Elham Khodabandehloo; Mohsen Azadbakht; Soheil Radiom; Davood Ashourloo; Abas Alimohammadi
Abstract
Rapid increase of the world population growth and the demand for food security makes increasing yield as an essential strategy for solving the food supply problem. What is more, because of the restrictions in increasing crop cultivation areas and the decrease in some crops such as wheat in Iran, increasing ...
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Rapid increase of the world population growth and the demand for food security makes increasing yield as an essential strategy for solving the food supply problem. What is more, because of the restrictions in increasing crop cultivation areas and the decrease in some crops such as wheat in Iran, increasing the yield potential can be an effective way to respond to this requirement. Fusarium Head Blight (FHB) is one of the most important wheat diseases and for prediction FHB some methods have already been developed in the USA, Canada, Argentina and Brazil. As there is no model for predicting FHB in Iran, in this study, a method for predicting severity of FHB based on spatial analysis and using environmental parameters and meteorological data was developed for the Moghan, which is in the northwest of Iran. An Internet of Things (IoT) network was established in the study area for measurement of environmental data, including relative humidity, rainfall and air temperature for evaluating the developed model. Random Forests (RF) and extracted indices were used for predicting FHB severity and calculating the relative importance of the indices. We evaluated FHB for the period of 1389 to 1396 and the results show the effectiveness of the developed model and the capability of IoT and spatial analysis for predicting FHB.
Mohammad Mansourmoghaddam; Iman Rousta; Mohammad Sadegh Zamani; Mohammad Hossein Mokhtari; Mohammad Karimi Firozjaei; Seyed Kazem Alavipanah
Abstract
The effect of urban thermal islands due to intersections with major environmental challenges of the 21st century is one of the most important studies on environmental phenomena, and in this regard, the study of the land surface temperature gives a clear perspective of the thermal islands in cities, which, ...
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The effect of urban thermal islands due to intersections with major environmental challenges of the 21st century is one of the most important studies on environmental phenomena, and in this regard, the study of the land surface temperature gives a clear perspective of the thermal islands in cities, which, according to the warm and dry climate of Yazd, examines the status and factors affecting the land surface temperature in this city seem to be necessary. This research, using the spectrally and spatially fused image of Landsat-8, for August 2020, and using machine learning algorithms, tries to model the changes in land surface temperature by calculating different parameters related to urban land perspective. Based on the results of this study, the spectral-spatial fusion of Landsat-8 with Sentinel-2 by Pan sharpening, increased 10.7% of the overall accuracy and 16.5% of the Kappa coefficient in the classification of this image. The study also showed that most neighboring parameters associated with land cover are ranked 1 to 11 of influencing the land surface temperature of Yazd city. In this area, the proximity to bare lands in the radius of 100, 50, and 150 meters ranked 1 to 3 of the most important parameters affecting the land surface temperature respectively. This study showed that the change in land cover arrangement could affect the land surface temperature and changing the bare lands to the built-up areas, up to 1.1°C, to vegetation, up to 2.1°C, and changing 30% of bare land to vegetation, up to 1.6°C can reduce the average land surface temperature in Yazd. Also, this study showed that two different models of vegetation simulation in Yazd city showed that the "land-sparing " model could reduce the average land surface temperature in Yazd by 1.3° and the "land-sharing" model by 1.4°C.
Azar Pouyanjam; Hassan Mahmoodzadeh
Abstract
The increase in urban population following migration from villages and sometimes the uneven development of villages and the transformation of villages into cities are problematic factors in the environmental structure of developing countries, resulting in ecological changes, especially the destruction ...
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The increase in urban population following migration from villages and sometimes the uneven development of villages and the transformation of villages into cities are problematic factors in the environmental structure of developing countries, resulting in ecological changes, especially the destruction of natural landscapes. And reducing them is in the interest of man-made landscapes and disrupting spatial patterns of natural cover. The purpose of this study is to evaluate the ecological changes and measure the patterns governing the landscapes of Varzeqan city using quantitative and qualitative metrics of land appearance in the years 1363, 1381, 1398. The nature of research is developmental-applied and descriptive-analytical. Data were collected through library studies and field studies to calculate the detection of changes, and to measure the quantitative and qualitative metrics of the landscape. After preparing the land cover maps in ENVI software environment, TerrSet software was used to calculate the detection of changes and quality indicators of the land appearance and Fragstats software was used to calculate quantitative metrics. The results showed that the most changes are related to vegetation classes, especially at low density level and in the second interval, which in addition to reducing the area, also includes increasing the number of spots. And has the largest share in becoming barren and pasture lands. Also, two quantitative metrics of spot number (NE) and landscape percentage (PLand) of quantitative metrics and patch area index and patch compactness (Patch Compactnees) more favorably show the changes and patterns governing the appearance of a land. they give.
