Maximum likelihood

Open Access Article

1 - Inference for generalized inverse exponential distribution based on generalized hybrid Progressive censored data and its application to plasma spray data
parya parviz hanieh panahi saeid Asadi

In many applied research, the researcher does not have access to all the data for some reasons such as time and cost constraints. So, the statistical inference based on the available data is important. In this paper, estimation of unknown parameters of a generalized inv More

In many applied research, the researcher does not have access to all the data for some reasons such as time and cost constraints. So, the statistical inference based on the available data is important. In this paper, estimation of unknown parameters of a generalized inverted exponential distribution is studied under generalized Type II progressive hybrid censoring. The maximum likelihood estimators and their existence and uniqueness are investigated. Based on the Bayesian approach, the estimators of the shape and scale parameters are derived under squared error loss function. Since closed - form expressions for the Bayes estimators cannot be obtained, we use Lindley’s approximation and important sampling procedure for obtaining them. Simulation study for comparing the different classical and Bayesian estimations is presented. Finally, two real data sets contain oblique impact of micro droplets onto surface in plasma spray coating process and repair time for a communication transmitter are analyzed for illustration purposes. Manuscript profile

Open Access Article

2 - Semi-parametric estimation of the strategic goods (OPEC oil price)
R. farnoosh M. Hajebi

In the global economy, crude oil is among the most important strategic goods that affects the performance of local and international markets. Prediction of the oil price has always been an important challenging topic in the global economy and producers and consumers hav More

In the global economy, crude oil is among the most important strategic goods that affects the performance of local and international markets. Prediction of the oil price has always been an important challenging topic in the global economy and producers and consumers have constantly been trying to improve their roll in the oil price changes and for many years OPEC has been one of the key players in this field of economy. Oil is considered as one of the most important financial resources for providing the budget of the OPEC country members. Oil price fluctuations, is one of the major causes of many economical crises among these countries. By applying the statistical models one can improve the performance of the oil price prediction dramatically and obtain results with less errors and higher precision. Therefore, in this paper, the nonlinear autoregressive model with a semi-parametric method is implemented to predict the oil price. Manuscript profile

Open Access Article

3 - The study of effective factors on probability of default banks' credit facilities (The case study of legal customer of Export Development Bank of Iran)
شمس اله شیرین بخش ندا یوسفی جهانگیر قربان زاد

The aim of this research is to verify effective factors of legal counterparty creditrisk of Export Development Bank of Iran (EDBI), and design a probability of defaultmeasurement model using logit regression.330 probability samples were selected from companies that took More

The aim of this research is to verify effective factors of legal counterparty creditrisk of Export Development Bank of Iran (EDBI), and design a probability of defaultmeasurement model using logit regression.330 probability samples were selected from companies that took loans in year 1387(2008-2009) including 256 good pay bank customers and 65 bad pay bank customers.Seven variables have been recognized which have significant influence atcompanies' credit risk among 13 selected financial ratios as effective explanatoryvariables in default probability based on statistics indexes and economic and financialtheories. after significant examining total of the regression with LR statistic finalmodel in 5% level of significance created by them.The results expressed that cash flow on total debt ratio (CSDT), assets turnoverratio (SATA), current ratio (CACD) and liquidity ratio (LR) have a reverse effect oncredit risk. Free cash flow ratio (RETA), total debt ratio (TDTE) and current debts tonet worth ratio (CDTE) have a direct effect on credit risk. Manuscript profile

Open Access Article

4 - Prediction of the land use change using markov chain and cellular automata (case study: Roze Chay basin, Uremia)
Farrokh Asadzadeh Kamal Khosravi Aqdam Laleh Parviz Hassan Ramazanpour Nafiseh Yaghmaeian Mahabadi

Land use surveys and investigations are a prerequisite for the study of watersheds, because regional planning is dependent on the awareness about land use type and future changes. As a result, modeling and predicting of land use is essential for land planning and manage More

Land use surveys and investigations are a prerequisite for the study of watersheds, because regional planning is dependent on the awareness about land use type and future changes. As a result, modeling and predicting of land use is essential for land planning and management in the future of a country such as Iran, where land use is changing rapidly In this regard, in order to reveal the land use changes in the 15 years and modeling the changes for the next 20 years, the markovin transmission estimator was used with Landsat 7 and 8 Landsat satellite imagery data from the Roze Chay basin of urmia. Based on the controlled classification algorithm with the maximum probability of land use as seven classes of land uses in this watershed were seven garden, irrigated farming, dry farming, grass land, residential area, water and salt marsh with a mean Kappa coefficient of 0.88 and overall accuracy of 0.9 for 2000 and 2015 were extracted. The changes of 15 years showed that the variation of water dependent uses in the region decreased during the mentioned time period (percentage reduction in the area of agricultural crops and gardens, 32.51). The modeling of land use changes in the region with the markovin transmission estimator suggests that the use of gardens, arable and dry lands, villages and water resources will decrease, and the use of grass land and salt marsh will increase in the region (from 2020 to 2035 percent increase in area grass land 13.11 and the percentage of dry farming 17.56). The results indicate that soil and water resources are used improperly in the studied area, which requires comprehensive planning and management in the watershed. Manuscript profile

Open Access Article

5 - Pixel Based Classificatrion Analyisis of Land Use Land Cover in Tarom Basin
seyed behrouz Hosseini Ali Saremi Mohammad hossein Noori Gheydari Hossein Sedghi Alireza Firoozfar Jaefar Nikbakht

The comprehensive management of a watershed requires basic information such as land use and land cover. The aim of this study is to conduct accuracy analyses of LULC classifications derived from Landsat-8 data, and to reveal that which kind of land use and land cover ca More

