Rasoul Atashi Deligani; Mina Moradizadeh; Behnam Tashayo
Abstract
Ground surface ozone is one of the most dangerous pollutants that has significant harmful effects on the residents of urban areas. The purpose of this study is to identify the factors affecting ozone concentration and modeling its changes using satellite data and different machine learning methods in ...
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Ground surface ozone is one of the most dangerous pollutants that has significant harmful effects on the residents of urban areas. The purpose of this study is to identify the factors affecting ozone concentration and modeling its changes using satellite data and different machine learning methods in Tehran. For this purpose, pollutant concentration and meteorological data were used along with the satellite product of land surface temperature (LST) in the period from 2015 to 2021. After calculating the correlation between ozone concentration and independent parameters, ozone concentration modeling was done in five different modes in terms of input parameters and learning method and applying data refinement. In the first and second mode, modeling was done using pollutant concentration and meteorological data through multivariate linear regression method. The only difference between these two modes is the filtering of the input data using the WTEST method in the second mode. In the third mode, the LST product was added to the input data, and in the fourth and fifth mode, ozone modeling was done using multilayer neural network and recurrent neural network, respectively. The comparison of the five modes showed that the modeling of the first to fifth stages with adjusted coefficient of determination of 0.5, 0.64, 0.69, 0.74 and 0.8 were able to recover the ozone concentration, respectively. It was also found that among different pollutants, nitrogen monoxide, nitrogen dioxide and nitrox have the greatest impact on ozone concentration, just as temperature, humidity and wind speed are the most influential among meteorological data. Although the use of WTEST statistics led to the identification and elimination of inconsistencies and errors in the observations of pollution measurement stations, the neural network learning method showed better performance in modeling than multivariate regression due to its less sensitivity to noise. As a notable result, adding the LST product to the input data brought a 5% increase in accuracy in estimating ozone concentration.
A.A Matkan; M Hajeb; M Eslami
Volume 7, Issue 2 , November 2015, , Pages 19-34
Abstract
The availability ofinformation about roads has great importanceinvariousapplicationssuch as transportation,traffic controlsystems, automatic navigation system, etc. In recent years, designing new road extraction algorithms has become the target of many studies by researchers. Despite the achieved progress, ...
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The availability ofinformation about roads has great importanceinvariousapplicationssuch as transportation,traffic controlsystems, automatic navigation system, etc. In recent years, designing new road extraction algorithms has become the target of many studies by researchers. Despite the achieved progress, there are some defects in this field. The gaps in detected roads are one the most important of them. The gaps are appeared due to getting placed under trees, shadow or any other reason. Since the continuity of roads is a momentous topological trait, so filling the gaps seems necessary. The main aim of this paper is to provide a method to automatic find and fill the existing gaps in the extracted road net. Our algorithm first applies the Radon transformation to find the source and destination endpoints of the gaps, then connect these points together using Spline interpolation. This algorithm is implemented on a real detected road which has 4 gaps in straight roads and 2 gaps in junctions. The experiment shows that the proposed algorithm can correctly fill all of the gaps in straight roads, but it is not able to fill the gaps in junctions. So, regardless of the location of the gap, straight road or junction, it can be said that about 66.7% of the existing gaps was filled by the algorithm. This gap filling algorithm is implemented in MATLAB software
M Jannati; M.J Valadan Zouj; A MohammadZadeh; A. Safdarinezhad
Volume 8, Issue 1 , November 2016, , Pages 19-36
Abstract
In normal images, which resampled according to epipolar geometry, all of spatial displacements of points in the space of stereo images occur only in one direction of the digital image coordinate system. This prominent characteristic makes normalized imagery as an important prerequisite for many photogrammetric ...
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In normal images, which resampled according to epipolar geometry, all of spatial displacements of points in the space of stereo images occur only in one direction of the digital image coordinate system. This prominent characteristic makes normalized imagery as an important prerequisite for many photogrammetric activities such as image matching, automatic aerial triangulation, automatic digital elevation model and orthophoto generation, and stereo viewing. In this paper, a novel approach for epipolar resampling of linear pushbroom satellite imagery is proposed based on Orbital Parameters Model (OPM). The proposed method is developed based on modifying the exterior orientation parameters of OPM in the object space. The most prominent advantage of this method is the capability of the correction of off-nadir viewing of the sensor through the physical interpretation of its parameters. Also, there is the capability of implementation of the proposed method by means of other common physical or interpolative mathematical models used in geometric correction of satellite imagery. According to the results, the average reminded vertical parallax x in the digital image coordinate system is determined 0.73 pixels with respect to the independent check points that demonstrates the high performance of the proposed method
Sogol Moradian; Mohammad Taleai; Ghasem Javadi
Abstract
Nowadays, water supply is one of the main causes of tension for scientists in arid countries. Therefore, the assessment of water resources can be considered as a main challenge for the authorities around the world. Successful dicision making in water resources management tries to resolve competing and ...
