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Open Access Article
1 - Detection and monitoring of land use change using supervised classification method and post classification comparison (case study of Assaluyeh area)
farhad hamze Hadi AbdollahiCoastal areas has been important for mankind because of the abundance of potential possibilities for development since long time ago and this can be the reason for concentration of %75 of the world's population at radius of 200 km from the coasts.The purpose of this stu MoreCoastal areas has been important for mankind because of the abundance of potential possibilities for development since long time ago and this can be the reason for concentration of %75 of the world's population at radius of 200 km from the coasts.The purpose of this study is the change detection of land use in coastal areas of assalouye by using landsat images in two 16-year periods (2984-2000-2016). Land use maps for these years was extracted from landsat satellite imagery and was corrected by using of available maps and also google earth images and was verified through the error matrix approach, kappa coefficient and overall accuracy. kappa coefficient ( 0.94; 08808 and 0.9517) and overall accuracy (96.56%; 95.4952% and 93.5883% ) was obtained for 1984, 2000 and 2016 years respectively.The results show that increase in area in the residential areas use unit (126.36 square kilometer ) and vegetation (6.13 square kilometer) and decrease in area in the bayer areas use class (-125.37) and water range (-7.15 square kilometer) has been created during 32 years. Bayer lands has the most changes and residential areas unit as the most development. Manuscript profile -
Open Access Article
2 - 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 NikbakhtThe 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 MoreThe 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
3 - investigation of land cover changes using remote sensing technique (Case study: Katalan unit)
Maryam Nazemi jalal Marzieh Alikhah-Asl Elham ForootanBackground 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 MoreBackground 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
4 - Tree Cover detection through Maxlike Classification of Land sat ETM + Images of the Year 2001 in Golestan Province
Aborasoul Salman Mahini Azadeh Nadali Jahangir Feghhi Borhan RiaziSparse vegetation gives rise to increased overland water flow، soil erosion، water pollution and decreased soil fertility. Golestan Province has witnessed a relatively extensive forest clearing during recent years causing intensified flooding. We used ETM+ land sat imag MoreSparse vegetation gives rise to increased overland water flow، soil erosion، water pollution and decreased soil fertility. Golestan Province has witnessed a relatively extensive forest clearing during recent years causing intensified flooding. We used ETM+ land sat imagery to classify forest cover of the Golestan Province using Max like classification and assessed its accuracy. Land uses and land covers were distinguished on the color composite images of the area and used as training sites for image classification that included all six bands of the imagery. We also used an ISO-Cluster unsupervised classification to derive 100 clusters for purifying initial training sites. Accuracy assessment was implemented through test set pixels that were randomized and set aside from the training set pixels. We also used a LISS III imagery to assess the accuracy of the classification. Our assessment proved the classification to be of high accuracy. Manuscript profile -
Open Access Article
5 - Tree Cover detection through Maxlike Classification of Land sat ETM + Images of the Year 2001 in Golestan Province
Abdorrasoul Salman Mahini Azade Nadali Jahangir i Feghhi Borhan RiyaziSparse vegetation gives rise to increased overland water flow, soil erosion, water pollution and decreased soil fertility. Golestan Province has witnessed a relatively extensive forest clearing during recent years causing intensified flooding. We used ETM+ land sa MoreSparse vegetation gives rise to increased overland water flow, soil erosion, water pollution and decreased soil fertility. Golestan Province has witnessed a relatively extensive forest clearing during recent years causing intensified flooding. We used ETM+ land sat imagery to classify forest cover of the Golestan Province using Max like classification and assessed its accuracy. Land uses and land covers were distinguished on the color composite images of the area and used as training sites for image classification that included all six bands of the imagery. We also used an ISO-Cluster unsupervised classification to derive 100 clusters for purifying initial training sites. Accuracy assessment was implemented through test set pixels that were randomized and set aside from the training set pixels. We also used a LISS III imagery to assess the accuracy of the classification. Our assessment proved the classification to be of high accuracy. Manuscript profile -
Open Access Article
6 - Classification and Assessment of the land use changes using Landsat satellite imagery (Case Study: Rey Plain)
pegah mohammadpour reza Arjmandi Amir Hesam Hasani Jamal GhoddousiBackground 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 MoreBackground 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
