علمی - پژوهشی
Ali akbar Matkan; Babak Mirbagheri; Abbas Beigi; Mostafa Ghiyasvand
Volume 7, Issue 4 , November 2015, Pages 1-12
Abstract
Researchers have always been looking for better ways to develop the design, operation and implementationofwater distribution networks in GIS.Despite,extensivegeographical potentials of GIS,however, it cannot be independently considered as a spatial decision support system (SDSS) for management of this ...
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Researchers have always been looking for better ways to develop the design, operation and implementationofwater distribution networks in GIS.Despite,extensivegeographical potentials of GIS,however, it cannot be independently considered as a spatial decision support system (SDSS) for management of this kind of networks. This research aimed to develop a spatial decision support systemin the form of standalone application for management of the water distribution network of FereidoonShahr City. This study attempts to combine advanced analytical hydraulic functions with the spatial analysis potentials of GIS software.In this regard, the identification and assessment stage was intended to develop the components of SDSS including Database Management component, Model Management component and Dialog Management component. By implementing conceptual, logical and physicalmodels in Database Management component, our geodatabase was developed. By using user friendly interfaces in order to communicate easily with users the Dialog Management component was developed. After that,in the Model Management component, some hydraulic models of water distribution networks such as velocity analysis model of pipes, and pressure on junctions were developed using ArcObjects components. Ultimately, alternative evaluating models were designed in order to solve semi-structured issues of urban water distribution networks. With the implementation of the above system for the first time in Iran which is based on a scientific approach, network administrators and analysts can use this software as a comprehensive SDSS in the analysis of urban water distribution networks.
علمی - پژوهشی
H.A Bahrami; S Mirzaei; A Darvishi Boloorani
Volume 7, Issue 4 , November 2015, Pages 13-26
Abstract
In recent years, dust storm has become a common phenomenon in West Asia and especially Iran. This phenomenon is affecting almost all aspects of life including fauna and flora as well as human life. This research aimed to investigate the effects of dust storms on the wheat canopy, that are the most important ...
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In recent years, dust storm has become a common phenomenon in West Asia and especially Iran. This phenomenon is affecting almost all aspects of life including fauna and flora as well as human life. This research aimed to investigate the effects of dust storms on the wheat canopy, that are the most important agricultural species, reflectance and best band for selected narrow band indices to discriminating wheat canopies which are under dust stress in different growing stages. Two wheat (Triticum aestivum L.) varieties, Aflak and Pishtaz, were grown in pots under controlled conditions. The treated samples were exposed to simulated dust storm, in the wind tunnel, at two growth stages including Tillering and Heading stages. In each stage the treatments were exposed in 2, 4 and 6 days. Field spectroscopy measurements were carried out at canopy level using a full range spectro-radiometer Fieldspec-3-ASD. New narrow-band vegetation indices from NDVI, RVI, PVI and SAVI2 indices were computed from the all measured canopy spectra, Tillering and Heading stageseparately. To assess the performance of the indices, the RMSE, R2 and cross-validation method were used. For most indices, the selected optimum narrow bands are very close to one another and located in visible and NIR spectral domains. The result showed that the PVI index performed the best for considering the dust effect on wheat crops. The result also show that the selected indices have better performance in the Tillering stage ( 0.77; 0.63 0.80)for estimating the dusty days, compared with Heading stage ( 0.91; 0.62 0.71). Therefore, determining the dusty days by narrow band indices could be done precisely in the early stage of wheat growing.
علمی - پژوهشی
Ehsan Tamassoki1; Asadollah Khoorani; Ali Dervishi Bolorany; Ahmad noheghar
Volume 7, Issue 4 , November 2015, Pages 27-44
Abstract
Wind erosion and dust storms are of major environmental hazards all around the world. Because of The extent of arid and semiarid regions in South and South- East of Iran and the successive incidence of this phenomenon in this region, it is important to study these phenomena. The aim of this study is ...
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Wind erosion and dust storms are of major environmental hazards all around the world. Because of The extent of arid and semiarid regions in South and South- East of Iran and the successive incidence of this phenomenon in this region, it is important to study these phenomena. The aim of this study is monitoring and predicting dust storms in south and south-east of Iran. For this purpose 92 Images of MODIS sensor as well as weather data of 18 stations are used. Dusty days (originating in outside and around the station) were extracted. After monthly and annually monitoring of storms, in order to predicting the frequency of dust storms based on spatial regression, climatic factors and NDVI are used. The results show that the number of storm are high in the beginning year and is decreasing in Jun and July. More than 78 percent of dust storms are of near station type. Spatial regression equations could predict amount of storms. Based on the origin of dust storms in this study combating desertification and wind erosion program could reduce frequency of this storms.
علمی - پژوهشی
M.R Mobasheri; E Amraie
Volume 7, Issue 4 , November 2015, Pages 45-60
Abstract
Detectors noises in satellite images are seen as either vertical or horizontal stripes. The directions of these stripes depend on the imaging technique (Pushbroom or Wiskbroom). The main reasons in appearance of stripe noises in TM images are; lack of matching between detectors, unsuitable calibration ...
