فصلنامه سنجش از دور و GIS ایران
سال نهم شماره 4 (پیاپی 36، زمستان 1396)

Soil moisture plays an important role in interactive processes between earth and atmosphere and global climate changes. In recent decades, there has been a great research interest to determine soil moisture from remote sensing methods. Triangular or trapezoidal methods are the most common remote sensing methods that apply the combination of thermal and optical satellite images to estimate soil moisture content. The accuracy of methods governed by the accuracy of saturated and dry edges that define from vegetation/ temperature scatter plot. A main limitation of these methods arose in some days or in some vegetation condition that dry and wet edges cannot be defined correctly. This concern is addressed in this paper by using the temperature and vegetation information during one year interval to form the temperature-vegetation scatter plot, saturated edge and dry edge exactly. The main contribution of the paper is, however, the introduction of co­-moisture lines in the one-year scatter plot. These lines are later applied to define the wet and dry edges of each individual day which are taken as the two closest co-moisture lines that contain all corresponding pixels of that day. The soil moisture index as a parameter dependent to evaporation efficiency is finally estimated from the slope and intercept of these two co-moisture lines. The proposed soil moisture index calculated from co-moisture was implemented and validated in Manitoba, Canada area while MODIS satellite images, taken in 28 cloudless days of year 2014, were used as the input data. The correlation between ground soil moisture data and proposed soil moisture index was estimated. Correlation of 0.92 was achieved for low vegetation days and lower in days with higher vegetation densities.

Distribution warehouses have great importance in the economy of the country, and a significant percentage of assets are accumulated in warehouses. Choosing the best place of the warehouses has a significant impact on the economic efficiency and performance of the warehouses and reduction of supply chain costs. In this research, a multi-criteria decision-making model based on a geospatial information system is presented to evaluate the potential areas for distribution depots in the province of Tehran. The proposed process consists of four main steps. In the first step, different criteria were extracted and the required data were collected in the context of GIS. In the second step, the evaluation factors were identified by experts and then weighted and integrated utilizing ANP method. At the third step, defining different scenarios based on the Ordinary Weighted Average (OWA) method taking into account the risk compensation in the decision-making process. Finally, using the data of Tehran province, the effectiveness of the proposed model was evaluated and results were analyzed. At the end, by combining the outputs of different scenarios, the places which recognized as good alternatives in most scenarios, were identified as appropriate options for doing additionalstudies to construct distribution warehouses.

City Development and its direction is always a potential problem in urban planning. In modeling this phenomenon, variety of criteria are involved, directly and indirectly. So it is assumed as a complex Multi-Criteria Decision Making problem. There is a wide range of rigorous methodology to analyze these types of problems. TOPSIS method is an appropriate approach to deal with MCDMs. This model accounts for the distance between each solution with ideal solution. In this research, the most appropriate direction of Zanjan city deployment is investigated considering economic, environmental, physical and climatically parameters employing TOPSIS method. The results illustrate that the eastern and north-western spaces are more suitable to city development. In contrast, the southern and northern parts are not primarily suitable in this case. It is notably to mention that 15% of city development since 2005 has been accrued in inappropriate areas.

This paper provides an advanced method to improve results of three stage inversion algorithm using polarimetric synthetic aperture radar interferometry (PolInSAR) technique based on Random Volume over Ground model. In conventional three stage method, the ground phase, extinction coefficient and volume layer is estimated in a geometrical way without the need for a prior information or separate reference DEM. The extinction and volume height estimation is done in the third stage by searching in the two dimension area. In the proposed algorithm, defining a new geometrical index, based on signal penetration in the forest, imposes a limited range for the extinction coefficient. The new index, as an axillary data, help search in a more appropriate space. The proposed algorithm was applied on L-band ESAR single baseline single frequency polarimetric SAR interferometry data. As a result of applying this restriction in the extinction range, a 2.5 meter improvement was observed in the RMSE of proposed algorithm compared to the three stage method.

Image registration process is one of the most important branches in the field of image processing, which is an essential preprocessing for the use of remote sensing. Scale invariant feature transform (SIFT) is one of the most commonly used feature-based methods for registration of images. However, a main weakness of this algorithm is the creation of a large number of mismatches. Based on the spatial relationships of the corresponding points of SIFT, the proposed method in this paper increases the accuracy of image registration in multi-sensor remote sensing images, changing mismatches into correct matches. Initially, key points matching is performed using the SIFT algorithm. Then, using the proposed affine-transformation-based approach, the mismatches are corrected and matching is done. Another novelty of the paper is suggesting two new criteria for assessing the efficiency of image matching methods in addition to the classical criteria of matching precision. As a weakness of the classical criteria that do not consider the total number of matches, feature repeatability rate and the number of correct matches are not defined efficiently. Simulation results show that the proposed method improves the rate of repeatability by 11.41% and cross- correlation coefficient by 14.20% on the average compared to the RANSAC method. Therefore, the proposed method can be used as a new and effective way of improving image matching.

The evidences showing that remote sensing has a significant role as a powerful tool around the world, which can reduced the costs and time of projects, especially since they have a comprehensive view of the large areas where are difficult to access. This study has aimed to predict gully erosion using remote sensing data and Maxent model in Alvand basin located in the western part of Kermanshah province, Iran. Alvand basin with a difficulty accessing due to the extent of the minefield during the imposed war and interconnected with Iraq, on the other hand, the shape of Marne lands and absence of proper vegetation have led to acceleration of gully erosion. Therefore, in this study with a combination method of fieldwork and remote sensing which used in the Google Earth environment, then the essential spatial analysis layout has prepared by Maxent model and the zonation of the gully area has digitized as independent variables that introduced to model. In addition, for analysing the ground surface, a digital elevation model of the Alos data has used with 15 environmental layers of 10/m resolution were prepared as dependent variables. Three goals have attained based on this quantitative and statistical model. First, the effect level of each environmental layer has obtained using the Jackknife test. Second, trend of maximum and minimum effects of each parameter has investigated using logistic regression and finally, Potential map of gully erosion was prepared for the whole region. Then the model validation has performed using the ROC curve and the area under the curve (AUC). It has concluded that the most effective index in gully erosion creation related to elevation index, vertical distance from channel level and flow accumulation. The validation is calculated equal to AUC = 0.899, which shows a good level of results.

Wheat rust is one of the important diseases of cereal crops in Iran and other countries in the world which imposes irreparable damages to the agricultural economy. In this study, the effects of the leaf and yellow rust disease on wheat leaves reflectance were studied. For this purpose, various vegetation indices derived from leaf spectra were measured. To do this, diseases ratio and varying degrees of disease were extracted by using digital camera and multi-step algorithm including color Transformation, mask preparation, texture and maximum likelihood classification. Results show variation in the values of the parameters with changing in proportion of disease whereas the data scattering of indexes Increase quickly. The highest correlation was for the NDVI (0.9) and the minimum was for the red slope (0.2). With the similarity criteria, range and inter-class scattering relations of spectra and disease were studied and with Increasing of the disease ratio. These criteria are altered by developing of disease ratio .Further investigation showed, spectrum mixing in different fraction of yellow, orange, brown and dead is a cause for data scattering with disease development.