7 - Extraction of Multiple Hybrid Features to Reduce the Semantic Vacuum with the Semi-Supervised Classification
Mahdi Jalali Tohid SedghiIn this paper, for the classification of images, the observed cooperative classification method is proposed with the aim of reducing the semantic vacuum. Because most classification methods are sensitive to cluster centers at initialization, the algorithm converges opti MoreIn this paper, for the classification of images, the observed cooperative classification method is proposed with the aim of reducing the semantic vacuum. Because most classification methods are sensitive to cluster centers at initialization, the algorithm converges optimally locally if the quantification is not done correctly. It is also very difficult to combine the results of the classification due to the fact that the labels of the work centers are not clear. Supervised semi-classification is used to solve these problems. To achieve the highest performance, the system classification results are combined with different color space and similarity criteria with multiple features as a semi-supervised cooperative. When the number of features is effective, the relevant feedback is used for its semi-regulatory classification. One of the most important parts of an image retrieval system and a classification algorithm is to determine the appropriate similarity between images. In this study, two methods of classification of cases have been used, which include k-NN and PNN classification, which according to the results in all proposed methods, a better response from k-NN classification than PNN has been observed. The proposed algorithm is very suitable for classifying large image databases due to the reduction of time complexity. Also, in image retrieval in different color spaces and using different similarity criteria, different classifications are obtained. Better results can be achieved if the classification results are combined. Manuscript profile -
Open Access Article
8 - The Comparison of Classification Algorithms for Remote Sensing Images
najmeh Cheraghi Shirazi Roozbeh Hamzehyan Ashkan MasoomiAlthough a large number of remote sensing image classification algorithm is proposed, but rarely performance of them are compared with each other. In this research, we classify satellite remote sensing images using two unsupervised classification algorithm and eight sup MoreAlthough a large number of remote sensing image classification algorithm is proposed, but rarely performance of them are compared with each other. In this research, we classify satellite remote sensing images using two unsupervised classification algorithm and eight supervised classification algorithm which includes a number of common algorithms twenty years, tested and compared. Our analysis focused on satellite images of the 12 spectral bands. In comparison of these algorithms, the number of training samples is equated. These algorithms are compared with each other in terms of complexity, accuracy and reliability. The results show that accuracy of classification directly proportion to the number of training samples. The user can also choose efficient algorithms, depending on the complexity, accuracy and reliability of each parameter. Manuscript profile -
Open Access Article
9 - Comparison of visual and digital interpretation methods of land use/cover mapping in Ardabil province
Azad Kakeh Mami Ardavan Ghorbani Farshad Kayvan Behjoo Amir Mirzaei MosivandLand use/cover mapping is one of the most common applications of remote sensing data. Remote sensing data by providing updated digital information, repetitive coverage, reduce costs and the possibility of processing and high potential for the preparation of land use/cov MoreLand use/cover mapping is one of the most common applications of remote sensing data. Remote sensing data by providing updated digital information, repetitive coverage, reduce costs and the possibility of processing and high potential for the preparation of land use/cover maps in natural resources, is of paramount importance. In this study, the land use and cover map prepared using Google Earth and the Operational Land Imager image sensor (OLI) of Landsat 8 satellite and methods of visual interpretation (GE images), supervised classification, neural networks and object-based classification methods (Landsat 8 images), and compared with each other. In order to evaluate the accuracy of the classification, the overall accuracy, Kappa coefficient, producer’s accuracy and user’s accuracy were used. The results showed that the visual interpretation method with overall accuracy and Kappa coefficient of 99.4 and 0.99, in comparison to the object-based, supervised and artificial neural networks (with an overall accuracy of 94, 82 and 60.8, and a Kappa coefficient of 0.92, 0.77 and 0.50) are more reliable. According to the map of visual interpretation, the rangelands with an area of 946687 ha and water bodies in the area of 217.42 ha were the largest and smallest land use/covers, respectively. In terms of accuracy, the visual interpretation method using Google Earth images had the highest accuracy, but it is time-consuming and cost-effective. In contrast, object-based method with acceptable accuracy and with low cost and time is the best method for land use/cover mapping. Manuscript profile -