Alireza Taheri Dehkordi; Mohammad Javad Valadanzouj; Alireza Safdarinezhad
Abstract
Map of croplands is one of the information layers required in the efficient management of these lands. Having such maps makes it possible to monitor agricultural fields during the growing season continuously. In this study, a solution to produce map of Shahrekord’s agricultural lands in two agricultural ...
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Map of croplands is one of the information layers required in the efficient management of these lands. Having such maps makes it possible to monitor agricultural fields during the growing season continuously. In this study, a solution to produce map of Shahrekord’s agricultural lands in two agricultural and non-agricultural classes is presented using the time series of different extracted indices from Sentinel-2 images. Since the use of large data sources is one of the obstacles to the development of methods based on the time series of satellite images, the Google Earth engine processing platform has been used in this study. The proposed method is based on integrating supervised pixel-based classification results with segmentation results. First, training data of supervised classification is provided in a rigorous refining process without the need of collected data from field surveys or interpretation of high-resolution satellite images. Then, by calculating the separability of the two target classes in the time series of each index, the optimal indices are selected. Finally, by combining the results of segmentation and classification methods based on the votes obtained from the classification results, agricultural or non-agricultural class is assigned to each of the image segments. In addition to incorporating spatial information including edges and spatial proximity, this method has been able to improve the noise and porous results of pixel-based classification and has increased the overall accuracy of the final map from 90.7% to 96.05%. Also, user accuracy of both agricultural and non-agricultural classes show an improvement of 3.27 and 7.97%, respectively.
Alireza Zahirnia; Hamid Reza Matinfar; Hossianali Bahrami
Abstract
Organic carbon plays a activate role in environmental sustainability, soil quality and health index, so identifying the spatial distribution of carbon sequestration is a requirement of environmental planning and soil management. The purpose of this study is to investigate the amount of carbon sequestration ...
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Organic carbon plays a activate role in environmental sustainability, soil quality and health index, so identifying the spatial distribution of carbon sequestration is a requirement of environmental planning and soil management. The purpose of this study is to investigate the amount of carbon sequestration in sugarcane and traditional uses of sugarcane, traditional agriculture and barren. In each land use, 60 soil samples were taken and organic carbon, salinity, lime, soil reaction and solution sodium were measured. Using Landsat 8 satellite OLI and TIRS spectral data, the variable of soil and vegetation indices including: NDVI, SAVI, TSAVI, OSAVI, MSAVI, SOCI, WDVI, PVI, RVI and BI in the sample points was obtained and the relationship between them and the amount of soil organic matter was calculated. The results show that in agro-industrial use, SOCI index with 50.30% and band 3 with 53.82% have the highest correlation, in traditional agriculture, PVI index with a correlation of 60.35% and band 7 with 60.63% and in Barren lands ,RVI index with a correlation of 34.27% and band 2 with 36.67% have the highest correlation with the amount of soil organic matter. The results of statistical analysis by partial least squares fitting method showed that the average of calibration and validation results are 43.48 and 39.08%, respectively. The results of estimating soil organic matter by kriging method and M5 tree model show that the correlation between measured and predicted organic matter was 66.20% and 82.00%, respectively. The results show that there is a significant correlation between soil organic matter and Landsat 8 satellite indices and bands, and it is possible to estimate the soil organic matter levels of the study area and other areas with similar conditions with acceptable probability.
Najme Satari; Malihe Erfani; FATEMEH Jahanishakib
Abstract
Trend analysis of growth of cities and predicting their changes in the future are essential for spatial planning. For this purpose, it is necessary to map build-up areas. In many areas, especially in arid climate, it is not possible to separate the build-up areas from the surrounding land cover simply. ...
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Trend analysis of growth of cities and predicting their changes in the future are essential for spatial planning. For this purpose, it is necessary to map build-up areas. In many areas, especially in arid climate, it is not possible to separate the build-up areas from the surrounding land cover simply. That's mean the usual methods of classifying satellite images or conventional indices can’t separate mentioned classes with acceptable accuracy. Hence, many researchers have developed different spectral indices to extract the build-up areas. The use of surface temperature changes to represent build-up areas using the Local Climate Zones (LCZ) algorithm is less considered and is a relatively new method. Therefore, in this paper, the separation of build-up areas from the other surrounding land cover was considered using LCZ algorithm. There is no limit to the number of bands in this method, thus four series of Landsat satellite images in the year 2020 were used and the LCZ algorithm’s accuracy was compared with the latest automatic classified build-up indices including DBI, BLFEI, BAEI and BAEM. The results of this study showed that the classification accuracy of the LCZ algorithm was 96%, while the BLFEI and BAEM indices were not able to completely separate the build-up areas from other types of land cover. The total accuracy of the BAEI index was 0.37. Therefore, the use of LCZ method has a high efficiency compared to build-up indices, and it is recommended in arid and semi-arid zones.
