Ata Amini; mehdi Karami Moghadam; Mohammad Hossein Sedri; Somayyeh Kazemi
Abstract
In recent years, with the change of use and development of agricultural lands in the country's basins, the rate of erosion and sediment production has increased. Given that in most sub-basins, the long term data of sedimentation stations have not been recorded, it is difficult to estimate the amount ...
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In recent years, with the change of use and development of agricultural lands in the country's basins, the rate of erosion and sediment production has increased. Given that in most sub-basins, the long term data of sedimentation stations have not been recorded, it is difficult to estimate the amount of sedimentation and erosion. The objectives of this study was to determine the factors influencing erosion and sedimentation and to determine the quantitative values of erosion in the Khorkhoreh watershed, Kurdistan, Iran. For this purpose, first, using topographic maps, geology and aerial photographs in GIS environment, type and shape maps of erosion were prepared and evaluated by field survey. Based on the MPSIAC model, the nine factors influencing erosion for all sub-basins were identified separately and the scores of each factor were determined. By summing the factors, the degree of sedimentation for each sub-basin was determined and the amount of sedimentation and special erosion and total erosion in each sub-basin were calculated. The results showed that the topographic factors and the current state of erosion have the most role and the weather factor has the least role in the sedimentation rate of the basin.Moreover, 92% of the total basin has a high degree of sedimentation in the fourth order erosion class. The amount of Sediment Delivery Ratio of the basin (SDR) varies between 32 and 50 percent. The lowest and highest specific erosion rates in different sub-basins were 10 and 35 ton/ha.yr, respectively. Also, the amount of special sediment and special erosion of the whole basin was 6.4 and 17.4 ton/ha.yr, respectively.
Volume 6, Issue 1 , April 2014
Abstract
Field spectrometry as a field of remote sensing, dealing with determination of spectral characteristics tries to provide the spectral libraries for different objects. The first objective of this study was to prepare and investigate the significant differences between the spectral signature of water samples ...
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Field spectrometry as a field of remote sensing, dealing with determination of spectral characteristics tries to provide the spectral libraries for different objects. The first objective of this study was to prepare and investigate the significant differences between the spectral signature of water samples with different amount of chlorophyll a (Chl a) of Anzali wetland in 15 cm depth. This was carried out using a full range spectrometer during the spring 2013. The second objective of this study was to discriminate the spectral signature of water samples with different amount of chlorophyll a (Chl a) of Anzali wetland in 30 cm depth. A total of 500 water sample spectral curves of illuminated and shaded samples were acquired of 80 water samples with different amount of chlorophyll between 2.07 and 23.9 (mg/lit. Following the measurements, chlorophyll and total phosphorus of the samples were extracted in laboratory. After quality control and noise remove, the spectral fingerprint of the samples was prepared along 400-900 nm. In order to investigate the spectral reflectance differences, one important index related to chlorophyll a of water were calculated and statistically analyzed. We conclude that three band model in 15 cm depth of water samples has the most relation (r=0.963) with chlorophyll a content. This result has been proved by statistical results obtained by chlorophyll and total phosphorus data in lab. We could conclude that the best wavelength region for spectral separately of eutrophication of turbid water is depend on different factors such as depth of water and amount of sediments of water.
Volume 6, Issue 2 , August 2014
Abstract
Morphology analysis which concentrates on spatial relations analysis between neighborhood pixels provides a better image processing compared to analyses which are only based on spectral signature of a single pixel. The proposed method in this paper integrates spectral and spatial information produced ...
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Morphology analysis which concentrates on spatial relations analysis between neighborhood pixels provides a better image processing compared to analyses which are only based on spectral signature of a single pixel. The proposed method in this paper integrates spectral and spatial information produced from morphology analysis to improve the final result of hyper spectral image classification. For this reason at first, primary components are extracted using limited training samples. Extended morphological profiles are then produced by applying morphological analysis on each extracted features. Afterwards, Final components are extracted by applying a supervised feature selections on a datasets composed of both the spectral and the extended morphological features. The extracted features are introduced into the Support Vector Machine (SVM) algorithm. The final results are then archived by implementing a majority filter as a post-processing step. The proposed method is implemented on aerial hyper spectral images of Rosis sensor taken from urban and semi-urban areas from. The obtained results proved the efficiency of the proposed method where classification accuracies are improved from 98.86 and 82.70 in conventional method to 99.36 and 95.75 in urban and semi-urban areas respectively. Keywords: Morphological Analysis, Support Vector Machines (SVMS), Feature Extraction (FE), Classification, Majority Vote
Volume 6, Issue 4 , October 2014
Abstract
The importance of groundwater as a source of water supply in arid, semi –arid areas in recent years due to the uncontrolled exploitation of groundwater have been doubled. Uncontrolled exploitation has been caused a drop in the water table in many areas of the country, including this region. With replace ...
