayin developing algorithms.

Invader species often drive ecological changes that may be irreversible and therefore preclude successful restoration of basins. Thus invasion prevention, early detection and removal of these plants play key roles for controlling and management of them. Remote sensing techniques in invader plants monitoring are memorable to avoid invasion and eradicate or control established invaders. The aim of this paper is investigating the efficiency of Landsat TM and IRS-LISS III data in zonation of invader species of Cirsiumarvense and Stachysbyzanthina in Vazroud rangelands. To gain the optimized results Cos(t) model was used for the atmospheric correction on images and then some vegetation indices were applied with extraction of digital average of pixels related to training samples of corrected images. Minimum distance of mean classifier has been used as classification technique which was evaluated with the ground truth map. The results showed that NDVI,Ratio, RVI, and TVIof TM image and AVI of IRS image are most suitable indices for discriminate the Cirsiumarvense but the best indices for the Stachysbyzanthina were NDVI, PVI 1, PVI 2, RVI and WDVI of TM image and NDVI of IRS image. Also out of the total indices analysed, WDVI and DVI of both images were able to discriminate these species but with varying degrees of separation and can produce well results as a measure separation in future studies.

Traffic analysis zones represent locations of trip generation and attraction. These zones are widely used in many urban transportation planning activities. Proper design of the mentioned zones is very important, because they cause considerable effects on the results of different transportation modeling and analyses. In this paper an efficient method is proposed to design these zones using geospatial information system. By using this method, various criteria can be considered to determine homogeneous traffic analysis zones whereas the number and size of the zones can be controlled as well. In the proposed method, the k-means technique is used for clustering basic areal units to homogeneous traffic analysis zones. To prevent overlaps and gaps between the zones and creation of the zones with too large or small sizes, some constraints are added to the k-means technique. However, definition of homogeneous zones without considering their geometric compactness results in generation of zones with complex shapes. For solving this problem, a geometric algorithm is developed to create continuous and compact zones. Efficiency of the proposed approach has been tested through to redesign the existing traffic analysis zones of Tehran city and its comparison with the existing traffic analysis zones. Results showed that the redesigned traffic analysis zones with the proposed method have been more homogeneous than the existing zones. Also, by using the shape compactness algorithm, compactness of the resulting zones increases whereas their homogeneity decreases. However the redesigned zones are more homogeneous than the existing ones.

One of the major challenges in the Iran water resources management is reduction of Helmand River’s inflows that has happened several times in recent years. Increasing in the basin’s cultivated area (mainly after Kajaki dam) and the recent droughts are declared as the reasons; however none of them is analyzed in a quantative manner. This paper aimed to address this issue and at this stage evaluated land cover changes in the last two decades using remote sensed data. Due to importance of the selected algorithm for classification, three methods have been applied for the evaluation, including maximum likelihood classifier (MLC), decision trees (DTs) and support vector machines (SVMs). It is obvious that direct sampling was not possible from the study area, therefore the FAO land use maps, watershed atlas of Afghanistan (AIMS), and Google Earth images were applied in this regard. Considering the capabilities of Landsat (ET and ETM), three images for 1990, 2001 and 2011 during growing season (all in May) were prepared and processed for this study. The results were evaluated using Kappa coefficient and overall accuracy, which showed almost similar performances of the algorithms. It was concluded that the total cultivated area in the region increased from 103’000 ha in 1990 to 122’000 ha in 2001. Notably that this amount increased to 167’000 ha in 2011. These changes show definite impact of land use change on the river inflows. In spite of similar results of the applied methods, we found DT method more suitable for such an analysis due to its computation cost and efficiency as well as less relying on training samples.

Edge detection is a fundamental concept of image processing in remote sensing which aims at detecting points of the image at which the brightness changes sharply. Sharp changes in the image properties usually represent important events and changes in environmental factors. This kind of detection method can be useful in different image processing applications such as satellite image segmentation. Many classical mathematical algorithms for the edge detection, such as Gradient operators, Laplacian and Laplacian of Gaussian operators,are based on the derivative of the original image pixels. In the remote-sensing imagery, because of the high rate of changes, these edge detection operators perform weakly in correct diagnosis of the feature boundaries and in keeping their consistency. In order to solve these problems, this research presents a knowledge-based system to detect the feature edges based on the properties of remote-sensing images. In this method, the threshold is firstly determined piecewisely in different regions of the image using a knowledge-based system, and then the area boundaries are extracted using the Shannon entropy, which safeguards the continuity of the edges. The advantage of this approach is simultaneous evaluation of the image features in both partial and general cases. In addition, the system is flexible in terms of the type of user needs and the intended details and applications. Eventually, to evaluate the method, the results are compared with Sobel, Perwitts and LOG standard algorithms. It is observed that the proposed technique can efficientlydetect the edges for different digital images.

This study aimed to compare the performance of pixel-based (vegetation indices, VIs) and subpixel-based (spectral mixture analysis, SMA) methods in mapping rangeland vegetation cover in Semirom region, Isfahan Province. First, the 2009 Landsat TM data was geometrically and radiametrically corrected. Then, the percentage of canopy cover was determined using step-point technique in radial direction in 30 sampling sites. The collected field data was correlated with different groups of VIs, including slop-based (NDVI), distance-based (PD54), orthogonal transformations (GVI and SBI) and plant-water sensitive (STVI-1) and also SMA in SPSS.16 software environment. To evaluate the performance of VIs and SMA techniques, vegetation cover data was classified in three percentage groups including 0-25, 25-50 and >50. In low vegetation cover group (0-25%), SMA in comparison with VIs had the highest relationship with field vegetation cover data (R2=0.52, p