Maedeh Behifar; Hossein Aghighi; Aliakbar Matkan; Hamid Salehi shahrabi
Abstract
Leaf area index (LAI) derived from remotely sensed images is considered as an important index for spatial modelling of vegetation productivity. Traditionally, the spectral vegetation indices (VIs) derived from the red (R) and near infrared (NIR) reflectance values have been utilized to statistically ...
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Leaf area index (LAI) derived from remotely sensed images is considered as an important index for spatial modelling of vegetation productivity. Traditionally, the spectral vegetation indices (VIs) derived from the red (R) and near infrared (NIR) reflectance values have been utilized to statistically estimate LAI. However, most of these VIs saturate at some level of LAI. This limitation was over-come by using the reflectance spectra in the red-edge region. Therefore, it is necessary to evaluate the capability of different VIs derived from RS data to estimate the LAI of silage maize. For this purpose, five field sampling campaigns which were near-simultaneous with Sentinel II over-passes were conducted by the Space Research Center, Iranian Space Research Center and totally 234 samples were collected from the silage maize fields, in Magsal, Qazvin. Then, 13 VIs from the time series of Sentinel-2 imagery were computed and employed to statistically estimate the LAI values. The results showed that Enhanced vegetation index (EVI) with outperformed other VIs to estimate LAI of silage maize. Moreover, the values of non-linear regression models were higher that the liner ones.
Volume 6, Issue 4 , October 2014
Abstract
Runoff is one of the major components of calculating water resource processes and is the main issue in hydrology. Many concept models are used to predict the amount of runoff, which in most cases depend on topographical and hydrological data. Conventional models are not appropriate for areas in which ...
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Runoff is one of the major components of calculating water resource processes and is the main issue in hydrology. Many concept models are used to predict the amount of runoff, which in most cases depend on topographical and hydrological data. Conventional models are not appropriate for areas in which there is little hydrological data. Changes in runoff are nonlinear, meaning it is time & space independent. Therefore it is not easy to simulate the runoff by simple models. Nowadays an appropriate method used in cases where there is a lack of data, is ANN (Artificial Neural Network). The precipitations, temperatures and flows of KAN watershed station between the years of 1996 to 2006 and physiographic characteristics were used as input data for the Artificial Neural Network to predict runoff. 80% of the data is randomly input into the program and the remaining 20% is used to check the accuracy of the result. For the purpose of determining an optimal network, two types of transfer functions, 12 types of training functions and between 1 and 9 kind of hidden neurons are used. After analyzing the hidden layers and various training functions, the results show that the best structure for estimating the runoff is using the precipitation, temperature, flow, LM training function and Tansig transfer function and 4 of the hidden neurons as input data. The results indicated that a Neural Network with such a structure can accurately estimate the runoff. (0.78≥ R2 ≥ 0.68 and 0.03 ≤ RMSE ≤0.53). Keywords: Estimation of Runoff, Artificial Neural Network, Back Propagation Algorithm, Kan basin
Volume 6, Issue 1 , April 2014
Abstract
The Motrabad epithermal system 30 km southwest of Bajestan is located in an assemblage of intermediate to silicic volcanic rocks. The mineralization occurs as irregular veins, veinlets and hydrothermal breccias. Hydrothermal alteration is developed around the veins and consists of silicic (
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The Motrabad epithermal system 30 km southwest of Bajestan is located in an assemblage of intermediate to silicic volcanic rocks. The mineralization occurs as irregular veins, veinlets and hydrothermal breccias. Hydrothermal alteration is developed around the veins and consists of silicic (
Volume 6, Issue 2 , August 2014
Abstract
In this study, a method for replacing MODIS measured flux densities using CRTM is introduced. For this, the Radiosonde measured temperature profiles in Bandar-abbass synoptic station along with night time flux densities measure by MODIS sensor on board of Aqua platform for the deep water region in the ...
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In this study, a method for replacing MODIS measured flux densities using CRTM is introduced. For this, the Radiosonde measured temperature profiles in Bandar-abbass synoptic station along with night time flux densities measure by MODIS sensor on board of Aqua platform for the deep water region in the Persian Gulf were used. Then, using standard predictors of OPTRAN version VIII which is the main part of CRTM model, it was tried to model the difference between modeled and MODIS measured radiance values. To evaluate the method, the averaged RMSE were used. The RMSE between CRTM calculated and MODIS measured radiation fluxes was found to be 0.47 . This value was improved to 0.39 using modified CRTM. The equivalent brightness temperature for these fluxes was 6.45 and 5.27 (K) respectively. So using the suggested method in this study, the CRTM calculated radiances fairly approaches the MODIS measured values. It is suggested that this method be used whenever there are high noises, cloud overcast and or any possible malfunctioning of MODIS sensor to replace the missing data.Keywords: Temperature Profile, MODIS, CRTM, Satellite.
