Hassan Sharafi; Reza Faraji
Abstract
In order to understand the site, it is necessary to obtain soil strength parameters, which are both costly and time-consuming. In this research, utilizing 135 geotechnical boreholes drilled in Kermanshah, the zonation of soil shear strength parameters (friction angle and cohesion) using ArcGIS software ...
Read More
In order to understand the site, it is necessary to obtain soil strength parameters, which are both costly and time-consuming. In this research, utilizing 135 geotechnical boreholes drilled in Kermanshah, the zonation of soil shear strength parameters (friction angle and cohesion) using ArcGIS software and ordinary kriging interpolation method (employing spherical, exponential, and Gaussian semi-variograms), Up to a depth of 9 meters in three-meter intervals was done. The selection of the best model for predicting these characteristics was determined by assessing the root mean square error (RMSE) and mean absolute error (MAE). Based on these error evaluation indicators, the optimal variograms for zonating friction angle and cohesion at depths of 0 to 3 meters are Gaussian, 3 to 6 meters is exponential, and 6 to 9 meters are Gaussian and spherical, respectively. The results indicate that, predominantly with increasing depth, the friction angle and cohesion have increased. The northern and southwestern parts of Kermanshah, in comparison to other regions, exhibit soil with a higher friction angle and lower cohesion (coarse-grained). Furthermore, the northwestern parts of the city have clay and alluvial soils, findings corroborated by the passage of the Qarasu river through this area and the location of the northern and southern regions of Kermanshah at the foot of the mountain.
Fateme Ameri1; Mohammad Javad Valadan Zoej; Mehdi Mokhtarzade
Volume 7, Issue 3 , November 2015, , Pages 33-48
Abstract
Nowadays, extraction of roads from digital aerial and satellite images is a common method of road database construction. Regarding to massive amount of road data and time and cost effective updating requirements, automation procedure is becoming an essential. In this research, which is mostly concentrated ...
Read More
Nowadays, extraction of roads from digital aerial and satellite images is a common method of road database construction. Regarding to massive amount of road data and time and cost effective updating requirements, automation procedure is becoming an essential. In this research, which is mostly concentrated on road vectorization process, an automatic approach of road centerline vectorization from detected road image with negligible operator interventions is designed. The proposed system consists of two main stages including road key points determination and connection. At the first stage, the road key points representative of the road centerline are determined using particle swarm optimization clustering. At the second stage, in order to model the road networks weighted graph theory is considered. In this model cost of each connection is calculated by aggregating appropriate road geometric criteria by means of ordered weighted averaging operators. The least cost connections constitute the vectorized road networks. The proposed approach was implemented on several high resolution satellite images and their results were compared with the results of the minimum spanning tree algorithm. On the whole, the obtaining results proved the efficiency of the vectorization approach in attaining the complete and accurate road network. Extracting different road shapes including direct and curved roads, roads with different widths, parallel roads with different distances, junctions and square with average RMSE value about 0.9 meter, average completeness of %94, and average correctness greater than %95 proves the efficiency of the algorithm in yielding complete road networks.
D Akbari; M Moradizaded
Volume 9, Issue 3 , February 2018, , Pages 33-44
Abstract
In recent years, the issue of improving the spatial resolution of thermal images in urban areas has been introduced as a new challenge. The purpose of this study is to use the impervious surfaces indices and vegetation indices to improve the spatial resolution of Landsat ETM + thermal band over Tehran ...
Read More
In recent years, the issue of improving the spatial resolution of thermal images in urban areas has been introduced as a new challenge. The purpose of this study is to use the impervious surfaces indices and vegetation indices to improve the spatial resolution of Landsat ETM + thermal band over Tehran as a part of the study area. After the initial pre-processing, images obtained using the mean filter was simulated at spatial resolutions 120, 240, 480, 720 and 960 m. The relationships between these simulated imaged with the image simulated at the resolution of 960 m were calculated by the use of regression models.These derived models, containing vegetation and impervious surface indices, were then used to simulation of surface temperatures in different pixel sizes. The accuracy of each output, has been evaluated using the thermal images of ETM + and MODIS sensors.The results showed that by increasing the spatial resolution, the errors increases while the gradient of error is not fixed. So that in all indices, there are more increasing in gradient of error when the pixel size goes to smaller than 240 meters.Moreover, the best performance was obtained by combination of impervious surfaces indices and vegetation indices due to the enhancement of spatial resolution of thermal images in Tehran city.Using the combination of these indices, the spatial resolution of the MODIS sensor can be reached to about 240 meters, while the absolute error value is less than 1 K Kelvin.
Arash Hazeghi Aghdam; Hossain Helali; Ali Asghar Alesheikh
Volume 10, Issue 2 , September 2018, , Pages 33-44
Abstract
All of the old paper-based land documents should be transformed to digital georeferenced form. Indigitization of parcels to be able to be imported into a cadastral system, the area of the digital form ofthe polygon should be in concordance with the analog document. However, during the digitizationprocess, ...
