Volume 7, Issue 1 , December 2015, , Pages 95-115
Abstract
Obtaining and Production of accurate and realistic information from mineral resources is one of the intellectual concerns of managers. To produce this kind of data and information, there are various methods that can be traditional methods combined with the field data and remote sensing techniques. Measurement ...
Read More
Obtaining and Production of accurate and realistic information from mineral resources is one of the intellectual concerns of managers. To produce this kind of data and information, there are various methods that can be traditional methods combined with the field data and remote sensing techniques. Measurement and generation data using of satellite data and remote sensing methods especially, in desert areas because existing unfavorable conditions, opened up a new horizon to the managers to overcome the problems of traditional conventional methods.The purpose of this paper evaluate remote sensing and GIS techniques is to map evaporate minerals in the eastern part of Semnan using ASTER data.We used field data and false color, PCA and Tasseled cap transformation, ratio and data fusion techniques to estimate amount of gypsum, salt, sodium and magnesium sulfate soils. Regression and correlation relationships between satellite and field data were determined. The results showed that the thermal bands 9, 10, 12 and PCA 9, 10 and 12 can be used for separating Gypsum, Halite as well as sulfate. Finally using maximum likelihood classification map was used to map Gypsum, halite and sulfate contents with accuracy of 73.33, 66.67, 66.67% also using Kappa coefficient were prepared respectively, 0.61, 0.53 and 0.55. Keywords: Evaporate minerals, Gypsum, Salt, Sulfate, Remote sensing.
K Aliabadi; H Soltanifard
Volume 8, Issue 1 , November 2016, , Pages 95-108
Abstract
Knowledge of temporal and spatial distribution of LST to determine the amount of earth energy is much applicable for climatology studies, examination of vegetation and also determination of urban structure. With respect to deriving LST from the studied area and its relationship with urban structure and ...
Read More
Knowledge of temporal and spatial distribution of LST to determine the amount of earth energy is much applicable for climatology studies, examination of vegetation and also determination of urban structure. With respect to deriving LST from the studied area and its relationship with urban structure and vegetation, the present study illustrates that climate conditions specially wind, urban structure and vegetation are some of the effective factors on LST. According to the importance of heat islands at pixel scale in this study, and the ability of Newton Interpolation Polynomial in this respect, urban construction and vegetation are derived by the stated polynomial and their relationship with LST is examined and the areas concluding heat island are known. In this study, Newton Interpolation Polynomials have presented two equations of grade 7 by received DN from 200 points of image including vegetation and the areas with urban structure. The produced error rate from deriving vegetation by using Newton Interpolation Polynomial in 100 locations of the studied area and in urban construction are calculated as 10.1 and 12.02 respectively. It should be stated that no research with similar method has been done yet. The use of mathematical techniques in remote sensing and the amount of accuracy and ability of them are considered some of the main purposes in this research
S Pirouzinejad; Solaimani, K Solaimani; M Habibnejad Roshan; R Zakerinejad
Volume 9, Issue 4 , May 2017, , Pages 95-110
Abstract
The evidences showing that remote sensing has a significant role as a powerful tool around the world, which can reduced the costs and time of projects, especially since they have a comprehensive view of the large areas where are difficult to access. This study has aimed to predict gully erosion using ...
Read More
The evidences showing that remote sensing has a significant role as a powerful tool around the world, which can reduced the costs and time of projects, especially since they have a comprehensive view of the large areas where are difficult to access. This study has aimed to predict gully erosion using remote sensing data and Maxent model in Alvand basin located in the western part of Kermanshah province, Iran. Alvand basin with a difficulty accessing due to the extent of the minefield during the imposed war and interconnected with Iraq, on the other hand, the shape of Marne lands and absence of proper vegetation have led to acceleration of gully erosion. Therefore, in this study with a combination method of fieldwork and remote sensing which used in the Google Earth environment, then the essential spatial analysis layout has prepared by Maxent model and the zonation of the gully area has digitized as independent variables that introduced to model. In addition, for analysing the ground surface, a digital elevation model of the Alos data has used with 15 environmental layers of 10/m resolution were prepared as dependent variables. Three goals have attained based on this quantitative and statistical model. First, the effect level of each environmental layer has obtained using the Jackknife test. Second, trend of maximum and minimum effects of each parameter has investigated using logistic regression and finally, Potential map of gully erosion was prepared for the whole region. Then the model validation has performed using the ROC curve and the area under the curve (AUC). It has concluded that the most effective index in gully erosion creation related to elevation index, vertical distance from channel level and flow accumulation. The validation is calculated equal to AUC = 0.899, which shows a good level of results.
Bahram Moradi Solooshi; Alireza Vafaei Nezhad; Hossein Aghamohammadi Zanjirabad; Ali Alesheikh
Abstract
The capacity of passenger and goods shipment in a railway network is affected by various parameters. Considering the high cost of railway construction, optimum utilization of capacity in a railway network can help to improve the efficiency of network. Therefore, the purpose of this study is spatial calculating ...