Volume 6, Issue 3 , October 2014
Abstract
Among the usual interpolation methods, kriging and co-kriging are frequently used in the interpolation of precipitation data as one the best linear unbiased estimators, Despite these advantages, there models show smoothness representation and because they are based on regional averages of the data, they ...
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Among the usual interpolation methods, kriging and co-kriging are frequently used in the interpolation of precipitation data as one the best linear unbiased estimators, Despite these advantages, there models show smoothness representation and because they are based on regional averages of the data, they predict maximum and minimum values lower and higher than real values respectively. Therefore, using these models alone is not sufficient in cases where the target is assessment of risk and study of variability. Variability of phenomenon could be measured by uncertainty index. In the study in order to calculation of local and spatial uncertainty of precipitation, geostatistical simulation algorithms CO-SGS and SGS were used. The main result of the study showed that, in simulation sample SGS and CO-SGS algorithms would be able generate the Max and Min probable value making variance as close as to the main data. The difference simulation variance is very low with main samples, in contrast, the difference of variance between main samples and interpolation method is very high. The result also showed that the mentioned algorithms could be able to compute the local and spatial uncertainty of the precipitation by different simulation. Keywords: Precipitation, Uncertainty, Geostatistical Simulation, SGS Algorithm, CO-SGS Algorithm
Ayoub Moradi; Hadiseh Babaei; Abbas Alimohammadi; Soheil Radiom
Abstract
The increasing shortage of the renewable water resources in the country has made the farm water needs estimation to become as one of the important priorities in agricultural water management. Farm water needs are normally controlled by climatologic factors. It equals to the reference evapotranspiration ...
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The increasing shortage of the renewable water resources in the country has made the farm water needs estimation to become as one of the important priorities in agricultural water management. Farm water needs are normally controlled by climatologic factors. It equals to the reference evapotranspiration which is corrected by a scaling factor associated to the crop kind and to local characteristics. In this research, using Landsat satellite imagery, we estimated and compared the crop coefficients for main agricultural crops in the Moghan cultivation industry, from two procedures: the first based on evapotranspiration measuring, and the second based on NDVI measuring. The comparisons in the case of the five main crops showed that the Root Mean Square Errors are within an acceptable range, leass than 0.28. In the following, the evapotranspiration based crop coefficient has been used in order to estimate farm water needs. Farm's water needs are indeed estimated by six methods: a combination of two actual evapotranspiration and three reference evapotranspiration ways. Among the six methods, the Metric/PenmanMonteith method was selected for final step, i.e. farm irrigation needs. The farm irrigation needs is equivalent to farm water need minus effective rain. We compared four different ways for estimating the effective rains but preferred the FAO method assigned for low slopes. Based on our results, farm irrigation needs in the Moghan cultivation industry range from 270 mm (for rainfed barley) to 1500 mm (olive groves). Statistical investigation in three years data revealed a dependency between yield performance and evapotranspiration rate. In addition, it showed that yield performance is correlated with crop spectral indices such as NDVI, LAI and SAVI. The primary goal of this research is to estimate local agricultural crop coefficient in the Moghan cultivation industry. The second goal is to investigate of relationships between crop coefficient and crop spectral indices in order to make the crop coefficient estimable directly from spectral indices.
H.A Bahrami; S Mirzaei; A Darvishi Boloorani
Volume 7, Issue 4 , November 2015, , Pages 13-26
Abstract
In recent years, dust storm has become a common phenomenon in West Asia and especially Iran. This phenomenon is affecting almost all aspects of life including fauna and flora as well as human life. This research aimed to investigate the effects of dust storms on the wheat canopy, that are the most important ...
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In recent years, dust storm has become a common phenomenon in West Asia and especially Iran. This phenomenon is affecting almost all aspects of life including fauna and flora as well as human life. This research aimed to investigate the effects of dust storms on the wheat canopy, that are the most important agricultural species, reflectance and best band for selected narrow band indices to discriminating wheat canopies which are under dust stress in different growing stages. Two wheat (Triticum aestivum L.) varieties, Aflak and Pishtaz, were grown in pots under controlled conditions. The treated samples were exposed to simulated dust storm, in the wind tunnel, at two growth stages including Tillering and Heading stages. In each stage the treatments were exposed in 2, 4 and 6 days. Field spectroscopy measurements were carried out at canopy level using a full range spectro-radiometer Fieldspec-3-ASD. New narrow-band vegetation indices from NDVI, RVI, PVI and SAVI2 indices were computed from the all measured canopy spectra, Tillering and Heading stageseparately. To assess the performance of the indices, the RMSE, R2 and cross-validation method were used. For most indices, the selected optimum narrow bands are very close to one another and located in visible and NIR spectral domains. The result showed that the PVI index performed the best for considering the dust effect on wheat crops. The result also show that the selected indices have better performance in the Tillering stage ( 0.77; 0.63 0.80)for estimating the dusty days, compared with Heading stage ( 0.91; 0.62 0.71). Therefore, determining the dusty days by narrow band indices could be done precisely in the early stage of wheat growing.