Read More
All of the old paper-based land documents should be transformed to digital georeferenced form. Indigitization of parcels to be able to be imported into a cadastral system, the area of the digital form ofthe polygon should be in concordance with the analog document. However, during the digitizationprocess, geometric properties, such as area, changes. Preserving the dimensions and area prevents thecontradiction in documents and, registration and trading quarrels. In order to solve this problem,angles of the polygon vertexes are considered as observations by keeping the dimensions fixed and amethod based on the least-square adjustment is proposed. Executing the invented process, therequired concordance is acquired. For checking this method, a paper map having six parcels each withabout one thousand m2 area in 1:1000 scale is scanned and digitized by preserving area anddimensions. Results showed that the mean, maximum and minimum movement by using adjustingmethod is in average 19.31, 26.96 and 10.50 respectively that is acceptable from the land registrationaspect because of map scale and cadastral law about cultivation and national parcels.
naeimeh ahmadi; zahra mousavi; zohreh mosoumi
Volume 10, Issue 3 , January 2019, , Pages 33-52
Abstract
Subsidence is a downward motion of ground surface with small horizontal displacement vector. It may happen due to natural factors or human activities. In Iran, subsidence may occur because of the human activities and excessive extraction of groundwater resources. In this study, we applied Synthetic aperture ...
Read More
Subsidence is a downward motion of ground surface with small horizontal displacement vector. It may happen due to natural factors or human activities. In Iran, subsidence may occur because of the human activities and excessive extraction of groundwater resources. In this study, we applied Synthetic aperture Radar Interferometry (InSAR) to investigate the rate of subsidence. We estimated the rate of subsidence in Khoramdareh plain using Permanent Scattering (PS) for the duration time 2003-2005. The mean velocity map indicated that the subsidence is occurring with the rate of 35 mm/yr in direction of Satellite Line of Sight. Afterward, we used Geospatial Information System (GIS) to evaluate subsidence relation with agricultural lands and wells in the case study area. Also the risks of subsidence are investigated in the area using GIS abilities. The results show some parts of the railways, main roads and highways are affected by subsidence.
Maedeh Behifar; Mohsen Azadbakht; Farzaneh Hadadi; AliAkbar Matkan
Abstract
Vegetation indices are used to estimate vegetation parameters from satellite images. Despite their capabilities, performance of some vegetation indices decreases in high vegetation densities, making them inappropriate for estimation of the desired parameters. Vegetation indices are saturated in alfalfa ...
Read More
Vegetation indices are used to estimate vegetation parameters from satellite images. Despite their capabilities, performance of some vegetation indices decreases in high vegetation densities, making them inappropriate for estimation of the desired parameters. Vegetation indices are saturated in alfalfa farms due to the high chlorophyll content and high vegetation density; therefore, monitoring the changes of this plant is hindered. However, all indices do not perform similarly. In this research, the performance of different vegetation indices at different LAI values were investigated. The results showed that the CIgreen, CIrededge and NGRDI indices gained the best performance at high LAI values and they were less saturated. In contrast, the NDVI, NDREI and GNDI indices did not perform well and they were saturated at medium and high levels of LAI.
Soheil Radiom; hossein Aghighi; Hamid Salehi Shahrabi
Abstract
Evapotranspiration is one of the most important components of energy and water balance. The most important way to get real large-scale evapotranspiration is to utilize satellite imagery and remote sensing. Implementation of evapotranspiration calculation algorithms such as SEBAL demands calculation of ...
Read More
Evapotranspiration is one of the most important components of energy and water balance. The most important way to get real large-scale evapotranspiration is to utilize satellite imagery and remote sensing. Implementation of evapotranspiration calculation algorithms such as SEBAL demands calculation of reference evapotranspiration and thus measuring air temperature, humidity and wind speed. Calculation of evapotranspiration is usually based on obtained information from the nearest weather stations to the study area, which can be error-prone. Therefore, in this study, IoT sensors were used to accurately measure air temperature at 2 m above the ground, as well as air humidity and wind speed in the study area. The study area is the farms of Moghan Agricultural Company in Ardabil province. In this study, 23 nodes were installed in a number of farms. The ground-based energy balance algorithm (SEBAL) was used to calculate the evapotranspiration using Landsat 8 images in 2015.
Mojdeh Mohammadi Khashoui; Mohammad reza ekhtesasi; Ali Talebi; Seyed Zeynalabedin hosseini
Abstract
Digital elevation models and its derivatives are important factors for watershed modeling. It is obvious that DEM errors adversely affect the accuracy and thereby modeling of natural processes. This problem along with the impossibility of measuring all elements of nature, has led to a major evolution ...