Read More
The capacity of passenger and goods shipment in a railway network is affected by various parameters. Considering the high cost of railway construction, optimum utilization of capacity in a railway network can help to improve the efficiency of network. Therefore, the purpose of this study is spatial calculating the capacity of railway network of Iran based on the effective parameters. In this paper using the transportation data of trains, a spatial software is developed for calculating the capacity of the railway network. Then, the outputs are compared and evaluated with the daily and real time data of the freight trains performance. In the next step, the amount of capacity utilization of each route and the amount of capacity remaining in each route and block is determined. Based on the analysis, the capacities of the selected passenger double-line route from Semnan to Shahrud, freight single-line route from Yazd to Bafgh and combination single-line route from Arak to Dorood were calculated 2.6 for Semnan to Shahrud path and 2.9 for return path, 13.6 and 12.6 (trains pair/day) respectively. Considering the common calculations, the online calculation with ability connection to related databases and the possibility of exchanging spatial web-based services with the different software, can improve the speed and accuracy of the railway network capacity calculation. On the other hand, in the equation of calculating the capacity (Scott equation) used in Iran, it is common that the adjustment coefficient of passenger train is determined by experts of transportation, while in this paper, since the data of each path is accessible, the aforementioned coefficient is calculated by the ratio of passenger trains number to their maximum number during the considered period of time. The outcome coefficient compared with the coefficient determined by experts, and the result was acceptable. Finally, with access to outputs in the GIS environment, the management solutions were proposed for optimum using the remaining capacity, enhancing the capacity in some parts of the network as well as eliminating the bottlenecks of the railway network.
I Yoosefdoo; A Khashei Siuki
Volume 9, Issue 2 , December 2017, , Pages 99-116
Abstract
The use of groundwater plays an important rule for agricultural and drinking water purposes in the north of Iran especially in Koochesfehan region. In these areas, the excessive use of chemical fertilizers, especially nitrogen based ones, beside the inadequacy in the treatment and release of urban and ...
Read More
The use of groundwater plays an important rule for agricultural and drinking water purposes in the north of Iran especially in Koochesfehan region. In these areas, the excessive use of chemical fertilizers, especially nitrogen based ones, beside the inadequacy in the treatment and release of urban and industrial wastewater are some of the most effective parameter in groundwater pollution, especially about the concentration of nitrate. Therefore, identification and mapping of vulnerable aquifer areas, i.e. areas where pollutants can be penetrated and discharged from the ground surface to the groundwater system, is an appropriate management tool for preventing the pollution of groundwater resources. In this study, with the purpose of identifying vulnerable aquifers and areas with high nitrate content as the main vulnerability areas, by using 7 variables the Drastic method and by using the Aller weighing criterion, vulnerability index of the region was estimated. Then, by comparing the vulnerability index and the amount of nitrate measured in the zoned area, the correlation between nitrate and Drastic vulnerability index was calculated. The results showed that the vulnerability of the Astaneh-Koushfahan plain aquifer is located in four areas: 56.16% of the plain has a low vulnerability, 51.29% has a low to moderate vulnerability, 28.46% has a moderate to high vulnerability, 67.1% is vulnerable. It is too much. The correlation between the Drastic (vulnerability) index and the concentration of nitrate was 80%, which confirmed that nitrate was the main cause of vulnerability in this the aquifer. So, finding a method for estimating the amount of nitrate in present and future in this area with high speed and precision was assumed as the goal of this study. The amoun of nitrate were estimated with four artificial intelligence methods: artificial neural network, fuzzy model, support vector model and fuzzy-neural network. For this purpose, the seven Drastic variables data assumed as input parameters and the measured nitrate content in 30 different wells of the area were zoned by use of GIS software and divided into two categories of training and experimentation and they give as output parameters to all data-driven models. The results showed that all used artificial intelligence models give a good estimation of the amount of nitrate, but the neural network model had the best results, so that there was a correlation of 98% between computational nitrate and observation nitrate value. Finally, by choosing the model of the neural network as the superior model, it was tried to estimate the nitrate by decreasing the input parameters. The results showed that with 5 parameters of soil environment-unsaturated medium-saturated environment -water-hydraulic and eliminating two parameters of nutrition and topography, the correlation of estimated nitrate with the actual amount of measured nitrate is 0.90.
Farzaneh Hadadi; Hossain Aghighi; Ayoub Moradi
Volume 10, Issue 4 , February 2019, , Pages 99-120
Abstract
The accurate estimation of crop biomass using satellite data is one of the important challenges in environmental remote sensing. Traditionally, spectral vegetation indices (VIs) derived from spectral reflectances in red (R) and near infrared (NIR) bands have been employed to statistically estimate ...
Read More
The accurate estimation of crop biomass using satellite data is one of the important challenges in environmental remote sensing. Traditionally, spectral vegetation indices (VIs) derived from spectral reflectances in red (R) and near infrared (NIR) bands have been employed to statistically estimate the crop biomass; however, most of these VIs saturate at some level of LAI. Therefore, most of the recent studies have been investigated on using the reflectance spectra in the red-edge region to overcome the saturation limitation. In order to evaluate the performance of different VIs for the estimation of crop biomass, we conducted five sampling campaigns during the growing season of silage maize in Magsal, Qazvin and we totally collected 182 silage maize biomass samples. Then, ten spectral indices from the time series of Sentinel-2 images of 2017 which were simultaneous with our campaigns were computed and employed to statistically estimate the silage maize biomass. The silage maize biomasses were evaluated with the field measurements. The results showed that index with and the lowest root mean square error () was the best index to estimate silage maize biomass. Moreover, this work also showed that Sentinel-2 satellite which delivers high spatial resolution images of the red-edge band can be employed to accurately estimate the silage maize biomasses. The accurate estimation of crop biomass using satellite data is one of the important challenges in environmental remote sensing. Traditionally, spectral vegetation indices (VIs) derived from spectral reflectances in red (R) and near infrared (NIR) bands have been employed to statistically estimate the crop biomass; however, most of these VIs saturate at some level of LAI. Therefore, most of the recent studies have been investigated on using the reflectance spectra in the red-edge region to overcome the saturation limitation. In order to evaluate the performance of different VIs for the estimation of crop biomass, we conducted five sampling campaigns during the growing season of silage maize in Magsal, Qazvin and we totally collected 182 silage maize biomass samples. Then, ten spectral indices from the time series of Sentinel-2 images of 2017 which were simultaneous with our campaigns were computed and employed to statistically estimate the silage maize biomass. The silage maize biomasses were evaluated with the field measurements. The results showed that index with and the lowest root mean square error () was the best index to estimate silage maize biomass. Moreover, this work also showed that Sentinel-2 satellite which delivers high spatial resolution images of the red-edge band can be employed to accurately estimate the silage maize biomasses.