J Sadidi; P Zeaiean Firozabadi; Z Darvari
Volume 9, Issue 3 , February 2018, , Pages 15-32
Abstract
Today, one of the limitations of water resources is the solutions weakness of water resource management. One of the management solutions to improve the problem above is the optimal allocation of users with the virtual water approach. In the present study, a model for optimization of user allocation with ...
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Today, one of the limitations of water resources is the solutions weakness of water resource management. One of the management solutions to improve the problem above is the optimal allocation of users with the virtual water approach. In the present study, a model for optimization of user allocation with a virtual water storage approach is provided with using of genetic algorithms (NSGA-II, GA) in HAJILAK lands, located in the city of BUKAN in the West AZARBAIJAN province. After the preparation of the land use layer in the GIS and the preparation of target function coefficients, the user allocation with using meta-heuristic algorithms, is optimized with particular attention to virtual water. The results show that the suggested user patterns in the GA and NSGA-II algorithms, respectively, increased the storage of virtual water at an optimum of 29% and 35%. This model, as the decision support system, can play an effective role in deciding managers for different purposes. Also, the repeatability, runtime, and convergence of algorithms in the model indicate the superiority of the NSGA-II algorithm than GA. So that NSGA-II algorithm has less time in executing model, more convergence and less variance in the repeatability test than GA algorithm. This model can act as a decision support system to play an effective role in decision makers based on different goals. In this research, the use of meta- heuristic algorithms to optimize the allocation of users with the virtual water approach can be expressed in the thematic innovation of this research.
Hamidreza Matinfar; H Mahmodzadeh; A Fariabi
Volume 10, Issue 2 , September 2018, , Pages 15-32
Abstract
Soil organic matter is one of the most important Physical and chemical properties of soil that it iscritical in determining the quality and management of soils. Quantify of soil organic carbon due to thehigh spatial variability and changes over time is difficult. Near-infrared-visible spectroscopy is ...
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Soil organic matter is one of the most important Physical and chemical properties of soil that it iscritical in determining the quality and management of soils. Quantify of soil organic carbon due to thehigh spatial variability and changes over time is difficult. Near-infrared-visible spectroscopy is afeasible method to reduce the time and cost to check the organic carbon. The aim of this study was toevaluate soil organic carbon through near-infrared and visible spectroscopy with the statistical modelsPLSR and PCR. For this purpose, 40 soil samples from depths of 0 to 30 cm were collected bysystematic random method based on previous studies and determination of different classes of soils inthe region. Chemical analysis of soils was performed according to standard methods. Spectralreflectance of soil samples in the range of 350 to 2500 nm was measured then after applying thepreprocessing methods such as Savitzky and Golay filter, Soil organic carbon were calculated byprincipal component analysis (PCA), regression partial least squares (PLSR) and principal componentregression (PCR) models. The results of this study showed that the Savitzky and Golay filter was thestrongest preprocessing method for spectral data. Coefficients of determination (R2), root mean squareerror of Prediction (RMSE) and ratio of prediction to deviation (RPD) in the calibration andvalidation to predict organic matter, respectively, 0.97, 0.05, 5.09 and 0.85, 0.14, 2.78 respectively.Therefore, for dry and semi-arid soils of the PLSR model, it is more efficient to predict the organiccarbon of the soil. The results showed that the PLSR model has better performance than the PCRmodel in soil organic carbon estimation.
mehrdad gobal; MirSaman Pishvaee; Barat Mojaradi
Abstract
From the beginning of the Earth until now, humans have affected their environment more than anyother creature. With increasing population, water and soil constraints, and climate change, foodsupply has faced serious challenges. Among agricultural products, wheat is one of the most widelyused strategic ...
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From the beginning of the Earth until now, humans have affected their environment more than anyother creature. With increasing population, water and soil constraints, and climate change, foodsupply has faced serious challenges. Among agricultural products, wheat is one of the most widelyused strategic crops in Iran and many countries in the world, which can be grown in hot and dryclimates with good yields. In Iran, about 10% of the demand for agricultural products is suppliedfrom Fars province. Fars province also has the second rank of wheat production among the provincesof Iran. This study intends to evaluate the suitability of lands in this province for dryland wheatcultivation. In the first step, climatic, soil and topographic data in the area of Fars province arereviewed and analyzed. In the next step, the information layers are entered into the GIS software andthe suitability map of wheat cultivation lands is determined using the VIKOR multi-criteria decisionanalysis method. The results of this study have shown that about 32% of the Fars province lands is inthe first and second rank of land suitability for wheat cultivation. Also, the distribution of suitableareas for wheat cultivation is higher in the western and northwestern regions of Fars province.