Read More
Digital elevation models and its derivatives are important factors for watershed modeling. It is obvious that DEM errors adversely affect the accuracy and thereby modeling of natural processes. This problem along with the impossibility of measuring all elements of nature, has led to a major evolution in the way of understanding and explaining phenomena. In this way, we can use the fractal geometry with the theory that many natural phenomena are order in the chaos. Each element of nature is represented as a fractal geometry number. The fractal geometry is a quantitative tool for studying the geomorphology of drainage networks and modeling many complex natural phenomena. In fact, geophysical phenomena such as drainage networks are fractal phenomena with fractal behavior. The purpose of this paper is to evaluate sensitivity of the drainage networks based on DEMs (ASTER & SRTM), flow direction algorithms (Single Flow Direction (D8) and Multiple Flow Direction (MD8)) and topographic maps of 1:25000 in order to study the fractal dimension of drainage network on geological formations of Yazd-Ardakan basin. The results showed that the least difference in the length and the rank of the stream belonged to the drainage network obtained from the topographic maps of 1:25000. After the topographic maps, ASTER and the multi-flow direction (MFD) algorithm and ASTER, and the single flow direction (SFD) algorithm are close to real ground map. Even though the multi-flow direction algorithm shows more detail on the drainage network. But it is not close to real ground map. The difference is particularly noticeable in the first rank of streams. SRTM and the flow direction algorithms showed only good results in routing the main rank of drainage networks. In fact, the results of this study demonstrate that accurate extraction of drainage networks from DEMs generated by remote sensing technologies such as SRTM or ASTER and SFD or MFD algorithms remains challenging. Therefore, the analysis of DEMs and flow direction algorithms should be considered as an important part of hydrological and geomorphological research. Due to the very high sensitivity of the fractal dimension to the smallest change in drainage network conditions, the drainage network obtained from topographic maps were used to calculate the fractal dimension. The mean fractal dimension of 1.149, 1.16 and 1.207, respectively, represents Taft, Granite and Kahar formations. There is a significant correlation between fractal dimension and sensitivity to erosion of geological formations (level 0.99). In fact, the fractal dimension increases with increasing the sensitivity to erosion along with the drainage density in geological formations. The results showed that fractal dimension allows for a quick and accurate analysis of sensitivity to erosion of the formations of this area.
H Kachar; M.R Mobasheri; A.A Abkar; M Rahimzadegan
Volume 7, Issue 2 , November 2015, , Pages 35-53
Abstract
Increase of temperature with height in the troposphere is called temperature inversion. Parameters such as strength and depth are characteristics of temperature inversion. Inversion strength is defined as the temperature difference between the surface and the top of the inversion and the depth of inversion ...
Read More
Increase of temperature with height in the troposphere is called temperature inversion. Parameters such as strength and depth are characteristics of temperature inversion. Inversion strength is defined as the temperature difference between the surface and the top of the inversion and the depth of inversion is defined as the height of the inversion from the surface. The common approach in determination of these parameters is field measurements by Radiosonde. On the other hand the Radiosonde data are too sparse, so using satellite images is essential for modeling the temperature inversion. Necessary condition for the temperature inversion modeling using satellite images, examine the relationship between the brightness temperature difference with the temperature inversion strength and depth of the resulting data is Radiosonde. Temperature inversion phenomenon is common in Tehran. So Mehrabad airport weather station was selected as the 1st study area. Then correlation coefficients between Brightness temperature differences of different band pairs and the inversion depth and strength collected by Radiosonde were calculated. The results showed weak linear correlation. This could be due to the change of the atmospheric water vapor content and the relatively weak temperature inversion strength and depth occurred in Tehran. Proving this hypothesis is an innovation in the present work, in continuation of this research, the factors increasing the linear correlation coefficient was investigated. Due to the presence of deeper and stronger temperature inversion in Kermanshah, this region was chosen as the second studied region. The calculated correlation coefficients increased for Kermanshah all due to increase in the strength and depth of the temperature inversion in this region. Knowing that the amount of water vapor in the atmosphere in winter is less than warm seasons, Tehran and Kermanshah data were divided into two all seasons and cold seasons.Increase of correlation coefficients for both Tehran and Kermanshah in the cold season verifies the effect of atmospheric water content. For instance, the correlation coefficient between BT7.2-BT11 with strength and depth of inversion for Kermanshah for all season are 0.51 and 0.70 respectively. This for cold season was boosted to 0.78 and 0.85.
H Lashkari; A.A Matkan; H Asakereh; Y Khosravi
Volume 8, Issue 2 , November 2016, , Pages 35-52
Abstract
Water vapor as one of the most important climate elements, plays an important role in decision-making, design and evaluation in hydrological models. Therefore, understanding the spatial changes of this important climate element have a significant effect on water management and planning. Accordingly, ...
Read More
Water vapor as one of the most important climate elements, plays an important role in decision-making, design and evaluation in hydrological models. Therefore, understanding the spatial changes of this important climate element have a significant effect on water management and planning. Accordingly, this study is tried to studythe spatial structureand estimation of watervapor pressurein south and south west of Iran, using by geostatisticsmethodand variography analysis. In this regard, the water vapor pressure data of 78 synoptic stations on 18 August 2007 as one of the sweeping days by the water vapor pressure was analyzed. The first step for this purpose was calculation of the spatial variable of water vapor pressure that were analyzed by plotting the variogram. After fulfilling this requirement, geostatistical methods such as simple kriging, ordinary kriging, Co-kriging with auxiliary variables, and circular, spherical, exponential, Gaussian and quadratic rational models were used and their performance were evaluated. The results of cross validation showed that the best method that being able to justify the amount of water vapor pressure is Co-kriging method with altitude. According to the drawn map with the optimum method it was found that near the Persian Gulf and Oman Sea and the North and North West of study area are the highest and lowest amounts of water vapor pressure, respectively. It was determined that three reasons 1. Far and near to the main sources of humidity, 2. Zagros Mountains and 3. The establishment of strong pressure has an important role in water vapor pressure distribution.
Saeed Saroei; Ali Asghar Darvishsefat; Manochehr Namiranian
Abstract
Estimating the biomass values in forests stands through remote sensing is important. It has been reported that the major reasons of uncertainty are the lack of concurrency in satellite data and field information as well as the use of global allometric equations for estimating the weight of biomass of ...