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Nowadays, water supply is one of the main causes of tension for scientists in arid countries. Therefore, the assessment of water resources can be considered as a main challenge for the authorities around the world. Successful dicision making in water resources management tries to resolve competing and conflicting needs of water users from different sectors including: domestic, agriculture and industry. In this research, a systematic framework for assessing different scenarios in the system, has been defined based on WEAP. The proposed model was used in the Urmia Lake basin as a case study; A scenario, proposed by the Urmia Lake Reconstruction Team, presists on transmission of water to the lake. In this study, this scenario and some others, proposed by the Urmia Lake Reconstruction Team, were used in this model and the best scenario was identified using a decision support tool based on the principles of TOPSIS, which has been written in FORTRAN. In the following section, water allocation in the catchment was investigated based on the principles of game theory and the outcome of this research shows that applying game theory and using full cooperation games (based on the Shapley Values method), provides better outcomes for all competing users of water. In other words, using the notion of coalition between different sectors including domestic, agriculture and industry, can save about 332 MCM of water which can be used in the dying lake.
maryam omidpour; Romina Sayahnia; Y Rezaei
Abstract
Urban development is an inevitable subject due to the increasing population growth in cities. The city consists of open and living systems and a combination of socio-ecological systems. On the one hand, the hurried process of urban growth resulted in the land use change. Subsequently, this manner will ...
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Urban development is an inevitable subject due to the increasing population growth in cities. The city consists of open and living systems and a combination of socio-ecological systems. On the one hand, the hurried process of urban growth resulted in the land use change. Subsequently, this manner will damage to the structure, yield, and ecological processes in each city. Meanwhile, the use of ecologic knowledge with the landscape and resilience approaches can help to analyze the present situation and discover the optimal solutions. The resilience in the pattern of the natural structure of the ecological networks depends on the extent and intensity of the green spots. This research was carried out to determine the vegetation changes in Hamadan city between1982 to 2015 to achieve the evaluation of the ecological network structure in the urban development process with a resilience approach. In this study, a conceptual framework derived from ecological knowledge, resilience ideas and the use of modern technologies, such as GIS and RS, was designed to determine the bio-sensitive areas caused by the substructure urban changes. Besides, this study was performed to preserve the remaining biological resources in this area and preventing damage to the natural ecosystem of this city. A series of satellite imagery was classified in four categories such as such as open area, built land, communication paths, vegetation. After the above classification, these maps were processed and analyzed by the TerrSet software and three types of landscape metrics including creating, loosening and separating were reviewed to identify the types of land use changes in these years. The results show that the vegetated area decreased from 2820.2 hectares in 1982 to 1304.2 hectares in 2015 and, on the other hand, built lands and communication paths were degraded from 606.4 hectares in 1982 to 4274.2 hectares in 2015. In general, it can be concluded that this level of change shows a high urban development, decreases in plant vegetation, and its discontinuation so that the above changes have reduced the resilience of the city's ecology network. At the end of the study, numerous strategies were also provided for the restoration of the entire damage in the natural ecological network of Hamadan and its development.
Amir Hossen Nazemi; Hamed Sabzchi; Aliashrafi Sadraddini; Abolfazl Majnooni Haris
Abstract
Application of the remote sensing methods in crop area mapping on a large spatiotemporal scale serves is as an alternative to costly time-consuming field data gathering methods. So far, some methods have been developed for wheat and rice area mapping using the images from optical and radar sensors. Some ...
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Application of the remote sensing methods in crop area mapping on a large spatiotemporal scale serves is as an alternative to costly time-consuming field data gathering methods. So far, some methods have been developed for wheat and rice area mapping using the images from optical and radar sensors. Some of these methods are appropriate for humid climates with several cloudy days, while others use complex processes in terms of combining both optics and radar images. Meanwhile, methods based on the unique variation of the vegetation index time series belongs to each crop are relatively simple methods that can be used for crop area mapping. The objective of this study is to improve one of the proposed methods for rain-fed wheat area mapping, in which a step-by-step elimination algorithm of non-wheat pixels was applied to MODIS images. The Improved algorithm took advantage of both MODIS and Landsat Images in terms of their high temporal and high spatial resolutions, respectively. The mentioned process could detect rain-fed wheat areas from the pastures and heterogeneous areas with higher accuracy in comparison with the previous algorithm. The overall accuracy, Kapa index, and F1 score for the final rain-fed wheat map was 92.5%, 0.67, and 0.71 respectively.