Amir Hedayati; Mohammad H Vahidnia; Hosseain Aghamohammadi
Abstract
Rice has become one of the most important food security items in many countries, especially Iran. In this study, a model was proposed to select Landsat-8 satellite time-series images in order to prepare a map of paddy lands. The method is based on the phenological characteristics of rice plants and annual ...
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Rice has become one of the most important food security items in many countries, especially Iran. In this study, a model was proposed to select Landsat-8 satellite time-series images in order to prepare a map of paddy lands. The method is based on the phenological characteristics of rice plants and annual surface temperature data from the MODIS sensor. After preprocessing satellite images, they were classified using an object-based approach and fuzzy functions. Various data such as a digital elevation model, land surface temperature, and spectral indices including NDVI, EVI, NDBI and LSWI are used to improve the classification process. In addition, information about the segmentation of the image is employed during the process of classification. Because of the different traits of paddy fields, a digital elevation model with a resolution of 12.5 meters was used to help differentiate paddy lands from other vegetation. In addition, a comparison was made between the results of classification based on object-based and pixel-based methods. The results showed that the object-based classification yields better results than the pixel-based method with special considerations. The classification result following validation using ENVI software pixel-based classification indicated an overall accuracy of 92 percent and a kappa value of 0.89. This is in contrast to the object-based classification technique in the eCognition software, which yielded an overall accuracy of 94 percent and a kappa coefficient of 0.92.
Ali Akbar Matkan; Babak Mansouri; Babak Mirbagheri; Fariba Karbalaei
Volume 7, Issue 3 , November 2015, , Pages 17-32
Abstract
Earthquake is one of the most destructive natural disasters which frequently occurs with different intensities. Earthquakes cause severe damage to buildings, main roads and most importantly, loss of life. Detection of damaged buildings caused by such an event at the right time is a critical issue for ...
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Earthquake is one of the most destructive natural disasters which frequently occurs with different intensities. Earthquakes cause severe damage to buildings, main roads and most importantly, loss of life. Detection of damaged buildings caused by such an event at the right time is a critical issue for crisis management and disaster relief. The aim of this study is to detect earthquake damaged buildings using very high resolution (VHR) satellite imagery. To achieve this result, the satellite images with very high resolution before and after the earthquake in Port-au-Prince in Haiti as well as the observed destruction map in 2010 were used. In this study, the optimum features extracted from the image were selected using correlation analysis. The buildings destroyed were classified using fuzzy inference system and the values of selected textures. Finally, the damage map obtained from the proposed algorithm was compared to the map of the area. The kappa criterion estimated from the results of the proposed method is 82% while the index- Jaccard parameter is 89.69%.
R Hosseini; A Alimohammadi; M.H Ghasemian
Volume 8, Issue 2 , November 2016, , Pages 17-34
Abstract
Change detection methods are powerful tools to present the changes on the Earth’ surface. The multi-scale approaches which proceed the observations at coarser and finer scales, can be applied to maximize the accuracy of the change maps. The multi-scale approach, based on discrete wavelet, has been ...
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Change detection methods are powerful tools to present the changes on the Earth’ surface. The multi-scale approaches which proceed the observations at coarser and finer scales, can be applied to maximize the accuracy of the change maps. The multi-scale approach, based on discrete wavelet, has been applied in this research. In addition to the spectral information, the contextual or local information- available in the image are set in the processing. The wavelet technique is exploited in many processing fields of images. The ability of the wavelet technique has been applied for the change-detection, based on the satellite images in this study. The necessary parameters for the wavelet modification are the quantity decomposition levels and kind of mother wavelet. Thus The effect of the mother wavelet boir3/7 and db4 and the levels of decomposition s=1 to s=6 on the final change detection map have been assessed . All the results have been stated on the basis of the detection accuracy kappa coefficient and overall accuracy. The results reveal the influences of the mother wavelet and levels of decomposition on the final change detection map. The Change detection map, using t bior3/7mother wavelet, reveals higher overall accuracy and better kappa coefficient in proportion to bd4 mother wavelet. It is 0/7966 and 89/8013 for band 3 of Mother wavelet bior3/7 and 9/8013 & 0/7966 for mother wavelet db4. The next parameter being investigated here is related to the analysis surfaces influence on the precision of the change detection map. It increases to the level 3 of analysis and then decrease down. Eventually, most of the overall precision and kappa coefficient is related to the analysis level 3 of both mother wavelet. A comparison has been also conducted between the wavelet technique and the three methods image differencing , image ratio and supervised classification. The final review reveals the priority of the wavelet technique, as it presents better results.