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The importance of groundwater as a source of water supply in arid, semi –arid areas in recent years due to the uncontrolled exploitation of groundwater have been doubled. Uncontrolled exploitation has been caused a drop in the water table in many areas of the country, including this region. With replace methods of modern irrigation instead of traditional irrigation methods can prevent drop the water table and also desertification. Water quality, is one of the major limitations of pressurized irrigation in arid and semi-arid areas. The aim of this research to identify the potential pressurized irrigation in areas of plain of Khaled -Abad with using the techniques Geostatistics and GIS. For the zonation of plain water quality used of quality parameters of 11 piezometric well. In order to zoning map of the quality of geostatistics method IDW and Kriging, selected Kriging method, because it has lower RMSE and MAE. Then by overlay layers of water quality based on Boolean logic, selected areas with good water quality for applying pressurized irrigation systems. The results showed that 21.9% of the area suitable for sprinkler irrigation and 54.4% of the area suitable for drip irrigation. Piezometers used for drip irrigation have been in broader range. Therefore, a large part of the study area executable for Drip irrigation system in compared to Sprinkler irrigation and also efficiency is higher. Keywords: GIS, Water quality, Pressurized irrigation, Locating, Badrood.
Volume 6, Issue 3 , October 2014
Abstract
Remote sensing can be used as a powerful tool by using data from different sources and combine them for vegetation and land cover classification. Pasture type classification provides key information for analysis of agricultural productivity, carbon accounting and biodiversity.The firstdata set thatused ...
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Remote sensing can be used as a powerful tool by using data from different sources and combine them for vegetation and land cover classification. Pasture type classification provides key information for analysis of agricultural productivity, carbon accounting and biodiversity.The firstdata set thatused in thisstudyLandsatTM (Thematic Mapper)optical image and the second ENVISAT ASAR radar image for the study area located within the North-West of Tehran (South Alborz). In this study after applying several methods which all of them are non-lambertian and regarding to evaluate them, topographic correction was performed for optical image. The usefulness and improvement of using texture features extracted from optical and radar images in integration with spectral bands of the optical image has been evaluated on the final classification results and genetic algorithm used to select features that are independent to derive the most accurate results. In another part of the study, the impact of elevation data and optical image vegetation indices evaluated on final classification result and optimal bands selected. The results indicate increase in the overall accuracy and maximum likelihood Kappa coefficientfrom 77.04 and 0.7317 for original optical image to 78.71 and 0.7495 incaseof usinggenetic algorithm and 83.37 and 0.8036 incaseof usingelevation data and vegetation indices. Keywords:Image Fusion, Pasture Classification, Topographic Correction, Image Texture, Remote Sensing.
Z Fazli,; M.R Delavar,; M.R Malek,
Volume 9, Issue 1 , October 2017, , Pages 65-92
Abstract
Urban property survey is one the of municipality activities for development and updating of the spatial database of the urban properties. With the emergence of mobile geospatial information system, new methods were developed to collect and update the location information. In mobile environment the calculations ...
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Urban property survey is one the of municipality activities for development and updating of the spatial database of the urban properties. With the emergence of mobile geospatial information system, new methods were developed to collect and update the location information. In mobile environment the calculations depend on to tasks undertaken and user dynamic environment. Reduction of user direct interaction with the system is one of the most important factors for system automation and leads to reduction of the human errors in collection and updating information. This can be done by understanding user situation using context information so that suitable maps and attribute information can be provided to the user at right place. Urban property survey information is a sample of spatial information that can benefit from such systems.The objective of this paper is to identify the role of context-aware mobile GIS in urban property survey, standardization and optimization of urban property survey process and identification of the implementation methods to present suitable spatial information relevant to the user’s context and also intelligent user interfaces for enhancement of the system capabilities. In addition to the client-server architecture, a stand-alone architecture is used in the system design and implementation for this research to prevent the survey process failure in case of disconnecting from server. Finally, Tehran urban blocks are used to test the system and the obtained results compared to the results of two former municipality urban property surveys. The results indicate their required time for data collection in the proposed method compared to the two Tehran urban property survey periods has been reduced to 50% and the time between spatial and attribute data collection and their upload to municipality database reduced nearly 100%. By using this system, direct interaction of the surveyor and the system application is reduced and it has helped to upgrade automation in data collection and updating which results in enormous improvements in urban property survey process. By using the proposed method, the block and property data based on paper maps in traditional urban property survey, have been directly corrected and updated in field.
Saba Kharyaband; S Attarchi
Abstract
In recent decades in Iran, due to the effect of climate change and population growth, the extent and depth of water in wetlands have been largely decreased. Therefore, it is worth finding the main reasons of the changes and if possible to reduce the rate of changes. Great advances in remote sensing technology ...