Nahid Haghshenas; Ali Shamsoddini
Abstract
Normally, images with a high resolution (temporal or spatial) are available, while there is a limitation in accessing images which are simultaneously high spatial and temporal resolution. While, in some applications, access to images with high spatial and temporal resolution is necessary. Therefore, ...
Read More
Normally, images with a high resolution (temporal or spatial) are available, while there is a limitation in accessing images which are simultaneously high spatial and temporal resolution. While, in some applications, access to images with high spatial and temporal resolution is necessary. Therefore, this study was conducted to downscaling MODIS images to Sentinel- 2 spatial resolution by STARFM, ESTARFM and FSDAF spatio-temporal downscaling algorithms in different land cover classes including urban, garden, pasture, agricultural and water classes. The study area was selected with a variety of land covers around the city of Mahabad, Iran. First, the corresponding visible and near-infrared bands in Sentinel- 2 and MODIS were selected and necessary pre-processes such as geometric correction were done on these images. Then, Sentinel- 2 images were simulated using downscaling algorithms. The results indicated the accuracy of downscaling in the urban, garden and pasture classes compared to the agricultural and water classes. So that the ESTARFM, FSDAF and STARFM algorithms averagely showed the coefficient of determination of 88.25, 87.25 and 86.5 for the urban class, the coefficient of determination of 83.75, 83.25 and 80.5 for the garden class and the coefficient of determination of 90.75, 70.5 and 87.5 for the pasture class in all bands
Samaneh Safaeian; ُSamereh Falahatkar; Mohammad Javad Tourian
Abstract
In recent years, the phenomenon of climate change and drought has become a global problem in the arid and semi-arid regions of the world. Climate change as a problem in the annual bio-farming cycle causes extinction of plant and animal species, reduced vegetation richness, impaired and reduced fertility ...
Read More
In recent years, the phenomenon of climate change and drought has become a global problem in the arid and semi-arid regions of the world. Climate change as a problem in the annual bio-farming cycle causes extinction of plant and animal species, reduced vegetation richness, impaired and reduced fertility severity in animals, changes in the pattern of migration of birds and animals (due to new habitats or food sources New) and changes in the spawning pattern of fish. Droughts and floods are one of the most severe climatic events that are likely to change faster than the average climate of any region. Today, access to freshwater resources is a very important issue in most countries, including the Middle East and Iran, according to FAO statistics, while the Middle East accounts for 14 percent of the Earth's surface, accounting for only 2 percent of water resources. The drying up of internationally valuable lakes and wetlands, the lowering of rivers to crisis levels, and the exposure of people in 12 provinces to drinking water shortages are among the consequences of a nationwide drought. Droughts have been particularly prevalent in the tropical and subtropical regions since the 1970s. Reduced ground precipitation and increased temperatures, which increase evaporation and decrease soil moisture, are important factors that have led to more drought zones. Recent droughts have emphasized the need for more research into the causes and effects of droughts and the need for additional planning to help reduce the potential consequences of future droughts. On the other hand, some studies consider the increase in greenhouse gases and disruption of sunlight transfer to and from the earth to the atmosphere as a reason for the recent drought. In the present study, monthly changes of atmospheric carbon dioxide and monthly changes of total water storage in the period 2003-2015 in Iran were investigated. Combined data with the Obsm4MIPs algorithm of GOSAT satellite and SCIAMACHY sensor were used to obtain the trend of changes in carbon dioxide concentration and GRACE satellite data for changes in total water storage from 2003 to 2015. The results of the canonical correlation show a strong relationship between carbon dioxide concentration and changes in total water storage. Stepwise regression model was used to model the relationship between changes in total water storage with CO2, discharge rate and groundwater consumption. The results of regression model showed that carbon dioxide with R2 = 0.91 had the highest relationship with total water reservoir changes in the model. It is noteworthy that the identification of these relationships on a large scale is tangible and at the local scale management practices are more influential in changing water resources, especially groundwater.
Kamal Omidvar; massumeh nabavi zadeh; Ahmad Mazidi; HamidReza Ghaffarian Malmiri; Peyman Mahmoudi
Abstract
Drought monitoring is critical for early warning of drought hazard. This study is attempted to develop remote sensing drought monitoring index (VDI), based on Accuracy of different bands of Moderate Resolution Imaging Spectroradiometer data MODIS, VDI focuses about the vegetation water stress. Spectral ...