Open Access Article
10 - Study of land use change and its effect on erosion in Nir city using GIS and RS (Case study: Nir county)
sayyad asghari saraskanroud Leila Aghayary Elnaz PirouziDue to human activities and natural phenomena, the face of the earth is always undergoing change. Therefore, for the optimal management of the natural areas, awareness of the land use ratio is a necessity. Soil erosion is one of the environmental disasters that annihila MoreDue to human activities and natural phenomena, the face of the earth is always undergoing change. Therefore, for the optimal management of the natural areas, awareness of the land use ratio is a necessity. Soil erosion is one of the environmental disasters that annihilates thousands of soil, crops each year, and land use change is one of the important factors in erosion. Therefore, the present study was conducted to investigate the land use change trend in Nair, Ardabil province, and its effect on erosion using GIS and RS in order to carry out the research, images from 2000 and 2016, OLI and TIRS sensors, Landsat 8 were used and land use map was prepared using a controlled classification method. The erosion zonation map was performed using landuse maps and factors such as slope, lithology, distance from the road, distance from the waterway, precipitation and soil using Critical Weighing and Weighted Linear Combination (WLC). The results showed that the highest amount of area in 2000 was related to dry land farming with 442.38 km2 and semi-condensing pastures with an area of 347.39 km2. In 2016, the highest area of use of rangelands density, and then the use of semi-metamorphic rangelands are 478.76 and 458.5 km2, respectively. According to the erosion zoning plan of 2000, 17.25% and 25.55%, respectively, according to the 2016 erosion zonation, 12.44% and 26.51% of the city area are located in two high risk and high risk categories. Mostly, high risk and high-risk areas are located in both dry land and aquaculture fields at both time periods. Manuscript profile -
Open Access Article
11 - Change detection of soil salinity using remote sensing in Zahed Shahr, Fars province
Roya Ranjbar Hamidreza Owliaie Hojjat Ranjbar Ebrahim AdhamiSoil salinization is a predominant process in the degradation of arid and semi-arid soils that causes reduced the yield of crops, increases erosion and intensifies desertification. The Zahed Shahr studied area is located in the south and east of Fasa city, Fars province MoreSoil salinization is a predominant process in the degradation of arid and semi-arid soils that causes reduced the yield of crops, increases erosion and intensifies desertification. The Zahed Shahr studied area is located in the south and east of Fasa city, Fars province and in recent years the soil salinity has affected large areas of the farms and gardens in this area. The aim of this study was to evaluate the salinity changes of the soils during a 17 year period using Landsat 7 and 8 satellite images using the spectral angle mapping method. To do research, the salty soil spectra were extracted from satellite data and then using supervised classification the areas with salty soil were identified changes in salinity soils in ArcGIS software were investigated. Field studies, soil surface sampling, chemical analysis and spectral analysis of soil were performed by Spectroradiometer, and X-ray diffraction analysis for mineral identification was done. The results obtained from visible and shortwave infrared Spectroradiometer, and X-ray diffraction analysis showed the presence of evaporites minerals (calcite, halite, gypsum). By comparison with real ground data, the spectral angle mapper method was identified to be efficient in the classification of soil salinity. The results of the SAM method for a period of 17 years show four times spatial increase in soil salinity in this area. The area of saline soils has increased from 1600 hectares in 2000 to 6500 hectares in 2017. Manuscript profile -
Open Access Article
12 - 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 MortazRemote 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 MoreRemote 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 -
Open Access Article
13 - Investigating the surface changes of Urmia Lake using the integration of Landsat-8 and Sentinel-2 satellite data
Amir Ghayebi Ahmad Ahmadi Behnaz BigdeliAmong environmental changes, water plays a very vital role in the political, social and economic issues of countries, which can be used as one of the most practical sources of water supply available to humans and animals. Investigating the fluctuations of the water leve MoreAmong environmental changes, water plays a very vital role in the political, social and economic issues of countries, which can be used as one of the most practical sources of water supply available to humans and animals. Investigating the fluctuations of the water level of the lakes in terms of the importance, location and nature of these water bodies has become especially important in recent years. Lake Urmia with an area of 51200 square kilometers is of special importance as the largest internal lake of Iran and the 20th lake in the world. Landsat-8 and Sentinel-2 satellite data for