The comprehensive management of a watershed requires basic information such as land use and land cover. The aim of this study is to conduct accuracy analyses of LULC classifications derived from Landsat-8 data, and to reveal that which kind of land use and land cover can be estimated more accurately. Tarom Basin and its near surrounding was selected as study area for this case study. Landsat-8 the data, acquired on 8 August 2017, were utilized as satellite imagery in the study. The RGB and NIR bands were used for classification. Required pre-processing and control of georeferenced of images were performed. After performing the required atmospheric corrections, using the FLAASH algorithm, classification maps were generated. LULC images were generated using 3 pixel-based supervised classification method method, Maximum Likelihood (MLC), Support Vector Machine (SVM) and Artificial Neural Network (ANN). As a result of the accuracy assessment, kappa statistics and overall accuracy for MLC method were 0.88 and 91.55 respectively. The obtained results showed that Landsat-8 OLI data, presents satisfying LULC images in water body, mountain and rock, bare land, Vegetation and forest classes. In addition, according to the obtained results, it can be stated that all three methods of classification in a region with heterogeneous (in terms of elevation elevation between 280 and 3000 m and land use and variety of vegetation) Such Tarom, can have good results. Among these methods, Classification with MLC method, had higher speed and lower complexity for execution, than two other methods in achieving the required maps. Manuscript profile

Open Access Article

6 - investigation of land cover changes using remote sensing technique (Case study: Katalan unit)
Maryam Nazemi jalal Marzieh Alikhah-Asl Elham Forootan

10.30495/jest.2024.74410.5882

Background and Objective: Updated and correct information is necessary for using and optimized managing of a land. Land cover map is one of the most important information resources in natural resource management. The goal of this research is to provide Katalan land cove More

Background and Objective: Updated and correct information is necessary for using and optimized managing of a land. Land cover map is one of the most important information resources in natural resource management. The goal of this research is to provide Katalan land cover map for investigating land use changes during 12 years in this area. Material and Methodology: For this purpose, satellite images such as Landsat ETM 2001 and OLI 2013 were used after performing necessary corrections whereas; GPS and topographic maps were implemented for surveying fields and gathering trained samples. Land cover maps were provided using supervised classification method with maximum likelihood algorithm. Findings: The results of this study revealed that the study area comprises six classes viz. irrigated farm land, rainfed farm land, bare land, rock stone, range land and mine class. The overall accuracy and kappa coefficient for 2013 map were estimated 86.11% and 0.82, respectively and theses values for 2001 land use map were 78.26%, and 0.71, respectively. Discussion and Conclusions: The results of this research revealed that the class of farm land, bare land and range land were increased 1.84%, 1.29%, and 1.21%from 2001 to 2013, and the class of rock stone and rainfed farmland were decreased 5.09%, and 0 .62%, respectively. Also, there was not mine class in 2001 but this class was 1.36% equivalent to 49.3939 hectare of the whole area in 2013. Manuscript profile

Open Access Article

7 - The value of tourism Village KANDOLE Kermanshah using logit model
Sayed tajedin mansoori Haydar jahanbakhsh

10.22034/jest.2018.15589.2406

Background and Objective: One of ecologically sustainable development, the value of natural capital and national. This concept brings us to the questions about the valuation of environmental guidelines that can occur in financial levels. Method: In this study, to measu More

Background and Objective: One of ecologically sustainable development, the value of natural capital and national. This concept brings us to the questions about the valuation of environmental guidelines that can occur in financial levels. Method: In this study, to measure the willingness to pay (WTP) logit model was used based on the maximum likelihood method, model parameters were estimated. For this purpose, the survey included 100 visits tourist village KANDOLE city of Kermanshah, randomly selected in October 1394 were used. Findings: The results of this study showed that the average willingness to pay to visit the historical-tourist village based on logit, 16240 rials. Discussion and Conclusion: So this village due to the large number of visitors during the year, a significant recreational value, the value for policy makers and decision makers, provides justification to support the environmental quality of the area and prevented from degrading them. Factors affecting the willingness to pay, including age, education, family size, income and social security is the satisfaction.   Manuscript profile

Open Access Article

8 - Analysis of morphodynamic changes of landforms in riverbed kalshour in the shirAhmad protected area in Sabzevar With an environmental perspective
ebrahim taghavi moghadam elahe akbari Ali akbar Ehsanzadeh

10.22034/jest.2018.15811.2432

Background and Objective: Knowledge of the characteristics of morphodynamic river systems, Landform and its evolution as one of the most vital components of the Earth's surface that In many studies and projects, including the flood control, watershed studies, and enviro More

Background and Objective: Knowledge of the characteristics of morphodynamic river systems, Landform and its evolution as one of the most vital components of the Earth's surface that In many studies and projects, including the flood control, watershed studies, and environmental hydrology of the basic requirements for environmental planners. This study aimed to evaluate the changes of landforms and the KalShour riverbed in protected area ShirAhmad is Sabzevar emphasis on environmental considerations. Method: in the Research using topographic map and satellite images of Landsat, years 1988, 2000 and 2015. For this purpose, was determined changes kaleshor of riverbed and morphodinamic landform changes occurred in the study period of 27 years and Using maximum likelihood map landforms and account for the years of research and changes in any areas identified and analyzed and assessed. Findings: Calculations show that the rivers in the region of 8.1 km2 kalshour shorter and less meandering, and become more the radius of the circle is tangent to any show that has become curved meander pattern to direct arterial. The map Landforms has changed was produced with maximum likelihood and overall accuracy0.78 and kappa coefficient 0.84. Discussion and Conclusion:  The results show a large changes of sand dunes and vegetation especially .Based on the results, because of having high groundwater levels and also enter wastewater Sabzevar and.31.5 km2 of sand dunes volume reduction and conversion to fields Tamarix hispida and forest Tamarix. This issue is for the benefit of the natural landscape and on the other hand depletes water resources and soil and adverse impact on animal life protected area's Shirahmd that require special strategic programs in order to preserve natural heritage and sustainable development. Manuscript profile

Open Access Article

9 - Market Depth and Noisy Prices: A Maximum Likelihood Approach
Jalal Seifoddini Fereydon Rahnamay Roodposhti Hashem Nikoomaram

The information content of high frequency data has made them the main instruments for studying market microstructure. However, the noise content of this data may negatively affect the results of studies on market microstructure. Using maximum likelihood methodology, we More