Read More
Drought monitoring is critical for early warning of drought hazard. This study is attempted to develop remote sensing drought monitoring index (VDI), based on Accuracy of different bands of Moderate Resolution Imaging Spectroradiometer data MODIS, VDI focuses about the vegetation water stress. Spectral studies have demonstrated that due to the large absorption by leaf water, shortwave infrared reflectance (SWIR) is negatively related to leaf water content. Being sensitive to leaf water content, SWIR is widly utilized to construct various remote-sensing indices for example VDI for reflecting vegetation water content, . In this study, Vegetation Drought Index (VDI) was evaluated Based on the sensitivity rate to moisture by shortwave infrared reflectance bands SWIR 5 and 6 (VDI5 and VDI6). The data included the MODIS sensor images from Terra satellite in a period of nineteen years from 2000 to 2018 and Precipitation data are obtained from the Global Land Data Assimilation System (GLDAS), in Sistan & Balouchestan Province, Pearson correlation coefficient was used to evaluate the accuracy of the Drought spatial distribution maps calculated based on the two bands.Results indicate high significant correlation rate between VDI6 and Precipitation data . Study also showed that shortwave infrared band 6 (SWIR) is more sensitive to agricultural drought than band 5,in Sistan and Baluchestan province . The study recommends to use VDI index with and 6 for better early detection and monitoring of agricultural drought in operational drought management programmes.
morteza Sharif; aboozar kiani
Abstract
Forest fires worldwide cause severe damage to vegetation, soil and natural habitats, resulting in direct and indirect negative environmental impacts such as deforestation, climate change and drought. Therefore, identifying and determining the hazards of vegetation that suffer from fire is crucial for ...
Read More
Forest fires worldwide cause severe damage to vegetation, soil and natural habitats, resulting in direct and indirect negative environmental impacts such as deforestation, climate change and drought. Therefore, identifying and determining the hazards of vegetation that suffer from fire is crucial for their management and development. The proliferation of remote sensing images such as the active fire products of the Terra and Aqua satellites over the past two decades has been one of the most essential methods in detecting these fires. However, the active fire product of the MODIS sensor in previous studies has shown that they alone do not provide good results in fire-affected areas. Therefore, it is necessary to evaluate vegetation with basic maps. The aim of this study was to investigate two types of plant products and to discover the active fire of MODIS sensor and FNF-JAXA forest and non-forest cover maps for better separation of burnt areas of vegetation in Iran between July 1 and 160 2020. The results show the highest area of fire on Julius 144 with more than 49 thousand hectares and Julius 128 with more than 45 thousand hectares. However, the largest area of the fire, forest cover is estimated at 120 to 160 in 2020 with more than 14 thousand hectares. Khuzestan province had the highest area of fires in the period under study that most of these areas in agricultural lands and the three provinces of Fars, Kohgiluyeh and Boyer-Ahmad and Bushehr had the highest area of fires in forest cover. The highest frequency of fires was observed in agricultural lands, the main reason for which could be human intervention. The evaluation of the results showed that the use of the FNF-JAXA product (accuracy of 87.4% and a Kappa coefficient of 0.85) compared to MODIS products (accuracy of 80.3% and a Kappa coefficient of 0.78) in the separation of forest areas has better capabilities. However, the ability of MODIS products to distinguish between pasture and agricultural vegetation is an important advantage, which the FNF-JAXA product does not have. In general, the findings of the research show that the MODIS product and FNF-JAXA maps can be used as reference maps to distinguish different types of vegetation that are subject to fire, in damage assessment and management.
Hamed nematollahi; Davoud Ashourloo; Abas Alimohammadi; Elham Khodabandehloo; Soheil Radiom
Volume 10, Issue 3 , January 2019, , Pages 105-122
Abstract
One of important objectives in sustainable agriculture is preservation of healthy ecosystems with focus on natural aquatic and terrestrial resources management in order to accomplish food security at local and global scales. Time-series remotely sensed datasets are precious and valuable resource of temporal ...
Read More
One of important objectives in sustainable agriculture is preservation of healthy ecosystems with focus on natural aquatic and terrestrial resources management in order to accomplish food security at local and global scales. Time-series remotely sensed datasets are precious and valuable resource of temporal and spectral information that could support researchers to access field management goals. Farm management have been always encountered some challenges such as lack of access to quantitative and qualitative information of agricultural crops. This research aims to develop crop and field condition indices using time-series of NDVI (Sentinel-2) and crop type maps of Moghan Agro-Industry (MAI) in 2016-2017 and also Shahid Rajaei Agro-Industry (SRAI) in 2017-2018. Then we tried to identify parts of the fields that are affected by Environmental factors such as disease, pest, weed, soil-related deficiencies and uneven distribution of water due to Inefficient irrigation system. To this end, Time-series of NDVI for four crops (wheat, maize, alfalfa and sugar beet) in various fields was provided. Finaly, field and crop condition indices were developed to show the variations of crop in each field and also the fields in comparison with each other. Finally, the proposed indices showed high accuracy with ground observations. The results were 88.88% for Alfalfa fields in MAI, and 94.11% for wheat fields in SRAI. After evaluation of the results of indices with ground observations, it was revealed that where field (homogeneity) index is low, growth limiting factors are activated.
ali reza shooreshi; hassan zoghi
Abstract
Among the network of urban roads, the network of emergency roads plays an important role in providing relief during an earthquake, especially in the crisis response phase. It is very important to maintain the function of the urban roads network in the first few hours after earthquake. Protecting and ...
