R Nazari,; A Kaviani, A
Volume 9, Issue 1 , October 2017, , Pages 63-74
Abstract
Increasing crop production depends on the supply crop water demands, thus accurate estimation of crop water demands helps not only to crop production, but also is effective in the management of water resources. SEBAL and METRIC algorithms are the most widely used methods for estimating evapotranspiration ...
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Increasing crop production depends on the supply crop water demands, thus accurate estimation of crop water demands helps not only to crop production, but also is effective in the management of water resources. SEBAL and METRIC algorithms are the most widely used methods for estimating evapotranspiration as a residual of the energy balance with using remote sensing data. Based on this, the purpose of the present research was to investigate the results of actual evapotranspiration of crop from SEBAL and METRIC models in the Qazvin plain. To evaluate the results of actual evapotranspiration, two sensors with different temporal and spatial resolution (images of MODIS sensor Terra satellite and ETM+ sensor Landsat 7 satellite) were used. In this regard, Qazvin weather station data as well as data Lysimeter were used in order to verify the results of METRIC and SEBAL algorithm. The results of METRIC and SEBAL models with a total of 10 images obtained from MODIS sensor Terra satellite and ETM+ sensor Landsat 7 satellite were evaluated with data Lysimeter for grass reference crop in 1380. MODIS sensor with r=0.88, RMSE=1.91 and SE=0.85 mm/day with r=1.00, RMSE=0.91 and SE=0.09 in METRIC model compared with the SEBAL model estimates are more accurate than the lysimeter operation and in this research recommended as a top model for estimating actual evapotranspiration in the Qazvin plain.
Ahmad Malek Nejad Yazdi; Hassan Ghassemian; Vahid Esavi
Volume 7, Issue 3 , November 2015, , Pages 65-82
Abstract
Most of common classification algorithms in remote sensing are based on spectral characteristics of the pixels. These approaches result in ignorance of many precious information, such as texture, in the classification process. The urban environment has an inhomogeneous texture, which makes land covers ...
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Most of common classification algorithms in remote sensing are based on spectral characteristics of the pixels. These approaches result in ignorance of many precious information, such as texture, in the classification process. The urban environment has an inhomogeneous texture, which makes land covers detection a complicated process. In this study, use of texture extracted from the panchromatic image of ALI detector for improvement of Hyperion image's classification accuracy in urban regions was analyzed. Classification carried out using Random Forests method and in five different scenarios. These scenarios included: 1- Classification of the fused image by CNT method (Without Incluion of Texture Information), The other four scenarios covered the classification used by simultaneous use of texture extracted by Gray Level Co-occurrence Matrix »GLCM« in 4 different window sizes: 3,5,7,9 and fused image. Results of these analyses revealed that use of texture information as a useful parameter can lead to an enormous improvement in classification accuracy. Our findings showed that use of texture resulted in an increase in overall accuracy by around 10 percent from 80.47 to 90.74 percent . Many of land use/land covers such as roads, residential and industrial areas also experienced the improvement in producer and user accuracies. OOB error as an essential random forests parameter inclined as far as 11 percent from 19.86 to 8.87 percent. Moreover, the GLCM feature vectors such as mean and contrast achieved high ranks in importance evaluation in random forests classification. Increase of window size also led to a rise of classification accuracy and the window size 9 gained the highest accuracy accordingly.
Nassim Hoorijani; Farzin charehjoo
Abstract
Physical activity is one of the most important aspects of life that has many environmental, economic, social and health benefits. According to the importance of the public space of cities in the occurrence of these activities, the issue of developing built environments as a framework that can promote ...
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Physical activity is one of the most important aspects of life that has many environmental, economic, social and health benefits. According to the importance of the public space of cities in the occurrence of these activities, the issue of developing built environments as a framework that can promote physical activities has become of the major issues in urban societies around the world. This research is aimed at objectively measuring the environmental qualities influencing the residents’ physical activities. It has been conducted in an analytical-applied manner, in four areas with a buffer of 500 meters selected from the four urban context of Sanandaj city in Kurdistan province. Through a review of the previous researches, the factors influencing the physical activities have been identified and the data related to each of them have been prepared using the layers of street networks and land uses in geographic information system. The obtained data have been placed in quantitative evaluation formulas related to each criterion. The mentioned evaluations have been studied in a combinational manner and in smartraq and pedestrian orientation indexes, as the two common one. Then, through comparison of the results of the mentioned indexes, the studied areas were classified in groups from high pedestrian oriented to non-pedestrian oriented. The data of physical activities were gathered from 421 inhabitants by questionnaire. The mean of the data has been calculated in SPSS software by variance tests. At the final stage of the research, the relation between pedestrian orientation qualities of the built environment and the level of the residents’ physical activities has been studied by regression analysis. The results of this research indicate the importance of the built environment on the level of physical activity and hence the public health of residents.
Ghasem Javadi; Mohammad Taleai
Abstract
Public satisfaction is a multidimensional and dynamic concept that changes over time, so it must be evaluated at appropriate times. A major challenge for this evaluation, especially in large geographical areas such as one country, is the lack of regular procedures and updated relevant index values. In ...