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Detectors noises in satellite images are seen as either vertical or horizontal stripes. The directions of these stripes depend on the imaging technique (Pushbroom or Wiskbroom). The main reasons in appearance of stripe noises in TM images are; lack of matching between detectors, unsuitable calibration and detector degradation in time. Due to the Wiskbroom technique in TM sensor, the stripes appear horizontally. Among these, the stripe noises in band4 are more profound in images acquired from dark surfaces such as sea surface. This kind of noise may produce sever errors in atmospheric correction based on dark surfaces. In this work, to correct the stripe noise, Mean Method (MM), Modified Spatial Momentum Matching (MSMM), and image filtering in frequency and spatial domain (IFFD & IFSD) are introduced. To evaluate the results, some statistical parameters such as averaging, standard deviation, histograms and Fourier spectrums before and after corrections are deployed. Reduction in standard deviation after denoising demonstrates enhancement in the image. To compare these methods with other known methods, parameters such as MSE, RMSE and PSNR along with simulated images for periodical striped noise are used. Among these, the maximum PSNR and naturally the minimum MSE belongs to MM and MSMM methods and consequently these methods perform better accuracies compared to IFFD and IFSD.
علمی - پژوهشی
A Sedaghat; H Ebadi
Volume 7, Issue 4 , November 2015, Pages 61-84
Abstract
A descriptor is computed on a local region around a feature point and is used to characterize and compare the features. Various descriptors have been proposed in the literature which have different properties and performance in different image data. Evaluation of the local feature descriptor is important ...
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A descriptor is computed on a local region around a feature point and is used to characterize and compare the features. Various descriptors have been proposed in the literature which have different properties and performance in different image data. Evaluation of the local feature descriptor is important to identify the strengths and weaknesses of each algorithm in different applications. In this paper a performance evaluation of the state of the art in local descriptors is performed on a set of satellite images under varying imaging conditions. Ten descriptors are included, which are spin image (SI), shape context (SC), SIFT, PIIFD, SURF, DAISY, LSS, LBP, LIOP and BRISK. 80 satellite image pairs in three groups including simulated images, multi-temporal, and multi sensor images are used as data set and descriptors are evaluated using four evaluation criteria including Recall, Precision, positional accuracy and speed. The evaluation results indicate that there does not exist one descriptor which outperforms the other descriptor for all scene types and all types of transformations, but in average DAISY and SIFT show the best performance
علمی - پژوهشی
M Rajabpour Rahmati; A.A Darvishsefat; N Baghdadi; Manochehr Namiranian; Nosrat ollah Zargham
Volume 7, Issue 4 , November 2015, Pages 85-98
Abstract
Forest volume as an important factor in forest management was aimed to be measured in mountainous forests in the North of Iran using spaceborne LiDar. Two missions of GLAS (L3K and L3I) were preprocessed to remove inappropriate waveforms. Several waveform metrics including waveform extent (Wext), lead ...
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Forest volume as an important factor in forest management was aimed to be measured in mountainous forests in the North of Iran using spaceborne LiDar. Two missions of GLAS (L3K and L3I) were preprocessed to remove inappropriate waveforms. Several waveform metrics including waveform extent (Wext), lead edge extent (Hlead), trail edge extent (Htrail) and quartile heights (H25, H50, H75 and H100) were extracted. Principal component analysis (PCA) was also applied to reduce noises from waveform signals and produce new components (PCs). In order to decrease the effect of terrain slope on waveforms, terrain index (TI) describing topographic information was extracted from a digital elevation model (DEM). Forest stand volume was measured on 60 circle plots with diameter of 70 m for developing volume models and their validation. Multiple regression and artificial neural network models were built based on two sets of variables including waveform metrics and PCs. Generally, both multiple regression and neural network methods produced approximately the same result. A neural network combining three first PCs of PCA and Wext estimated forest volume with an RMSE and of 119.9 m and 0.73, respectively (RMSE%=26.6). Interesting points regards to this model is employing PCs and Wext as input variables which are not affected by terrain slope, achieving slightly better accuracy without adding any ancillary data (DEM), and showing better performance in short sparse stands in comparison with other developed models.
علمی - پژوهشی
E Taherian; H Samadi
Volume 7, Issue 4 , November 2015, Pages 99-116
Abstract
Strategic decisions about the construction of engineering structures along the river which are essential for the management of sediment entering the reservoir will be facilitated by understanding the behavior and characteristics of the sedimentation of rivers leading to large dam reservoirs. Multi-temporal ...
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Strategic decisions about the construction of engineering structures along the river which are essential for the management of sediment entering the reservoir will be facilitated by understanding the behavior and characteristics of the sedimentation of rivers leading to large dam reservoirs. Multi-temporal and spectral remote sensing technology has been applicable for detecting of the rivers morphological changes. However, the specific nature of the narrow and shallow rivers is responsible for increasing the complexity of morphology with available data. This study was undertaken in order to detect narrow and shallow rivers by assessing the ability of six famous water indices, including: Normalised Difference Water Index, Modified Normalised Difference Water Index, Automated Water Extraction Index no shadow, Automated Water Extraction Index shadow, Enhanced Water Index and Water Index 2015 which were derived from two Landsat ETM+ and OLI sensors. The optimal threshold for each of these indices was determined using ROC curves and validation process was carried out using Google Earth images captured in August 2013. The accuracy of results was evaluated by using different statistics including combined error, producer’s accuracy, user accuracy and omission and commission errors. Consequently, the results of this study have shown that the ETM+ sensor was generally more accurate than OLI sensor. All in all the Modified Normalised Difference Water Index and the Automated Water Extraction Index shadow was the most accurate indices. Also Automated Water Extraction Index no shadow index had the lowest accuracy for the river’s detecting process