Read More
Among the network of urban roads, the network of emergency roads plays an important role in providing relief during an earthquake, especially in the crisis response phase. It is very important to maintain the function of the urban roads network in the first few hours after earthquake. Protecting and strengthening vulnerable parts of the network before the crisis (especially bridges) plays a significant effect in reducing damages and injuries. However, retrofitting all vulnerable components is practically impossible due to budget constraints. The existence of this limitation requires identifying the vulnerable components accurately. Therefore, retrofitting options are prioritized first, and the most suitable ones are finally selected. In this research, after identifying the bridges that need to be retrofitted on the emergency roads network through a five-step methodology, we also considered the financial limitations and budget allocation options, and prioritized retrofitting options based on the network of layers created in the Geographic Information systems environment (GIS) under the title of input. Examining all possible situations for the stability of bridges after a specific earthquake, designing the emergency road network for all these situations, examining different options for retrofitting bridges, evaluating the effect of this retrofitting on the length of the emergency network, and finally, the prioritization of retrofitting options according to their impact during the emergency network, are the main steps of the proposed method in this study. The efficiency of the above method was evaluated after applying it on a part of the emergency roads network of Tehran as a real network with large scale.
Volume 4, Issue 1 , March 2012
Abstract
Taking the advantages of polarimetric radar data has a decisive role in target detection purposes. In this way, comprehensive geometric and descriptive information could be derived through processing this kind of data. However, the selection of optimal features could be considered as a major challenge ...
Read More
Taking the advantages of polarimetric radar data has a decisive role in target detection purposes. In this way, comprehensive geometric and descriptive information could be derived through processing this kind of data. However, the selection of optimal features could be considered as a major challenge in order to classification of the polarimetric radar imagery. In this paper, a novel approach is proposed for optimal feature selection based on mapping the extracted features to the prototype space. As a key result of the paper, fitness index is introduced to facilitate the optimal feature selection in polarimetric radar images. On the other hand, the mixture of backscattering mechanisms in a pixel level is another limitation to obtain precise spatial information. Thus, utilizing soft classifiers is indispensible to acquire the sub-pixel information. Positivity and sum to unity of the fractions within each pixel are major challenges in results of the soft classifiers. In this paper, integration of the soft classifiers and unsupervised algorithms of end-member extraction is proposed to solve this problem. Likewise, soft classifiers just provide fractional maps and the spatial arrangement of sub-pixels remains unknown. In this regard, Super Resolution Mapping (SRM) techniques are developed to enhance the spatial resolution of the results of soft classifiers. This research attempts to provide a sub-pixel classification of polarimetric radar images using the pixel swapping technique. Towards this end, a non-random procedure is suggested for initial arrangement of the sub-pixels. According to the results, the proposed method for optimal feature selection is demonstrated more accurate results than genetic algorithm. Next, three algorithms including Linear Spectral Unmixing (LSU), Multi-Layer Perceptron (MLP) and Support Vector Machines (SVM) are performed to soft classifying of the polarimetric radar image into three classes (residential, vegetation and bare earth). SVM present accurate results in comparison to others; its resulted fractional maps are used in SRM procedure. Finally, pixel swapping technique is performed based on the results of SVM classification and the land cover map of the study area is produced in a finer spatial resolution.
Volume 6, Issue 1 , April 2014
Abstract
Timely and accurate detection of changes in land use/ cover is important for land planning and management. Remote sensing images have been primary sources for change detection in recent decades. Due to its simplicity, thresholding of difference image is a popular method for change detection. The traditional ...
Read More
Timely and accurate detection of changes in land use/ cover is important for land planning and management. Remote sensing images have been primary sources for change detection in recent decades. Due to its simplicity, thresholding of difference image is a popular method for change detection. The traditional thresholding methods such as Otsu are based on exhaustive search, so that they are time consuming. Since these methods are mainly developed for one-dimensional problems, the computation time grows exponentially with the number of thresholds when these methods are extended to be used for multi-dimensional problems. If thresholding is supposed to be as an optimization problem, optimization methods can potentially decrease the computation time. In this paper, a fast, simple and effective multi-dimensional image thresholding technique based on Particle Swarm Optimization (PSO) method is presented. This technique calculates the optimal threshold values by maximizing the Otsu objective function and minimizing the inter-class variance objective function. The proposed method has been implemented on two multispectral and multi-temporal datasets. The first dataset includes a couple of images acquired by the TM sensor taken form south islands of Aurmia Lake (Iran) in Jun 1984 and July 2010, respectively. The second dataset is obtained from a couple of images acquired by the same sensor on the Khodafarin dam (Iran) in July 2000 and July 2009, respectively. In order to evaluate the proposed method, the computational time and change detection accuracy were computed. In addition, statistical test was carried out in order to evaluate the robustness of the developed method. The experimental results show that the proposed PSO-based multi-dimensional thresholding method could provide optimum thresholds values by decreasing 98% and 15% of the time complexity compared with the most widely used Otsu and inter-class variance-based thresholding methods.
Volume 6, Issue 2 , August 2014
Abstract
The increasing concentration of greenhouse gases has been identified as a main cause of increase of global mean temperatures since the mid-20th century. The effect of human-induced climate change could be unprecedented and far-reaching. Carbon sequestration into trees and forests is an effective and ...
