List of Articles satellite data

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  1 - Change prediction of Karoon river lengths by using historical and quantitative geomorphologic data (From Shoshtar to Arvandrod)
  Jafar Morshedi Seyed Kazam Alavi panah
  The study area is a part of Karoon river located in Khuzestan province in southwestof Iran. The length of this reach is about 364 km from the north of Shoshtar to theArvandrod. The changes and local difference on the river reaches consider togeological, tectonicaly, hyd More

  The study area is a part of Karoon river located in Khuzestan province in southwestof Iran. The length of this reach is about 364 km from the north of Shoshtar to theArvandrod. The changes and local difference on the river reaches consider togeological, tectonicaly, hydrological and artificial parameter in the dry flood plain ofKhuzestan has caused some damages, risks and hazards during the time. By recognizeof fluvial environment of Karoon River and determining the changes of the river,control of these hazards is possible. Because of morphometric characteristics study ofKaroon River, for changes prediction, with use of satellite images of IRS and land satin the years of 1991 and 2007, channel length of the river has drawn, measured andanalyzed by GIS software. so total length of Karoon consider to the number of theircurves(100 curve) divided to smaller limits and crossing point selected as upper andlower limits of each curves. Then geometric parameter of channel like radius ofcurvature, mean central point of each curve, curve direction and annual rate of channelmigration measured. The results show that the most risks belong to meanderingreaches. Therefore the land use and sensitive area of river to erosion spatially oncurves if dose not controlled. There are a lot of area like farms, roads, settlement,national fields and other mankind Struthers that will be destroyed. Manuscript profile
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  2 - Using Sentinel-2 satellite image data and ground data to surveying and mapping poplar plantation of Tehran province
  fatemeh Ahmadloo khosro Mirakhorlou Mohsen Calagari Azadeh Salehi
  10.30495/jest.2021.52793.5074
  Background & Objective: The Lack of timely, documentary and scientific information from the current status (level and distribution) of poplar plantation of Tehran province is one of the main problems facing the managers of the wood production sector in the planning More

  Background & Objective: The Lack of timely, documentary and scientific information from the current status (level and distribution) of poplar plantation of Tehran province is one of the main problems facing the managers of the wood production sector in the planning and management of wood supply in the province and the country. Preparing a map and determining the areas of poplar plantation and their distribution in Tehran province are the objectives of this study to monitor and evaluate changes of poplar plantation area in short-term periods.Material and Methodology: The present study was conducted from April 2018 to March 2020 for 2 years in the whole of Tehran province. In this study, multi-temporal data, from the beginning to the end of the poplar growing season (second half of March to December 2018), at least 6 time periods of 30 to 40 days were used from Sentinel-2 satellite image. Then, 355 poplar plantation fields with uniformly distribution in the province were taken as a training sample for use in the SVM classifier. Post-test and calibration of SVM model based on the phenology of poplar genus and harvested field samples, poplar plantation distribution map of province was extracted.Findings: The results showed that the total area of poplar plantation of Tehran province is 511.1 ha which covers 0.04% of the total area of province. One percent of the total poplar plantation fields were randomly selected for field control and after that, the overall mapping error obtained was calculated. In this study, the exact location and area of current poplar plantations were estimated with acceptable accuracy (96.7%). The highest level of poplar plantations was obtained in Damavand (196.8 ha), and the lowest in Varamin (0.22 ha).Discussion and Conclusions: Using the resulting information (distribution map and mapping poplar plantation of province), can be initiated in studies on cultivation planning and development of wood farming for the present and future of the province. Manuscript profile
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  3 - Identification of Satellite Image Ability for Vegetation Cover Crown Percentage Mapping in Arid and Semi Arid Region (Case study: Mouteh wild life sanctuary)
  Vahid Rahdari Alireza Soffianian Seyed Jamalaldin Khajaldin Saedeh Maleki Najfabdai
  IntroductionRemote sensing provides the basic data to undertake inventory of land resources specially vegetationmapping.Material and MethodsIn this study for producing vegetation cover percentage map in Mouteh wild life sanctuary, IRS-P6,LISS III data was used for June More

