فصلنامه سنجش از دور و GIS ایران
سال چهارم شماره 1 (پیاپی 13، بهار 1391)

The necessity of awareness of water resources conditions in different watersheds for executing the water plans on the one hand and the absence of reliable network of gauging stations measuring the meteorological parameters on the other hand relieve how essential taking advantage of the indirect methods for simulating and evaluating the run-off flow in watersheds are. Hence, amongst the indirect methods, the sophisticated techniques such as Remote Sensing and Geographical Information Systems (GIS) play an important role in the field of hydrology and water resources. The fact is that such methods provide us with comprehensive knowledge of watershed and accelerate the analysis in a much more precise manner. This plays an important role in developing the spatial-distributed models in GIS in which the spatial data as the pixel units along with the hydrologic data contribute to the better understanding of the complicated and dynamic processes. WetSpa model is known as one of the best examples in GIS-based spatial-distributed models. This hydrological model was developed with the purpose of sustainable watershed management strategies. The WetSpa performs based on the grid networks in a watershed with a continuously function. In the present study first the daily flow for Merek watershed in Kermanshah Province – 1380 km2 area – was simulated using WetSpa model in GIS environment and following the calibration and validation the effect of land use changes on stream flow – particularly the peak discharge- was analyzed. Also, the watershed optimal land use map was produced based on Makhdoom land use planning and the stream flow was simulated again. The results of applying the WetSpa model in Merek watershed showed a Nash-Sutcliffe (NS) efficiency of 77% whereas there seems to be a strong consistency between measured and simulated hydrographs. Meanwhile simulating the stream flow under the optimal land use scenario illustrated that there is a lag time for peak flows compare to the present land use hydrograph, and the hydrograph peak discharge has decreased after the optimal land use change.

Taking the advantages of polarimetric radar data has a decisive role in target detection purposes. In this way, comprehensive geometric and descriptive information could be derived through processing this kind of data. However, the selection of optimal features could be considered as a major challenge in order to classification of the polarimetric radar imagery. In this paper, a novel approach is proposed for optimal feature selection based on mapping the extracted features to the prototype space. As a key result of the paper, fitness index is introduced to facilitate the optimal feature selection in polarimetric radar images. On the other hand, the mixture of backscattering mechanisms in a pixel level is another limitation to obtain precise spatial information. Thus, utilizing soft classifiers is indispensible to acquire the sub-pixel information. Positivity and sum to unity of the fractions within each pixel are major challenges in results of the soft classifiers. In this paper, integration of the soft classifiers and unsupervised algorithms of end-member extraction is proposed to solve this problem. Likewise, soft classifiers just provide fractional maps and the spatial arrangement of sub-pixels remains unknown. In this regard, Super Resolution Mapping (SRM) techniques are developed to enhance the spatial resolution of the results of soft classifiers. This research attempts to provide a sub-pixel classification of polarimetric radar images using the pixel swapping technique. Towards this end, a non-random procedure is suggested for initial arrangement of the sub-pixels. According to the results, the proposed method for optimal feature selection is demonstrated more accurate results than genetic algorithm. Next, three algorithms including Linear Spectral Unmixing (LSU), Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM) are performed to soft classifying of the polarimetric radar image into three classes (residential, vegetation and bare earth). SVM present accurate results in comparison to others; its resulted fractional maps are used in SRM procedure. Finally, pixel swapping technique is performed based on the results of SVM classification and the land cover map of the study area is produced in a finer spatial resolution.

Urban land suitability evaluation (ULSE) is necessary to allocate proper land uses to available sites. GIS and MCDM based methods have considerable results to facilitate ULSE. Furthermore, ULSE asks participation of various experts with different point of view and interest, as a group decision making problem. In this research, a GIS and Fuzzy-MCDM integrated method is used to evaluate urban land suitability and allocate proper land use to predefined urban sites, at a group decision making approach. Main steps of ULSE include: selection of evaluation factors, determining of factors’ weights, producing of factor maps in GIS, aggregating of factor maps and finally calculating of land suitability index for each urban site, have been presented and discussed. The method is tested in a case study area at 6th municipality region of Esfahan city. Evaluation factors for four urban land uses and related weights are modeled and used to define priority for each urban site to allocate to specific land uses, using of Fuzzy AHP method. The results indicate usefulness of method for ULSE.