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Public satisfaction is a multidimensional and dynamic concept that changes over time, so it must be evaluated at appropriate times. A major challenge for this evaluation, especially in large geographical areas such as one country, is the lack of regular procedures and updated relevant index values. In recent years, several indicators have been presented based on traditional methods of data collection, including the use of questionnaires, to measure public satisfaction. Since, in recent years, the use of User Generated Geo-Content (UGGC) has been widely considered, in this research, with a new perspective by using of location-based social networks (LBSNs), extraction of information and criteria that can somehow reflect public satisfaction has been done. Finally, considering the uncertainties in the input data and the definition of public satisfaction, a fuzzy inference system was used to evaluate and compare public satisfaction in Iranian provinces. The extracted indices in this study, include negative/positive tweet ratio, the ratio of joy and love tweets to all tweets, and the ratio of sadness, anger and fear tweets to all tweets. The results of the proposed method resulted in the classification of the provinces of Iran from favorable to unfavorable situations. The results of this study demonstrated the potential of UGGC for public satisfaction assessment in the role of complementary data rather than as an alternative to official data. The proposed method in this study is a step towards evaluating public satisfaction using data shared by users on location-based social networks.
Manouchehr Manteghi; Yazdan Rahmatabadi
Abstract
Remote sensing is the science of obtaining information from the surface of the earth without explicitcontact with the components studied. Commercialization is a set of activities that converts aninnovation into a product or service that brings economic benefits. Given the widespread use formeasurement ...
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Remote sensing is the science of obtaining information from the surface of the earth without explicitcontact with the components studied. Commercialization is a set of activities that converts aninnovation into a product or service that brings economic benefits. Given the widespread use formeasurement and the high importance of its application in agriculture, commercialization of thistechnology in agriculture has been a top priority and investigated in this study. The target populationof this research is active and passive companies in this field to use their experience to provide suitablefield for cultivation of remote sensing technology through in-depth interviewing and snowballsampling. The catch is used. In this research, using product and technology life cycle diagrams,examining the challenges of technology and infrastructure commercialization, commercializationelements, types of software used in the world agricultural industry, remote sensing investment chartsand analysis The viability of remote sensing in agriculture as a business has been scrutinized. As aresult, the best way to commercialize the product is to reduce constraints for active companies, buildthe necessary infrastructure, especially timely data, and be independent in deploying this technologyto allow users to use a variety of business methods. Provide.
A.A Abkar
Volume 7, Issue 2 , November 2015, , Pages 69-88
Abstract
Investigation of various types of vegetation’s characteristics as an effective parameter in the energy exchange between the atmosphere and Earth's surface is very important in environmental, natural resources and agriculture studies. Nowadays, using remote sensing techniques with a wide range of valuable ...
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Investigation of various types of vegetation’s characteristics as an effective parameter in the energy exchange between the atmosphere and Earth's surface is very important in environmental, natural resources and agriculture studies. Nowadays, using remote sensing techniques with a wide range of valuable spectral information facilitate the study of vegetation, especially in estimation of the biophysical parameters. One of the most important biophysical parameters used in the various analyses related to the study of vegetation is Leaf Area Index (LAI). In this study, in addition to the analyzing and modeling of the relationship between LAI and vegetation indices (VIs)via spectrometry observations, the limitations of the mathematical model for estimation of LAI has been explored, some practical guidelines have been provided to improve the accuracy of the model as well as a new vegetation index has been designed. Finally, the results showed that through the conventional vegetation index, Simple Ratio (SR) and Second Soil Adjusted Vegetation Index (SAVI-2) have the minimum RMSE (about 0.08 in LAI unit) and the fitted models using their formulas in comparison with the other indices have the minimum rate of saturation. In other words, these indices are more efficient to estimation of the LAI; especially in high density vegetation area and can be used with high reliability in linear models for LAI estimation.
Mir Hamed Mirloye Mousavi; Behzad Zamani Gharechamani
Abstract
Nowadays remote sensing science is widely used in the earth sciences. One of these branches is the identification, investigation and interpretation of the surface structures of the earth. The study of the fracture systems, the recognition of the geometry and spatial distribution of them, could helped ...
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Nowadays remote sensing science is widely used in the earth sciences. One of these branches is the identification, investigation and interpretation of the surface structures of the earth. The study of the fracture systems, the recognition of the geometry and spatial distribution of them, could helped to development of the oil fields. In this research, using Landsat 8 satellite imagery data, STA algorithm and remote sensing processing techniques, geometric analysis of the surface structures in the Siyahmakan oil field has been investigated. Then, the ground surface temperature (LST), calculated by Landsat project science office model for the oil field, and finally examined the relationship between the distribution of surface fractures and the surface temperature distribution pattern of the study area. The results show that the parallel axial (SA) and shear fractures(SO1), that have the N-S and NW-SE trend ordinary, have the highest frequency. According to the density map, the density of the lineaments in the mid-zone to the southeast of the field is the highest. Since underground resources are located along the tectonic lineaments near the surface, these structures have seen cooler and darker in the satellite images of the surrounding areas. Structural lineaments have shown conformity with surface temperature map so that lineaments often located in the regions with low to medium and medium – level Temperatures. Therefore low temperatures correspond to the highly fractured areas.
M Danesh; R Darvishzadeh; A.A Noroozi
Volume 8, Issue 1 , November 2016, , Pages 71-94
Abstract
Satellite image fusion and creating data with spectral and spatial capabilities greater than those of the existing data is of special interest and position in Remote Sensing. However, the accuracy and efficiency of all processing stages of using these data depend on the precision and reliability of the ...