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In recent decades in Iran, due to the effect of climate change and population growth, the extent and depth of water in wetlands have been largely decreased. Therefore, it is worth finding the main reasons of the changes and if possible to reduce the rate of changes. Great advances in remote sensing technology offer valuable opportunities to monitor the trend of changes in natural environment. Landsat satellites from 1970s has the largest archive of remote sensing images. Remote sensing images provide data in wide area with high temporal resolution and low cost. Anzali Wetland is one of the most important international wetlands of Iran which has been registered in Ramsar Convention. In recent decades, population growth and expansion of cities and farm lands near Anzali wetland, climatic changes in this region and also changes in Caspian Sea’s water level threaten this wetland. The present study investigates wetland depth changes using Landsat imagery. Furthermore the depth changes have been thoroughly explained concerning rain fall, temperature and the Caspian Sea’s water level changes over a 30-year period from 1988 to 2018. Our Findings emphasizes that the depth of water in this wetland is more related to the changes of the Caspian Sea’s water level and the rainfall and temperature are not the main reason of decreasing of the wetland’s depth.
K Borna; F Fathi
Volume 10, Issue 2 , September 2018, , Pages 75-94
Abstract
Repairing incorrect polygons for use in GIS software is semi-automated and time-consuming.Automatic polygon repair, interpretation of obscure polygons, and elimination of all existing bugsbased on definitions and global standards that have many uses in software related to GIS. Due to thecomplexity of ...
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Repairing incorrect polygons for use in GIS software is semi-automated and time-consuming.Automatic polygon repair, interpretation of obscure polygons, and elimination of all existing bugsbased on definitions and global standards that have many uses in software related to GIS. Due to thecomplexity of the computation and data volumes in working with big data, there is always acompetition between the speed and the amount of memory used. In this paper, while introducing thestandard of the characteristics of simple complications in polygons, using Delaunay Triangulation andGTS functions in Java and with the help of the H2 database, a method is presented that receivespolygons in the form of a file in the CSV format and applies several effective algorithms toautomatically repair them. The polygons in the spatial data set are automated at optimal time and withminimal memory consumption and are repaired if necessary. The results show that this method,compared with the previous ones, our method leads to relative improvement in execution speed andprovides more than 50 percent saving (in average) in the main memory while working with big data.
Mehran Shaygan; Marzieh Mokarram
Abstract
Due to the fact that droughts can affect both water quality and quantity, the purpose of this study is to determine the effect of droughts on water quality and quantity in Northern Fars province, Iran, based on drought indicators. The drought indices PCI, TVDI, and NDVI are used to study drought from ...
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Due to the fact that droughts can affect both water quality and quantity, the purpose of this study is to determine the effect of droughts on water quality and quantity in Northern Fars province, Iran, based on drought indicators. The drought indices PCI, TVDI, and NDVI are used to study drought from 2000 to 2020. Also, the kriging method is used to generate zoning maps of elements in water (Ca, Cl, EC, K, Na, Mg). Then, using the neural network (MLP) method, the amount of elements in the water is predicted based on drought indices. Based on the values of the drought indicators, the trend of drought changes in the region is increasing from 2000 to 2020, with the southern areas of the region experiencing a more acute drought than the rest of the region. In addition, the zoning map of the elements in water indicated that salt concentrations are higher in the southern parts than in the northern parts. Correlation between drought indices and the amounts of elements in water showed that Ca has a high correlation (R2= 0.820) with TVDI index, and also Cl, EC, K, Na, and Mg have significant correlations (R > 0.8) with the index. Using drought indicators, MLP results for predicting water quality status show that southern regions have more solutes and lower water quality. Furthermore, the R2 values of the model for predicting the elements Cl, EC, K, Na, Mg, TDS, TH using PCI index equal to 0.85 and for Ca using TVDI index equal to 0.71, which indicates high accuracy.
Hossein Sadeghi; Ali Hoseinypoor; Rouzbeh Shad
Volume 7, Issue 3 , November 2015, , Pages 83-96
Abstract
The change detection using satellite images is one of the main fields of remote sensing researches. Numerous errors in the images are one problem with the use of satellite imagery. Errors caused by surface illumination, are the main problems in the process of change detection. Therefore, in this study, ...
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The change detection using satellite images is one of the main fields of remote sensing researches. Numerous errors in the images are one problem with the use of satellite imagery. Errors caused by surface illumination, are the main problems in the process of change detection. Therefore, in this study, in order to reduce errors in the results of the detection of changes using change vector analysis, a simple, yet effective method to reduce errors caused by topography is proposed. After applying Tasseled Cap Transform on images, the proposed method compare the angle between direction of change vector with the direction of vector of the pixel in the base image using spectral space, and then calculates the angular threshold. The area under the ROC curve, the Probability Detection and False alarm of proposed method are 0.970, 0.97 and 0.32 respectively.
Mohammad Saadat; Reza Shahhoseini
Abstract
Preparation of proper land use maps has always been one of the important goals of researchers and policymakers. The aim of this study was to provide a new method for preparing land use maps using remotely sensed data and satellite data imagery. For this Purpose, we used Landsat 8 data, Digital Elevation ...