The information content of high frequency data has made them the main instruments for studying market microstructure. However, the noise content of this data may negatively affect the results of studies on market microstructure. Using maximum likelihood methodology, we disentangle from high frequency observations on the transaction prices of a sample of Tehran Stock Exchange stocks a fundamental component and a microstructure noise component. We then relate these statistical measurements of market microstructure noise to different financial measures of their market depth. We find that stocks with higher market depth have higher noise measured from their high frequency returns. This is in accordance with Fischer Black’s hypothesis that the existence of noise traders and the noise, which can be caused by the activities of this group of traders, to be the vital condition of a liquid market. We also find that pre-trade depth measures are the most powerful depth measure in explaining the noise in the market. Manuscript profile

Open Access Article

10 - Evaluation of the most efficient supervised classification algorithm in monitoring growth changes in Tehran
Aida Ashjaee Seyed Masoud Monavari Jalil Imany Harsini Maryam Robati Zahra Azizi

Background and Objectives: The urban sprawl is a dynamic and complex phenomenon, and the most effective factor is land use-cover change Coordinated by with the growth of population and economy, and the resulting changes affect vegetation and the functioning of urban eco More

Background and Objectives: The urban sprawl is a dynamic and complex phenomenon, and the most effective factor is land use-cover change Coordinated by with the growth of population and economy, and the resulting changes affect vegetation and the functioning of urban ecosystems. In this paper, identification of the most appropriate classification algorithm to investigate the effect of urban sprawl growth in the east of Tehran city in the time period of 1986 to 2016 on land use-cover changes of Jajroud protected area has been studied. Material and Methodology: In this research, the land cover-use changes map was prepared using the supervised classification method and the comparison of three neural network algorithms, minimum distance and maximum likelihood was done in ENVI 5.3.1 software environment. Findings: Land use-cover changes from 1986 to 2016 (period of 30 years) shows the increase of land use-cover area including compact rangelands 58.45%, arid region 91/19%, urban 65/57%, and forest 74/47%. In 2016 compared to 1986. Discussion and Conclusion: By comparing and examining three supervised classification algorithms including neural network, minimum distance, maximum likelihood, the neural network method has been the most suitable algorithm to identify land use-cover changes. Manuscript profile

Open Access Article

11 - Preparing Ghoorichay Catchment Land Cover Map Using Satellite Image Analysis
Marzieh Alikhah-Asl Dariush Naseri

Background: Land use/land cover has long been considered for natural resource planning and management and remote sensing techniques are the best tools to produce land use/cover maps. There are various methods for preparing land use maps. Objective: The purpose of this More

Background: Land use/land cover has long been considered for natural resource planning and management and remote sensing techniques are the best tools to produce land use/cover maps. There are various methods for preparing land use maps. Objective: The purpose of this study is to prepare a land cover map of Ghoorichay watershed using processing and classification of satellite images, which is one of the most important watersheds of Ardabil province. Materials and Methods: For this purpose, Landsat 8 satellite images related to June 2015 were classified using supervised maximum likelihood and fuzzy classification methods. Results: The results showed that rangelands, bare lands, dry lands, and residential lands (village) are the major land uses in the area, respectively. According to the results, maximum likelihood method with kappa coefficient of 0.82 and overall accuracy of %88 is more accurate than fuzzy classification method with kappa coefficient of 0.81 and overall accuracy of %87. Discussion and Conclusion: Based on the results of this study, despite the high capability of satellite images in the preparation of land use map, in order to increase the accuracy of classification, peripheral parameters should be used. Manuscript profile

Open Access Article

12 - Classification and Assessment of the land use changes using Landsat satellite imagery (Case Study: Rey Plain)
pegah mohammadpour reza Arjmandi Amir Hesam Hasani Jamal Ghoddousi

Background and Purpose :Land use change due to human activities is one of the important issues in regional and development planning. Lack of attention to land use changes in recent decades has created many environmental problems such as pollution of water resources, soi More

Background and Purpose :Land use change due to human activities is one of the important issues in regional and development planning. Lack of attention to land use changes in recent decades has created many environmental problems such as pollution of water resources, soil, etc. Therefore, the study and analysis of land use at different scales with the aim of sustainable development in the proper management of the environment and natural resources is essential. Remote sensing and GIS provide the necessary and sufficient facilities for extracting and updating land use maps and determining its amount. This study aims to investigate changes in land use conversion using remote sensing technology and satellite images for four periods It has been done for 3 years, from 2008 to 2020 in Rey plain. Material and Methodology: TM and OLI satellite images of Landsat 5 and 8 satellites were used to prepare land use maps for the studied years. Then the satellite images were monitored by classification method and were classified using the maximum neighborhood probability algorithm with an overall accuracy of 87.39 to 95.78% and a kappa coefficient of 85 to 93% in four user classes.. In the next step, land use maps were compared. Results: Based on the analysis, it was found that in the period under study, 26.07 square kilometers of Barren lands in this area has changed to agricultural, industrial and residential lands. As a result, the area of Barren lands has decreased and other uses have increased during the studied years. , So that the area of land with agricultural, industrial and residential use has increased by 14.66 square kilometers, 9.77 square kilometers, 1.64 square kilometers, respectively. Discussion and Conclusion: The results of the research show that the most important factor in land use change in the region is human activities that have caused many changes in land use. Analysis of the area of these uses showed that the level of agricultural land has increased significantly, mainly this increase. The result is the conversion of agricultural land use. Finally, the results of this study indicate that the combination of remote sensing techniques and GIS in the implementation of models for assessing spatial-temporal changes in land use, in order to know the type and percentage of land use and the extent of their changes, is very effective. The title of a management parameter can help planners of different executive departments in monitoring and managing the environment.   Manuscript profile

Open Access Article

13 - Producing Islamshahr Land-Use Maps in 2015 Using Maximum Likelihood and Fuzzy Classification Methods
Sanaz shafiee Marzieh Alikhah-Asl Mohammad Rezavani

Related data to land cover and land use has a great importance for land use planning and land management. Nowadays, satellite imagery and remote sensing techniques are the best ways to extract land cover and land use maps as a fundamental map in territory planning. Comp More