, A.A Matkan; , A. Alimohammadi; , B Mirbagheri; , K Akbari; , M Tanasan
Volume 9, Issue 1 , October 2017, , Pages 17-36
Abstract
Commensurate with the complexity of human behavior, social systems are complicated. Population management in these systems are crucial and need to spend too much cost. Because of the interaction between humans and the environment and then the impact of these interactions on social systems in the process ...
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Commensurate with the complexity of human behavior, social systems are complicated. Population management in these systems are crucial and need to spend too much cost. Because of the interaction between humans and the environment and then the impact of these interactions on social systems in the process of population movements, there is a need to identify and study these interactions, especially in emergency situations.In this study, the results of agent based geosimulation of pedestrian movements and fire simulation at Hafte-Tir subway station were used to investigate the behavior of individuals and the environment during fire. Then, the discomfort indices, including environmental and human-environmental indicators, were calculated to examine the effect of the environment and agents on the movement process. This research has introduced two new discomfort indices i.e. environmental index AM1 and environmental-humanity index AM2 to evaluate the behavior of individuals and the environment during the fire. The innovation of these indices relates to the integration of the results of the agent based simulation and the fire simulation in the environment and after that using of visibility, in addition to the interactions of individuals with each other and their interactions with the physical components of the environment. Calculating results of indices and the results of people movement’s simulation in the station represented an inverse relationship between the level of discomfort and speed of crowd in the station. Also, the discomfort induces in the successful environmental scenario shows a reduction in the discomfort in hot spots rather than current situation scenario. The use of agent based geosimulations and the result of discomfort indices in different periods of crisis, can contribute population management strategies and emergency evacuation.
M Panahi; M.J Valadan Zoej; S Yavari
Volume 10, Issue 1 , June 2018, , Pages 17-40
Abstract
Non-physical models have attracted the attention of experts in the field of photogrammetry and remote sensing due to the lack for need of ephemeris data at the time of imaging and not providing raw images by owners of these images. In this paper, a comprehensive research was performed on non-physical ...
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Non-physical models have attracted the attention of experts in the field of photogrammetry and remote sensing due to the lack for need of ephemeris data at the time of imaging and not providing raw images by owners of these images. In this paper, a comprehensive research was performed on non-physical models including: 3D Affine Model, First Order Rational Function Model with unequal denominator, SDLT, DLT, Rational Function Model with equal denominator, with the emphasis on the effect of linear and point features as control information to geometrically correct the high spatial resolution images. In addition, a new form of Pushbroom-Projective function is introduced, as a new idea for geometric correction of satellite images. The satellite images used in this research are GeoEye-1 from Urmia and Ikonos from Hamedan. Based on the results obtained, in the case of GeoEye-1 satellite image, First Order Rational Function Model with unequal denominator when using point features as control and +XY term of Rational Function Model with equal denominator when applying linear features as control reached the highest accuracy of 0.75 pixel and 2.03 pixel respectively. In the case of Ikonos satellite image, the +XY term of Rational Function Model with equal denominator when using control point features and First Order Rational Function Model with unequal denominator when using linear control features reached the accuracy of 0.68 pixel and 1.5 pixel respectively at the best. It is worth mentioning that the remaining systematic errors in the case of using linear features as control are always more than those obtained using point control features.
Hamid Ezzatabadi Pour
Volume 10, Issue 3 , January 2019, , Pages 17-32
Abstract
K-Means is one of the most frequently used unsupervised classification approaches for remotely sensed image analysis. In standard K-Means version, the Euclidean distance (ED) has used to estimate the dissimilarity between an unknown vector data and the cluster center. Since, this measure is very sensitive ...
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K-Means is one of the most frequently used unsupervised classification approaches for remotely sensed image analysis. In standard K-Means version, the Euclidean distance (ED) has used to estimate the dissimilarity between an unknown vector data and the cluster center. Since, this measure is very sensitive to topographic and environmental effects on spectral observations, we have proposed to replace it with a new one for goal of hyperspectral image clustering. The Spectral Information Divergence (SID) is a stochastic measure that is a more reliable dissimilarity measure when compared to ED as a deterministic measure. Where the ED measure the spectral distance between vector data and the clusters, SID models the probability distributions for vector data and clusters by normalizing their spectral signatures and measures the distances between them. This idea has applied to develop an enhanced clustering framework. The experimental results on three real hyperspectral images collected by HyMap, HYDICE and Hyperion sensors show that the proposed method improves classification results. In the manner that the Kappa coefficient of the classification results of three hyperspectral imagery datasets increased by about 7%, 56% and 10%, respectively.