Zohreh Roodsarabi; ali Sam Khaniani; Abbas Kiani
Abstract
Numerous studies on the phenomenon of fire over the past several decades have provided an extensive set of input data and implementation and evaluation methods. However, this vast array of results and research is structured to provide a roadmap to new users in the field and guidance on various applications ...
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Numerous studies on the phenomenon of fire over the past several decades have provided an extensive set of input data and implementation and evaluation methods. However, this vast array of results and research is structured to provide a roadmap to new users in the field and guidance on various applications and conditions that have not yet been analyzed. In other words, the absence of coherent research on the relative performance of different remote sensing processes in the fire is felt to produce various products or the resulting utilities. To fill this gap, a relatively comprehensive analysis of fire studies in remote sensing publications has been performed in this study. Some of the general factors evaluated in the pre, during, post-fire studies were the manipulation of input data, the review of algorithms, and their development, as these are factors that can be controlled by analysts to improve the Final accuracy of analyzes and results. One of the important issues in the field of fire after the identification and discovery of fire, due to the permanent changes in the structure and composition of vegetation, is to study how vegetation is restored and its growth rate during the years after the fire. According to a study of fire studies in the country, about 48% of them are related to the identification and spread of fire and the remaining 52% are related to resuscitation and recovery. In a review of research related to identification studies, it was found that approximately 5% of its share was done using learning methods and the remaining 43% was done using traditional methods. At the same time, of the study-related share of Resuscitation studies approximately 21% to examine vegetation and 31% of the soil under the fire surface. The findings of this study can be useful in helping researchers to make decisions in the selection of data and algorithms used according to the purpose of study, in different branches of studies associated with fire. However, in addition to these general guidelines, an analyst can consider personal preferences or the benefits of a particular algorithm that may be relevant to a particular program.
Mina Hamidi; Hamid Ebadi; abbas kiani
Abstract
By improvement of the spatial resolution of remote sensing images, more accurate information are provided from the image scene such as texture structures. However, extraction of land cover information from these datas has become a challenging process due to the high spectral diversity and the heterogeneity ...
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By improvement of the spatial resolution of remote sensing images, more accurate information are provided from the image scene such as texture structures. However, extraction of land cover information from these datas has become a challenging process due to the high spectral diversity and the heterogeneity of surface materials. Visual interpretation is costly and time consuming and automatic interpretation of images does not necessarily lead to high accuracy. Achieving optimal interpretation accuracy requires the design of automatic algorithms that are capable of dealing with the complexity of the image scene. To overcome this problem, object-based image analysis (OBIA) that is sensitive to the image scene morphology, can be particularly effective in an urban area where the density of man-made structures is high. In object-based classification, pixels of a segment are analyzed in combination with each other. So the dimensions of the problem space are reduced, in compared to the pixel-based method, which leads to increasing the computational speed. Meanwhile, due to the different sizes of image segments, supervised object-based classification faces challenges in creating an optimal training set. In this research, AdaBoost algorithm was selected for the object-based classification, to overcome the problem of feature space imbalance, due to the small number of training samples in comparison with the high dimensions of the feature space (including spectral, spatial and geometric features), two strategies were proposed. In the first approach an active learning mechanism was integrated with AdaBoost to produce optimal training data set (OTD) and in another approach based on the feature-to-feature correlation (redundancy) and the feature-to-class correlation (relevance), the candidate feature subset (CFS) was generated to reduce the size of the feature space. To evaluate the proposed method, the developed algorithm was performed on the standard dataset of Vaihingen in Germany and the results were compared with the pixel-based classification. In order to evaluate the signification of the results, the McNemar statistical test was used. The experimental results showed that the proposed object-based approach improved the overall accuracy by 6% and the kappa coefficient by 7% compared to the pixel-based approach. Also, the computational speed of proposed object-based AdaBoost was significantly increased compared to the pixel-based approach. These results indicate the superiority of the proposed approach both in terms of accuracy and processing speed.
Volume 7, Issue 1 , December 2015, , Pages 21-38
Abstract
The result of epipolar rearrangement process is pseudo normal images; which conjugate points are located along their rows or columns. But, unlike normalized images resulted from epipolar resampling process, there is no guarantee that parallel parallax of conjugate points to be linearly proportional with ...