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Satellite image fusion and creating data with spectral and spatial capabilities greater than those of the existing data is of special interest and position in Remote Sensing. However, the accuracy and efficiency of all processing stages of using these data depend on the precision and reliability of the produced data. The optimum utilization of fused images relies, ultimately, on the precision of the employed fusion method. Evaluation of this important aspect requires selection of an optimum assessment metric which is appropriate for the objectives and application areas of fused images. Different application areas such as, natural resources, civil areas and etc. have different preferences with regard to maintaining the spectral and spatial data. Therefore, selection of the best fusion method, that is appropriate for the application area of the image, through image quality assessment metrics is one of the users’ challenges in this field. The present paper, thus, attempts to provide an analysis and assessment of 20 common image quality assessment methods so as to identify and introduce the most optimum metrics based on the area of application of fused images. It also tries to introduce the factors causing differences in the way quality is assessed by the metrics. And then present a model for identifying the capabilities of each metric for displaying the distortions that occur in the spectral and spatial aspects of data. To this end, two metrics of high-pass filter and spectral angle mapper are taken into consideration as spectral and spatial data comparison bases, and the performance of metrics with regard to their assessment of the quality of simulated data, that contain images with controlled spectral and spatial distortions, is evaluated. Spectral distortions were introduced by high-pass filter effect, band displacement and changing color tone. Low-pass filter and attrition filters with structural elements of different dimensions were also used for introducing spatial distortions. Due to offering different spectral and spatial resolutions, images from Landsat8, EO-1, and Angular Mapper method that are suitable for assessment of images with sensitive applications as they display the spectral distortions with greater precision; These methods include BIAS, RASE, Q, MSSIM, NQM, FSIM, SRSIM, and SAM indices. The third group is also compatible with high-pass filter of HPF, RFSIM and MAD that are of a greater capability for displaying spatial distortions.
M Rahimpour; N Karimi; R Rouzbahani; A Rezae
Volume 9, Issue 3 , February 2018, , Pages 71-90
Abstract
Cocurrent access to high spatial and temporal resolution imageries is essential in many studies. However, this will not be provided by using images from one sensor. To achive this goal, the incorporation of different satellites with high spatial (e.g., Landsat) and temporal (e.g., MODIS) images can be ...
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Cocurrent access to high spatial and temporal resolution imageries is essential in many studies. However, this will not be provided by using images from one sensor. To achive this goal, the incorporation of different satellites with high spatial (e.g., Landsat) and temporal (e.g., MODIS) images can be used. In present study, one of newest data fusion model, Enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was evaluated with actual satellite data (OLI image). For emplementation and evaluation of this model, two different periods were selected (the first period selected between the days 204 to 220 and the second one were between the days 220 to 236). For evaluating the obtained results, OLI satellite images were used as a refrence data. Results show that ESTARFM not only improves the accuracy of predicted fine-resolution reflectance, especially for heterogeneous landscapes but it preserves spatial details also. The Coefficient of Determination (R2) of blue, green, red and near-infrared estimation bands with actual satellite data was 0.90, 0.91, 0.91 and 0.85 respectively, and the average Root-Mean-Square Error (RMSE) in four bands are 0.025, 0.030, 0.036 and 0.049 successively. In addition, a comparison between obtained NDVI from estimated reflectance values and observed NDVI, indicates outputs of ESTARFM have acceptable accuracy of (R2 =0.87 and RMSE =0.056). Thereby, this model can be successfully utilized to fusion images for enhancing the spatial and temporal resolution of reflectance.
Z Hossein-Nejad; M Nasri
Volume 9, Issue 4 , May 2017, , Pages 72-94
Abstract
Image registration process is one of the most important branches in the field of image processing, which is an essential preprocessing for the use of remote sensing. Scale invariant feature transform (SIFT) is one of the most commonly used feature-based methods for registration of images. However, a ...
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Image registration process is one of the most important branches in the field of image processing, which is an essential preprocessing for the use of remote sensing. Scale invariant feature transform (SIFT) is one of the most commonly used feature-based methods for registration of images. However, a main weakness of this algorithm is the creation of a large number of mismatches. Based on the spatial relationships of the corresponding points of SIFT, the proposed method in this paper increases the accuracy of image registration in multi-sensor remote sensing images, changing mismatches into correct matches. Initially, key points matching is performed using the SIFT algorithm. Then, using the proposed affine-transformation-based approach, the mismatches are corrected and matching is done. Another novelty of the paper is suggesting two new criteria for assessing the efficiency of image matching methods in addition to the classical criteria of matching precision. As a weakness of the classical criteria that do not consider the total number of matches, feature repeatability rate and the number of correct matches are not defined efficiently. Simulation results show that the proposed method improves the rate of repeatability by 11.41% and cross- correlation coefficient by 14.20% on the average compared to the RANSAC method. Therefore, the proposed method can be used as a new and effective way of improving image matching.
A Daneshi; M Panahi
Volume 8, Issue 2 , November 2016, , Pages 73-86
Abstract
Because the various algorithms have been developed for the land use classification by using remote sensing, the suitable algorithm selection plays an important role in achieving good results. For this purpose, by efficiency comparison of two algorithms classification i.e. support vector machines (SVM) ...