Read More
The increasing concentration of greenhouse gases has been identified as a main cause of increase of global mean temperatures since the mid-20th century. The effect of human-induced climate change could be unprecedented and far-reaching. Carbon sequestration into trees and forests is an effective and inexpensive way for mitigating the CO2 level in the atmosphere. Hence, accurate measurement of biomass will be of great importance to global carbon cycle and climate change. This study performed a wavelet-based forest aboveground biomass estimation approach in a temperate deciduous forest, Kheyroud Kenar forest in north part of Iran. Wavelet analysis, specifically two-dimensional discrete wavelet transform (DWT) was applied to ALOS PALSAR images to obtain wavelet coefficients (WCs), which were correlated with forest inventory data using multiple linear regression analysis to investigate the relationship. The results indicate that Db wavelet coefficients correlate better with field biomass data than other parameters. For the first level of the decomposition, the correlation coefficient is 0.5 while for second level, the overall R value increased up to 0.75. This study demonstrates that wavelet-based biomass estimation could be a very promising approach for providing better biomass estimation; however, further research is needed for identifying robust wavelet coefficients and optimizing procedures. Keywords: ALOS PALSAR, Wavelet analysis, Forest biomass, Multiple regression analysis.
Volume 6, Issue 4 , October 2014
Abstract
Accurate mapping of geological structures play an important role in the land management, some of which are considered as the origination of wind deposits, dust and aerosols. During the last decades, remote sensing has considerably helped the studies of desert areas especially in change detection of land ...
Read More
Accurate mapping of geological structures play an important role in the land management, some of which are considered as the origination of wind deposits, dust and aerosols. During the last decades, remote sensing has considerably helped the studies of desert areas especially in change detection of land phenomena. This study aims to study the facies morphology of playa in Tabas Kavir, based on the geometric mean of ASTER imagery bands. To do so, 40 topsoil samples have been collected during the fieldwork in 2010/06/23. Soil samples were analyzed in the laboratory and various pedagogical variables (including Anions, Cations, soil moisture, texture and pH) were measured. Digital elevation model (DEM) was used to investigate the effect of topography on the facies morphology. Pixel values of each spectral band have been extracted in the training samples points (26 samples). Then, the correlation models have been established by using multiple linear regression method. Estimated values and the accuracy of models were assessed by using regression models in the test samples points (14 samples). Related maps have been produced by applying the models on the corresponding ASTER bands and the accuracy of produced maps have been assessed by using the ground truth map. The results of this study indicate that the most important factors in the studying of morphology of the region's facies are the interaction among components of bicarbonate-lime, potassium-lime and bicarbonate-lime-potassium with the overall accuracy more than 53 percentage. As well, the results show that there is no meaningful relationship between Na, CO3, EC, topography and ASTER bands. Keywords: Facies, Geometric Average, Aggregation, Geomorphology, Playa, Tabas
Volume 6, Issue 3 , October 2014
Abstract
In the last two decade the use of Aerial Laser Scanner (ALS) or LiDAR (Light Detection and Ranging) sensor in geomatics engineering and surveying application has augmented significantly. The main reason of the mentioned phenomenon is the reliability and accuracy of the data obtained by LiDAR sensors. ...
Read More
In the last two decade the use of Aerial Laser Scanner (ALS) or LiDAR (Light Detection and Ranging) sensor in geomatics engineering and surveying application has augmented significantly. The main reason of the mentioned phenomenon is the reliability and accuracy of the data obtained by LiDAR sensors. The output of LiDAR is unclassified 3D point cloud. Classification of the LiDAR point clouds in different and distinguished classes is the first step in applying such data in different geomatics applications. The purpose of this article is to classify Full- Waveform LiDAR data with the compilation of geometric and physical parameters of each point in the point cloud. First of all the geometrical parameter is extracted from raw 3D coordinate of the points. This geometrical parameter is the calculation of the relational association of the point in the construction of a plane with the help of 3D Hough transform. The feature vector also includes physical features that exclusively belong to full-waveform LiDAR. These features are amplitude of the pulse, width of the pulse and the number of the returned pulse. After the construction of the feature vector for each point, the next step is to classify the point cloud into three classes; bare earth, building and vegetation with the utilization of Support Vector Machines classification method. The final step is accuracy assessment of the classification method. The results are promising; 81.04% Overall Accuracy, 0.69 Kappa Coefficient and 79.21% Average Accuracy. Keywords:Full-waveform LiDAR, Support Vector Machine Classifier, Point Cloud Processing, 3D Hough Transform, Urban Areas.
M Shaygan; M Mokarram
Abstract
The aim of this study was to use the attraction model to increase the spatial resolution of the Digital Elevation Model (DEM) and to use the genetic algorithm to predict stream network in the future and compare its results with stream of extraction of DEM with resolution of 30 m. In the quadrant neighborhood, ...
Read More
The aim of this study was to use the attraction model to increase the spatial resolution of the Digital Elevation Model (DEM) and to use the genetic algorithm to predict stream network in the future and compare its results with stream of extraction of DEM with resolution of 30 m. In the quadrant neighborhood, a neighbor pixel is the only pixel in the same quadrant while in touching neighborhood a neighbor pixel that is the pixel, which physically touches a subpixel. In this method, the pixels were divided into a number of sub-pixels according to the values of the neighboring pixels. The results of the attraction model showed that Scale 2 with the Neighborhood model 2 is more accurate than other Neighborhoods for extracting DEM with higher resolution. The results showed that the predicted stream-network landscapes created using the GLE algorithm had the self-similar tree structure of natural stream networks. Also, the results of the genetic algorithm showed that a change in the degree of waterways in the study area over time compared to the current situation, so that the degree of number of first-class waterways in the future will change to grade 3 due to erosion in upper lands. Therefore, using these models, the condition of waterways can be predicted in the future and better management can be adopted for watersheds.
