kntu
Engineering Journal of Geospatial Information Technology
2008-9635
7
3
2019
12
1
Localization Boyan algorithm to detect forest fires from MODIS sensor images
1
16
FA
Omid
Azari
K.N. Toosi University of Technology
N
Ali
Mohammadzadeh
K.N. Toosi University of Technology
almoh2@gmail.com
Y
10.29252/jgit.7.3.1
Of phenomena which much damage and irreparable import to forests and natural resources is the fire that each year, more than 100 fires occur in Iran and thousands of hectares of trees and plants eliminates. Given that fire risk is high in most parts of the world, full and continuous monitoring on this natural phenomenon, is essential. Use remote sensing is a way to identify and manage fire. Ahead goal in the study, development and improvment, Byun algorithm and compare it with some fire detection algorithms using the MODIS sensor images. Therefore, in addition to the developed algorithm, algorithms Byun in 2007, Lingli Wang in 2008 and Jing Wang in 2011 for the forest area in Golestan Province is localized and implemented. To evaluate the results, the matrix ambiguity and ground data collected from the forests and natural resources in Golestan province has been used that for each algorithm, fire detection rate, false alarm rate and Kappa statistics were calculated and compared which Fire detection rate for Byun algorithms, Ling Wang, Jing Wang and development algorithm by 78.95, 53.84, 46.15 and 72.22 percent respectively and kappa coefficient, 81.02, 32.37 , 28.37 and 81.11 percent have been achieved which shows the superiority of the algorithm is developed in the study area.
Forest fire, Fire detection algorithm, improved algorithm, MODIS sensor.
http://jgit.kntu.ac.ir/article-1-738-en.html
http://jgit.kntu.ac.ir/article-1-738-en.pdf
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
7
3
2019
12
1
Locating Hospital Centers By an Integration of BWM، DANP، VIKOR and COPRAS Methods (Case Study: Region 1, City of Tehran)
17
42
FA
Kamal
Mohammadi
K.N. Toosi University of Technology
N
Ali Asghar
Alesheikh
K.N. Toosi University of Technology
Alesheikh@kntu.ac.ir
N
Mohammad
Taleai
K.N. Toosi University of Technology
Y
10.29252/jgit.7.3.17
Land use planning is the main element of urban planning. Today, the expansion of cities and the increase of urban populations have led city managers to face the challenge of integrated planning and optimal use of land for sustainable development. Finding solutions to the optimal location of urban centers for citizens to achieve prosperity among these issues. In this regard, the application of a spatial information system along with multi-criteria decision making can be a suitable option for policymakers. Given the importance of hospitals as subcategories of health centers and their vital role in community health, the decision to allocate the required service, entail the use of efficient tools and techniques, as well as the expertise of the experts. In the present study, with the aim of improving decision output related to traditional methods and previous studies, the effective measures of hospital site selection are weighted in two sages: Best-Worst and Dematel-based ANP methods. In the first method, access to the main street and distance from the health centers are categorized as the most and the least criteria while in the second, distance from other hospitals and the slope outlined the most important and least important criteria. Due to the different weighting process of the two methods, their results were combined, and the weighted weights of the criteria were adjusted to 21.8%. Then the corresponding factor maps were prepared and combined with the final weights. Then, from the zoning of the study area, nine suitable construction options were proposed, which were ranked in two steps using the Vikor and Coopers methods. The final rating was derived from the comparison of the output of the two methods. Then, in selecting the sites 6 and 7 as the best selective options, we evaluated the expected improvement in the health status of the region. Based on the results of this research, increasing the number of effective criteria, combining new and powerful methods of decision making and turning decision-making from individual to group and prioritizing needs can be important factors in improving the quality of the results of the analysis.
