farzaneh hadadi; Davod Ashourloo; Alireza Shakiba; Aliakbar Matkan
Abstract
Climate change is one of the most important challenges facing mankind. This phenomenon has already had significant impacts on agricultural products in most parts of the world, especially arid and semiarid regions. Also, average temperature has risen in many regions in recent decades. Nowadays, in various ...
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Climate change is one of the most important challenges facing mankind. This phenomenon has already had significant impacts on agricultural products in most parts of the world, especially arid and semiarid regions. Also, average temperature has risen in many regions in recent decades. Nowadays, in various researches, remote sensing indices are used as one of the new methods in identifying climate change. One of the important indices of remote sensing is the phonological characteristics of vegetation, which in recent studies has shown great potential in identification and estimation of vegetation. In the present study, using the 5-day normalized vegetation index (NDVI) time series of NOAA-AVHRR images and plant phenology parameters, vegetation changes in rangelands and dryland areas of Lake Urmia Basin during 1984-2013 were investigated. Climatic temperature and precipitation data was obtained from the meteorological stations of Lake Urmia basin and was compared with the results of satellite images. The results of time series analysis over thirty years of statistical period in Lake Urmia basin showed that the beginning of the growing season in Oshnavieh, Saghez and Sarab started earlier in 2013 than in 1984. But in the Maragheh area it began later. The end of the growing season in Oshnaviyeh, Saghez and Takab has ended earlier. Also, the peak growth parameter in the above mentioned vegetation reached its maximum value earlier. The length of the growing season has been decreased in the cities of Oshnavieh, Maragheh and Saghez, respectively. The results of statistical analysis obtained from satellite images and climatic data showed that changes in phonological parameters are location dependent and also decreased and increased in cold nights and hot days at the beginning of the growing season, respectively. But at the end of the growing season, the warm days have increased. These changes increased the slope of the plant growth phenology curve at the time of plant aging and ultimately reduced the length of the growing season.
Maedeh Behifar; Hossein Aghighi; Aliakbar Matkan; Hamid Salehi shahrabi
Abstract
Leaf area index (LAI) derived from remotely sensed images is considered as an important index for spatial modelling of vegetation productivity. Traditionally, the spectral vegetation indices (VIs) derived from the red (R) and near infrared (NIR) reflectance values have been utilized to statistically ...
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Leaf area index (LAI) derived from remotely sensed images is considered as an important index for spatial modelling of vegetation productivity. Traditionally, the spectral vegetation indices (VIs) derived from the red (R) and near infrared (NIR) reflectance values have been utilized to statistically estimate LAI. However, most of these VIs saturate at some level of LAI. This limitation was over-come by using the reflectance spectra in the red-edge region. Therefore, it is necessary to evaluate the capability of different VIs derived from RS data to estimate the LAI of silage maize. For this purpose, five field sampling campaigns which were near-simultaneous with Sentinel II over-passes were conducted by the Space Research Center, Iranian Space Research Center and totally 234 samples were collected from the silage maize fields, in Magsal, Qazvin. Then, 13 VIs from the time series of Sentinel-2 imagery were computed and employed to statistically estimate the LAI values. The results showed that Enhanced vegetation index (EVI) with outperformed other VIs to estimate LAI of silage maize. Moreover, the values of non-linear regression models were higher that the liner ones.
Mohammad Reza Gili; Davoud Ashourloo; Hossein Aghighi; Ali Akbar Matkan; Alireza Shakiba
Abstract
Changes in crop growth at relatively short intervals, asymmetry of cultivation of similar crops, the spectral similarity between different crops at certain times of the growing season, and lack of ground data make classifying crops in satellite imagery a challenging task. Changing the amount of canopy ...
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Changes in crop growth at relatively short intervals, asymmetry of cultivation of similar crops, the spectral similarity between different crops at certain times of the growing season, and lack of ground data make classifying crops in satellite imagery a challenging task. Changing the amount of canopy and greenness during the growing season is one of the most prominent characteristics of vegetation, including agricultural products, which can be monitored by using time series of vegetation indices that have useful information about the sequence of phenological features of crops. The use of deep learning methods with the ability of learning sequential information obtained from these time series can be useful in crop mapping and reducing dependence on ground data. The LSTM network is one of the types of RNNs in sequential data analysis that has the ability to learn long-term sequences of time-series information. Therefore, in this study, after extracting the NDVI time-series of 9 different dates from Sentinel-2 satellite images for a region located in Moghan plain, with ground labeled data related to the type of crops cultivated, we trained a convolutional LSTM network. Then we used this trained network to classify agricultural products in another region of the plain as a test site, and achieved an overall accuracy of 82% and a kappa coefficient of 0.8. Increasing the number of ground samples and selecting the exact boundary of crops, can increase the efficiency of the method used.