the years 2013 to 2021 were used to investigate and evaluate changes in the water level of Lake Urmia, vegetation and soil around it. First, radiometric and atmospheric corrections were made on the images, and then, while using Gram Schmidt and LMVM integrators to increase spatial resolution, NDWI, AWEI, WI2015 and NDVI indices were extracted in order to differentiate the lake water level from non-water. paid. Finally, by combining the indicators with each other and by sampling the training and test samples, in order to classify the images, supervised classifiers such as Maximum Likelihood, Support Vector Machine, Neural Network and Minimum Distance to Mean were used. Also, in order to improve the results, the output of the classifiers was merged using the majority voting method. The results of the research showed that the majority voting method was chosen as the most suitable classification method with the highest level of accuracy. The water level of Lake Urmia, the vegetation and soil around it have also undergone significant changes during the years 2013 to 2021, so that in 2021, compared to 2020, the water level decreased by 29.89%, and the vegetation increased by 16.08%. And the soil has increased by 17.50%. Manuscript profile -
Open Access Article
14 - Comparison of three common methods in supervised classification of satellite data for vegetation studies
Amir Ahmadpour Karim Solaimani Maryam Shokri Jamshid GhorbaniUsage of modern technologies such as GIS and RS in plant ecosystems studies and especially land cover mapping is needed to recognition of these instruments efficiency and also identification of the best methods for applying them. This study aimed to compare the efficien MoreUsage of modern technologies such as GIS and RS in plant ecosystems studies and especially land cover mapping is needed to recognition of these instruments efficiency and also identification of the best methods for applying them. This study aimed to compare the efficiency of three common supervised classification methods of satellite data (Minimum to Distance, Parallelepiped and Maximum Likelihood) to identification of plant groups in Goloul-via-Sarani protected area, Northern Khorasan Province, Iran. In order to this, 143 training samples (>30m2) were collected from areas that shown a homogenous plant species composition. These data recorded by GPS device and so transported to a GIS database. Satellite data included Landsat ETM+, and IRS-P6 LISS III that were prepared and analyzed by ENVI 4.2 software. Amount of efficiency for each method was evaluated by measurement of overall accuracy (OA) and Kappa coefficient (KC) criteria. Results showed that ML method makes the highest accuracy for two data series (OA=90.35, 82.19 and KC=0.878, 0.772 for Landsat and IRS data respectively). In the face, PP method showed the worst results (OA=67.09, 58.76 and KC=0.593, 0.478). These results suggested that collection of sufficient training samples from natural classes and surveying probability of image pixel's dependency on these classes can be useful for classification of plant groups. Manuscript profile -
Open Access Article
15 - Quality assessment and detection of forest area changes using satellite images (Case study: Rustam, Fars)
Mahmoud Ahmadi Mehdi NarangifardThis paper has been conducted to estimate the detection capability of LandSat satellite data for the detection and qualitative assessment of forest area and vegetation changes, land uses and vegetation percent in Rustam city. In this regard, using Landsat satellite imag MoreThis paper has been conducted to estimate the detection capability of LandSat satellite data for the detection and qualitative assessment of forest area and vegetation changes, land uses and vegetation percent in Rustam city. In this regard, using Landsat satellite images (1987 and 2010), forest land use map, Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI) were obtained by Maximum Likelihood and Supervised Classification algorithms. The results showed that the area of extracted layers of forests with high, moderate and low density as well as barren regions has been estimated as 48.78, 348.67 and 281.42 and 81.68 km2, respectively. Assessing the classification results indicated that the overall accuracy, producer accuracy and user accuracy were given as 94.3, 91 and 95%, respectively and also, Commission error and Ommission error have been computed as 0.09 and 0.05. The highest and lowest producer accuracy estimated as 99 and 80% was related to low-density and high density forests and the highest and lowest percent of user accuracy given as 100 and 87% was attributed to the barren and moderate density forest. Also, comparing maps of vegetation percent and Ratio Vegetation index during 1987 and 2010 has shown no significant changes. Manuscript profile -
Open Access Article
16 - Evaluation and Comparison of Different Supervised Classification Algorithms in Lands User Map Preparation Using Satellite Images (Case Study: Miandoab City)