Related data to land cover and land use has a great importance for land use planning and land management. Nowadays, satellite imagery and remote sensing techniques are the best ways to extract land cover and land use maps as a fundamental map in territory planning. Comparison between maximum likelihood and fuzzy methods to extract land cover maps and satellite images of Islamshahr using OLI Landsat for 2015 is the main objective of this study. To achieve this goal, by applying indispensable pre-processing, implementation and operation of processing images using maximum likelihood and fuzzy thematic map covers and land use algorithm in five classes, including man-made, bare land, agricultural land, orchard, landscape and road have been prepared. The overall accuracy was evaluated and determined by accuracy of the two methods. Based on the results, in both methods, man-made land use accounted for most of the area and the road showed the lowest and results of evaluation have shown that the classification using maximum likelihood algorithm with overall accuracy of 88.10% and kappa coefficient 0.84 compared to fuzzy method with 87.83 accuracy and kappa coefficient 0.83 are much more accurate. Manuscript profile

Open Access Article

14 - Land use map production of Kaftareh catchment using remote sensing technique
Marzieh Alikhah-Asl Dariush Naseri Teimour Tanha Ghezeli

One of the important factors for programming and management of natural resources, especially toachieve sustainable development purposes is acquiring enough information about current land use/landcover. Remote sensing techniques are the best methods to produce land use m More

One of the important factors for programming and management of natural resources, especially toachieve sustainable development purposes is acquiring enough information about current land use/landcover. Remote sensing techniques are the best methods to produce land use maps. In this research,supervised classification method with maximum likelihood algorithm was used to prepare the land usemap. The case study is Kaftareh catchment located in Ardabil province and in this investigation;Landsat 8 images acquired in 2014 were applied. The results showed that dry farming, irrigated lands,rangelands and bare lands are the major land uses respectively in the area. The classification accuracywas assessed by kappa index and overall accuracy that 93.20% and 96.24% were obtainedrespectively. According to our results, Landsat 8 images have high capability to produce the land usemaps. NDVI index was used to prepare the vegetation density map and the results showed that themajor parts of the rangelands are covered by weak vegetation density class in the studied area Manuscript profile

Open Access Article

15 - Comparing fuzzy and maximum likelihood methods to land cover mapping in Gandoman wetland using Landsat satellite data
Leila Samiee Marzieh Alikhah-Asl Mohammad Rezvani

  Preparation of land use and land cover maps in order to inform from land use and planning with optimal planning one of the basic measures to achieve sustainabll developement. This issue is more important in sensitive areas and particularly in wetland due to depe More

  Preparation of land use and land cover maps in order to inform from land use and planning with optimal planning one of the basic measures to achieve sustainabll developement. This issue is more important in sensitive areas and particularly in wetland due to dependence of hydrologic and biogeological performance of them to surface and groundwater flows and their status in the landscape. Nowdays, many algorithms have developed to compare maps of land use / cover maps. The aim of this study is comparison of two methods of maximum likelihood and fuzzy method for preparation of land use/cover maps in Gandoman international wetland and the land around it using Landsat images. For this study, Images (2014) were used related to landsat. Based on obtained results the study area was classified into farmland, bare land, rangeland and lagoon. Thereafter overall accuracy and kappa computed for each land use. Finally results showed that fuzzy method more accurately produced land were used than maximum probability method. The result can be staded maps of land use fuzzy algorithm with overall accuracy 86،70 and kappa coefficient 0.79 to maximum likelihood in overall accuracy 81.20 and kappa coefficient is 0.71, higher accuracy.   Comparing fuzzy and maximum likelihood methods to land cover mapping in Gandoman wetland using Landsat satellite data Manuscript profile

Open Access Article

16 - Land cover mapping of Roudbar-e Qasran (Shemiranat County) using Remote Sensing
Fatemeh Karami-ghahi Marzieh Alikhah- Asl Mohammad Rezvani Fatemeh Bokaeian

One of the most important issues of urban and regional development throughout the world is land use and planning for its sustainability. Nowadays, organization of land use and understanding of land cover situation is a great importance due to the increasing development More

One of the most important issues of urban and regional development throughout the world is land use and planning for its sustainability. Nowadays, organization of land use and understanding of land cover situation is a great importance due to the increasing development of cities and imbalance in the spatial distribution of users. Remote sensing techniques are the best application to extract the land use map. So in this study, for preparing the map of land use a Supervised Classification Technique and the Maximum Likelihood Classification Algorithm were used. The study area is located in the Roudbar-e Qasran region of Shemiranat county and in this research the Landsat-8 satellite images (June) of the OLI sensor in 2015 was used. The results showed that the barren lands, rangelands, man-made and farms comprise the largest area of use, respectively. To evaluate the accuracy of the classifications carried out, the total accuracy and Kappa coefficient were determined by 97.74% and 0.92%, respectively, representing the high resolution of Landsat images to map the land use. Manuscript profile

Open Access Article

17 - Comparing the accuracy of time series classification of Landsat images in monitoring land use change
Ahmad Azimi Najarkolaei Ali Akbar Jamali Zeynolabedin Hosseini

In this research, artificial neural network, maximum likelihood and minimum distance classification methods for analysis of land use changes, during 1989 to 2015, were evaluated and compared images from three Landsat satellite sensors in Sari. After geometric and atmosp More

In this research, artificial neural network, maximum likelihood and minimum distance classification methods for analysis of land use changes, during 1989 to 2015, were evaluated and compared images from three Landsat satellite sensors in Sari. After geometric and atmospheric corrections, images of 1989, 2002, and 2015 were categorized under three artificial neural network algorithms, maximum likelihood and minimum distance in five land use classes. After assessing the accuracy of the methods, the Kappa coefficients were calculated for maximum likelihood, artificial neural network and minimum distance of 1989 were 92%, 87% and 65% in 2002, were 89%, 87% and 60%, and in 2015 were 91% %, 90% and 73%, respectively. These coefficients indicate the superiority of the maximum likelihood method in comparison with the other two methods in 1989. Also, the results of land use change over the whole period of the survey (from 1989 to 2015), showed that the areas of residential and irrigated lands were increased by 3615 and 575 hectares, but bare lands, gardens and forests were decreased to 1791, 1127 and 1272 hectares, respectively. According to the results, the two methods of maximum likelihood and neural network were more suitable for land use classification. The maximum likelihood method was better than the neural network method with a difference of 5% in 1989 and 2% in 2002 and 1% in 2015 in the Kappa coefficient. Manuscript profile