Read More
Estimating the biomass values in forests stands through remote sensing is important. It has been reported that the major reasons of uncertainty are the lack of concurrency in satellite data and field information as well as the use of global allometric equations for estimating the weight of biomass of forest trees inside the country. Minimizing the above problems and the investigation of data performance in developing appropriate model for estimating the forest biomass in the Bankoll region of Karazan District of Sirvan County in Ilam province using Sentinel-1 satellite data in 27th of June, 2017 was the main goal of this study. Average size of the trees crown in 53 rectangular plots related to the coppice growth form with dimensions of 30×30 mwhich during 23 may 2017 to 10 June 2017 through applying DGPS by RTK method have been implemented on the ground were entered in the process of estimation the value of biomass. The average harvested field biomass was 10.63 Mg ha-1. After extraction of radar features, those features which had the greatest correlation with the values of biomass were selected using genetic algorithm by two models including K-Nearest Neighbor (K-NN) regression and Support-Vector Regression (SVR), then the most appropriate combination was identified and the biomass values were modelled. Models were validated using 26 test plots. Correlation of features obtained from radar data and the value of biomass indicated that features of VH، Mean VV، Mean VV GLCM (Correlation) and Mean VH GLCM (Dissimilarity) had the greatest sensitivity towards the value of biomass. Using regression models indicated that SVR model (Relative RMSE of 0.08) was more precise compared with K-NN regression (relative RMSE of 0.10). The best combination in the use of K-NN regression model with a relative RMSE of almost 0.99 Mg ha-1 (equal to 10%) and the coefficient of determination (R2) of 0.22 and the best combination when using SVR model was a relative RMSE of 0.87 Mg ha-1 (equal to 8%) and the R2 of 0.14. The results indicated that the final models, obtained from the optimal features extracted from radar data in the wavelength of C band and used parametric and non-parametric regressional methods in this research, were not abled to improve the saturated effect in data for estimation of biomass in the studied forests and it was not resulted in presenting an estimating model with an acceptable accuracy.
Moslem Torky; Seyed Abolfazl Masoodian
Abstract
The expansion of urbanization and the increase of population in metropolises and the growth ofindustrial activities of cities, It has caused changes in urban area climate. One result of these changesis the city's heat islands. The city of Mashhad has also grown rapidly in recent years. This studyinvestigates ...
Read More
The expansion of urbanization and the increase of population in metropolises and the growth ofindustrial activities of cities, It has caused changes in urban area climate. One result of these changesis the city's heat islands. The city of Mashhad has also grown rapidly in recent years. This studyinvestigates the heat/cold island of Mashhad metropolis based on the background climate in order toidentify its spatiotemporal behavior. For this purpose The MODIS Terra and Aqua land surfacetemperature (LST) data were obtained and the heat island was examined accordingly. A new wasused to measure the heat island. In this method, Modis land use data was used to determine the urbanand suburban boundaries as well as to determine the land use type of the study area. The backgroundclimate was determined based on Far-side temperature and the representative non urban area wasselected based on the most frequent temperature and the heat island was calculated. Survey ofheat/cold island in the daily period showed that during the day the average temperature of city islower than non urbun temperature and at night is higher. Also the seasonal survey of heat island/couldisland of Mashhad metropolitan shows that daily cold island is the highest during the warm seasonsand lowest in the cold seasons and the seasonal variability of nightly heat island is less than the dailycold island. The core of the daily cold island is located between the Haram and the Shahid FehmidahSquare towards the western area of Mashhad. The day time cold island matches the areas of the citywith high vegetation coverage. The core of the nightly heat island is consistent with the old textureand dense area around the Haram towards the northwest of the city. The heat/cold island intensity isalso directly related to the wind speed. The role of land use in intensifying or reducing the intensity ofthe heat island of Mashhad is well seen. In the development of the city, more attention can be paid tothe use of urban land use in order to moderate the temperature of the city.
M Effati
Volume 8, Issue 1 , November 2016, , Pages 37-54
Abstract
Developing intelligent and novel methods for crash prevention or reducing crash severity in regional highway corridor is one of the major goals of road safety research. This study presents a comprehensive approach using geospatial information systems and data mining to analyze the severity of highway ...
Read More
Developing intelligent and novel methods for crash prevention or reducing crash severity in regional highway corridor is one of the major goals of road safety research. This study presents a comprehensive approach using geospatial information systems and data mining to analyze the severity of highway corridors crashes and identify the most spatial contributing factors. The approach implements Fuzzy Classification and Regression Tree (FCART) on a database of spatial data and four year period accident records in the study corridor (Qazvin-Rasht). The proposed method is tested on the crash data using a 10-fold cross validation process and the results are compared with Classification and Regression Tree (CART) model. The results show that FCART model inducts crash severity better than CART model and its overall accuracy is higher than CART model. Moreover, the sensitivity analysis of FCART model indicates that beside vehicle failure, using seatbelt and weather condition factors, curve and the spatial distribution and prevalence of activities and land uses in the proximity of highway corridors are among the most important factors affecting the severity of injuries and increase opportunities for crash occurrences.
, Z. Fazilatpour; , K Rangzan; G.R Eskandari,; , A Saberi
Volume 9, Issue 1 , October 2017, , Pages 37-48
Abstract
Marine environment, including the ocean and coastal areas provide enormous opportunities for the growth in fisheries and the exploitation of natural resources. Fishing is an important source for the food production industry and in Iran. Due to the high demand to find fish resources, data from satellites ...