  IntroductionRemote sensing provides the basic data to undertake inventory of land resources specially vegetationmapping.Material and MethodsIn this study for producing vegetation cover percentage map in Mouteh wild life sanctuary, IRS-P6,LISS III data was used for June 2006. First geometric and atmospheric correction was done.Vegetation cover sampling was done with 290 plots in heterogeneous cover areas and Data werecollected from overall region. Vegetation Indices were produced using satellite image. Simple linearregression was done between plots information and vegetation indices and models were produced foreach index and vegetation maps were produced using each index model.ResultsResults showed that SAVI index had highest correlation with field sampling equal 0.78 and it wasused for vegetation cover percentage mapping. Using SAVI model vegetation cover was classified infour classes: 0-10%, 10-20%, 20-40% and 40%<.Disscusion And ConclusionsResults showed that 10%> and 10-20% crown cover were dominate in region. SAVI index with soilcoefficient reduced soil background reflectance effects. In this study NDVI, TSAVI1 and RVI hadhigh correlation (0.77, 0.78 and 0.76). Manuscript profile
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  4 - Land use/cover mapping usig satellite data and geographic information system (GIS) (Case study: Mouteh wild life sanctuary)
  Vahid Rahdary Alireza Soffianian Saeideh Maleki Najfabdai Seyed Jamaleddin Khajeddin Meysam Rahdari
  Introduction: Nowadays remote sensing and geographic information systems (GIS) are excellent tools to use in land use and land cover mapping. Identification land use /cover arrangement can help to proper land management. Material and Methods:In this study for Mouteh wil More

  Introduction: Nowadays remote sensing and geographic information systems (GIS) are excellent tools to use in land use and land cover mapping. Identification land use /cover arrangement can help to proper land management. Material and Methods:In this study for Mouteh wild life sanctuary’s land use land cover mapping IRS-P6, LISS III data which is planned at the same time with field sampling was taken in jun 2006were used. After preparing the satellite data, the geometric correction was applied to an image with the 0.65 mean square error. In the next step, due to being the mountainous zone topographic correction was performed on the image.Finely land use/cover maps were produced by using combinatorial classification method. Vegetation cover percentage map was prepared by using SAVI index and field sampling. Each land use/cover map was produced using several image processing. Using GIS technique Land use/cover layer combined together and land use/cover map was produced. In order to thematic accuracy assess Kappa coefficient and total accuracy were calculated respectively equal: 0/92 and 0/94 that shown proper image classification. Results: Study result show that vegetation cover with 0-10% crown has highest area in region with 81690(ha) either mining residential area was respectively 828 and 249(ha). Discussion And Conclusions:Result shown that hybrid classification method has high ability for land use/cover mapping especially when land use/cover have similar reflectance that common classification methods such as supervise and unsupervised classification can not produce proper maps.  Manuscript profile
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  5 - Determining the optimal method for classification and mapping of land use/land cover through comparison of artificial neural network and support vector machine algorithms using satellite data (Case study: International Hamoun wetland)
  amir houshang ehsani Mojtaba Shakeryari
  10.22034/jest.2019.13711
  Background and Objective: Images classification is one of the important techniques for interpretation of satellite images that is widely used in survey of earth changes. In the meantime, satellite data has been recognized as the best tool for detection and evaluation of More

  Background and Objective: Images classification is one of the important techniques for interpretation of satellite images that is widely used in survey of earth changes. In the meantime, satellite data has been recognized as the best tool for detection and evaluation of changes due to its update information, low costs and variety of forms. Therefore, land use/land cover map is one of the most important information required by the environmental managers and planners. On the other hand, in recent years, artificial neural network method has been used widely for the classification of satellite data. The aim of this study is to compare three different methods for land cover classification using 2014 OLI image over a 26-year period. Method: In this study, digital data of OLI (2014) sensor was used in order to optimize image classification method. Initially, the image was corrected in terms of geometry and radiometry in the ENVI software. Then IDRISI software was used for image classification using three different methods: fuzzy artmap, multilayer perceptron artificial neural networks and support vector machine. Finally, land cover maps were classified into five categories: water, vegetation, canebrake, barren lands and saline lands. To evaluate accuracy with the help of user accuracy, producer accuracy, overall accuracy, kappa coefficient and error matrix, the created map was compared with the ground reality map created by GPS, Google Earth images and field observations. Discussion and Conclusion: The results of image accuracy evaluation showed that among the applied methods the fuzzy artmap algorithm had the highest accuracy in classification of satellite data with an overall accuracy of 94.68 and kappa coefficient of 0.91 compared to both multilayer perceptron artificial algorithm with an overall accuracy of 92.99 and kappa coefficient of 0.89 and support vector machine with an overall accuracy of 90.93 and kappa coefficient of 0.85. This study showed that classification of fuzzy artmap artificial neural network algorithm has a high capability to create the land cover map with high accuracy. Manuscript profile
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  6 - Evaluation of RapidEye satellite data for estimation some quantitative structure variables in the Caspian forests of Gorgan region
  Noureddin Noorian Shaban Shataee Jahangir Mohamadi
  Estimation of quantitative forest attributes is important for its applications in order to understand the forest condition and performance. The aim of this study was the estimation of some quantitative forest attributes (stand volume, basal area, and tree stem density) More