Open Access Article

18 - Comparison of object-oriented and pixel-based classification methods for land use mapping (Case study: Isfahan-Borkhar, Najafabad and Chadegan plains)
Sedigheh Ghafari Hamid Reza Moradi Reza Modarres

Change detection algorithms of remote sensing image can be divided into two categories: pixel-based and object-oriented, according to the minimum processing unit. This paper deals with the comparison between application of pixel-based and object-oriented approaches in l More

Change detection algorithms of remote sensing image can be divided into two categories: pixel-based and object-oriented, according to the minimum processing unit. This paper deals with the comparison between application of pixel-based and object-oriented approaches in land use classification in Isfahan-Borkhar, Najafabad and Chadegan plains and evaluation of land use changes with Landsat TM (1985) and OLI (2015) data during the study period. The object-oriented approach involved the segmentation of image data into objects with multi-resolution segmentation algorithm by eCognition  software. Then objects were assigned and classified with the nearest neighbour algorithm in object-oriented classification The supervised pixel-based classification involved the selection of training areas and a classification using a maximum likelihood algorithm. Accuracy assessments of both classifications were undertaken. The results show better overall accuracy (higher 90%) of the object-oriented classification over the pixel-based classification. The land use maps indicate that residential area is increased 2.09, 9.66 and 3.74% and rangeland area is decreased 7.48, 10.94 and 17.73% in Isfahan-Borkhar, Najafabad and Chadegan plains in the study period, respectively. In Chadegan plain the increase in agriculture and fallow land use has been equal to 8.31 and 5.64%, respectively. Manuscript profile

Open Access Article

19 - Performance evaluation of principal component analysis, independent component analysis and minimum noise fraction method in increasing the information extracting accuracy of Sentinel-2 satellite data
Sayyad Asghari Saraskanrood Hasan Hasani Moghaddam Hossein Fekrat

10.30495/girs.2021.677443

Background and ObjectiveProblem The use of various transformations to improve the accuracy of data extraction from satellite images is increasing sharply. In the meantime, the choice of optimal conversion is very important and will affect the output results. Due to the More

Background and ObjectiveProblem The use of various transformations to improve the accuracy of data extraction from satellite images is increasing sharply. In the meantime, the choice of optimal conversion is very important and will affect the output results. Due to the correlated nature of remote sensing images, the use of various transformations to improve the accuracy of information extraction from these images is essential. According to the studies, the purpose of this study is to investigate different methods of image conversion in improving the process of classification of satellite images and increasing the accuracy of land use maps. Considering that the study area and in general the northern regions of Iran are facing special conditions of entanglement of land uses, so the use of various conversion methods as well as the combined method proposed in this study increases the accuracy and the accuracy of the output information and finally the possibility of more detailed separation and review of uses and identification of factors changing them for future planning.Materials and Methods In this study, in order to evaluate the performance of principal component analysis methods, independent component analysis, and minimum noise fraction method, Sentinel-2 satellite images of Rezvanshahr city were used. Gram-Schmit algorithm was used to integrate this data with each other and achieve a resolution of 10 meters. After applying the necessary pre-processing and merging the images together, all three transformations were applied to the image, as well as a combination of the components of these three methods. Then, the results of the transformations were classified into 8 user classes using the maximum likelihood algorithm. Using Sheffield coefficient and statistical calculations between the obtained components, the combination of the first components of principal component analysis, the first component of minimum noise fraction, and the second component of independent component analysis were selected as the optimal combination. General knowledge of the area and accordingly the visual interpretation of the outputs, as well as the perception of 120 ground points by GPS, has been the basis for assessing the accuracy of the output maps.Results and Discussion After applying the required preprocessors, each of these algorithms was applied to the image, and the output of each was classified into 8 user classes using the Maximum Likelihood algorithm. The results of output maps showed that the conversion of principal component analysis, considering that it considers Gaussian distribution for variables and tries to decompose the extracted components, is weak in samples with non-Gaussian distribution and shows low performance. The minimum noise fraction algorithm works similarly to the principal component analysis algorithm, except that it classifies the noise better. This algorithm has less error in separating classes and this factor has resulted in better performance and higher accuracy than the other two conversions. In the independent component analysis algorithm, the image correlated bands of the study area have been converted to independent components and new information has been extracted from the area. The visual interpretation shows the high accuracy of the classification result and an error matrix (confusion) is used to quantify the accuracy of the classified image. The results of the evaluation of overall accuracy and kappa coefficient showed that the classification of the original image without applying transformations and with the same training samples of output with an overall accuracy of 76% and kappa coefficient of 0.78 had the highest error. Also, the results of other outputs for classification resulting from principal component analysis conversion are 80% overall accuracy and kappa coefficient of 0.83, respectively, for classification resulting from minimum noise fraction conversion, total accuracy of 85% and kappa coefficient of 0.88 and for the classification obtained from the analysis of independent component analysis, the overall accuracy was 77% and the kappa coefficient was 0.80. After selecting the optimal combination of components of principal components analysis methods, independent component analysis and minimum noise fraction method and selecting the first components of principal component analysis algorithms and minimum noise fraction and the second component of total component analysis to 92% independent coefficient and Kappa increased 0.94.Conclusion In this study, after evaluating the conversion performance of principal component analysis, independent component analysis, and minimum noise fraction method, an optimal combination of components of these transformations was proposed. As the results of the research showed, the classification of the original image without conversions and with the same training samples had low overall accuracy and kappa coefficient. The results show the close performance of these transformations to each other, which indicates the existence of both Gaussian and non-Gaussian distributions of variables. MNF conversion has minimized the amount of data noise and results in better output than ICA and PCA conversion. Since these transformations alone are not able to extract all the components of the image, so a combination of the components of these transformations based on the Sheffield coefficient was chosen to assume the Gaussian and non-Gaussian distributions of the variables with the least possible noise. Manuscript profile

Open Access Article

20 - Evaluation of supervised classification capability of Landsat-8 and Sentinel-2A Satellite images in determining type and area of Pistachio Cultivars
Hadi Zare khormizi Hamid Reza Ghafarian Malamiri Morad Mortaz

10.30495/girs.2020.672378

Remote sensing technique is one of the most effective tools for monitoring, studying and determining the cultivation area of agricultural and horticultural crops, especially on a large scale. Planners, managers, and farmers, with knowledge of the type and extent of crop More