Farzaneh Aghighi; Omid Mahdi Ebadati E.; Hossein Aghighi
Abstract
Lidar point cloud dataset and 3-D models are widely used in urban feature extraction, forest, urban and tourism management, robotics, computer game production etcetera. On the other hand, The existence of outliers in the lidar point cloud is inevitable. Therefore, outlier detection and removing them ...
Read More
Lidar point cloud dataset and 3-D models are widely used in urban feature extraction, forest, urban and tourism management, robotics, computer game production etcetera. On the other hand, The existence of outliers in the lidar point cloud is inevitable. Therefore, outlier detection and removing them from lidar point cloud data have been known as necessary steps in lidar point cloud processing. Over the past decade, several outlier detection techniques have been introduced in the literature; however, most of them are time-consuming, expensive, and computationally complicated. For overcoming these limitations, this article introduces a new automatic approach for outlier detection using a support vector machine-based conditional random field (SVM-CRF) technique and box plots methods. In this approach, a box plot analyzes the output energyvector of SVM-CRF to recognize outliers. The methods were evaluated using ISPRS benchmark datasets of Vaihingen provided in order to urban classification and 3D building reconstruction. To evaluate this method, first of all, outliers, that are almost closed to objects, were added to the data set manually. Then the research steps were done to evaluate the proposed method's ability for detecting outliers. The evaluation of this research showed an overall accuracy of 62% as the performance of the proposed model. Although the RANSAC algorithm has better performanc, it is a more costly and time-consuming technique than the proposed outlier detection technique.
S.A.R Nouredini; A.A Bonyad
Volume 9, Issue 1 , October 2017, , Pages 93-110
Abstract
Reflectance of different of land surface phenomena on remote sensing data was influenced by different conditions including atmospheric conditions. Variety methods of atmospheric correction have been developed for remove and reduction of its effects. In this study three atmospheric correction methods: ...
Read More
Reflectance of different of land surface phenomena on remote sensing data was influenced by different conditions including atmospheric conditions. Variety methods of atmospheric correction have been developed for remove and reduction of its effects. In this study three atmospheric correction methods: Dark Object Subtraction (DOS), Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubus (FLLASH) and Second Vector Simulation of Satellite Signal in the Solar Spectrum (6SV) have been applied on OLI sensor of Landsat8 inthe forest regions of Guilan province. Numbers of 10 vegetation indices were extracted from each image. Forest area was extracted on various indices detected by global land cover layer. Forest areas segmented on Landsat8 image by object-based method. In the total 91 segments, randomly were selected. Forest canopy density of any segment plot estimated on Google images using 20×20 m network dotted. Person test was used for correlation between indices and training samples and two linear and nonlinear regression models were used for forest canopy density estimation. The results confirmed that 6SV method dominates than other methods in the forest regions of Guilan province. The lowest root means square error (RMSE) with 17.72 was shown in the green atmospherically resistant vegetation index (GARI) extracted from DOS. The results indicated that the lowest RMSE was in atmospherically resistant vegetation index (ARVI) using 6SV, FLAASH and OLI original image with 18.38, 15.87 and 21.78 respectively. The results of this study were shown that use of atmospheric correction methods in preparing vegetation indices is cause of increasing information accuracy from satellite images. Reduction of atmosphere effects in preprocessing before modeling is necessary and suggestible.
Faraham Ahmadzadeh; Negar Amiri; Elham Ebrahimi
Volume 10, Issue 2 , September 2018, , Pages 95-108
Abstract
Today, it is well-known that predicting the distribution potential of endangered species by usingspatial modeling methods is highly beneficial and using these methods can greatly contribute toecological conservation and management. Rana pesudodalmatina is one of the Iranian endemicamphibian species of ...
Read More
Today, it is well-known that predicting the distribution potential of endangered species by usingspatial modeling methods is highly beneficial and using these methods can greatly contribute toecological conservation and management. Rana pesudodalmatina is one of the Iranian endemicamphibian species of Iran. In order to predict the potential geographic distribution of the species itsoccurrence points were collected through field work and 19 so-called bioclim climate variables asspatial environmental predictors were extracted from the Worldclim database. By applying Pearsoncorrelation test, the highly correlated variables with correlation coefficient of 0.75 were eliminated.Species distribution modeling was done using newly published R package which includes GLM,GAM, RF, MARS, CART, FDA, BRT and SVM models. All individual models were compound as anensemble to reduce the uncertainty which increase the accuracy and predictive power. The resultsrevealed that the long-legged wood frog has maximum distribution potential in Hyrcanian forest ofIran. Also, the results of the valuation of the models showed that the AUC and TSS had better statusand the SVM model was the most credible. In addition, the results of measuring the importance ofeach of the variables showed that BIO6 had the highest and BIO19 had the least importance for this.
Hamid Ezzatabadi Pour; Saeid Homayouni
Volume 7, Issue 3 , November 2015, , Pages 97-114
Abstract
C-means clustering models are one of the most widely used methods for unsupervised classification of any data. Fuzzy c-means (FCM) is one of the most well-known clustering models in which, each data may be belonged to multiple clusters with different membership degree between 0 and 1. This model has ...