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Because the various algorithms have been developed for the land use classification by using remote sensing, the suitable algorithm selection plays an important role in achieving good results. For this purpose, by efficiency comparison of two algorithms classification i.e. support vector machines (SVM) and maximum likelihood (ML), the more precision method was determined and it was used for investigating land use changes trend. The present research was carried out using TM, ETM+ and OLI sensors images in Siminehroud watershed. The research results showed that SVM algorithm classified satellite images better than ML algorithm and radial basis function (RBF) kernel has the highest overall accuracy among the studied methods. Therefore, SVM algorithm with RBF kernel was used to derive land use maps and monitor land use changes in the studied periods. By analysis of land use changes trend using this kernel, it was found that during studied periods, irrigated farming from 30535ha to 67210ha, dry farming from 79909ha to 123387ha, residential from 474ha to 1934ha land uses have been increased but rangeland from 259811ha to 178397ha and water resources from 30535ha to 67210ha land uses are decreasing
S.M Pourbagher Kordi
Volume 10, Issue 1 , June 2018, , Pages 73-90
Abstract
Identification of dominant landforms is important in a landscape because they applicable in various types of urban planning, tourism planning, spatial planning, etc. In this study, the landforms of Yazd-Ardakan basin were identified by two visual and automatic methods and then were compared. In automatic ...
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Identification of dominant landforms is important in a landscape because they applicable in various types of urban planning, tourism planning, spatial planning, etc. In this study, the landforms of Yazd-Ardakan basin were identified by two visual and automatic methods and then were compared. In automatic method, were used by Multiresolution and Contrast Split image segmentation in the object based concepts for identification of geomorphological landforms. The results showed using “Multiresolution Segmentation” due to consider the shape parameter is appropriate in the recognition of the landforms structure and their natural boundary such as alluvial fan but using the “contrast split image segmentation” is appropriate for micro-landform recognition such as braided river at the surface alluvial fans. The results of the comparison of visual and automated landforms maps showed that the visual approach was only useful for macro-landforms such as mountain masses, types of pediments, Ardakan playa and were barely detectable dunes, But the object based automatic approach not only mentioned landforms but also smaller landforms were identified such as the transverse dunes, alluvial fans, badlands, inselbergs. To evaluate the accuracy of automatic landforms identification models were used both qualitative and quantitative methods; in the qualitative evaluation were used the overlay technique to the visual investigation of matching the map of the model with Google Earth images. The quantitative evaluation was used the confusion matrix. The results of the evaluations showed that Overall Accuracy and Kappa coefficient for the multiresolution algorithm in landforms recognition are 97.46% and 96.53% respectively. Also, Commission and Omission errors showed that the minimum identification errors are related to soft surfaces such as Plain, but the maximum of the identification errors are related to rough surfaces like mountainous.
Zeinab Ghodsi; Mir Masoud Kheirkhah Zarkesh; Bagher Ghermezcheshmeh
Abstract
Land-cover/land-use maps are necessary for monitoring land changes and proper planning for managers in agriculture, natural resources and environment fields each year. The method of field data collection using GPS and land survey is time-consuming and costly. Therefore satellite images which have entire ...
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Land-cover/land-use maps are necessary for monitoring land changes and proper planning for managers in agriculture, natural resources and environment fields each year. The method of field data collection using GPS and land survey is time-consuming and costly. Therefore satellite images which have entire coverage and repetition of collection, low cost and real-time data, are usually used so that land-cover/land-use maps are produced. Accurate mapping using technique suitable for today is a key factor. Although in the past, conventional classification methods have been applied to images such as Landsat, using new satellite images and modern classifiers specially machine learning has been growing recently and their effectiveness in preparing land-cover/land-use maps has been very successful. Another advantage of satellite images is repetitious collection and according to that, vegetation changes through time can be used to differentiate land cover types. The Sentinel-2 satellite with the superiority of a pixel rating of 10 meters is one of the appropriate tools to discriminate land cover types. In the current study, Support Vector Machine and Random Forest classifiers on multi-temporal Sentinel-2 images were used to differentiate land use and crop types of Sanjabi plain in Ravansar and their accuracies were compared. To do so, after sampling, Principal Component Analysis was performed for four dates in crops’ growing season and PC1,2,3 bands of the images were combined. The two techniques were implemented on the layerstacks of PC1,2,3 bands of the images and the training samples. Results of accuracy assessments showed that Support Vector Machine, with overall accuracy of 91.36% and Kappa coefficient of 0.8927, produces a more precise land use and crop map rather than Random Forest method.
Rozhin Moradi; Bubak Souri; Marzieh Reisi
Abstract
The aim of this study was to estimate soil properties using Landsat 8 satellite bands in part of farmlands of Qorveh-Dehgolan plain in western Iran. Soil sampling was conducted at a total number of 107 points from 0-15cm depth throughout the study area and their physicochemical properties were measured ...
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The aim of this study was to estimate soil properties using Landsat 8 satellite bands in part of farmlands of Qorveh-Dehgolan plain in western Iran. Soil sampling was conducted at a total number of 107 points from 0-15cm depth throughout the study area and their physicochemical properties were measured in the laboratory. In order to extract information from the Landsat 8 satellite image following application of the vegetation mask; DOS values for bands 1-7 were extracted for the sampling points. Correlation Analysis, Stepwise Linear Regression and Principal Component Regression were used to determine the relationship between soil properties and digital value of Landsat 8 bands. Validation of Regression Analysis was evaluated using two parameters of Coefficient of Determination and Root Mean Square Error. The results showed that there was a positive and significant correlation between the digital value of most Landsat8 bands to the amounts of sand and free iron oxides in the soil but a negative and significant of it to amounts of clay and silt in the soil. There was no significant correlation between heavy metals concentration and digital value in visible and near infrared bands while Regression Analysis did not provide acceptable performance in estimating soil properties of the study area. According to the obtained results, it seems that Landsat 8 satellite images can be used to estimate the soil texture and the amount of free iron oxides across the study area.