Read More
Marine environment, including the ocean and coastal areas provide enormous opportunities for the growth in fisheries and the exploitation of natural resources. Fishing is an important source for the food production industry and in Iran. Due to the high demand to find fish resources, data from satellites play an important role in fisheries applications. Remote sensing of ocean color can be used in many applications, such as commercial fishing, marine transport, ocean mining, oil and gas exploration, hydrography, etc. Since the satellite images can provide a wide area coverage and good temporal resolution, they can be of great help in detecting potential fishing zones. The MODIS sensor that has the capability of high spectral resolution and multiple thermal bands make it a superior sensor compared to other types. In this research, the correlation coefficient value between MODIS satellite images and in-site water samples were at 0.84, which indicates a high accuracy of SST MODIS Images. This research aims to determine potential fishing zones in the Persian Gulf by using layers of sea surface temperature, sea surface height, chlorophyll - a and sea surface temperature gradient of water surface. Fuzzy methods provide a good decision making algorithm to determine the location of important areas, including uncertain and unclear ones. In this decision was given weighting layer through FAHP procedure was performed and the highest weight in the sea surface temperature as a parameter. After overlaying layers, the results indicate that 82% of the selected areas coincide with that of commercial fishing zones. The application of targeted fishing can be used to increase fishing efficiency at lower time compared to the current longer time that is possible by integrating remote sensing and Global geographic information system (GIS).
Z Masoumi; M Amiraslani; A Rezaee
Volume 9, Issue 4 , May 2017, , Pages 37-58
Abstract
City Development and its direction is always a potential problem in urban planning. In modeling this phenomenon, variety of criteria are involved, directly and indirectly. So it is assumed as a complex Multi-Criteria Decision Making problem. There is a wide range of rigorous methodology to analyze these ...
Read More
City Development and its direction is always a potential problem in urban planning. In modeling this phenomenon, variety of criteria are involved, directly and indirectly. So it is assumed as a complex Multi-Criteria Decision Making problem. There is a wide range of rigorous methodology to analyze these types of problems. TOPSIS method is an appropriate approach to deal with MCDMs. This model accounts for the distance between each solution with ideal solution. In this research, the most appropriate direction of Zanjan city deployment is investigated considering economic, environmental, physical and climatically parameters employing TOPSIS method. The results illustrate that the eastern and north-western spaces are more suitable to city development. In contrast, the southern and northern parts are not primarily suitable in this case. It is notably to mention that 15% of city development since 2005 has been accrued in inappropriate areas.
Mina Moradizadeh
Abstract
Atmospheric column water vapor, which is the total atmospheric precipitable water vapor contained in a vertical air column, is one of the most important factors in all surface-atmosphere interactions (such as energy fluxes between the earth and the atmosphere) and plays a key role in wide variety of ...
Read More
Atmospheric column water vapor, which is the total atmospheric precipitable water vapor contained in a vertical air column, is one of the most important factors in all surface-atmosphere interactions (such as energy fluxes between the earth and the atmosphere) and plays a key role in wide variety of environmental studies, ecological and agricultural applications. However, measuring this parameter at meteorological stations requires the use of radiosonde instruments, which being pointwise and costly are limitations of these observations. Therefore, remote sensing is used as an alternative to estimate this important atmospheric parameter. Compared to other atmospheric parameters, atmospheric water vapor which attenuates remotely sensed radiance is of great importance. Although this atmospheric parameter is measured by AIRS (Atmospheric Infrared Sounder) sensor, its low resolution (about 40 km) is not acceptable for many applications. Therefore, developing an algorithm to downscale the AIRS-derived column water vapor is the main goal of this study, so that its spatial resolution can be improved. To do this, using the ratio method, the AIRS-derived column water vapor is fused with the MODIS (Moderate Resolution Imaging spectroradiometer) data. Then, due to the major influence of this parameter on Land Surface Temperature (LST) estimation, the role of improved resolution atmospheric column water vapor in the estimation of LST is investigated as a secondary goal. In order to validate the estimated parameters and evaluate their accuracy, independent datasets were used. Results of the implementation indicate that proposed downscaling method has high potential to enhance the spatial resolution of AIRS-derived atmospheric column water vapor, without significant degradation of the RMSE. It was also found that the atmospheric column water vapor when moving into higher spatial resolution can dramatically increase the accuracy of the LST estimation.
Eslam galehban; Saeid Hamzeh; Shadman Veysi; Seyed Kazem Alavipanah
Abstract
Determination of the Crop Water Requirement (CWR) of different crops and the value of crop water consumption is one of the problems at a large scale and in real-time to the soil and water expert. The first step to compute this variable is to determine the reference evapotranspiration (ET0). The standard ...