Mehdi MohamadpourPreparation of land use maps using traditional methods, in addition to spending a lot of time and money, is mainly about efficiency and it does not have the necessary accuracy. Today, satellite imagery and remote sensing techniques have a wide range of applications in a MorePreparation of land use maps using traditional methods, in addition to spending a lot of time and money, is mainly about efficiency and it does not have the necessary accuracy. Today, satellite imagery and remote sensing techniques have a wide range of applications in all sectors, including agriculture, natural resources, and land use mapping, due to the provision of timely data and high analysis capabilities, variety of shapes, digitality, and the possibility of processing. Satellite imagery Landsat 8 for August 2020 was used, which after making the necessary corrections in the pre-processing stage, action experimentation or fusion of the desired image using the panchromatic band and spatial resolution of the image was increased from 30 meters to 15 meters. In the next step, four different classification methods, including backup vector machine, maximum probability, Mahalanoob distance, and minimum mean distance were compared. The results showed that the classification method of backup vector machine with average overall coefficients and kappa of 100 and 1, respectively, has higher accuracy than other methods. Priority accuracy of classification methods is in the form of backup vector machine, maximum probability, Mahalanoob distance, and minimum distance from the mean, respectively. Finally, by assessing the accuracy using user accuracy, producer accuracy, overall accuracy, kappa coefficient and error matrix, land use map was prepared in three separate classes. Manuscript profile -
Open Access Article
17 - Assessment of the capability of supervised classification algorithms in the preparation of vegetation map Case Study: Abyek, Tehran
ensiyeh mihanparastIn order to properly manage rangeland ecosystems and vegetation, it is necessary to know the relationship between their components. One of the main components of these ecosystems is vegetation and its composition, which is under the control of environmental factors. In MoreIn order to properly manage rangeland ecosystems and vegetation, it is necessary to know the relationship between their components. One of the main components of these ecosystems is vegetation and its composition, which is under the control of environmental factors. In other words, the pattern of vegetation distribution is affected by many environmental factors. Today, business awareness and its health play an important role in soil and plant management. Also, the use of vegetation maps is one of the most important pillars in the production of information for regional planning. Therefore, using remote sensing knowledge and careful study of elements, it is possible to identify vegetation and its types and its distribution using satellite images. Therefore, in the present study, two supervised classification algorithms including Maximum likehood and Spactral angel mapper have been used to evaluate the vegetation cover and its distribution for Abyek region. The overall accuracy for the MLC and SAM algorithms is 91.86 and 68.85, respectively, and the MLC and SAM kapa ratios are 0.89 and 0.62, respectively. Therefore, the MLC algorithm was recognized as a more appropriate method for assessing and estimating the vegetation distribution of the Abyek region, and therefore the MLC algorithm has a higher accuracy than the SAM algorithm. It goes without saying that the correlation between vegetation and environmental factors is one of the most important issues affecting the formation of the structure of plant communities and their distribution in each region and study area, and the specific conditions of each region are different. Recognize and manage vegetation. Manuscript profile -
Open Access Article
18 - Ability to prepare methods land use maps using satellite images (Case study: Kamyaran city)
saman javaheri ali asghar torahi seyed mohammad tavakoli sabourIt is important to have new land use plans in many areas, including natural resource management and land planning. Remote sensing data has a high potential for preparing up-to-date land use maps and land cover. The purpose of this study is to evaluate the methods of pre MoreIt is important to have new land use plans in many areas, including natural resource management and land planning. Remote sensing data has a high potential for preparing up-to-date land use maps and land cover. The purpose of this study is to evaluate the methods of preparing land use maps of Kamyaran city using satellite images. In this study, OLI Landsat 8 satellite sensor data for June 2018 were used. Initially, preliminary processing, including radiometric, atmospheric, and geometric corrections, was performed on raw data. ground control points were used for training, accreditation, and land use mapping. The Landuse class was identified at each point by field survey and using Google Earth images in 9 user classes of agricultural lands, forest, garden, rich pastures, medium pastures, residential areas, water area, barren lands and rocky outcrops. In the following, maximum probability, minimum distance, support vector machine and Mahanalubi distance were used for the supervised classification in ENVI 5.3 software. To evaluate the accuracy of classification methods, two criteria of general accuracy and capa coefficient were used with ground control data. The results showed that the support vector machine method was 91.4% more accurate and the Kappa coefficient was 0.88% more accurate than other methods. Manuscript profile -