Homayoun Khoshravan; Parastoo Karimi; Payam Alemi Safaval; Parisa poursafari yekrang
Abstract
This study aims to evaluate and compare coastline displacement and erosion intensity on the Caspian Sea's southern shores in the largest ports of Northern Iran, including Amirabad, Fereydunkenar, Nowshahr, Anzali, and Astara. Landsat satellite images were used to estimate the morphological status of ...
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This study aims to evaluate and compare coastline displacement and erosion intensity on the Caspian Sea's southern shores in the largest ports of Northern Iran, including Amirabad, Fereydunkenar, Nowshahr, Anzali, and Astara. Landsat satellite images were used to estimate the morphological status of the coasts in terms of erosion, sedimentation characteristics, diversity of existing coastal landforms, and changes in the GIS environment were used by digital coastline analysis software (DSAS) over the years 1995 to 2021. It has been indicated that the southern shores of the Caspian Sea differed in how they responded to the construction of port structures and Caspian Sea level (CSL) changes, and the areas of Amirabad and Astara ports had the highest displacements as well as accurate measurements of sedimentation and erosion rates, respectively. Given this situation, the beaches overlooking the ports of Nowshahr and Anzali have had significant changes in sedimentation, while the coast of Fereydunkenar had a very slow erosion rate. The northern ports of Iran, as well as changes in the Caspian Sea level (CSL), have direct physical impacts on the adjacent coasts. The management of concentrated sediment resources on the coast is a reliable solution to reducing erosion rate very effectively and the use of sand resources to mitigate coast erosion.
Mohammad Momeni Esfahani; Amir Shahrokh Amini
Abstract
Colored dissolved organic matter (CDOM) is an important measure of water quality. CDOM can reduce the amount of light in water layers, disrupt the biological activity of photosynthesis, and inhibit the growth of phytoplankton populations that are essential for the aquatic food chain. Contrary to conducted ...
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Colored dissolved organic matter (CDOM) is an important measure of water quality. CDOM can reduce the amount of light in water layers, disrupt the biological activity of photosynthesis, and inhibit the growth of phytoplankton populations that are essential for the aquatic food chain. Contrary to conducted research to date, which uses a specific wavelength, in this paper, we first examined the possibility of using visible portion of the spectrum to determine CDOM at 254-443 nm (254, 260, 350, 375, 400, 412, 440, 443 nm) in Landsat 8 . we then selected the most appropriate band ratios to measure CDOM at measurable wavelengths using the SVR algorithm (the parameters of which have been optimized using the genetic algorithm). It is noteworthy that in this study, the ratio of Coastal to red bands (), blue to red (), and the ratio of green to red bands () were considered for CDOM retrieval. Based on the results, considering the coefficient of determination ( = 0.71) and the amount of errors (MSE = 1.161 , RMSE = 1.077 and MAE = 0.946 ), it was concluded that the ratio of green to red bands in Landsat 8 is the most suitable choice for determining the colored dissolved organic matter. Moreover, according to the results from this study, the measurement of CDOM (440) is the most appropriate index for evaluating the quality of lake water resources in terms of their concentrations.
Marjan Teheri; MahmodReza Sahebi; Mehrnoosh Omati
Abstract
Synthetic aperture radar (SAR) sensors with various properties offer potential in various remote sensing applications, such as land cover and land use segmentation. Despite the two independent approaches of region-based segmentation and boundary-based segmentation, it isn't easy to obtain satisfactory ...
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Synthetic aperture radar (SAR) sensors with various properties offer potential in various remote sensing applications, such as land cover and land use segmentation. Despite the two independent approaches of region-based segmentation and boundary-based segmentation, it isn't easy to obtain satisfactory results if either process is used in SAR images. In contrast, complementary information can be obtained using both region-based and boundary-based segmentation methods, removing existing limitations and improving results.In this research, with the help of polarimetric SAR images, a new segmentation method is presented, aiming to improve segmentation results by combining the two region-based and boundary-based approaches. From the set of superpixel methods, the Felzenszwalb method as a proposed region-based algorithm is compared with Quickshift and SLIC methods. The proposed method was able to prevent over-segmentation of the image and significantly increased the efficiency of segmentation analysis. Also, as the proposed method of boundary-based segmentation, Shannon entropy has considerably preserved the boundaries of the image segmentation compared to the two gradient-based methods, Canny and Laplacian. Comparison of the results of this method with reference data shows the total error of 10.39% and 11.25% for the first and second-time images, respectively. Compared to the performance of the other two methods, the absolute error has been decreased to 5.81% and 9.73% in the first image, and 11.16% and 13.86% in the second image, respectively. Finally, as a significant achievement of this research, integrating the two proposed segmentation algorithms improves the accuracy of polarimetric image segmentation.
Karim Solaimani; Shadman Darvishi; Fatemeh Shokrian; mostafa rashidpour
Volume 10, Issue 3 , January 2019, , Pages 77-104
Abstract
Snow is a major source of water flow in each region. Therefore, knowledge of the spatial and temporal distribution of snow is essential for proper management of water resources in the region. Due to the severe physical conditions of mountainous environments, there is no permanent ground measurement for ...