Read More
Determination of the Crop Water Requirement (CWR) of different crops and the value of crop water consumption is one of the problems at a large scale and in real-time to the soil and water expert. The first step to compute this variable is to determine the reference evapotranspiration (ET0). The standard method to compute this parameter is to utilize the climate data and experimental equations. The problem with classic methods is that the meteorological station isn’t available in the agricultural lands and usually, we have data limitations. The optimized solution is to utilize remote sensing data. So with the combination of different datasets then the reference evapotranspiration and actual evapotranspiration will be estimated. The goal of the study is to an evaluation of open-source WaPOR and ERA5 to compute daily reference evapotranspiration based on the FAO-Penman Monthis equation at the meteorological stations of Sistan and Baluchestan province. The result has shown that the open-source dataset estimated the reference evapotranspiration as more than 80 percent accurate at the place of the meteorological station and in all of the stations RMSE was less than 2 mm per day. The accuracy assessment of results shown at different crop seasons that ET0 in the autumn season is better than in the spring season. So that the ERA5 combined with the GLDAS Wind data has a better correlation with in situ measurement of ET0 than to the WaPOR. All of the results shown that this dataset can be used in each place in the province to estimate ET0.Therefore, the present study is to investigate the possibility of using the products of WaPOR and ERA5 systems to calculate the amount of daily reference evapotranspiration based on the experimental method of Penman-Monteith and to evaluate and validate its outputs in Sistan and Baluchestan Province of Iran.The results showed that remote sensing systems with an accuracy of over 80% at meteorological stations estimated the amount of reference evapotranspiration and an error of less than 2 mm was reported in all stations. Also, studies during the growing season (June 15 to November 6) compared to the growing season (1 November to 15 May) showed that the reference evapotranspiration obtained from satellite data in the first growing season has a higher (R2). Also, the results of NRMSE index evaluation indicate that the reference evapotranspiration obtained from ERA-GLDAS2.1 data is appropriate.Therefore, since the estimated and validated values had acceptable accuracy, in the next step, these systems can be used anywhere in the province.
FATEMEH KAFI; Elham Yousefi; FATEMEH Jahanishakib
Abstract
The world is warming and the world's population is moving to cities. These two truths do not seem to be related; But a phenomenon called urban heat island connects the two. UHI is one of the most common urban climate phenomena in which some urban areas, especially urban centers, become several degrees ...
Read More
The world is warming and the world's population is moving to cities. These two truths do not seem to be related; But a phenomenon called urban heat island connects the two. UHI is one of the most common urban climate phenomena in which some urban areas, especially urban centers, become several degrees warmer than the surrounding areas. Studying this phenomenon and examining its mechanism is very important for urban planning. In the present study, in order to estimate LST, four single-channel Landsat algorithms, single window, Planck equation and radiation transfer equation in QGIS software environment between 2000 and 2019 in summer and winter seasons in Birjand city have been used. The effect of land use change on the thermal island has also been investigated. In the present study, ground surface temperature in Birjand city was first extracted using Landsat 7 ETM + satellite imagery and Landsat 8 TIRS / OLI sensors in 2000 and 2019 by four methods. In order to investigate the general ability of algorithms to calculate the surface temperature, the statistical indices of mean square error, Nash-Sutcliffe coefficient, mean absolute error and coefficient of determination were used. The results showed that the Landsat single-channel algorithm for calculating the surface temperature in Birjand is more accurate than other algorithms.
Volume 7, Issue 1 , December 2015, , Pages 39-57
Abstract
With the increase in population and consequent increasing needs of society, land use planning is of particular importance. Land use planningdue to being involved with several conflicting aims, multi- objective evolutionary algorithm would be a useful tool to solve land use planning. But use of these ...
Read More
With the increase in population and consequent increasing needs of society, land use planning is of particular importance. Land use planningdue to being involved with several conflicting aims, multi- objective evolutionary algorithm would be a useful tool to solve land use planning. But use of these algorithms should be examined according to the type of issues. In the study, addition to introducing a model to optimize land use, effective solution for the application of multi- objective genetic algorithm on a variety of problems related to land use planning was presented. In order to land uses optimization in the study, the algorithm NSGA-II was use in the model. Output of the model might be introduced patterns for reduction of erosion to an acceptable level and enhancing the economic benefits. This will be skillfully carried out while the land use adaptation is in the highest level and land use changes are easy with high level of continuity.An innovative operator which producing the initial population and an innovative operator with an appropriate Crossover of land use planning issues were developed.The developed model in the study was implemented in Kerman-Rodbar watershed. Evaluation results show that the model is able to suggest patterns to land use planning that reduce erosion about 30 to 35%. While the economic benefits of the changes will be about 40 to 50 %. Furthermore all models have a high consistency and low difficulty to change. These operators have had a significant impact on problem solving. Keywords: Multi- objective optimization, NSGA-II algorithm, Innovative Operators, Land use planning, Ecological potentiality
Saeid Ahmadi; Hadiseh Hasani
Abstract
Nowaday, there are wide applications for satellite images in agriculture monitoring and management. According to high spatial, spectral and temporal resolution of Sentinel-2 images, we used them for precise agriculture in Qorveh country. Proposed methd consist of five step: firstly, multi-temporal images ...
Read More
Nowaday, there are wide applications for satellite images in agriculture monitoring and management. According to high spatial, spectral and temporal resolution of Sentinel-2 images, we used them for precise agriculture in Qorveh country. Proposed methd consist of five step: firstly, multi-temporal images are collected based on agriculture calender of crops. Then feature space is generated based on spectral reflectance and vegetation indices which consists of 70 features. According to high dimensionality of feature space, principle component analysis is applied to reduce its dimension. Four power classifiers include support vector machine, k-nearest neighbour, multi-layer perceptron and random forests classify the reduced spectral feature space. On the other hand, spatial information are extracted from multi-temporal multispectral images. For this pupose, strandard deviation (STD) maps are generated for red, NIR and SWIR bands of each epoch. Then, by averaging the STD maps, final STD map is obtained. Edge detection is performed on STD map and it improves by removing small lines, smoothing, thining, etc. Finally, crop mapping is done by fusion of four classification maps and agriculture farm boundaries. The obtained results show that classification accuracy of k-nearest neighbour, support vector machine, multi-layer perceptron and random forest classifiers are 77.78%, 79,16%, 76.41% and 76.89%, respectively. The overall accuracy of the proposed method improve up to 94.72% which proves high potential of the proposed method.