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Preparation of proper land use maps has always been one of the important goals of researchers and policymakers. The aim of this study was to provide a new method for preparing land use maps using remotely sensed data and satellite data imagery. For this Purpose, we used Landsat 8 data, Digital Elevation Model (DEM), Principal Component Analysis (PCA), and Spectral Indices to extract land use map in the study area. After all required preprocessing, the training samples were provided. In this study, the training samples were utilized in two parts; in the first part they were used as inputs for image classification using supervised algorithms of maximum likelihood Classification (MLC) and support vector machine (SVM). In the second part, in order to applying Decision Tree Classification (DTC), these training samples were used to determine the spectral reflection of each end-member in the spectrum of electromagnetic waves (image bands, PCA, spectral indices, and DEM).Then, using these binary data and DTC, each end-member was identified and the Landuse/Landcover (LULC) map was extracted. In order to combine the classification results and achieve higher accuracy, the Majority Vote Classification (MVC) method was applied to prepare a new compilation of land use in the area. In order to evaluate the accuracy of produced maps, the statistical parameters extracted from the confusion matrix including overall accuracy, kappa coefficient, user and producer’s accuracy were utilized. According to the results, the combined method (MVC) with a total accuracy of 93.37% and kappa coefficient of 0.91 had the highest accuracy. The overall accuracy of the DTC, SVM, and MLC were 89.61, 88.01 and 87.6%, respectively. Due to the fact that in the nature most of the landuse are mixed and complicated, it would be better to use new methods that cover all aspects of the phenomena. In this research, the data extracted from the supervised classifications as well as the data derived from the DTC were combined and the results clearly illustrate the improvement of the final accuracy of the classification.
M Rajabpour Rahmati; A.A Darvishsefat; N Baghdadi; Manochehr Namiranian; Nosrat ollah Zargham
Volume 7, Issue 4 , November 2015, , Pages 85-98
Abstract
Forest volume as an important factor in forest management was aimed to be measured in mountainous forests in the North of Iran using spaceborne LiDar. Two missions of GLAS (L3K and L3I) were preprocessed to remove inappropriate waveforms. Several waveform metrics including waveform extent (Wext), lead ...
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Forest volume as an important factor in forest management was aimed to be measured in mountainous forests in the North of Iran using spaceborne LiDar. Two missions of GLAS (L3K and L3I) were preprocessed to remove inappropriate waveforms. Several waveform metrics including waveform extent (Wext), lead edge extent (Hlead), trail edge extent (Htrail) and quartile heights (H25, H50, H75 and H100) were extracted. Principal component analysis (PCA) was also applied to reduce noises from waveform signals and produce new components (PCs). In order to decrease the effect of terrain slope on waveforms, terrain index (TI) describing topographic information was extracted from a digital elevation model (DEM). Forest stand volume was measured on 60 circle plots with diameter of 70 m for developing volume models and their validation. Multiple regression and artificial neural network models were built based on two sets of variables including waveform metrics and PCs. Generally, both multiple regression and neural network methods produced approximately the same result. A neural network combining three first PCs of PCA and Wext estimated forest volume with an RMSE and of 119.9 m and 0.73, respectively (RMSE%=26.6). Interesting points regards to this model is employing PCs and Wext as input variables which are not affected by terrain slope, achieving slightly better accuracy without adding any ancillary data (DEM), and showing better performance in short sparse stands in comparison with other developed models.
karim solaimani; Fatemeh Ruhani; Morteza Shabai; Mohsen Rohani
Abstract
The increase in population and the development of urbanization and, consequently, diforested areas have caused an increase in the surface temperature in urban areas, which results in an urban heat island. The heat islands of the city is one of the factors that has become important at the same time with ...
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The increase in population and the development of urbanization and, consequently, diforested areas have caused an increase in the surface temperature in urban areas, which results in an urban heat island. The heat islands of the city is one of the factors that has become important at the same time with the development of the city and today it can be calculated and evaluated using satellite images. The objectives of this study are to evaluate the points of temperature changes, land use, vegetation, traffic and soil types relationship with surface temperature in Sari and the trend of its spatial changes during the two time periods of 1988 and 2018. For this purpose, TIRS and Landsat 5 and 8 TM images in a period of 30 years (1988-2018) were used to study the heat island changes and calculate the surface temperature with a single-channel algorithm. The results showed that during a period of 30 years with a decrease of 235.3 hectares of green space and a 34% increase in land occupation in Sari, the area of heat islands increased by 21.83%. Also, considering the value of P-value less than 0.05, it showed that there is a significant relationship between vegetation index and city occupation level with land surface temperature and it can be argued that land use change, vegetation and traffic due to population growth and land use change is one of the main factors in increasing spatial changes in the heat islands of Sari.
Parinaz Ahmadi; Hossein Mostafavi
Abstract
Since climate change is one of the most important and biggest threats to nature and biodiversity, it makes it difficult to manage and protect species. Predicting and determining its effects will considerably help to provide appropriate protection solutions as well as management plans. In the present ...