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Snow is a major source of water flow in each region. Therefore, knowledge of the spatial and temporal distribution of snow is essential for proper management of water resources in the region. Due to the severe physical conditions of mountainous environments, there is no permanent ground measurement for estimating snowfall resources and the establishment of a database. So, using remote sensing data to monitor snow level changes is very effective. Therefore, the aim of this study was to investigate the temporal and spatial variations of snow cover in Kurdistan province using MODIS (MOD10A1, MOD10A2) snowstorm products in the 17-year period (2000-2017). Also, to evaluate the accuracy of the images and to analyze the relationship between snow changes with rainfall and temperature data, the synoptic station data of the study area was used. The results of the evaluation of the images with the weather station data show that these images have the appropriate accuracy in extracting snow surfaces. Also, the results of snow cover variations in Kurdistan province indicate that the highest snow cover area was in 2000, 2001, 2004, 2006, 2007, 2008, 2010, 2011, 2012, 2013, and 2015, respectively, and the lowest in the years 2005, 2009, 2016 and 2017, with the largest snow cover area in December 2007 with a 2.8914 square km area. The study of snowfall variations in the province shows that the highest snowfall in the province from November to March was in the city of Diwandareh (November 2004, 59.57%) in Bijar (Feb. 2000, 25.93%) and Qorveh city (January 2017, 25.38%). Also, the analysis of the relationship between snow melting and climatic data shows that in the months of April and May rainfall increased and in June, with decreasing rainfall, the increasing trend of temperature caused the snow depths to melt in the province.
T Ensafi Moghaddam,; F. Khoshakhlagh; A.A Shamsipour; R Akhavan; T Safarrad; F Amiraslani
Volume 9, Issue 2 , December 2017, , Pages 79-98
Abstract
Dust in the atmosphere and their interactions with precipitation have great impacts on regional climate where there are large arid and semiarid regions. Dust is one of the factors affecting precipitation. There are many ambiguities about the cause of the difference between amount of rainfall from an ...
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Dust in the atmosphere and their interactions with precipitation have great impacts on regional climate where there are large arid and semiarid regions. Dust is one of the factors affecting precipitation. There are many ambiguities about the cause of the difference between amount of rainfall from an area to another area and from time to time. So that even with the spread of knowledge and technology yet still there is not completely specified the cause of these fluctuations. Nowadays, satellite images are broadly used for monitoring the effects of dust variations on the precipitation changes. Nowadays, satellite images are broadly used for monitoring the effects of dust variations on the precipitation changes. The aim of this study was to investigate the relationship between dust dynamic and precipitation variations. This research can be help to find the impact of dust occuarrances on precipitation changes in the South-West parts of Iran during thirty years by cluster analysis, remote sensing, and aridity zoning in GIS software. In this investigation, we analyzed data sets of daily average visibility(a proxy for surface aerosol concentration), daily No of reports frequency of dust occurance and daily precipitation at 45 meteorological stations during past 30 years(1986-2016)were obtained from the Iran Meteorological Organization. The consistent trends in observed changes in visibility, precipitation, and daily No of reports frequency of dust occurance appear to be a testimony to the effects of dust. In present study, we tried to determine the relationship between dust events data and measured precipitation changes in a ground stations. Therefore frequency of dust occurrence from 1986 to 2016 at 30 stations, compared with rainfall anomalies for South-West of Iran as a whole. Rainfall is expressed as a regionally averaged, standardized departure (departure from the long-term mean divided by the standard departure), but the axis of the rainfall graph is inverted to facilitate comparison with dust occurrence. Dust is represented by the number of days with dust haze. Then‚ dust days ratios which measured by number of days with dust in month and horizontal viewing which measured by number of days with visibility min < 2000 were compared by map of mean annual rainfall of stations in South-West of Iran. Precipitation maps were created using the inverse distance weighting interpolation (IDW). Too, In this study, (MODIS) Aerosols Optical Thickness(AOD)product is applied in order to estimate dust intensity. AOD images and MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) were utilized to assessment of move pattern of the dusts in the study area. Our results indicated that MODIS products could be a reliable tool to assess dust events patterns and to survey the concentration of particulate matter .So AOD images and MODIS/Terra Calibrated Radiances 5-Min L1B Swath 10km were utilized to assessment of special move pattern of the dusts frequency in the study area‚and indicated the opposite response of light rain to the increase in dust, have seen in mountainous and plain areas.
Zahra hemmati; Karim solaimani; Mir hasan Miryaghoubzadeh
Abstract
Takab watershed basin is one of the most important basins of Lake Urmia. The basin is quite hilly and mountainous, and the runoff from its snow melting is of substantial significance. Snow accumulation in winter is considered to be crucial in the spring of the following year, and the water from snow ...
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Takab watershed basin is one of the most important basins of Lake Urmia. The basin is quite hilly and mountainous, and the runoff from its snow melting is of substantial significance. Snow accumulation in winter is considered to be crucial in the spring of the following year, and the water from snow melting is especially important for water facilities in a way that it results in serious floods when the snow melts with warm spring rain. Therefore, the prediction of snow melting seems necessary. Furthermore, managing water resource and reservoirs as well as planning of rivers hydrology would not be possible without considering this factor. The SRM snow melt runoff model was used to simulate the flow considering the 83-84 water years. Furthermore, to test the validity of the model, the 84-85 water years was used. Due to the fact that the MODIS images have the appropriate time resolution, such images have been used to estimate the underlying snow area. Results of the study showed that the use of snow cover maps, derived from MODIS images, is useful in predicting the runoff of the basin. The findings also show that the model has the ability to simulate the snowmelt runoff. To evaluate the model, two indexes, namely, the coefficient of determination and volume difference were used which were obtained as 0.75 and 27.84%, respectively. The obtained values indicate that the model has high accuracy in estimating the runoff from snow melting in this basin and represents the applicability of the model to other basins in the region.