َAliakbar Matkan; Aliraze Vafaeinezhad; Iman Baharloo; Ahmad Khademolhoseini
Abstract
One of the most important modes of public transport is the metro transport network, which has a significant impact on reducing traffic and air pollution. On the other hand, due to the exorbitant costs of setting up the metro network, step-by-step development with the operation of one line and the implementation ...
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One of the most important modes of public transport is the metro transport network, which has a significant impact on reducing traffic and air pollution. On the other hand, due to the exorbitant costs of setting up the metro network, step-by-step development with the operation of one line and the implementation of other lines is of interest by urban managers. Therefore, the aim of this study is to investigate the extent of access to Isfahan metro network in the horizon of 1410 with the spatial equity approach from both horizontal and vertical aspects in 5 social classes using Gini coefficient indicators, Lorenz curve, service (supply) level index of public transportation and access. What distinguishes this research from the other similar studies is the investigation of access to the network of each metro line and finally all the lines and the estimation of population growth up to the horizon of 1410, which plays a significant role in the decisions of urban planners to develop other modes of urban transportation. The results of this study showed that Gini coefficients in access to the metro network in Isfahan in the study of horizontal equity are higher than the vertical equity, which shows a great injustice in the distribution of access of sensitive and needy groups to the metro network. So that the Gini coefficient in citizen's access to all lines in horizontal equity is 0.42 and in vertical equity in 5 classes including people over 60 years, under 15 years, without private cars, immigrants and households with an area of less than 50 square meters are 0.45, 0.49, 0.5, 0.54 and 0.6, respectively
Maedeh Behifar; Mohsen Azadbakht; Farzaneh Hadadi; AliAkbar Matkan
Abstract
Vegetation indices are used to estimate vegetation parameters from satellite images. Despite their capabilities, performance of some vegetation indices decreases in high vegetation densities, making them inappropriate for estimation of the desired parameters. Vegetation indices are saturated in alfalfa ...
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Vegetation indices are used to estimate vegetation parameters from satellite images. Despite their capabilities, performance of some vegetation indices decreases in high vegetation densities, making them inappropriate for estimation of the desired parameters. Vegetation indices are saturated in alfalfa farms due to the high chlorophyll content and high vegetation density; therefore, monitoring the changes of this plant is hindered. However, all indices do not perform similarly. In this research, the performance of different vegetation indices at different LAI values were investigated. The results showed that the CIgreen, CIrededge and NGRDI indices gained the best performance at high LAI values and they were less saturated. In contrast, the NDVI, NDREI and GNDI indices did not perform well and they were saturated at medium and high levels of LAI.
Jamileh Tavakkoli-nia; Aliakbar Matkan; Mozaffar Sarrafi,; faezeh borbori
Volume 10, Issue 1 , June 2018, , Pages 109-126
Abstract
Ecotourism is a part of the tourism industry that has attracted the attention of many officials and people in recent years and it is one of the levers of economic and social development of many developed and developing countries. Since the non-systematic activity of the ecotourism can negatively affect ...
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Ecotourism is a part of the tourism industry that has attracted the attention of many officials and people in recent years and it is one of the levers of economic and social development of many developed and developing countries. Since the non-systematic activity of the ecotourism can negatively affect the environment, evaluating the ecotourism activities using valid scientific frameworks and methods, such as DPSIR, can be effective and useful in the managers’ planning of this industry. The main purpose of this research was to investigate the ecotourism status in Rudbar-e Qasran and Lavasanat Zone using the DPSIR framework. Each of the five sections of this evaluation model was analyzed and the findings were presented in the form of a table. According to the results from the classification of images in 2004 and 2016, the constructed spaces have increased from 3625 square meters to 8744 square meters. One of the reasons for this can be the increase in the population, proximity to the capital, the ease of commuting, the expansion of second homes, and increasing the construction of tourist-related service sites.
The conducted evaluations and the obtained results of this research can be used as a decision support structure for managers and planners in this area to adopt appropriate strategies for implementing sustainable ecotourism.
D Ashourloo; H Aghighi; A.A Matkan; H Nematollahi
Volume 9, Issue 4 , May 2017, , Pages 111-128
Abstract
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 ...
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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.
Volume 7, Issue 1 , December 2015, , Pages 39-57
Abstract
With the increase in population and consequent increasing needs of society, land use planning is of particular importance. Land use planningdue to being involved with several conflicting aims, multi- objective evolutionary algorithm would be a useful tool to solve land use planning. But use of these ...