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Since climate change is one of the most important and biggest threats to nature and biodiversity, it makes it difficult to manage and protect species. Predicting and determining its effects will considerably help to provide appropriate protection solutions as well as management plans. In the present study, the impacts of climate change on the distribution of Mesopotamichthys sharpeyi species were forecasted by using the MaxEnt model in the R software environment. The environmental variables included slope, temperature annual range, flow accumulation, annual precipitation, annual mean temperature, and upstream drainage area. According to the results, the performance of the model in predicting the species was excellent (0.989) based on the AUC (Area Under the Curve) criterion. Moreover, the annual mean temperature and slope have been the most important environmental variables in determining the distribution of this species, respectively. In addition, the distribution range of this species will decrease in both the optimistic (RCP 2.6) and pessimistic (RCP 8.5) scenarios of 2050 and 2080. In conclusion, in order to protect this species, it is necessary for decision-makers to identify and implement appropriate actions in order to adapt the effects of climate change and reduce the related threats.
K Nosrati; S.H Pourali
Volume 8, Issue 2 , November 2016, , Pages 88-101
Abstract
With regard to the hydrologic and agricultural issues, the capacity of water available to soil is considered to be an important variable and its estimation in catchment basin is deemed a principle. Due to the lack of consistency in taking the samples, unavailability of sufficient data for recognizing ...
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With regard to the hydrologic and agricultural issues, the capacity of water available to soil is considered to be an important variable and its estimation in catchment basin is deemed a principle. Due to the lack of consistency in taking the samples, unavailability of sufficient data for recognizing the characteristics of a region and also it is being time-consuming and costly to estimate the water available to soil and its space changes, the use of satellite images is more feasible and less costly. That being said, it is of the essence to develop simple method and models for estimating the capacity of water available to soil from distance. The theoretical background of this research is based on the relationship between vegetation and the temperature of the surface of the earth in estimating the capacity of water available to soil. In the study, in order to estimate the capacity of water available to soil in catchment basin in Hiv located at Hashtgerd, Landsat 7 satellite was used. For the earth control, 50 samples of soil were taken which were distributed in systematic ways. 80 percent of the taken samples were used for combined-model process of the earth surface, using a normalized index for vegetation. Also, 20 percent of the samples were used for the validation of the model. The validity, using multivariate regression with a coefficient determination of 0/85 was significant at 0/01 and the square mean error was 2/6.
H Taji
Volume 7, Issue 2 , November 2015, , Pages 89-106
Abstract
Remote sensing (RS) data is widely applied for estimation of actual evapotranspiration (ETa) and different methods are also developed in this regard. Among them, SEBAL and METRIC as energy balance models and TVT as vegetation-based have received more attentions. However, they are based on some parameters ...
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Remote sensing (RS) data is widely applied for estimation of actual evapotranspiration (ETa) and different methods are also developed in this regard. Among them, SEBAL and METRIC as energy balance models and TVT as vegetation-based have received more attentions. However, they are based on some parameters that need to evaluate while being used. For this, 12 MODIS images for the 2008-2009 water year period that covers the Northern Ahwaz study region (based on the classification of Ministry of Energy), were prepared to be applied for estimation of ETa using the RS models. In order to evaluate and compare the results, estimation of actual evapotranspiration using the conventional water balance model was also used for the same period. Furthermore, the best estimations of ETa using RS models for alfalfa were compared with the respected values in the national water document. The results showed better performance of SEBAL and then METRIC and TVT. While, the water balance model showed 341 mm/yr of ETa, SEBAL showed it 347.24 mm/yr for the same period. However, evaluation of these parameters revealed that application of calibrated soil heat flux andMomentum roughness length equations have significant effect of its performance and reducing of the uncertainties
Ali Asghar Alesheikh; Saeed mehri
Abstract
About 80% of world transportation happens at sea. Therefore the safety of vessels, in particularduring vessels’ movement, is crucially important. As different contextual parameters affect vessels’movement, selecting optimal contextual parameters is one of the main changes in vessels’ ...
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About 80% of world transportation happens at sea. Therefore the safety of vessels, in particularduring vessels’ movement, is crucially important. As different contextual parameters affect vessels’movement, selecting optimal contextual parameters is one of the main changes in vessels’ Context-Aware movement analysis. Toward this end, a Long Short-Term Memory (LSTM) network is usedfor wrapper feature selection to identify optimal contextual parameters for vessels’ movementprediction. To do this, the Automatic Identification System (AIS) dataset from the eastern coast of theUnited States of America collected from December 2017 is used. All possible combinations of threecontextual parameters, including speed, course and vessels’ presence probability in different positionsat sea, were evaluated using the wrapper method in the LSTM network. In all evaluations, 70% ofdata was used for training and the remaining for cross-validation. The results selected speed andpresence probability as optimal contextual parameters for vessel movement prediction. The modeltrained with optimal contextual parameters is 26.98% more accurate than a model trained with allavailable contextual parameters and 16.14% better than a model without contextual parameters.Therefore, selecting optimal parameters from available contextual parameters can help improve theaccuracy of vessels’ predictions. Keywords: Context-Aware, Long Short-Term Memory, AutomaticIdentification System, wrapper, Movement prediction, Context.