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The result of epipolar rearrangement process is pseudo normal images; which conjugate points are located along their rows or columns. But, unlike normalized images resulted from epipolar resampling process, there is no guarantee that parallel parallax of conjugate points to be linearly proportional with z-coordinate of corresponding point in the object space. However, pseudo normal images can positively affects many photogrammetric activities such as image matching, automatic aerial triangulation, automatic digital elevation model and orthophoto generation, and stereo viewing. In present paper, a novel approach for epipolar rearrangement of linear pushbroom satellite imagery is proposed based on Multiple Projection Centers model, and rearrangement procedure is separately investigated for both Cross Track and Along Track imaging systems. The proposed method is developed based on refinement of trajectory and attitude parameters of the sensor. One of advantages of this method is the capability of the correction of off-nadir viewing of the sensor through the physical interpretation of its parameters. According to the results of the accuracy assessment of pseudo normalized images using the proposed method in independent check points, the mean of residual vertical parallaxes in stereo model is determined 0.94 pixels; that corroborates the feasibility, correctness, and applicability of the method. Keywords: Photogrammetry, Epipolar rearrangement, Pseudo normal image, Pushbroom linear imagery, Multiple projection centers model, Trajectory refinement, Along track and cross track imaging system.
S Salehi; M.J Valadan Zoej; M. Sahebi
Volume 9, Issue 2 , December 2017, , Pages 21-40
Abstract
In applications related to environmental monitoring and disaster management, multichannel synthetic aperture radar (SAR) data present a great potential, owing both to their insensitivity to atmospheric and Sun-illumination conditions and to the improved discrimination capability they may provide as compared ...
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In applications related to environmental monitoring and disaster management, multichannel synthetic aperture radar (SAR) data present a great potential, owing both to their insensitivity to atmospheric and Sun-illumination conditions and to the improved discrimination capability they may provide as compared with single-channel SAR. However, exploiting this potential requires accurate and automatic techniques to generate change maps from images acquired over the same geographic region in different polarizations or at different frequencies at different times. Furthermore, sensitivity to contextual information of each pixel reduces the error rates in labeling process, thus generates accurate change maps. The smoothing effect of despeckling and the isotropic formulation of the Markov Random Field model cause over-smoothing of the spatial boundaries between changed and unchanged areas in the final change maps. In order to reduce this drawback, edge-preserving MRF models could be integrated in the labeling process. This method improves the precision of edges at spatial boundaries and increases the change detection accuracy. In this paper, a contextual unsupervised change-detection technique (based on a data-fusion approach) is proposed for two-date multichannel SAR images. A Markov Random Field model is formulated by using “energy functions” that combines the information conveyed by each SAR channel, the spatial contextual information concerning the correlation among neighboring pixels and the edge information. In order to estimate the model parameters, the expectation–maximization algorithm is combined with the recently proposed “method of log-cumulants.” The proposed technique was experimentally validated with semisimulated data produced by ASAR-ENVISAT images. Experiments illustrate a significant improvement (average 12%) with the proposed technique over the other change detection approaches. Integrating edge information yielded accurate results in exploiting various levels of changes (low-medium-high) whereas contextual information and information conveyed by channels were unable to detect low and medium level changes. Considering the small number of iterations, computation time is reduced considerably. Generally the highest accuracy achieved by the proposed algorithm is 99/67%.
Mojgan Ahmadi; abbas kaviani; Peyman Daneshkar Arasteh; Zohreh Faraji
Abstract
Determination of precipitation in energy balance calculations are vital for hydrological studies and meteorology. Due to the importance of precipitation data in different sciences and the absence of a wide and appropriate rainwater meter, precipitation data needs to be estimated in some way. One ...
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Determination of precipitation in energy balance calculations are vital for hydrological studies and meteorology. Due to the importance of precipitation data in different sciences and the absence of a wide and appropriate rainwater meter, precipitation data needs to be estimated in some way. One of the ways to estimate precipitation is the use of satellite data In this study, GLDAS, CRU, GPCP, TRMM, CMAP and NCEP-NCAR models are evaluated with station data in Alborz, Qazvin, Zanjan, Kurdistan and Hamedan provinces. The results showed that GPCP, TRMM, CMAP and NCEP-NCAR had good outcomes in these regions, among which GPCP and TRMM provided better results.In the evaluation of GPCP with mean stations in the study area in pixel 3 in 2003, the explanatory factor (R2), EF coefficients, average error margin (MBE), absolute error of error (MAE) and root mean square error (RMSE) were 0.96, 0.94, 3.13, 5.30, and 6.58, respectively. In addition, precipitation data model of GLDAS is evaluated with station data. Results show that GLDAS model is not very accurate in areas with high precipitation such as Rasht and Noshahr stations.
Naser Farajzadeh; Mehdi Hashemzadeh
Abstract
Generally, the photos captured by drones and satellites include both natural scenes and man-made objects. Having these two categories classified, we will be able to extract important information from aerial scenes such as the shapes and the alignments of the structures and then, create labeled aerial ...