Remote sensing technique is one of the most effective tools for monitoring, studying and determining the cultivation area of agricultural and horticultural crops, especially on a large scale. Planners, managers, and farmers, with knowledge of the type and extent of crop cultivation, can adopt appropriate management and enforcement policies. The purpose of the present study was to evaluate the supervised classification ability to classify Landsat 8 and Sentinel-2A multi-band satellite imagery in determining the cultivated area and type of four varieties of Pistachio namely such as; Akbari, Kalle Ghuchi, Ahmad Aghaei and Fandooki in an orchard in the Yazd province. In the present study, the accuracy of four classification algorithms, namely: Parallelepiped classification, Minimum distance, Mahalanobis distance and Maximum likelihood, as well as the optimum time in the separation of pistachio cultivars, were investigated. According to the classification results of a Landsat-8 image, on June 12, 2018, the Maximum likelihood algorithm with a final accuracy and Kappa coefficient of 76.8% and 0.67% and Parallelepiped classification algorithm with the final and Kappa coefficients of 64.7 and 0.47, were of highest and lowest accuracy among others, respectively. Also, according to the results, the best time for the separation of Pistachio cultivars was in late June. The Kappa coefficient of maximum likelihood classification algorithm on June 22, July 23, August 24 and September 25 of 2018 were 0.67, 0.64, 0.63 and 0.63, respectively. The final accuracy and Kappa coefficient of maximum likelihood classification algorithm on the Sentinel-2A Satellite images on 12 June  2018, were 80% and 0.71, respectively. By applying the median filter with a 3×3 dimensional kernel window size on the classified image, the final accuracy and Kappa coefficient was increased to 82.6% and 0.75, respectively. The final accuracy and Kappa coefficient of classification and separation of Pistachio cultivars in Sentinel-2A images were higher than in Landsat-8 images. Overall, based on our results, the remote sensing classification techniques, as well as multi-spectral satellite imagery, are suitable for agricultural and horticultural mapping. Manuscript profile

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21 - Estimating changes in forest cover in the Rudsar county by using neural network and maximum likelihood methods
Seyed Reza Fatemti Talab Morteza Madanipour Kermanshahi Seyed Armin Hashemi

The acquisition of knowledge about the vegetation plays an important role in soil management.  However, vegetation estimating in the usual way, including an overall assessment of the vegetation is  time consuming and does not also provide accurate enough infor More

The acquisition of knowledge about the vegetation plays an important role in soil management.  However, vegetation estimating in the usual way, including an overall assessment of the vegetation is  time consuming and does not also provide accurate enough information. Therefore, remote sensing technology is a desirable way for reducing time and cost compared to other usual methods. In this study, forest cover maps were prepared using remote sensing techniques and  LandSat ETM+ imagery of year 2000 and LandSat 8 of year 2013. The classification of the study area digital images was performed  to prepare  land use map classification using maximum likelihood and neural network with participation of different bands. The results showed that the best overall accuracy of image classification using neural networks ETM+ in 2000 and LandSat 8  in 2013  was 0.95 and 0.95 respectively. It was also indicated that the kappa coefficient was estimated 0.91 and 0.91 respectively. The overall accuracy of maximum likelihood method of the collected images of  2000 and 2013 was  0.95 and 0.85, but it was 0.86 and 0.84 for Kappa statistics method. The results also showed a 1054.507 and 635.319 hectares decreasing of forest cover using  neural network classification  and maximum likelihood classification methods respectively. According to classification accuracy and Kappa statistics, it was observed that the accuracy and kappa coefficient of neural network classification was higher than accuracy and the Kappa coefficient of maximum likelihood method. Manuscript profile

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22 - Detecting environmental change of Shadegan international wetland using remote sensing and WRASTIC index (Case study: Shadegan international wetland)
Leila Rahimi Blouchi Azadeh Zarkar Bahram Malekmohammadi

The goal of this paper is reviewing and comparing of Shadegan international wetland changes during last two decades. To achieve this goal, the trend of changes in Shadegan international wetland and the relative consequences were examined by supervised classification of More

The goal of this paper is reviewing and comparing of Shadegan international wetland changes during last two decades. To achieve this goal, the trend of changes in Shadegan international wetland and the relative consequences were examined by supervised classification of LandSat satellite images. For this purpose, maximum likelihood algorithm, in ENVI®4.8 software was utilized during 20 years period (1990-2011). WRASTIC index, one of the existing methods for evaluating risk and vulnerability of the surface water, was used for finding inflow water quality to the wetland. Results of this study show six percent decrease in area of Shadegan wetland during these years, about 1796.61 Km2 (25.71%) declination in water and soil areas, and by 1796.76 (9%) increase in the total area of vegetation cover. Growing vegetation cover denotes water pollution, eutrophication, and early devastation of this international wetland. A result of calculation WRASTIC index showed that wetland basin components have the great impact on pollution of inflow water to wetland. Continuing of this trend, make irreparable effects on this existence and integrity of this wetland. Manuscript profile

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23 - Remote Sensing and Land Use Extraction for Kernel Functions Analysis by Support Vector Machines with ASTER Multispectral Imagery
E. Akbari N. Amiri H. Azizi

Land use is being considered as an element in determining land change studies, environmental planning and natural resource applications. The Earth’s surface Study by remote sensing has many benefits such as, continuous acquisition of data, broad regional coverage, More