Open Access Article
19 - Detection of land use changes using satellite imagery during the period 1984-2019 (Case study of Kamyaran city)
saman javaheri Ali asghar TorahiLand use change due to human activities is one of the important issues in regional and development planning. Given the advantages and capabilities of satellite data, this technology can be of great help in identifying and detecting these changes. The purpose of this stu MoreLand use change due to human activities is one of the important issues in regional and development planning. Given the advantages and capabilities of satellite data, this technology can be of great help in identifying and detecting these changes. The purpose of this study is to detect land use changes in Kamyaran city using satellite images over a period of 35 years. In this study, data from 1984 TM sensor, 2000 ETM + sensor and 2019 Landsat OLI sensor were used. Initially, preliminary preprocessions including radiometric, atmospheric and geometric corrections were performed on the raw data. Land control points were used for training, accreditation and to prepare land use map. Land use class was determined by field survey and using Google Earth images in 9 land use classes of agricultural lands, forests, gardens, rich and wooded pastures, medium rangelands, residential areas, water area, barren lands and rock outcrops. Next, the neural network method was used to monitor the images in ENVI 5.3 software. The evaluation results showed that the overall accuracy and kappa coefficient of OLI classified images are 94.3 and 0.92%, ETM + 92.6 and 0.91% and TM 90.3 and 0.87%, respectively. The results showed that forest lands and rich and wooded pastures decreased significantly during three time periods, which decreased by 11.64 and 19.12 percent, respectively. So that rich and wooded pastures have an increasing trend until 2000 and in the next period until 2019 has a decreasing trend. Residential lands, water areas and gardens increased by 2.27%, 0.57% and 3.98%, respectively. Due to the growing trend of population and urbanization, the results of this study provide the necessary information to make basic decisions in the development of management policies for planners and regional managers for the sustainability and evaluation of natural resources. Manuscript profile -
Open Access Article
20 - Comparison of three visual, object-based, and supervised classification methods of land use/cover mapping in Mollah-Ahmad Watershed, Ardabil
Azad Kakehmami Ardavan GhorbaniDue to the growing population and consequently the degradation and uncontrolled use of land, awareness of the state of the land and its use is necessary. In this study, the main purpose was land use /cover mapping of the Mollah Ahmad Watershed. Two types of Google Earth MoreDue to the growing population and consequently the degradation and uncontrolled use of land, awareness of the state of the land and its use is necessary. In this study, the main purpose was land use /cover mapping of the Mollah Ahmad Watershed. Two types of Google Earth images (Quickbird images, 2013) and Operational Land Imager (OLI) sensor of Landsat 8 image (2014) with three methods of visual interpretation (Google Earth images), object-based classification (Google Earth images), and supervised classification (Landsat 8) were used. In order to evaluate the land use /cover map produced from the methods, 51 points were selected as control points for the accuracy assessment. The results showed that the overall accuracy of the map generated from visual interpretation, object-based classification, and supervised classification were 100, 90, and 72 percent, respectively and their kappa coefficients were 1, 0.85 and 0.6, respectively, which the high accuracy of the maps generated from two methods of visual and object-based interpretation. The method of interpreting the visually using the high-resolution images (Quickbird with 0.65 to 2.9 m resolutions) of Google Earth is the most accurate method; however, the object-based classification method due to its low cost in terms of time in large environment has relatively acceptable accuracy. Manuscript profile -
Open Access Article
21 - Study on land-use changes using GIS and RS techniques and . economic evaluationcompared to soil loss changes. Case study: Azad dam watershed
maarof emamgholi kaka shahedi mohamad hosein farhodi kheh bat khosraviThis study was carried out for assess changes in land-cover and land-use from 1987 to 2006 in the Azad dam watershed of Kurdistan province. For this purpose, initially using Landsat satellite images, percent of vegetation cover map was provided then by correction geomet MoreThis study was carried out for assess changes in land-cover and land-use from 1987 to 2006 in the Azad dam watershed of Kurdistan province. For this purpose, initially using Landsat satellite images, percent of vegetation cover map was provided then by correction geometric, radiometric and the season difference in imaging, the best band combination was selected and land-use maps was prepare using Maximum Similarity Likelihood algorithm and supervised classification. The overall accuracy test used to determine the accuracy of produced map. The result showed that, the area of irrigated land from 14.33% to 13.70%, dry land from 15.43% to 26.63% and poor rangelands from 24.37% to 42.17% have increased but, the average rangelands from 28.57% to 14.83% and good rangelands with shrub cover from 17.30% to 2.64% have been reduced. Also, classification accuracy in irrigation land, dry land, poor and average and good rangelands were determined 66, 74, 82, 76 and 84 percent respectively. Subsequently, the amount of soil loss and sediment yield using EPM model have been estimated that in 1987 this amount was 8.7 m3/ha/y and to 10.2 m3/ha/y for 2006 was increased. Finally, obtained results of economic estimation and occurred soil loss showed that stakeholders were damaged to 10 billion rails in the study area Manuscript profile -