Best-Worst-Method, Dematel-based ANP, Complex Proportional Assessment, VIKOR, SIte Selection. Multi-criteria group decision making.
http://jgit.kntu.ac.ir/article-1-739-en.html
http://jgit.kntu.ac.ir/article-1-739-en.pdf
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
7
3
2019
12
1
Investigation of MODIS mission capability in tropospheric delay estimation for precise point positioning
43
55
FA
Saeid
Haji-Aghajany
K.N. Toosi University of Technology
N
Yazdan
Amerian
K.N. Toosi University of Technology
amerian@kntu.ac.ir
Y
10.29252/jgit.7.3.43
Tropospheric delay is always considered as one of the factors limiting the accuracy of GPS. In this paper, the three-dimensional ray tracing technique is proposed to calculate the tropospheric delay. The ability of the MODIS mission to calculate the tropospheric delay is also examined. For this purpose, an area in central Europe was selected and a MODIS acquisition on 2008/08/01 was studied. In addition, the radiosonde observations as well as ERA-Interim meteorological data were used to evaluate the obtained results. After applying corrections to the MODIS acquisition, the three-dimensional ray tracing method was implemented at the location of a GPS station using all three types of data to extract the tropospheric delay. The RMS of difference between the results of MODIS and results of radiosonde and ERA-Interim data was 1.11 and 0.89 cm respectively. Then, precise point positioning was done using the Bernese software and tropospheric correction from MODIS, radiosonde and ERA-Interim data and compared with precise coordinate of station. The accuracy of position with MODIS tropospheric correction is less than ones corrected with radiosonde and ERA-Interim tropospheric data. The results show the low efficiency of MODIS data for tropospheric correction of GPS observations compare to radiosonde and ERA-Interim data.
Tropospheric Delay, Precise Point Positioning, MODIS, Meteorological Data, Radiosonde, Three Dimensional Ray Tracing
http://jgit.kntu.ac.ir/article-1-741-en.html
http://jgit.kntu.ac.ir/article-1-741-en.pdf
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
7
3
2019
12
1
Combining Neural Network with Genetic Algorithm for prediction of S4 Parameter using GPS measurement
57
77
FA
Ali Reza
Atabati
K.N. Toosi University of Technology
N
Mohammad Mahdi
Alizadeh
K.N. Toosi University of Technology
alizadeh@kntu.ac.ir
Y
10.29252/jgit.7.3.57
The ionospheric plasma bubbles cause unpredictable changes in the ionospheric electron density. These variations in the ionospheric layer can cause a phenomenon known as the ionospheric scintillation. Ionospheric scintillation could affect the phase and amplitude of the radio signals traveling through this medium. This phenomenon occurs frequently around the magnetic equator and in low latitudes, mid as well as high latitude regions. ionospheric scintillation is a very complex phenomenon to be modeled. Patterns of ionospheric scintillation occurrence are depended on spatial and temporal ionospheric variabilities. Neural Network (NN) is a data-dependent method, that its performance improves with the sample size. According to the advantages of NN for large datasets and noisy data, the NN model has been implemented for predicting the occurrences of amplitude scintillations. In this paper, the GA technique was considered to obtain primary weights in the NN model in order to identify appropriate S4 values for GUAM GPS station in Guam country (latitude: 144.8683, Longitude:13.5893). The modeling was carried out for the whole month of June 2017, while this model along with ionospheric physical data was used for predicting ionospheric scintillation at the first day of July 2017, the day after the modeling. The designed model has the ability to predict daily ionospheric scintillation with the accuracy of about 78%.
Neural Networks, Genetic Algorithm, Ionospheric Scintillation
http://jgit.kntu.ac.ir/article-1-742-en.html
http://jgit.kntu.ac.ir/article-1-742-en.pdf
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
7
3
2019
12
1
Determining Effective Factors on Land Surface Temperature of Tehran Using LANDSAT Images And Integrating Geographically Weighted Regression With Genetic Algorithm
79
102
FA
Amer
Karimi
University of Tehran
N
Parham
Pahlavani
University of Tehran
pahlavani @ut.ac.ir
Y
Behnaz
Bigdeli
Shahrood University of Technology
N
10.29252/jgit.7.3.79
Due to urbanization and changes in the urban thermal environment and since the land surface temperature (LST) in urban areas are a few degrees higher than in surrounding non-urbanized areas, identifying spatial factors affecting on LST in urban areas is very important. Hence, by identifying these factors, preventing this phenomenon become possible using general education, inserting rules and also retaining efficient management policies and more monitoring to counter the stimulating factors of increasing land surface temperature. The goal of this research is to identify the effective factors on land surface temperature in Tehran. In this regard, a geographically weighted regression (GWR) was used to identify the effective factors and a genetic algorithm (GA) was employed to select the best combination of these factors. The recommended combination method is a suitable method for spatial regression issues, because it is compatible with two unique properties of spatial data, i.e. the spatial autocorrelation and spatial non-stationarity. In this study, land surface temperature data in Tehran was obtained on August 18, 2014 and August 21, 2015 using Landsat 8 satellite imagery, and was used in two methods of Gaussian and Tri-cubic weighting in GWR. The values of 1-R2 by using the Gaussian kernel were equal to 0.21752 and 0.23448, as well as by using the the Tri-cubic kernel were equal to 0.10452 and 0.14494 for August 18, 2014 and August 21, 2015, respectively. The results showed that the effects of factors such as land use, construction density, and distance from roads on land surface temperature in Tehran were more than other factors. Also, using the tri-cubic kernel for GWR provided more accurate results.