Land use is being considered as an element in determining land change studies, environmental planning and natural resource applications. The Earth’s surface Study by remote sensing has many benefits such as, continuous acquisition of data, broad regional coverage, cost effective data, map accurate data, and large archives of historical data. To study land use / cover, remote sensing as an efficient technology, is always desired by experts. In this case, classification could be considered as one of the most important methods of extracting information from digital satellite images. Selecting the best classification method and applying the proper values for parameters extremely influence the trust level of extracted land use maps. This research is an applied study which attempts to introduce Support Vector Machines (SVM) classification method, a recent development from the machine learning community. Moreover, we prove its potential for structure–activity relationship analysis on Aster multispectral data of central county of Kabodar-Ahang region in Hamedan, Iran. Accuracy of SVMs method is varied by the type of kernel functions and its parameters. The purpose of this research is to find the accuracy of Land use extraction by SVM method by Polynomial and radial basis functions kernel with their estimated optimum parameters in addition to compare the results with Maximum Likelihood method. Most of the scientists imply that Maximum Likelihood method is suitable for classification. Therefore, we try to compare SVM with ML method and to deliberate the efficiency of this new method in classification progress on Aster multispectral data. The accuracy of SVM method by Polynomial and radial basis functions kernel with optimum parameters and ML classification methods achieved 93.18%, 91.77% and 88.35 % respectively as an overall accuracy. By comparing the accuracy of these methods, SVM method by Polynomial kernel was evaluated as suitable. Therefore, we can suggest using SVM method especially with the use of Polynomial kernel to determine land use. In general, the results of this research are very practical in natural resources conservation planning and studies. Also, this study verifies the effectiveness and robustness of SVMs in the classification of remotely sensed images. Manuscript profile

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24 - Urban sprawl trend analysis using statistical and remote sensing approach Case Study: Mashhad City
susan shirvani moghadam sanaz saeidi mofrad

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25 - Evaluation of Land Use Change in Lali City Applying Maximum Likelihood Algorithm
Hadis Rezaei Mirghaed Ladan khedri gharibvand

Urban land use maps, in addition to different classes of land use with spatial patterns, specify the type and intensity of land use; therefore, they can be used for current and future planning of urban land. In this study, land use changes in Lali city in 30 years (1987 More

Urban land use maps, in addition to different classes of land use with spatial patterns, specify the type and intensity of land use; therefore, they can be used for current and future planning of urban land. In this study, land use changes in Lali city in 30 years (1987-2017) were investigated. To evaluate the land use changes in this time interval, several spectral images of Landsat satellites 5, 7, and 8 from the years 1987, 2001 and 2017 were utilized. After collecting data and the application of necessary pre-processing on them, also for the preparation of land use maps for the specified time intervals, data analysis was carried out by Maximum Likelihood Classification Algorithm. The findings obtained each year were monitored and controlled through field operations, and land use maps in 7 classes of agriculture, rangeland, forest, mountain, residential, river, and other areas were produced. Then, the changes in each land use were determined in the specified periods during 1987 to 2001, 2001 to 2017, and eventually 1987 to 2017. While the results obtained from the final changes illustrate that the overall level of vegetation compared to the beginning of the period has declined markedly which is an indication of deforestation in the region, urban areas, agriculture, and rangelands have maintained an ascending trend which can be due to increasing urban development and rural expansion, and the growing need of residents for housing, agriculture, and gardens. Manuscript profile

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26 - Change Point Estimation of a Process Variance with a Linear Trend Disturbance
Rassoul Noorossana Majeed Heydari

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27 - Evaluation and assessment of changes in forest area Harra (mangrove) Using remote sensing techniques Case Study: Bandar Abbas
محمد علی زنگنه اسدی ابراهیم تقوی مقدم elahe akbari

Knowledge of changes is first, most important action planners, and authority’s natural and human environment. Satellite images and satellite image processing techniques and methods very precise tool for navigation and assessment of changes in forest areas is the p More

Knowledge of changes is first, most important action planners, and authority’s natural and human environment. Satellite images and satellite image processing techniques and methods very precise tool for navigation and assessment of changes in forest areas is the purpose of of this study is assess the changes in forest areas mangrove in Bandar using the technique of remote sensing. To achieve this purpose of we used the information and topographic maps, satellite images and the algorithm of maximum likelihood and minimum distance 1989, 2005 and 2015 years of area. The results show that the maximum likelihood method with 98/32% overall accuracy and kappa coefficient 0/978 accurate method than using support vector machine and the minimum distance for mapping land cover changes and monitoring changes in forest. According to calculations forest surface area’ of 76/09 sq km in 1989 has increased to 125/08 square kilometers in 2015. Which indicates the shores of the Strait of Hormuz is the hydrodynamic change. Thus adopting every environmental protection measures in the area is necessary, any facilities and infrastructure projects must comply with environmental considerations and ecological. Manuscript profile

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28 - Land Use Mapping of Sabzevar using Maximum Likelihood and Artificial Multilayer Perceptron Neural Network
Elahe Akbari Majid Ebrahimi Abolghasem AmirAhmadi

Among the important factors in urban planning and management, particularly in line with the achievement of the sustainable development in the urban areas as well as regarding the optimal use of the land, is on-time access to the data of land cover conditions in these re More

Among the important factors in urban planning and management, particularly in line with the achievement of the sustainable development in the urban areas as well as regarding the optimal use of the land, is on-time access to the data of land cover conditions in these regions. The remote sensing data has a high potential for the preparation of the update urban land cover maps. In order to present on-time and digital satellite data, a variety of shapes and possibility of processing during land cover maps are of high significance. In order to use the satellite photos Landsat/ETM+ and two algorithm of supervised classification including the maximum likelihood and the artificial neural network, land cover maps were prepared. During classification, the neural network algorithm of a perceptron network with a hidden layer and 7 input neurons, nine middle neurons and 4 output neurons were used. The input neurons are the same in number as the bands of the Landsat photos and the number of output neurons are the same as land cover map classes. Eventually, land cover map of the region has been classified into four classes of residential areas, barren lands, plant coverage, and roads. In order to evaluate the correctness of the classification results, many photos have been taken using GPS. Using overall accuracy and Kappa Coefficient the precision evaluation results of these two methods indicate that perceptron neural network has an overall accuracy of 98/24 and Kappa Coefficient 97/03 compared to the algorithm of maximum likelihood with an overall accuracy of 94/23 and Kappa Coefficient 90 / 34 is of higher precision. The findings of this study also show that the classification method for multilayer perceptron neural network as compared with the maximum likelihood method is of higher separation and capability for preparing the land cover map in the urban regions. Manuscript profile

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29 - Distributed Agreement Based Ml Approximation
Mohamad Mohamadi Hamid Parvin Eshagh Faraji Sajad Parvin

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30 - Rice Yield Distribution and Risk Assessment in South Asian Countries: A Statistical Investigation
Mahadeb Prasad Poudel Shwu-En Chen Raju Ghimire