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Generally, the photos captured by drones and satellites include both natural scenes and man-made objects. Having these two categories classified, we will be able to extract important information from aerial scenes such as the shapes and the alignments of the structures and then, create labeled aerial images accordingly. Obtaining such information is of great interest in, for example, military, urban, and environmental protection applications. However, due to a huge amount of data that is collected in form of images, it seems that manually processing of such data is impossible. Therefore, employing automatic techniques based on artificial intelligence has become more on demand. There are numerous researches on this topic from which detection of buildings, vehicles, roads, and vegetation are of more interest. In this paper, we aim to introduce a method to detect man-made objects in aerial images based on a new set of color statistical features, which can be easily extracted, together with a learning model. Experimental results on a publicly available dataset, Massachusetts dataset, have shown promising results in terms of both accuracy and processing time; the accuracy and the average processing time are 90.07% and 0.96 seconds, respectively.
zohreh hashemi; Hamid Soodaei zadeh; Mohammad Hossein Mokhtari
Abstract
Land surface temperature is considered a key parameter in the physic processes of land surface at all scales of local to global. In this study, the relationship between land surface temperature with vegetation and soil surface moisture in land uses of Zahak plain of Sistan area was investigated. In order ...
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Land surface temperature is considered a key parameter in the physic processes of land surface at all scales of local to global. In this study, the relationship between land surface temperature with vegetation and soil surface moisture in land uses of Zahak plain of Sistan area was investigated. In order to, Landsat TM (1987), TM (2001) and OLI (2018) satellite imagery were used. After the preprocessing and image processing steps, the extraction of land use maps was performed based on the monitored classification method and through maximum probability algorithm for a period of 30 years. Also, land surface temperature was evaluated statistically by separate window method and the relationship between land surface temperature with vegetation and soil moisture. The results showed that the accuracy of classification by maximum probability method through geomorphic facts data, TM and OLI images in terms of kappa coefficient of 0.89, 0.95 and 0.84, respectively, based on the overall accuracy of 91.8, 96.45 and 87.89% was obtained. During 1987, 2001 and 2018, average of the land surface temperature indices were 38.13, 45.73 and 41.14 ° C, the normalized difference vegetation index was -0.11, -0.13 and -0.16, and the normalized difference moisture index was estimated 0.64, 0.63 and 0.58. The relationship between land surface temperature and normalized difference of vegetation index was no correlative. The correlation between land surface temperature and the normalized difference of humidity index was also inverted and negative. Plant regeneration and growth was decreased owing to factors including hydrological drought and Climatic conditions due to reduced rainfall, rising air temperature and Dust storms. Therefore, due to the lack of suitable vegetation, vegetation is not effective in reducing the surface temperature of the study area.
H Habibi; M Taleai; Gh Javadi
Volume 9, Issue 4 , May 2017, , Pages 22-36
Abstract
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 ...
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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.
Samira Karbasi; Hossein Malakooti; Mehdi Rahnama; Majid Azadi
Abstract
In this report, we compare data products from three different algorithms with the reference data obtained by ground-based high-resolution Fourier Transform Spectrometers (g-b FTSs) in the Total Carbon Column Observing Network (TCCON), with the 8 selected sites in five years(2011-2015). The algorithms ...
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In this report, we compare data products from three different algorithms with the reference data obtained by ground-based high-resolution Fourier Transform Spectrometers (g-b FTSs) in the Total Carbon Column Observing Network (TCCON), with the 8 selected sites in five years(2011-2015). The algorithms evaluated are NIES, ACOS and SRFP algorithms. These algorithms are focused on retrieving the column abundance of the CO2 to take advantage of the molecular amounts of dry air carbon dioxide (XCO2). To evaluate the products of each algorithm with its equivalent ground observations, statistical indices such as Bias error, root mean square error (RMSE), absolute error (MAE), standard deviation (SD), and Pearson correlation coefficient (CR) were used. By examining the values presented by each algorithm and comparing it with the ground observation values, it can be concluded that the NIES, ACOS, and RemoTeC (SRFP) algorithms have the lowest RMSE, Bias and MAE error respectively. The best agreements with TCCON measurements in the most stations were detected for NIES 02.xx. The SRFP algorithm has a significant difference in estimating CO2 retrieving rates compared to the other two algorithms. So that the lowest correlation values belong to the SRFP algorithm and the highest correlation, values belong to the NIES algorithm.
Abbas Kiani; Hamid Ebadi; Hekmat allah Khanlou
Volume 10, Issue 4 , February 2019, , Pages 27-54
Abstract
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 ...