Volume 7, Issue 1 , December 2015, , Pages 81-94
Abstract
Classification is one of the most widely used remote sensing analysis techniques. In the conventional remote sensing supervised classification, training information and classification result are represented in a one-pixel-one-class method. Fuzzy methods have been widely applied in image classification, ...
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Classification is one of the most widely used remote sensing analysis techniques. In the conventional remote sensing supervised classification, training information and classification result are represented in a one-pixel-one-class method. Fuzzy methods have been widely applied in image classification, which are believed to be more appropriate for handling uncertainty and mixed pixels in remote sensing. Also recent researches show that using neighborhood information with spectral information lead to higher accuracy in classification. Due to the dependence on initial classifier,the use of neighborhood information in the post processing of classification results is one of the reasons for its use in this research. Connectivity rules in fuzzy topological space are one of methods for using neighborhood information in post processing step. In case of using more than one classifier, it is possible to integrate the results. In this research two methods have been proposed for spatial integration results by using connectivity rules in fuzzy topological space. In first method, one of the two classifiers will be based and in second method, only pixels that are classified in the same manner in both and simultaneously not boundary pixel, will keep their own labels in final image. The results show that first method Provides better accuracy compared with second method and generally accuracy is improved when spatial integration results is used in compare with using only one classifier. The maximum overall accuracy and overall kappa values are obtained respectively 89.01 and 88.98 when maximum likelihood classifier is based in first method. Keywords: Fuzzy Classification, Fuzzy Topological Space, Integration, Connectivity Rules.
Reza Jafari; Morteza Ansari; Mostafa Tarkesh
Abstract
Temperature is the most important parameter for studying spatiotemporal phenological changes in plants. Thus, the current study was aimed to investigate the potential of MODIS land surface temperature (LST) data for mapping growing degree days (GDD) and different phenological stages of Bromus tomentllus ...
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Temperature is the most important parameter for studying spatiotemporal phenological changes in plants. Thus, the current study was aimed to investigate the potential of MODIS land surface temperature (LST) data for mapping growing degree days (GDD) and different phenological stages of Bromus tomentllus and Astragalus effusus in Chaharmahal and Bakhtiari Province. MODIS extracted maps of maximum, minimum and mean temperature, GDD index and phenological stages from 2018 to 2019 during growing season were assessed against weather station data and also field-based phenilogical data using Pearson analysis in three regions with different altitudes. Results showed that MODIS LST and GDD maps had more than 91 and 99% correlations with field-based air temperature and GDD data, respectively (p<0.001). In early growing season, GDD values were less than 16 degree-days and they were more than 5200 degree-days in the late growing season which explained one and all the phenological stages of the studied species in the study area, respectively. The study findings indicated that MODIS data have high capability in spatiotemporal stratification of phenological stages of the Bromus tomentllus and Astragalus effuses plant species. The knowledge of different phenological stages is essential in species conservation and rangeland sustainable utilization, therefore, species phenology map can be used as an effective tool in rangeland management in the related organizations.
Behzad Hessari; Sajjad Karimzadgan
Volume 10, Issue 4 , February 2019, , Pages 85-98
Abstract
Digital elevation model (DEM) reconditioning methods are being used to improve its quality to be used in simulation and cellular hydrological modeling. In this paper , methods of improving DEM and creating “Hydrologic DEM” with using the ArcHydro10.2 extention were studied and also evaluated in 53 ...
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Digital elevation model (DEM) reconditioning methods are being used to improve its quality to be used in simulation and cellular hydrological modeling. In this paper , methods of improving DEM and creating “Hydrologic DEM” with using the ArcHydro10.2 extention were studied and also evaluated in 53 subbasins of Karkheh river basin(KRB) and one of its applications in “accumulation runoff map” was implemented .Streamflow information is available at hydrometric stations as points but for determining the amount of river discharge continually at any point along the river and branches, it is needed to accumulate the runoff map. The 8 direction method (D8) is the basic model for river network delineation from DEM accumulated cells. In this algorithm, the flow of each cell pours to one of the eight adjacent cells in the "hydrologic DEM ". In hydrologic DEM the accumulated cells in the river line forward to the downstream increase continuously. With "Agree algorithm" and with trial and error the DEM improved and the upstream area of accumulation cells, have been compared with a vector area in each subbasin which correlation coefficient was 0.9975. After preparing runoff map of the basin, accumulation flow(accumulated runoff map) using a weighted cumulative flow function was created with 200m cell size then the continuous flow map was presented. The results indicate that the error between estimated discharge from accumulated flow map with observed discharge in 53 subbasins varies between 0.28 to 3.1 percent.
Mohammad Hajeb; Saeid Hamzeh; Seyed Kazem Alavipanah; Jochem Verrelst
Abstract
Leaf Area Index (LAI) plays a critical role in the mass and energy exchanges between the earth and the atmosphere. Like of other plants, LAI of sugarcane is a good indicator of the health status and growth of this crop which is of great economic importance due to its role in the food and energy industries. ...