Open Access Article
22 - Land use mapping of Kaftareh Watershed of Ardabil using visual and digital processing of ETM+ image
ardavan ghorbani farnoosh aslami saeed ahmadabadi sahar ghaffariAbstract The availability of land use information permits decision-makers to develop plans in short to long-term period for the conservation, sustainable use and development of natural resources and watersheds. In this study, ETM+ image (2006), GPS and GIS were used fo MoreAbstract The availability of land use information permits decision-makers to develop plans in short to long-term period for the conservation, sustainable use and development of natural resources and watersheds. In this study, ETM+ image (2006), GPS and GIS were used for image interpretation, field data collection and land use mapping. Preprocessing and required correction have conducted. Initially, field visit have been conducted and different land uses have been defined. In the second step, image was visually interpreted and then training area has selected and using the maximum likelihood algorithm image was classified. According to the lack of the capability for detecting river beds and residential areas in digital image processing, integration of visual and digital interpretation (supervised classification) and object-based image analysis were used. Results show that, in visual interpretation, there is almost no capability to discriminate rangeland from dry farming land uses; however garden, residential areas and riverbeds are discriminated. Results of supervised classification show that there are problems to detect and discriminate different land uses; however, by integration of digital and visual interpretation, it is possible to use Landsat data to discriminate different land uses in the areas such as Kaftareh watersheds and Arshagh region of Ardabil province. The results of the evaluation of object-based classification accuracy showed the highest overall accuracy, because the method parameters such as scale, shape, tone and texture, in addition to using pixel values were used in classification, hence with appropriate segment creation, there is the possibility of precise discrimination of land uses such as residential areas from dryland farming. In the future studies, according to the importance of land use map in the studies such as natural resources, watershed managements and agriculture, it is better to use high spatial imagery and object-based methods. Manuscript profile -
Open Access Article
23 - Evaluation of Land Cover Changes Remote Sensing Technique (Case Study: Hableh Rood Subwatershed of Shahrabad Basin)
Khadijeh Abolfathi Marzieh Alikhah-Asl Mohammad Rezvani Mohammad NamdarThe growing population and increasing socio-economic necessitiescreates a pressure on land use/land cover. Nowadays, land use change detection using remote sensing data provides quantitative and timely information for management and evaluation of natural resources. This MoreThe growing population and increasing socio-economic necessitiescreates a pressure on land use/land cover. Nowadays, land use change detection using remote sensing data provides quantitative and timely information for management and evaluation of natural resources. This study investigates the land use changes in part of Hableh Rood Watershed of Iran using Landsat 7 and 8 (Sensor ETM+ and OLI) images between 2001 and 2013. Supervised classification was used for classification of Landsat images. Four land use classes were delineated including rangeland, irrigated farming and plantations land, and dry farming lands,urban. Visual interpretation, expert knowledge of the study area and ground truth information accumulated with field works to assess the accuracy of the classification results. Overall accuracy of 2001 and 2013 image classification was 81.48 (Kappa coefficient: 0.7340) and 87.04 (Kappa coefficient: 0.7841), respectively. The results showed considerable land cover changes for the given study area. Land cover change detection showed that in a period of 12 years, 277.57 hectaresof dry farming lands and 340 hectares of dense range have been lost. But, 341 hectares for low dense range, 280 hectares for semi dense range and 1.4 hectares for urban areas, have been added in area. Manuscript profile -
Open Access Article
24 - Parallel Shared Hidden Layers Auto-encoder as a Cross-Corpus Transfer Learning Approach for Unsupervised Persian Speech Emotion Recognition
Yousef Pourebrahim Farbod Razzazi Hossein Sameti
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