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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 11.5%, respectively.
Mahvash Naddaf; seyedReza Hosseunzadeh; Jose Martin; mahnaz jahadi; Naser Hafezimoghaddas
Abstract
In the early 1990s, radar interferometry was introduced and used as a useful tool in the study of all phenomena that cause land surface deformations. If the land surface deforms between two radar images, a surface displacement map can be created with millimeter resolution and accuracy. This paper reports ...
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In the early 1990s, radar interferometry was introduced and used as a useful tool in the study of all phenomena that cause land surface deformations. If the land surface deforms between two radar images, a surface displacement map can be created with millimeter resolution and accuracy. This paper reports the findings of the Sentinel1 –A data time series results using the SBAS algorithm to detect surface deformation in the Sangan iron ore mine. Sangan Iron Ore Mine is the largest open pit iron ore deposit in the Middle East. Due to mining activities, this mine has undergone many changes in terms of topography and geomorphology, which can intensify geomorphological processes. To detect and obtain the amount of land deformation, 48 SAR images of Sangan iron ore mine obtained by the European Space Agency's Sentinel 1-A satellite were used. The time series (2014-2020) obtained from the deformation in the range of placer mines were analyzed. The results show the average displacement rate of -20 to -35 mm per year and the maximum cumulative rate of deformations of -120 mm. Investigation of the cross-section in the two parts of the apex and the center of the alluvial fan in the placer mines during the period 2014-2017 shows the topographic changes well. To evaluating the reliability of the results, the results derived from SBAS have been compared due to the lack of data in the range of placer mines with the values measured by the total station related to the mountain unit in the years 2020-2014. The results showed that the rate of deformations from radar data using the SBAS algorithm compared to the leveling data has followed a similar pattern. However, there may be some error due to the different nature, ie in the leveling of elevation deformations measured for a point, but in interferometry the average rate is obtained from adjacent points.
Mehdi Amiri; Saif ollah Soleimani; Fakhteh Soltani Tafreshi
Abstract
Dust storm increased in both spatial and temporal aspects during last decade. Middle East dust storms have caused countless social, economic and environmental damages for the residents of South and Southwest regions of Iran. MODIS satellite imagery has certain advantages, including available and useful ...
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Dust storm increased in both spatial and temporal aspects during last decade. Middle East dust storms have caused countless social, economic and environmental damages for the residents of South and Southwest regions of Iran. MODIS satellite imagery has certain advantages, including available and useful spectral bands, with high spatial and radiation resolution and MODIS data are used in the present study. In this study, two MODIS datasets were used. Part one, model development data (January 18-21, 2018) and part two, model evaluation data. Metrological data are collected with respect to time interval studied. After preprocessing MODIS data and preparing field observations, features (artificial neural network input) were generated by proposed method from MODIS data. A model through artificial neural network analysis was developed. This model extracts dust storm and estmates visibility. Model outputs were compared visually with NDDI outputs.To evaluate the effectiveness of the proposed method, the developed model was tested with other time data. Model outputs were compared visually with NDDI outputs. Eventually, in order to reveal the strengths and weaknesses of the proposed method, an accuracy assessment has been carried out by comparing the models output with visibility parameter of synoptic stations. The observation root mean squared error are10%, 10%, 15% and 10% related to January 18th, January 19th, January 20th and 21th, and also, 20% and 25% related to January 26th, 2019 and October 28th, 2018, respectively.
Volume 6, Issue 1 , April 2014
Abstract
Due to the rapid population growth and urban development which results in limited arable land, land evaluation for selecting optimum utilization of land and increase in production per unit area as well as purposeful decisions in allocation of agricultural land to the best land use type is very important. ...
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Due to the rapid population growth and urban development which results in limited arable land, land evaluation for selecting optimum utilization of land and increase in production per unit area as well as purposeful decisions in allocation of agricultural land to the best land use type is very important. In this paper, a crop land use allocation model is developed in which with the calculation of land suitability using fuzzy inference systems as well as demand determination and also consideration of crop rotation pattern, crop types within the agricultural area in the planning period are determined. The developed model is applied to Borkhar & Meymeh district in Isfahan province as a case study using GAMS 23.7, MATLAB and ArcGIS 9.3 softwares. Results of the crop allocation considering three existing crop rotation patterns showed that 27.82, 21.64, 7.27, 5.85, 7.36, 6.36 and 1.74 percent of agricultural areas were allocated to wheat, wheat-maize, barley, barley-maize, maize, alfalfa and potato, respectively for the year 2011. Also, spatial distribution of the crops allowed us to suggest that in order to have optimum production in the study area, southern part of the region is suitable for cultivation of wheat, barley and maize in double cropping systems while alfalfa and potato should be cultivated in northern parts of the region. The presented approach is found to be advantageous to determine the best crops for a given area and provide useful information for agricultural planners with the definition of various scenarios.