  Estimation of quantitative forest attributes is important for its applications in order to understand the forest condition and performance. The aim of this study was the estimation of some quantitative forest attributes (stand volume, basal area, and tree stem density) using the RapidEye satellite data (2011) and non-parametric algorithms in the part of Hyrcanian forests in the Gorgan region. For this purpose, 418 plots each with an area of 1000m2 were established using a simple random sampling method. In each plot, information including a position of plot center, diameter at breast height of all trees and height of selected trees were recorded. Based on which the standing volume and basal area per ha were derived. A RapidEye image was processed by different synthetic bands derived from rationing, principal component analysis, texture analysis, and Tasseledcap, and the pixel gray values corresponding to the ground samples were extracted from spectral bands. These were further considered as the independent variables to predict the Quantitative characteristics. Modeling was carried out based on 75% of sample plots as training set using K-Nearest Neighbor, support vector machine, and random forest methods. The predictions were cross-validated using the left-out 25% samples. The results showed Random forest comparatively returned the best estimates for stand volume, basal area and tree stem density with root mean square error of 39.83%, 29.71%, and 50.11% and relative bias of 0.01, 1.69 and 2.11 as well, respectively. The results of this study also showed that due to the heterogeneity and density of Caspian forests, RapidEye satellite spectral data have a moderate ability to estimate the quantitative forest attributes. Manuscript profile
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  7 - Rock Units Erosion Susceptibility Detection and Classification Using Nonlinear Correlation Analysis and Landsat ETM+ Data
  Ahmad Mokhtari Kourosh Shirani Farzad Heidari
  the lithological maps is inevitable in the preparation of rock unit’s erosion susceptibility maps. In this study, rock unit outcrops in the Soh Basin (50 km Northern Isfahan) were extracted using nonlinear correlation analysis of satellite data. Moreover, rock uni More

  the lithological maps is inevitable in the preparation of rock unit’s erosion susceptibility maps. In this study, rock unit outcrops in the Soh Basin (50 km Northern Isfahan) were extracted using nonlinear correlation analysis of satellite data. Moreover, rock unit’s erosion susceptibility such as marl, shale, and quaternary deposits and resistant rock units such as sandstone and limestone were extracted based on soil erosion intensity factors. The lithology of the basin was studied usingthe virtual variables method. Initially, rock units, as a virtual independent variable, and the PC1 (the first principal component) of ETM+ multispectral bands were by amultiple linear regression model. Afterward, rock units were in logistic regression analysis as virtual dependent variables. The results revealed that logistic regression analysis is a suitable model for rock unit’s extraction.           Manuscript profile
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  8 - 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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  9 - Study of Sea Surface Temperature (SST) & wind speed over coastal area of Hormozgan Province by satellite data
  M. Torabi Azad A. Mohammadi
  Interaction between sea and air is an important factor in controlling seasonal climatological variations in each environment. In this research, relationship between Sea Surface Temperature (SST) and wind speed over coastal area of Hormozgan province has been studied. In More

  Interaction between sea and air is an important factor in controlling seasonal climatological variations in each environment. In this research, relationship between Sea Surface Temperature (SST) and wind speed over coastal area of Hormozgan province has been studied. Initially the SST data of the area were collected using AVHRR sensor of NOAA satellite and the wind speed data were collected from QuikSCAT satellite. After analyzing the satellite data for the SST and wind speed, monthly, seasonal and annual variations of these data were studied. The significance of their variations were evaluated during years 1985-2008, using a long term study group of each variable as a control and then comparison was made using Duncan test. As the increasing trend of mean annual surface temperature and the decreasing trend in wind speed was significant, correlation coefficient between SST and wind speed was obtained. After plotting the time against surface temperature during the study period, it was observed that the minimum temperature was 21.11 ˚C in 2008 and the maximum was 33.12 ˚C in 2002. It was shown that there was 4.2 ˚C temperature difference between western and eastern region of the sea coast in summer and the temperature gradient reaches 3 ˚C in winter. It can be concluded that for summer 75% and for winter 33% increase (decrease) of the mean sea surface temperature takes place and the deviation of mean wind speed will be increase (decrease).                                         Manuscript profile