Availability of the loan in the construction area of soil structures is one of the main factors. The importance of this study is determination of areas with clay soil that can be used to construct earthy structures. At first by using of GPS and available map of Alborz Dam Project, the basic data was collected. To ensure samples were taken from available clay map and with hydrometric method the texture of soil was determined. Finally 10 points with clay texture and proper distribution in Babolrood basin, Mazandaran province were selected. 6 points for training data and 4 points for assessment were used. Also 10 points for vegetation and shade Endmembers were identified. Then we corrected the atmospheric errors with Dark-object subtraction method. The images do not required radiometric and geometric correction. Pure pixels were identified using pure pixel index (PPI). Using pure pixels spectral library was prepared and was used to classify with spectrum analysis and tree classification techniques. These modeling techniques operate as a tool to identify the position and seperability between vegetation, clay and shade in the space of complex spectral extracted from satellite images Landsat ETM+ sensor. The results showed that clay, vegetation and shade can be separated from each other with 68, 77.5 and 67.86 percent accuracy respectively. Overall accuracy and kappa coefficient of the model were 71.96% and 61% respectively.

Landslide is deemed a natural disaster, causing vast losses to lives and properties every year. One major characteristic for landslide is its predictability. Having a landslide predicted, we can prevent consequent losses from occurring or propagating. One method to predict this phenomenon is to study landslide patterns in the region and to zone the region accordingly. The current literature attempts to zone the entire Alang Dare region which is marked by landslides in various sections, with regard to landslide occurrence risk, through studying template landslide characteristics (or landslide characteristic patterns) by means of Analytical Hierarchy (AHP), fuzzy and LNRF methods. This literature compares different methods in this regard and also introduces points of risk for further stabilization. The results proved that LNRF, amongst other methods is of more compatibility to the above region. The expertise interference is minimized in this method and thus it is less likely to develop error. In AHP method, is more likely to develop error in rating parameters due to direct expertise and also greater number of variables. In fuzzy method the process of defining minimum and maximum limits for effective values in rating parameters is directly dependent on expertise which magnifies the possibility of developing error. In the current study, the above methods have been compared and with regard to field investigation at the site of landslide, the LNRF method was chosen as the ultimate method in this respect.

Actual evapotranspiration is one of the most important components of the water balance equation that is hard to estimate due to need of huge data. The use of remote sensing technology allows retrieving spatial data and minimizing meteorological data and is an efficient approach to overcome this problem. Different methods have been suggested for this task, so far. However, rare studies are available to compare their performances. This issue constructs objective of the present study. To end this three different schemes have been tested including 1) the widely used one-source SEBAL and METRIC models, where soil and vegetation are considered as a sole source, 2) the two-sources TSEB and STSEB models, where soil and vegetation components of the surface energy balance are treated separately and 3) one-two source SETEB model, which is a combination of the SEBAL and TSEB algorithms. In order to implement and evaluate the models, MODIS remote sensing data were acquired on 6 days of July 2011 and simultaneously Lysimeter observations of Tabriz University experimental farm on the similar days were recorded while the Tera satellite was passing the region. The results revealed acceptable and close estimations of ETA by the methods. Nevertheless, the highest and lowest performances were belonged to SETEB and TSEB with absolute error (MAPD) of 8.73 and 27.94, respectively. Despite the complexity of the two sources models, SEBAL and METRIC with simpler methodology resulted proper estimation of ETA that makes them suitable for large-scale studies. The SETEB also gains this simplicity aspect and recommendable for basin scale application.

In spite of strength of conventional and persistent scatterer Interferometry in deformation monitoring, they face limitations making estimation of deformation rate in rural area with high deformation rate using poor temporal sampling radar data impossible. Therefore, Sadeghi et al. presented two new approaches improving the Persistent Scatterer interferometry in order to estimate the deformation in areas lacking man-made structure with high and approximately linear deformation. In this paper, the first method using the merits of StaMPS and DePSI method and the second method improving StaMPS method are called enhanced algorithm based on persistent scatterer and increasing applicability of StaMPS, respectively. The aim of this paper is comparison between two mentioned approaches in rural area with high deformation rate. Therefore, a subset of Tehran basin undergoing maximum subsidence rate was selected. After applying both approaches in the study area using radar data of track 149, the results were compared to the extracted rate from SBAS algorithm using data acquired from track 378. The obtained results show, the second approach in spite of faster response, faces larger unwrapping error and more incorrect estimation.