Volume 6, Issue 2 , August 2014
Abstract
The purpose of presenting this paper is to determine the snow covered area on Karaj and Latyan basins using MODIS Images, and evaluating Salomonson et al method which is applied in this study. The importance of snow cover_ Such as its impact on radiation budget, water balance and modeling_ has led to ...
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The purpose of presenting this paper is to determine the snow covered area on Karaj and Latyan basins using MODIS Images, and evaluating Salomonson et al method which is applied in this study. The importance of snow cover_ Such as its impact on radiation budget, water balance and modeling_ has led to several researches. In this research, MODIS data for snow cover mapping and LISSIII_IRS image for accuracy assessment have been used. Up to now, various methods have been applied to compute pixels’ snow fraction. We have used Salomonson etal. method. The method has showed proper accuracy in global scale and doesn’t need priori knowledge of surface characteristics. Also, to increase accuracy, the coefficients of the Salomonson et al. model were modified using regional data. And the results were evaluated in a new region.Accuracy assessment results showed that Salomonson et al. method can calculate snow fraction of MODIS pixels with RMSE of 0.20 pixels. Furthermore, Kappa coefficients and overall accuracy of Salomonson et al. method were 0.84 and 92.12 respectively suggesting proper accuracy of the method. Local accuracy assessment showed that in Iran, river margins with low density tree cover and sparsely scattered orchards, this method has got more errors; therefore, it is important to exclude thisarea. Moreover, it is recommended to use proper masks which allow the narrow rivers to be removed. RMSE of the modified model was 0.258 while, RMSE of Salomonson et al. model was 0.266 at the same area. So, the results showed that modifying the coefficient could improve the result slightly. Keywords: NDSI, Salomonson method, Snow fraction, Subpixel.
Volume 6, Issue 4 , October 2014
Abstract
Land cover information is one of the most important prerequisite in urban management system. In this way remote sensing, as the most economic technology, is mainly used to produce land cover maps. Considering the complicated and dense urban areas in third world countries, object based approaches are ...
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Land cover information is one of the most important prerequisite in urban management system. In this way remote sensing, as the most economic technology, is mainly used to produce land cover maps. Considering the complicated and dense urban areas in third world countries, object based approaches are suggested as an effective image processing technique. The purpose of this paper are the introduction of a new object based approach for classification of complicated urban area using high resolution satellite image and approaching to a standard and effective process of map generation by satellite images. This paper used a new approach to select the segmentation parameters and a new hierarchical classification model based on a rule based strategy is used to overcome the confusions between urban classes too. In this article an innovative hierarchical model is proposed for object-based classification of complicated urban areas. In this way, beside of feature space optimization in a multi scale analysis, rule based and fuzzy nearest neighbor approaches are used as the object-based classification strategies. The proposed method is implemented on an urban IKONOS image where 84% and 87%overall accuracies are obtained for rule based and fuzzy nearest neighbor classification approaches respectively. The implementation of the devised algorithm on another IKONOS image moved its general ability to other urban areas. Keywords: Land cover classification, Rule based, Object based, Fuzzy nearest neighbor, Complicated urban areas.
Volume 6, Issue 3 , October 2014
Abstract
Employing recent technological advances in surveying and mapping soil salinity is a step forward in controlling saline soils. The aim of this study was to map the topsoil salinity, the depth of 0-5 cm, using different methods within the environmental context of the area around Tashk & Bakhtegan Lake, ...
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Employing recent technological advances in surveying and mapping soil salinity is a step forward in controlling saline soils. The aim of this study was to map the topsoil salinity, the depth of 0-5 cm, using different methods within the environmental context of the area around Tashk & Bakhtegan Lake, with the area of 8062 ha, that in this region soil salinity appears to be a major threat to agriculture production. We used three different methods to produce soil salinity map and then compared the results with the soil salinity data that were measured on the ground. A set of 143 soil salinity sample, electrical conductivity of the water extracted from saturated past (ECe), was systematically sampled on a 750-m grid and was used to assess two mapping methods; regression models (RM) and ordinary kriging (OK). As a third method, supervised classification (Scl) of LISS-III sensor satellite images was employed. We used linear, power and exponential regression models for estimating of salinity values. In these regression models, digital numbers of the satellite images were set as independent variables and ECe values as dependent variable. In order to provide a prediction map of the soil, the salinity data were interpolated using the ordinary kriging method. In case of the satellite images, we classified the training pixels with maximum likelihood algorithm and then the land cover map was prepared. Our results revealed that regression models could not appropriately predict the salinity values and the vegetation indexes had poor correlation with the topsoil salinity values. The salinity percentages obtained from OK and Scl were nearly similar where the salinity was high (≥16dS/m), but differed in other salinity classes. Therefore, in the supervised classification of LISS-III sensor data the bare soil surfaces with high salinity (≥16dS/m) were successfully identified and separated from the rest of the soils. The regression model estimated 100 % of the study area as saline soil. The kriging method predicted 87.6 % of the area to be classified as saline soils (> 4dS/m), while supervised classification predicted that to be 62.5 %. Each of these methods has constrains. Therefore, we recommend the integration of these methods for estimating of soil salinity. Keywords: Ordinary Kriging, Regression Models, Supervised Classification, Topsoil Salinity.