F Aghighi; O.M Ebadati; H Aghighi
Volume 9, Issue 2 , December 2017, , Pages 41-60
Abstract
Light Detection and Ranging (LiDAR) point cloud dataset and 3 dimensional (3-D) models have been extensively used for urban feature extraction, urban management, forestry management, managing urban green space, tourism management, robotics, and video and computer games' production. One of the main steps ...
Read More
Light Detection and Ranging (LiDAR) point cloud dataset and 3 dimensional (3-D) models have been extensively used for urban feature extraction, urban management, forestry management, managing urban green space, tourism management, robotics, and video and computer games' production. One of the main steps toward reaching accurate 3-D models is clustering and classification of LiDAR point clouds data. The main purpose of this research is to find out, particular machine learning techniques, which are promising for best learning and classification of LiDAR point cloud data in an urban area. Therefore, the performances of K-nearest neighbor (KNN), Decision Trees (D3), Artificial Neural Networks (ANN), Naive Bayes (NB), Support Vector Machine (SVM), and Markov Random Field (MRF) classifiers were evaluated on the LiDAR and aerial image dataset of Vaihingen, Germany, in the context of the "ISPRS Test Project on Urban Classification and 3D Building Reconstruction." In regard to the literature review, MRF model has not been used to classify LiDAR point cloud data in Iran. In this research, we utilized all the geometrical features, intensity values of LiDAR and aerial images as well as extracted eigenvalues based features to distinguish five urban object classes, including impervious surfaces, buildings, low vegetation, trees and cars. In order to compute eigenvalues using local point distribution, this paper introduces a new cubic structure, which has been not found in previous studies. The final results of 3D classification techniques in this research were 2D maps that evaluated by the benchmark ISPRS tests maps. The evaluation shows that the performance of MRF model with an overall accuracy of 88.08% and the kappa value of 0.83 is higher than other techniques to classify the employed LiDAR point clouds.
M Zakipour; M Taleai; Gh Javadi
Volume 10, Issue 1 , June 2018, , Pages 41-56
Abstract
Flood is one of the most common and destructive natural events in the world. Conventional methods which aim to prevent floods, mostly are based on resistance approaches. Considering uncertainties about time and location of flood occurrence and design variables such as discharge, structure resistance ...
Read More
Flood is one of the most common and destructive natural events in the world. Conventional methods which aim to prevent floods, mostly are based on resistance approaches. Considering uncertainties about time and location of flood occurrence and design variables such as discharge, structure resistance and physical characteristics of a basin, resistance methods are not suitable solutions. However, resistance methods for preventing from the flood are effective on lower discharges, they may become less effective or ineffective in extreme-case emergencies. As an example, levees are only effective when flood waters remain below their design capacity. Therefore, in order to manage and reduce human casualties and financial losses, more suitable solutions based on resilience are introduced. Flood resilience is interpreted as the capacity to tolerate flooding to avoid disaster when undergoing- not preventing- flooding, or when physical damage and socioeconomic disruption still occur, the capacity to reorganize and recover quickly. Recovery is defined as assisting of communities affected by flood waters to achieve a proper and effective level of functioning. Resilience approach in water resources management plays an important role in flood risk management. In this study, several strategies of flood risk management with an emphasis on the concept of resilience have been evaluated. A case study was carried out on the Ghezel Ozan river, located in the Mahneshan basin. In order to model the flood, the data related to the topographic conditions of the river are adapted using the HEC GeoRAS extension in the ArcGIS. Then, the flood characteristics in the 25, 50, and 100-year return periods are estimated by the HEC RAS model. Flood flow modeling has been carried out based on eight different management strategies including resistance and resilience strategies based on structural and non-structural approaches. The comparison of these strategies is based on the values of resilient indicators including the amplitude, graduality and recovery rate. Indicators for the amplitude are the expected annual damage (EAD) and the expected annual number of casualties (EANC). The graduality is measured by comparing the relative increase of discharge in a river by the corresponding relative increase of damage. Recovery rate is a function of social, economic and physical condition. In this research, in order to quantify the recovery rate, it is presented as a function of evaporation, transpiration and water penetration into the soil. Finally, after calculating the resilience indicators for each scenario, in order to prioritize the scenarios, the entropy method is used for weighting and TOPSIS is utilized to prioritize the scenarios. According to the results, it has been observed that resilience based methods are preferred to resistance methods and dry farming with flood warning and flood insurance has been determined as the best strategy.
Pouya Ahmadi; Tayebe Managhebi; Hamid Ebadi; Behnam Asghari
Abstract
With the development of remote sensing science, the use of hyperspectral images is becoming more widespread. Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a number of methods have been proposed to address the problem of hyperspectral data classification.In ...