In the last decades, rice yields in South Asian countries grew tremendously in one hand and a noticeable yield fluctuation on the other. The objective of this study was to examine the rice yield distributions, estimate yield risks at country level, and compare risks bet More

In the last decades, rice yields in South Asian countries grew tremendously in one hand and a noticeable yield fluctuation on the other. The objective of this study was to examine the rice yield distributions, estimate yield risks at country level, and compare risks between five countries namely Afghanistan, Bangladesh, Nepal, Sri Lanka, and Pakistan. Anderson Darling (AD) test was applied to test the goodness-of-fit for four distributions by using country level de-trended rice yields from 1961 to 2010. Results showed the Normal distribution was fitted well in Afghanistan and Sri Lanka, whereas the Wei bull distribution in Bangladesh, Nepal, and Pakistan. The average yield risks at 85% of the expected yield were found 5.29, 4.27, 3.86, 1.55, and.15% in Afghanistan, Sri Lanka, Pakistan, Nepal, and Bangladesh, respectively. Wilcoxon signed rank test results of mean absolute percentage differences showed yield risk in Bangladesh was significantly lower than the rest four counties and that in Afghanistan was significantly higher than Nepal and Bangladesh at 0.1 level. The outcome of this study could give policy implications for designing and implementing the risk reducing programs in the countries with higher yield risk. Manuscript profile

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31 - Analysis of Technical Efficiency of Smallholder Cocoa Farmers in Cross River State, Nigeria
Agom Damian Ila Susan Ben Ohen Kingsley Okoi Itam Nyambi N. Inyang

The technical efficiency involved in cocoa production in Cross River State was estimated using the stochastic frontier production function analysis. The effects of some selected socio- economic characteristics of the farmers on the efficiency indices were also estimated More

The technical efficiency involved in cocoa production in Cross River State was estimated using the stochastic frontier production function analysis. The effects of some selected socio- economic characteristics of the farmers on the efficiency indices were also estimated. The study relied upon primary data generated from interviewing cocoa farmers using a set of structured questionnaire. A multi-staged random sampling technique was adopted in selecting two hundred (200) cocoa farmers from Ikom Agricultural Zone in the state. The data on the socioeconomic characteristics of the farmers were analyzed using descriptive statistics, while the stochastic production function, using the Maximum Likelihood Estimating (MLE) techniques was used in estimating the farmer’s technical efficiency and their determinants. Result of the analysis showed that farmers were experiencing decreasing but positive returns to scale in the use of the farm resources. The efficiency level ranged between 0.20 and 0.93 with a mean of 0.69. The result of the generalized Likelihood Ratio (LR) tests confirmed that the cocoa farmers in the area were technically inefficient. The major contributing factors to efficiency were age of farmers, farm size, and level of education, sex of farmer and age of the farms. The study observed that there is enough room to improve efficiency with the farmers’ current resource base and available technology and concluded that policies that would directly affect these identified variables should be pursued. Manuscript profile

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32 - Robustness-based portfolio optimization under epistemic uncertainty
Md. Asadujjaman Kais Zaman

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33 - Bootstrap confidence intervals of CNpk for type‑II generalized log‑logistic distribution
Srinivasa Rao Gadde K. Rosaiah SVSVSV Prasad

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34 - Identifying the change time of multivariate binomial processes for step changes and drifts
Seyed Taghi Akhavan Niaki Majid Khedmati

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35 - Identifying the time of a step change in AR(1) auto-correlated simple linear profiles
Majid Khedmati Seyed Taghi Akhavan Niaki

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36 - A Comparative Study of the Efficiency of Insurance Companies in Iran and MENA Countries
Sousan Shokoohigol Ali Dehghani

The critical role and status of the insurance industry in the modern economy is inevitable. In recent years, particular attention has been paid to the efficiency of insurance companies in Iran. In the sixth program policies, this issue has also been emphasized. A brief More

The critical role and status of the insurance industry in the modern economy is inevitable. In recent years, particular attention has been paid to the efficiency of insurance companies in Iran. In the sixth program policies, this issue has also been emphasized. A brief review of the indicators of the level of insurance penetration, insurance density and the percentage of the total insurance premiums of the world indicate that so far this industry has not achieved its status in the national economy. The first step in the development of insurance is to consider whether insurance companies in Iran, with regard to their resources, are efficient? To this end, insurance industry of Iran and other countries of the MENA were examined. In this study, to measure technical efficiency, non-parametric method of data envelopment analysis and parametric method of stochastic boundary function estimation - to identify the factors affecting the efficiency of insurance companies - and non-parametric method adjustment have been used. Based on the results, the growth of GDP per capita and technical reserves has a positive and significant effect on insurance industry efficiency and the insurance industry of Iran has the highest efficiency among MENA countries Manuscript profile

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37 - Reactive Power Management in Micro Grid with Considering Power Generation Uncertainty and State Estimation
Mohammad Reza Forozan Nasab Javad Olamaei

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38 - Distance to default in banks with the approach of transformed- data maximum likelihood estimate method
samane shafiee mohammadhamed khanmohammadi

We introduced estimation methods include the market value proxy , volatility restriction , KVM , and the transformed-data maximum likelihood with strengths and weaknesses in order to estimate distance to default . If the correct estimation method is not used, there will More

We introduced estimation methods include the market value proxy , volatility restriction , KVM , and the transformed-data maximum likelihood with strengths and weaknesses in order to estimate distance to default . If the correct estimation method is not used, there will be distortion in the results . Considering the different balance sheet structure , the transformed- data is introduced by considering the coefficient of other debts as an optimal method in order to estimate distance to default in banks. Then, we used Merton's adjusted model and the transformed- data method during 2012 to 2019 to calculate market value of assets, asset volatility, distance to default, and probability of default in some private banks. The results show that the highest market value of assets is related to Bank Mellat and the lowest is Post Bank . The results achieved by comparing are different regarding volatility of assets, distance to default, and the probability of default. Additionally, the average market value of banks' assets is increasing and the average volatility of assets and the average distance to default is decreasing . In other words, Banks have become closer to default . The Dickey-Fuller test confirms the Stationary of the research model. Manuscript profile

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