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.
Mahshid karimi; Kaka Shahedi; Tayebe Raziei; Mirhassan Miryaghoobzadeh
Abstract
Drought is one of the natural disasters that may occur in any climate. In recent decades, Iran has been affected by severe droughts and its harmful effects in various sectors, such as agriculture, environment and water resources of the country. Today, vegetation indices, which are obtained through remote ...
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Drought is one of the natural disasters that may occur in any climate. In recent decades, Iran has been affected by severe droughts and its harmful effects in various sectors, such as agriculture, environment and water resources of the country. Today, vegetation indices, which are obtained through remote sensing technology, are used to identify and analyze agricultural droughts. Accordingly, the aim of this study was to investigate the effectiveness of NDVI, EVI and VCI vegetation indices in agricultural drought identification and analysis in Karkheh basin. In order to calculate these indices, MODIS sensor Images (Terra satellite, MOD13A2 product) were used during the 2000-2017 statistical period. The accuracy of these profiles was evaluated with the ZSI index calculated at 11 meteorological stations located in Karkheh basin for the statistical period of 2000-2017. The results showed that the changes of NDVI, EVI and VCI in the studied stations were approximately the same during the statistical period. Based on NDVI, EVI and VCI values, the lowest and highest vegetation cover was observed in 2000, dehno station and 2001, helilan-seymareh station, respectively. The ZSI survey showed that most stations Faced with droughts from 2000 to 2008, and the most severe drought occurred in 2008, nazarabad station. Then, in order to validation of the results, the vegetation indices with ZSI index were evaluated. Pearson correlation between mean vegetation indices of NDVI, EVI and VCI with mean ZSI was 0.766, 0.725 and 0.776, respectively, and all vegetation indices with ZSI index are significant at 0.99% confidence level. As seen, according to the results, the ZSI index confirms the results of NDVI, EVI, and VCI. So, according to the results, there is no conformity of meteorological and agricultural droughts in all years, Therefore, in addition to other precipitation, climate variables should also be considered.
alireza mahmoodi; Marzieh Mokarramb
Abstract
Today, remote sensing is used for plant studies, such as determining nutrient levels, plant diseases, water deficiency or excess, weed identification, and so on. As electromagnetic waves strike the plants, they react in different ways (absorption, reflection or passage) based on the characteristics of ...
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Today, remote sensing is used for plant studies, such as determining nutrient levels, plant diseases, water deficiency or excess, weed identification, and so on. As electromagnetic waves strike the plants, they react in different ways (absorption, reflection or passage) based on the characteristics of the plants. The quantity of nutrients in a plant can be determined through measurement science in plant studies. Since the amount of nutrients in the plant can be determined, it is possible to know how much fertilizer the plant needs. On the other hand, identified the nutrients in the plant, especially rangeland plants. A spectrometer was used to measure the plant's response to electromagnetic waves in the range of 0.3 to 1.1 m. Following that, the relationship between the amount of electromagnetic waves and the amount of nutrients in these plants was determined. The results showed that in Fagonia bruguieri b1026 nm, in Peganum harmala b1040 nm, in Ziziphus spina-christi b1046 nm, in Tecurium persicum band 1030 nm, in Vitex pesedo-negundo b400 and b1038 and in Otostegia persica band They are effective in predicting the value of P. For the prediction of Zn in F. bruguieri b1026 nm band, in P.harmala b1040 nm band, in Z. spina-christi ba1045 nm band, in T. persicum pea b1030 nm band, in V. pesedo-negundo plant b1010 nm and in O. persica band They are the most effective bands. To predict Cu, it is determined using spectral band values that in F.bruguieri band is b402 nm, in P. harmala band is b410 nm, in Z. spina-christi band is b1046 nm, in T. persicum band is b1030 nm, in V.pesedo and O. persica b1038 are the most effective bands.