Majid H.tangestani; Marjan Karimi
Abstract
In recent years, maritime and aerial surveillance have become commonplace for marine pollution control; however, these methods alone cannot provide rapid and systematic monitoring due to the limitations of weather conditions, time, and location. In this regard, satellite remote sensing can play an important ...
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In recent years, maritime and aerial surveillance have become commonplace for marine pollution control; however, these methods alone cannot provide rapid and systematic monitoring due to the limitations of weather conditions, time, and location. In this regard, satellite remote sensing can play an important role in the initial detection and continuous monitoring of oil spills at sea. The synthetic aperture radar (SAR) sensor is an active microwave sensing system that can be used for oil spill detection, along with optical sensors such as MODIS, with simultaneous imaging capability. The aim of this study was to detect the oil spills around oil platforms in the northern part of the Persian Gulf on June 15, and 17, 2015, using MODIS thermal infrared imagery and Sentinel-1 images. To estimate the sea surface temperature, the split-window algorithm was applied to band 20 of MODIS. Results showed that the sea surface covered by oil spill has lower temperature than surroundings. For accurate detection of oil slicks and accuracy assessment of the results of applied image processing method on the MODIS data, the Sentinel-1 vertical polarization image and noise removal processes such as filtering and multi-looking were used. Finally, by comparing the field temperature measured by Boushehr marine waveguide and the temperature estimated for the MODIS image, and review of the geographical location of detected oil slicks, the accuracy of the results of this study and the applied image processing methods were confirmed. Application of MODIS band 20 aiming the extraction of sea-surface temperature, and its thermal infrared bands for oil spill detection at sea surface are evaluated in this study for the first time.
Mohammad Reza Gili; Davoud Ashourloo; Hossein Aghighi; Ali Akbar Matkan; Alireza Shakiba
Abstract
Changes in crop growth at relatively short intervals, asymmetry of cultivation of similar crops, the spectral similarity between different crops at certain times of the growing season, and lack of ground data make classifying crops in satellite imagery a challenging task. Changing the amount of canopy ...
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Changes in crop growth at relatively short intervals, asymmetry of cultivation of similar crops, the spectral similarity between different crops at certain times of the growing season, and lack of ground data make classifying crops in satellite imagery a challenging task. Changing the amount of canopy and greenness during the growing season is one of the most prominent characteristics of vegetation, including agricultural products, which can be monitored by using time series of vegetation indices that have useful information about the sequence of phenological features of crops. The use of deep learning methods with the ability of learning sequential information obtained from these time series can be useful in crop mapping and reducing dependence on ground data. The LSTM network is one of the types of RNNs in sequential data analysis that has the ability to learn long-term sequences of time-series information. Therefore, in this study, after extracting the NDVI time-series of 9 different dates from Sentinel-2 satellite images for a region located in Moghan plain, with ground labeled data related to the type of crops cultivated, we trained a convolutional LSTM network. Then we used this trained network to classify agricultural products in another region of the plain as a test site, and achieved an overall accuracy of 82% and a kappa coefficient of 0.8. Increasing the number of ground samples and selecting the exact boundary of crops, can increase the efficiency of the method used.
B Tashayo; A Alimohammadi
Volume 9, Issue 3 , February 2018, , Pages 91-110
Abstract
This article develops and demonstrates a new quantitative modeling approach for environmental health impact assessment of traffic scenarios. For this purpose, two models based on hierarchical fuzzy inference system (HFIS) are developed. In order to develop HFIS for modeling the effect of transportation ...
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This article develops and demonstrates a new quantitative modeling approach for environmental health impact assessment of traffic scenarios. For this purpose, two models based on hierarchical fuzzy inference system (HFIS) are developed. In order to develop HFIS for modeling the effect of transportation system on the PM2.5 concentrations, the data from an air dispersion model are utilized. There are several advantages to this approach such as modeling the spatial variation of PM2.5 with high resolution, suitable processing requirements, and consideration of interaction between emissions and meteorological processes. Moreover, the resulting fuzzy landuse regression (LUR) is capable of using accessible qualitative and uncertain data. In order to develop HFIS for modeling the impact of traffic-related PM2.5 on health, a metric derived from epidemiological studies is employed. The suggested model improved the metric capabilities by modeling the uncertainty of relationships among parameters and parameter value. Two solutions are used to improve the performance of both models. First, the topologies of HFISs are selected according to the problem. Second, used variables, membership functions and rule set is determined together through learning. We examine the capabilities of the proposed approach with assessing the impacts of three traffic scenarios to deal with air pollution in Isfahan, Iran and compare the accuracy of the results with representative models from existing literature. The models are first developed based on the current traffic conditions. Then; Low Emission-Zone and Odd/Even scenarios are examined. The examination shows that, they are the most and least effective scenarios in reducing air pollution and improving environmental health, respectively. The obtained results demonstrate that the proposed approach has desirable accuracy; beside that the model can provide better understanding of phenomena and investigating the impact of each of parameters for the planners.