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With the increase in population and consequent increasing needs of society, land use planning is of particular importance. Land use planningdue to being involved with several conflicting aims, multi- objective evolutionary algorithm would be a useful tool to solve land use planning. But use of these algorithms should be examined according to the type of issues. In the study, addition to introducing a model to optimize land use, effective solution for the application of multi- objective genetic algorithm on a variety of problems related to land use planning was presented. In order to land uses optimization in the study, the algorithm NSGA-II was use in the model. Output of the model might be introduced patterns for reduction of erosion to an acceptable level and enhancing the economic benefits. This will be skillfully carried out while the land use adaptation is in the highest level and land use changes are easy with high level of continuity.An innovative operator which producing the initial population and an innovative operator with an appropriate Crossover of land use planning issues were developed.The developed model in the study was implemented in Kerman-Rodbar watershed. Evaluation results show that the model is able to suggest patterns to land use planning that reduce erosion about 30 to 35%. While the economic benefits of the changes will be about 40 to 50 %. Furthermore all models have a high consistency and low difficulty to change. These operators have had a significant impact on problem solving. Keywords: Multi- objective optimization, NSGA-II algorithm, Innovative Operators, Land use planning, Ecological potentiality
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.
Ali Akbar Matkan; Babak Mansouri; Babak Mirbagheri; Fariba Karbalaei
Volume 7, Issue 3 , November 2015, , Pages 17-32
Abstract
Earthquake is one of the most destructive natural disasters which frequently occurs with different intensities. Earthquakes cause severe damage to buildings, main roads and most importantly, loss of life. Detection of damaged buildings caused by such an event at the right time is a critical issue for ...
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Earthquake is one of the most destructive natural disasters which frequently occurs with different intensities. Earthquakes cause severe damage to buildings, main roads and most importantly, loss of life. Detection of damaged buildings caused by such an event at the right time is a critical issue for crisis management and disaster relief. The aim of this study is to detect earthquake damaged buildings using very high resolution (VHR) satellite imagery. To achieve this result, the satellite images with very high resolution before and after the earthquake in Port-au-Prince in Haiti as well as the observed destruction map in 2010 were used. In this study, the optimum features extracted from the image were selected using correlation analysis. The buildings destroyed were classified using fuzzy inference system and the values of selected textures. Finally, the damage map obtained from the proposed algorithm was compared to the map of the area. The kappa criterion estimated from the results of the proposed method is 82% while the index- Jaccard parameter is 89.69%.
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
Volume 6, Issue 4 , October 2014
Abstract
Runoff is one of the major components of calculating water resource processes and is the main issue in hydrology. Many concept models are used to predict the amount of runoff, which in most cases depend on topographical and hydrological data. Conventional models are not appropriate for areas in which ...
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Runoff is one of the major components of calculating water resource processes and is the main issue in hydrology. Many concept models are used to predict the amount of runoff, which in most cases depend on topographical and hydrological data. Conventional models are not appropriate for areas in which there is little hydrological data. Changes in runoff are nonlinear, meaning it is time & space independent. Therefore it is not easy to simulate the runoff by simple models. Nowadays an appropriate method used in cases where there is a lack of data, is ANN (Artificial Neural Network). The precipitations, temperatures and flows of KAN watershed station between the years of 1996 to 2006 and physiographic characteristics were used as input data for the Artificial Neural Network to predict runoff. 80% of the data is randomly input into the program and the remaining 20% is used to check the accuracy of the result. For the purpose of determining an optimal network, two types of transfer functions, 12 types of training functions and between 1 and 9 kind of hidden neurons are used. After analyzing the hidden layers and various training functions, the results show that the best structure for estimating the runoff is using the precipitation, temperature, flow, LM training function and Tansig transfer function and 4 of the hidden neurons as input data. The results indicated that a Neural Network with such a structure can accurately estimate the runoff. (0.78≥ R2 ≥ 0.68 and 0.03 ≤ RMSE ≤0.53). Keywords: Estimation of Runoff, Artificial Neural Network, Back Propagation Algorithm, Kan basin
Volume 6, Issue 3 , October 2014
Abstract
Among the usual interpolation methods, kriging and co-kriging are frequently used in the interpolation of precipitation data as one the best linear unbiased estimators, Despite these advantages, there models show smoothness representation and because they are based on regional averages of the data, they ...
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Among the usual interpolation methods, kriging and co-kriging are frequently used in the interpolation of precipitation data as one the best linear unbiased estimators, Despite these advantages, there models show smoothness representation and because they are based on regional averages of the data, they predict maximum and minimum values lower and higher than real values respectively. Therefore, using these models alone is not sufficient in cases where the target is assessment of risk and study of variability. Variability of phenomenon could be measured by uncertainty index. In the study in order to calculation of local and spatial uncertainty of precipitation, geostatistical simulation algorithms CO-SGS and SGS were used. The main result of the study showed that, in simulation sample SGS and CO-SGS algorithms would be able generate the Max and Min probable value making variance as close as to the main data. The difference simulation variance is very low with main samples, in contrast, the difference of variance between main samples and interpolation method is very high. The result also showed that the mentioned algorithms could be able to compute the local and spatial uncertainty of the precipitation by different simulation. Keywords: Precipitation, Uncertainty, Geostatistical Simulation, SGS Algorithm, CO-SGS Algorithm
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.