Read More
With the development of remote sensing science, the use of hyperspectral images is becoming more widespread. Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a number of methods have been proposed to address the problem of hyperspectral data classification.In the present study, a structure based on learning capsule networks has been used to classify hyperspectral images, so that the network structure can have the most optimal generation of features by using a convolution layer and a capsule layer, and at the same time Avoid overfitting the on training data. The obtained results show the high quality of production features in the proposed structure. With the development of remote sensing science, the use of hyperspectral images is becoming more widespread. Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a number of methods have been proposed to address the problem of hyperspectral data classification.In the present study, a structure based on learning capsule networks has been used to classify hyperspectral images, so that the network structure can have the most optimal generation of features by using a convolution layer and a capsule layer, and at the same time Avoid overfitting the on training data. The obtained results show the high quality of production features in the proposed structure.In order to improve the classification accuracy, the feature extraction approach through the designed network and the classification by the Extreme Gradient Boosting was compared with the classification method by the global deep network. The proposed capsule approach consists of 3 basic layers: 1) Prime caps, which are capsules of size 8 and 32 with 9 × 9 filters and movement step 2, 2) Digitcaps with 10 16-dimensional capsules, and 3) fully connected layer. The results of examining two approaches for deep networking as well as combining capsule networks with XGBoost reinforcement tree algorithm were compared. Approaches such as SVM, RF-200, LSTM, GRU and GRU-Pretanh were considered to compare the proposed approach based on the configurations mentioned in their research.Up in addition to the study and quality measurement of production vector deep features by the proposed method in different classifiers, the ability of deep global networks in the application of classification should also be examined. The results of examining two approaches for deep network and also combining CapsNet with XGBoost show that by using the proposed combined method, images are classified with 99% accuracy on training data and 97.5% accuracy on test data.Up in addition to the study and quality measurement of production vector deep features by the proposed method in different classifiers, the ability of deep global networks in the application of classification should also be examined.The results of examining two approaches for deep network and also combining CapsNet with XGBoost show that by using the proposed combined method, images are classified with 99% accuracy on training data and 97.5% accuracy on test data.
Sasan Alirezaei; Amir Sadeg Naghshineh; Jalal Karami
Abstract
Data collection and recording of Archaeological sites in Archaeological research is costly and requires a lot of manpower and time. Accordingly, the use of methods that can predict the presence of ancient monuments without direct observation will play a significant role in saving time and cost of Archaeological ...
Read More
Data collection and recording of Archaeological sites in Archaeological research is costly and requires a lot of manpower and time. Accordingly, the use of methods that can predict the presence of ancient monuments without direct observation will play a significant role in saving time and cost of Archaeological surveys. The main issue of this research is to assess the ability of the logistic regression model to predict the dispersal of ancient sites in the Harsin-Bisotun plain. Predictor variables for this study include the environmental variables of slope, height, distance to river, vegetation, distance to modern cities, density of modern villages and distance to main roads, and dependent variable is the most turbulent of area in terms of existence prehistoric Archaeological sites. For modeling, using logistic regression, GIS and IDRISI softwares was used. By analyzing the results of the logistic regression model, results showed that, the logistic regression model was successful in prediction the dispersion of ancient sites in the Harsin-Bisotun plain. As well, the introduction of densly populated areas due to the presence of ancient sites as a modle-dependent variable in the plain-bound areas is more important than the mere introduction of GPS points of the ancient sites as a dependent variable. and, Accordingly, the cultural variability of village density in the Calcolithic Age, village density, distance to cites and the distance to main roads in the Bronze Age and distance to cites, distance to main roads in Iron Age have had the greatest impact in prediction the dispersion of ancient sites.
Rasta Nazari; Hadi Ramezani Etedali; Peyman Daneshkar Arasteh
Abstract
Estimation of the production potential of a crop is a function of climatic conditions, crop genetic potentials and various other environmental and managerial factors. Assessing the ability of regions to realize the genetic potential of crops is one of the important points of macro-planning in agriculture. ...
Read More
Estimation of the production potential of a crop is a function of climatic conditions, crop genetic potentials and various other environmental and managerial factors. Assessing the ability of regions to realize the genetic potential of crops is one of the important points of macro-planning in agriculture. Considering the position of Qazvin province in the production of Maize and the importance of cultivating this crop, estimating the yield of this strategic product as accurately as possible is very necessary. In this regard, by studying an 11-year statistical period, Maize yield was estimated with the new crop model AquaCrop-GIS. The zoning of key product indicators was simulated through the model in the province. By examining the results of these parameters, it finds that Qazvin and Moallem Kelayeh study stations with higher reference evapotranspiration rates have higher water productivity. Then, with the help of the computational yield, components of water footprints, and total water footprint of the crop was estimated within the study stations. By examining the regression equations in each station, it was found that the relationship between blue water footprint and crop yield compared to other water footprint components for all stations has a higher coefficient of determination (R2 = 0.43, R2 = 0.51, R2 = 0.43, R2 = 0.77 and R2 = 0.79 for Qazvin, Avaj, Moallem Kelayeh, Takestan and Buin Zahra stations, respectively) and level of significance. In general, the coefficient of determination of these relationships in Buin Zahra station with R2 = 0.88, R2 = 0.79, R2 = 0.56 and, R2 = 0.53, respectively, for green, blue, gray, and total water footprints compared to other stations were rated higher. This reduction in yield at the station had a significant effect on increasing the total water footprint of the crop.