Melika Haghparast; Mehdi Mokhtarzade
Volume 10, Issue 1 , June 2018, , Pages 91-108
Abstract
Due to the global scope of water resources, ground measurements of the quality parameters are not feasible, as well as traditional sampling of water and laboratory analysis is very costly and time-consuming. In studies, estimation of turbidity and chlorophyll a concentrations as the most important water ...
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Due to the global scope of water resources, ground measurements of the quality parameters are not feasible, as well as traditional sampling of water and laboratory analysis is very costly and time-consuming. In studies, estimation of turbidity and chlorophyll a concentrations as the most important water quality parameters using artificial neural networks have been done by researchers. Considering the difficulties in providing a high number of training data in aquatic environments, the use of more robust hybrid networks such as the wavelet neural network is suggested. In this research, various types of wavelet functions were used as a network activation function, and the best network was used to estimate chlorophyll a and turbidity respectively, wavelet neural networks with a Morelt and a Mexican hat activation function, the data used for the reflection of the ocean reflectance of the modis sensor, Due to the use of multi-time images, the radiometric normalization of data was done and the results were significantly improved compared to the time when the non-normalized images were used. in addition to increasing the number of training data, the network generalization capability is provided to other days, and the accuracy of the network in this case increased compared to the one-day condition. the RMSE for the best model to estimate turbidity And chlorophyll a concentration was 0.047 and 0.071, respectively, which is acceptable in comparison with field accuracy of 0.1, and can be a alternative method for field measurements.
Mahmood Ahmadi; Abasali Dadashiroudbari; Neda Esfandiari
Abstract
Urban heat islands is the result of urban Climate and one of the important environmental challenges in the 21st century. The aim of this research is to evaluate the combined effects of biophysical components and Land surface temperature with a special Fractal Net Evolution (FNEA) in order to extract ...
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Urban heat islands is the result of urban Climate and one of the important environmental challenges in the 21st century. The aim of this research is to evaluate the combined effects of biophysical components and Land surface temperature with a special Fractal Net Evolution (FNEA) in order to extract the urban heat islands in Tehran. In the first stage, Landsat 8 satellite TIRS 3 images for August, 2013, 2015, and 2015 were calculated for land surface temperature (LST) and the urban heat islands were extracted by adopting a special Fractal Net Evolution (FNEA) Approach. In order to evaluate the role of biophysical components in the formation of urban thermal islands, BI, MNDWI, NDBI, NDVI, SAVI and UI indices were calculated and evaluated. The results showed that there is a negative linear correlation between vegetation and urban heat islands. Also, a strong positive relationship was found between the impenetrable surfaces with urban heat islands in Tehran metropolis. The UHIs of Tehran metropolis with FNEA approach was classified into five categories: cold UHIs, cold second UHIs, medium UHIs, second-order thermal UHIs and warm UHIs, with an average of 95 km2 hot warm islands and 73 km² of the total urban heat islands Tehran metropolis. The most important identified UHIs are also in the 1- zone 21 due to the intense focus of most factories, industrial workshops and warehouses; 2- Zone 9 due to the location of Mehrabad airport, terminals of passenger transportation and main access passage; 3- Zone 22 and North Zone 19 is located because of the focus of Barren land and 4- Zone 13 (uncovered land around the former Dashan Tape airport) and the southern regions of Tehran (due to the existence of educational and industrial workshops).
Reza shakerir; kamran shayesteh; Mehdi ghorbani
Abstract
Natural and human activities in coastal areas cause dynamic changes in land use and land cover. Rapid population growth in these areas accelerates the process of land use and natural land cover changes, and the transition to residential use and infrastructure development. This research was conducted ...
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Natural and human activities in coastal areas cause dynamic changes in land use and land cover. Rapid population growth in these areas accelerates the process of land use and natural land cover changes, and the transition to residential use and infrastructure development. This research was conducted to investigate and modeling land use changes in Anzali wetland basin between 1975 and 2015 using satellite imagery and predicting possible land use change in 2045 using the LCM model. In order to achieve quantitative and qualitative changes in the study area, the land use maps of the Anzali wetland basin have been produced based on Landsat satellite images for years 1975, 1989, 2000, and 2015. For this purpose, six land use classes including agriculture, rangeland, forest, wetland areas, urban lands, and wetland surface were considered. The accuracy of the land use maps was verified by overall accuracy and kappa coefficients using 323 points based on stratified random sampling and these two parameters were 87% and 0.71, respectively. The LCM model was used to detect and map the changes of different land use categories in the Anzali wetland basin during the periods 1975-1989, 1989-2000, 2000-2015, and predict land use changes in 2045. Analysis of the change detection matrix shows that during the period 1975 to 2015, the total change and transfer of different land uses to each other is 76648.14 hectares. The most changes among different land use during this time are related to the transfer of different land uses to agriculture for 49827.69 hectares, which is equivalent to 65% of the total changes of different land uses. Changing of different land uses to agricultural use is the main change in the uses of this period. forests (64%), rangelands (16%), wetland areas (10%), wetland surface (8%) and residential areas (2 %) have the largest share, respectively. Throughout the study, the expansion of urban land use has always been a positive trend in line with population growth. Based on these changes and by taking 7 independent variable and 8 sub-models, transition potential modeling was done using Artificial Neural Network. The results of modeling in most scenarios showed high accuracy (60.14 to 88.73 percent). To verify modeling accuracy, the standard Kappa coefficient (0.8948) and Null Successes error (77.9%), Hits (3.1%), Misses (15.9%), False Alarms (3.1%) were calculated and accuracy of the position and number of pixels in each class was determined. The ratio of Hits to the total pixels has changed (14.2) indicates that model results are acceptable in predicting land-use changes. Comparison of the results of the changes and conversion of land use classes in the period 2015 to 2045 (predicted) in the region shows that if the land utilization trend continues with current management mode, 10036.26 hectares of forest lands would change to agricultural lands (67.69%), rangeland (32.04 %), urban areas (0.16 %) and wetland surface, and considering the transfer of other uses to forestry, eventually the 9963.36 hectares of forest will be reduced during this period. In general, agriculture, rangeland, and urban areas will increase during this period.