Read More
C-means clustering models are one of the most widely used methods for unsupervised classification of any data. Fuzzy c-means (FCM) is one of the most well-known clustering models in which, each data may be belonged to multiple clusters with different membership degree between 0 and 1. This model has been employed for different application including remotely sensed data classification. FCM model uses Euclidean distance for clustering and assumes the same shape/distribution for all of clusters. However, this causes misclassification in data in which the classes have different shape and size. In this paper, Gustafson-Kessel clustering model is presented to overcome this problem. This model is based on using a fuzzy covariance matrix for each cluster which does not consider the same geometric shape, size and orientation for all clusters. The above models were applied for clustering of hyperspectral imagery issue of Hyperion, ROSIS and CASI sensors. The results of Gustafson-Kessel clustering model prove that the accuracy of classification increased about 12.5% for Hyperion imagery and about 8.45% for ROSIS imagery. Also, the visual test on CASI imagery show that Gustafson-Kessel clustering model has better performance.
E Taherian; H Samadi
Volume 7, Issue 4 , November 2015, , Pages 99-116
Abstract
Strategic decisions about the construction of engineering structures along the river which are essential for the management of sediment entering the reservoir will be facilitated by understanding the behavior and characteristics of the sedimentation of rivers leading to large dam reservoirs. Multi-temporal ...
Read More
Strategic decisions about the construction of engineering structures along the river which are essential for the management of sediment entering the reservoir will be facilitated by understanding the behavior and characteristics of the sedimentation of rivers leading to large dam reservoirs. Multi-temporal and spectral remote sensing technology has been applicable for detecting of the rivers morphological changes. However, the specific nature of the narrow and shallow rivers is responsible for increasing the complexity of morphology with available data. This study was undertaken in order to detect narrow and shallow rivers by assessing the ability of six famous water indices, including: Normalised Difference Water Index, Modified Normalised Difference Water Index, Automated Water Extraction Index no shadow, Automated Water Extraction Index shadow, Enhanced Water Index and Water Index 2015 which were derived from two Landsat ETM+ and OLI sensors. The optimal threshold for each of these indices was determined using ROC curves and validation process was carried out using Google Earth images captured in August 2013. The accuracy of results was evaluated by using different statistics including combined error, producer’s accuracy, user accuracy and omission and commission errors. Consequently, the results of this study have shown that the ETM+ sensor was generally more accurate than OLI sensor. All in all the Modified Normalised Difference Water Index and the Automated Water Extraction Index shadow was the most accurate indices. Also Automated Water Extraction Index no shadow index had the lowest accuracy for the river’s detecting process
H Heydari; M.J Valadan Zouj; Y Maghsoudi; M.R Beheshtifar
Volume 8, Issue 2 , November 2016, , Pages 101-112
Abstract
Iran as one of the countrieslocated in arid and semi-arid regions of the world, has been in drought danger. Shortage information about long-term weather conditions in many regions of the country, is one of the most important problems in drought monitoring. In this article, spectral vegetation indices ...
Read More
Iran as one of the countrieslocated in arid and semi-arid regions of the world, has been in drought danger. Shortage information about long-term weather conditions in many regions of the country, is one of the most important problems in drought monitoring. In this article, spectral vegetation indices (SVIs) have been employed in order to drought modeling and its forecast. To this end, SPI drought indicator (standardized precipitation index) used to represent period of drought and its intensity. Some broad band spectral vegetation indices including Normalized Difference Vegetation Index (NDVI), Temperature Condition Index (TCI) and Vegetation Condition Index (VCI) were extracted by using NOAA-AVHRR satellite imagery. These indices entered to SVM classifier model to gain the SPI index as its result. After comparing the results, TCI was diagnosed as the best index to predict drought condition via 3 months SPI (trimester SPI).
Nesa Farahmand; Vahid Sadeghi; Shohreh Farahmand
Abstract
In this study, processing and interpretation methods in remote sensing such as visual and spectral analysis have been performed on the EO-1, ASTER and ETM+ data from Meshkinshahr North area, and as a result, the alteration zones in the area have been identified. Then result Aeromagnetic data, using geological ...
Read More
In this study, processing and interpretation methods in remote sensing such as visual and spectral analysis have been performed on the EO-1, ASTER and ETM+ data from Meshkinshahr North area, and as a result, the alteration zones in the area have been identified. Then result Aeromagnetic data, using geological information, alteration and mineralization from the area. Development of advanced tools in remote sensing and geophysical exploration during recent decades indicates the necessity and importance of these tools in industry. For this purpose, a variety of image processing methods are used Aeromagnetic methods have an important role for exploration of metallic ore deposits. To achieve good results from these methods. In order to identify alteration zones, image processing methods such as PCA (principal component analysis), SAM (spectral angle mapping) and MTMF (Matched Filtering MF) using ENVI software were applied on the Hyperion EO-1, ASTER and ETM+ images from the study area. After removal of the noise from observed magnetic data, processing steps were considered, including IGRF subtraction for the proper years, reduction to pole, Signal Analytic, Tilt (TDR), THDR, and upward continuation 1000 meters. Identification of alteration zones in the study area using remote sensing and image processing methods, and interpretation of the geophysical Aeromagnetic results using geological and Mineralization and Hot Springs and Faults information in the area have been led to the identification of Alteration zone. Many Anomaly and Alterations Kaolinite and silica located in the Meshkinshahr north area (northwest Sabalan) and the other many situated in the northwest Sarab. For credibility of results, samples were taken and analyzed by XRD methods. Confirmed the results of remote sensing and aeromagnetic processes. Conclusions of this research revealed that applying concurrency both the remote sensing and aeromagnetic data could be led to improve the precision of the results.