Nahid Haghshenas; Ali shamsoddini; Hossein Aghighi
Abstract
It is necessary to know about the quantity of urban tree canopy cover due to its role in air and noise pollution reduction, wind prevention, saving rain water, and runoff control. Being expensive and time consuming, the manual extraction of tree canopy has been replaced by remote sensing techniques conducted ...
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It is necessary to know about the quantity of urban tree canopy cover due to its role in air and noise pollution reduction, wind prevention, saving rain water, and runoff control. Being expensive and time consuming, the manual extraction of tree canopy has been replaced by remote sensing techniques conducted on the images, digitally. There are several parameters which must be optimized prior to use of object oriented classification. One of these parameters is Scale affecting the segmentation results, significantly. Scale is usually set by trial and error which is an experimental approach. One of the aims of this study is to optimize Scale parameter, automatically. In addition, after segmentation process based on a proper Scale, it is required to classify the identified segments based on the attributes which are extracted from these segments. In this stage, the selection of suitable classification method fed by the proper attributes is critical. In this research, LiDAR data and aerial image acquired on Vaihingen, Germany, were utilized for segmenting the urban area. In order to identify suitable attributes, random forest feature selection was applied on the attributes derived from the identified segments. Machine learning methods including support vector machine, random forest, and decision tree were compared for classifying the segments based on their suitable attributes into two classes including tree canopy cover and others. The results indicated that Scale of 25 is the best one to segment this area. Also, the tree canopy cover map derived from support vector machine with quality index of 79.90 showed the best performance among different classifiers used in this study.
Mojtaba Akhoundi Khezrabad; Mohammad Javad Valadan Zoej; alireza safdari nezhad
Abstract
Due to the wide applications of hyperspectral images, economical and innovative imaging systems are developed to acquire such images. In order to use hyperspectral images, it is necessary to establish an accurate relation between the ground space and the image space, which needs numerous Ground Control ...
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Due to the wide applications of hyperspectral images, economical and innovative imaging systems are developed to acquire such images. In order to use hyperspectral images, it is necessary to establish an accurate relation between the ground space and the image space, which needs numerous Ground Control Points (GCPs). This fact highlights the need for developing geometric corrections methods for any camera design. BaySpec OCI-F (400-1000 nm) is one of the innovative cameras that acquires pushbroom hyperspectral images. In addition to the pushbroom sensor, the camera uses a frame sensor that acquires images at the same time as the pushbroom sensor and with the same temporal rate. In this article, a geometric correction method for pushbroom images of OCI-F camera is proposed. Based on the camera’s imaging design, the first step of the method determines a set of calibration parameters which geometrically relates the pushbroom and the frame sensors. Then using this relation and the geometric relations among consecutive frames, the pixels of the pushbroom scene are rearranged and form the corrected image. The proposed method determines the relation among the consecutive images via Least Square Matching (LSM) method. The results show that the correction method has decreased the geometric distortions of the raw pushbroom scene by 62.2% on average. Such a reduction causes the average accuracies of two-dimensional and three-dimensional generic models which relate image space and ground space together, to increase by 34.1% and 39.9% respectively.
Farideh Taripanah; Abolfazl Ranjbar; Abbasali Vali; Marzieh Mokarram
Abstract
One of the new and unique sections, especially in internal studies, is the quantitative examination of unevenness. The scientific and quantitative study of topographic position has always been one of the topics that have received little attention in domestic research. So, classification and identification ...
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One of the new and unique sections, especially in internal studies, is the quantitative examination of unevenness. The scientific and quantitative study of topographic position has always been one of the topics that have received little attention in domestic research. So, classification and identification of different morphometrically distinct regions are necessary. Thus, the present study aims to classify landforms in the northwest of Fars province, Kharestan region and investigate its factors affecting. In this regard, the Topographic Position Index (TPI) method was used in the first stage to classify landforms, followed by the CORINE method to determine erosion risk classes. Additionally, Landsat 8 satellite images from June 2017 were used to determine the normalized differential vegetation index (NDVI). The next step was to determine the relationship between different types of landforms and terrestrial factors such as height, slope, slope direction, topographic wetness index (TWI), Terrain Ruggedness Index (TRI) and NDVI. Finally, the status of different landforms was determined based on erosion risk classes. Results showed ten different types of landforms existed within the study area. Small plains (1.18%) were the lowest in the study area, while waterways (27.71%) and high peaks (27.48%) were the highest. The TWI was significantly correlated with landform classes at 95% level. Most of the region (91.71%) had NDVI classes of 0.1 to 0.3. Stream and u-shaped valleys were found to have higher NDVI values. Real erosion risk was classified into three classes: low, medium, and high with areas of 31.14, 31.11, and 37.78%. There were 44, 57, and 59% erosion levels in the low, medium, and high erosion classes, respectively.