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Leaf Area Index (LAI) plays a critical role in the mass and energy exchanges between the earth and the atmosphere. Like of other plants, LAI of sugarcane is a good indicator of the health status and growth of this crop which is of great economic importance due to its role in the food and energy industries. Launched in 2019, the PRISMA satellite provides one of the most recent hyperspectral data sources which are applicable especially for mapping plant variables. In this study, a new kind of Artificial Neural Networks (ANN) so-called Bayesian Regularized Artificial Neural Networkk (BRANN) which applies Bayes' theorem to overcome the overfitting problem of neural networks is used. The model was implemented on a data set consisting of spectrum obtained by PRISMA satellite as an independent variable and sugarcane LAI measurements as a dependent variable. The ground measurements of sugarcane LAI were carried out in 118 elementary sampling units on the fields of Amir Kabir sugarcane cultivation and industry in Khuzestan province and on seven different dates during a sugarcane growth period in 2020. Comparing the performance of BRANN in retrieving sugarcane LAI from PRISMA spectra with that of a conventional ANN trained with the Levenberg-Marquardt algorithm (LMANN) indicates that the retrieval RMSE is reduced from 2.26 m2/m2 applying LMANN to 0.67 m2/m2 applying the BRANN method. In this study, the principle component analysis was also used dimensionality reduction. Retrieving LAI from the first 20 principle components, RMSE was also reduced from 1.41 m2/m2 applying LMANN to 0.71 m2/m2 applying BRANN. Exploiting principal components significantly reduced computational time. By implementing the calibrated BRANN model over the PRISMA image pixel by pixel, the sugarcane LAI map was generated. Evaluating this map showed that this map represents the spatial variations of sugarcane LAI well. The results of this study indicate the high performance of the BRANN method and high potential of PRISMA images to retrieve sugarcane LAI.
Sara Attarchi; Mehdi Rahnama
Abstract
Full polarimetric SAR sensors can capture full polarimetric characteristics of targets. Therefore, in comparison with single and dual polarimetric sensors they offer more capabilities in target detection. However, operation in full polarimetric mode increases complexity, data volume and need more power. ...
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Full polarimetric SAR sensors can capture full polarimetric characteristics of targets. Therefore, in comparison with single and dual polarimetric sensors they offer more capabilities in target detection. However, operation in full polarimetric mode increases complexity, data volume and need more power. Full polarimetric sensors acquire images with less swath compared to dual mode. As a result, most of SAR sensors operate in dual mode and provide dual polarimetric images. Due to high availability, dual polarimetric images are increasingly being used in many researches. In this research, the efficiency of dual polarimetric images is compared with full polarimetric mode. The main goal is to find the best combination of two polarimetric bands which has the nearest results to full polarimetric mode.One Advanced Land Observing Satellite / Phased Array L-band Synthetic Aperture Radar scene had been processed. The scene was multi-looked and converted to the backscattering coefficient (sigma nought, dB). The image was decomposed by cluode-pottier method into alpha and entropy components. Three different combination of two polarimetric bands were considered; HH-HV; HH-VV and HV-VV. Alpha and entropy of each dual polarimetric mode were also computed. Then alpha and entropy driven from full-polarimetric mode were separately compared with alpha and entropy of each dual mode. Since different land cover types (i.e. built-up, cropland, bare land and water) exist in the scene, the computations were done separately for each land cover type. The comparison among alpha values from full polarimetric mode and dual polarimetric mode reveals that HH-HV combination shows the best conformity with full polarimetric mode. HH-VV dual mode has the poorest results. Entropy values of HH-HV mode had the least difference with full polarimetric mode. Entropy values of HH-VV shows the weakest similarity. The MAE values of HH-HV, HH-VV and HV-VV were 0.06, 0.22 and 0.17, respectively. The findings of this research shows that polarimetric features driven from HH-HV combination are more compatible with full-polarimetric mode. In case, no full polarimetric image is available, this dual combination can be substituted. Based on quantitative results, HH-HV combination is recommended to be used in case no full polarimetric image is availableOne Advanced Land Observing Satellite / Phased Array L-band Synthetic Aperture Radar scene had been processed. The scene was multi-looked and converted to the backscattering coefficient (sigma nought, dB). The image was decomposed by cluode-pottier method into alpha and entropy components. Three different combination of two polarimetric bands were considered; HH-HV; HH-VV and HV-VV. Alpha and entropy of each dual polarimetric mode were also computed. Then alpha and entropy driven from full-polarimetric mode were separately compared with alpha and entropy of each dual mode. Since different land cover types (i.e. built-up, cropland, bare land and water) exist in the scene, the computations were done separately for each land cover type. The comparison among alpha values from full polarimetric mode and dual polarimetric mode reveals that HH-HV combination shows the best conformity with full polarimetric mode. HH-VV dual mode has the poorest results. Entropy values of HH-HV mode had the least difference with full polarimetric mode. Entropy values of HH-VV shows the weakest similarity. The MAE values of HH-HV, HH-VV and HV-VV were 0.06, 0.22 and 0.17, respectively. The findings of this research shows that polarimetric features driven from HH-HV combination are more compatible with full-polarimetric mode. In case, no full polarimetric image is available, this dual combination can be substituted. Based on quantitative results, HH-HV combination is recommended to be used in case no full polarimetric image is available.