alireza bazrgar; morteza tayebi
Abstract
Land surface temperature (LST) monitoring has been widely used as one of the most important environmental parameters by the high temporal resolution sensors such as the MODIS sensor (daily temporal resolution capability and spatial resolution of one kilometer). One of the main problems of these sensors ...
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Land surface temperature (LST) monitoring has been widely used as one of the most important environmental parameters by the high temporal resolution sensors such as the MODIS sensor (daily temporal resolution capability and spatial resolution of one kilometer). One of the main problems of these sensors is their low spatial resolution, which limits the performance of these sensors for applications such as fire detection in forest areas and the study of urban thermal islands. In contrast, high spatial resolution sensors such as the ASTER sensor (90 meter spatial resolution and 16-day temporal resolution at the land surface temperature product), they have low temporal resolution, which results in application such as rapid change monitoring. In fact, due to technical limitations, there is no sensor that has a high resolution in spatial and temporal dimensions. To solve this problem, low-cost and efficient spatial-temporal fusion methods have been developed. The most important methods for fusion spatial-temporal methods are enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and Spatial and Temporal Data Fusion Approach (STDFA). This work uses the ESTARFM and STDFA algorithms and a new method (SWT-STDFA) based on the STDFA method and the two-dimensional stationary wavelet transformation to fuse LST data spatially and temporally. The LST products of ASTER and MODIS sensors were fused for a part of Tehran city and finally, a virtual image was obtained with a spatial resolution equal to that of the ASTER sensor and a temporal resolution equal to that of the MODIS sensor. Also, based on the existence of a classification map prepared on the basis of normalized vegetation difference index (NDVI) in STDFA and SWT-STDFA algorithms, the effect of using normalized Green Difference Vegetation Indices (GNDVI) and soil adjusted vegetation Index (SAVI) on the accuracy of the synthetic image of the output is discussed. The results of the research indicate the high accuracy of the proposed method with the root mean square error of 3.03 Kelvin, standard deviation of 2. 21 Kelvin, mean absolute difference 1.72 Kelvin and correlation coefficient of 0.92 between the image of the actual land surface temperature and the predicted synthetic image Compared to the other two methods. Also, the increase of vegetation’s indices GNDVI and SAVI in the classification of both STDFA and SWT-STDFA methods did not have much effect on the accuracy of the synthetic image of the output.
Saeed Mojarad
Abstract
The study area is located in the northeast of neyriz and near the village of Ghori in Fars province. Geologically, the units of the study area are located in the zone-Sanandaj-Sirjan and with the general northwest-southeastern trend. Most of these Units calcareous units, units sericitic - chlorite schist ...
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The study area is located in the northeast of neyriz and near the village of Ghori in Fars province. Geologically, the units of the study area are located in the zone-Sanandaj-Sirjan and with the general northwest-southeastern trend. Most of these Units calcareous units, units sericitic - chlorite schist and amphibolite units up. In this research, ASTER sensor images and ground magnetometric data were used to explore and identify iron-rich regions in the study area. In this investigation, we applied methods of False Color Composite (FCC), Band Ratio (BR), Principle Component Analysis (PCA) using ASTER images and areas with severe alterations propellitic, phyllic and sericite. Using methods of ground magnetometric processing, many methods containes reduce to pole (RTP), upward continuation, Analytic Signal, Tilt Angle, Vertical Derivative were used to identify the sources and we were able to identify the edges of these anomalies. In the study area, we were able to identify four anomalies under the ground that it is very important. The results of both methods explored four anomalies. Aster imager process and magnetometric data led to primary potential mineral map of the area. For credibility of results, 52 samples were taken and analyzed by XRD methods. Five boreholes have been drilled to a depth of 140 meters and all results are consistent with each other. The methods used are important and valuable