Land Surface Temperature, Geographic Weighted Regression, Genetic Algorithm.
http://jgit.kntu.ac.ir/article-1-743-en.html
http://jgit.kntu.ac.ir/article-1-743-en.pdf
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
7
3
2019
12
1
Improvement of the Classification of Hyperspectral images by Applying a Novel Method for Estimating Reference Reflectance Spectra
103
114
FA
Hamid
Ezzatabadi Pour
Sirjan University of Technology
hezzatabadi@sirjantech.ac.ir
Y
Abdol Reza
Kazeminia
Sirjan University of Technology
N
10.29252/jgit.7.3.103
Hyperspectral image containing high spectral information has a large number of narrow spectral bands over a continuous spectral range. This allows the identification and recognition of materials and objects based on the comparison of the spectral reflectance of each of them in different wavelengths. Hence, hyperspectral image in the generation of land cover maps can be very efficient. In the hyperspectral classification methods that use a dissimilarity measure for classification, the reference reflectance spectra of each class are usually estimated through averaging the image pixel's reflectance spectra of training data. This estimation method yields a reference reflectance spectrum in which minimize the total sum of squared Euclidean distances between the reference reflectance spectrum itself and the image pixel's reflectance spectra of training data. For this reason, the method is acceptable only for the Minimum Distance algorithm in which is used the squared Euclidean distance for classification. In this paper, we propose a method in which the reference reflectance spectrum is estimated by taking into account the dissimilarity measure that is used in the classification algorithm. Two SAM and JMD classification algorithms have been used to present and implement the proposed method. The evaluation of the accuracy and efficiency of the proposed method has been done by investigating and comparing the results of the classification of SAM and JMD algorithms by considering both averaging and proposed methods. The tests performed on four real hyperspectral images collected by AVIRIS, HYDICE, Hyperion and HyMap sensors show that the proposed method improves classification results, in a manner that the Kappa coefficient of the classification results of four hyperspectral imagery datasets increased by 13.18%, 1.06%, 0.75% and 2.18%, respectively, in the SAM algorithm and 10.79%, 2.17%, 0.34% and 2.4%, respectively, in the JMD algorithm.
Classification, Hyperspectral Images, Dissimilarity Measure, Estimating Reference Reflectance Spectra.
http://jgit.kntu.ac.ir/article-1-744-en.html
http://jgit.kntu.ac.ir/article-1-744-en.pdf
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
7
3
2019
12
1
Developing a model for simulating urban expansion based on the concept of decision risk: A case study in Babol city
115
135
FA
Mohammad
Karimi Firozjaei
University of Tehran
N
Amir
Sedighi
University of Tehran
N
Mohammadreza
Jelokhani-Niaraki
University of Tehran
mrjelokhani@ut.ac.ir
Y
10.29252/jgit.7.3.115
Today, the study of the spatial-temporal pattern of urban physical expansion and the identification of the parameters affecting the expansion play a crucial role in urban-related decision-making and long-term planning processes. Consequently, the use of precise and efficient methods to predict the physical expansion of urban areas is of great importance. The objective of present study is to provide a new conceptual model for implementing a simulation model for predicting physical expansion of urban areas based on the degree of risk in multi-criteria spatial decision making. This model has been implemented to predict the physical expansion of Babol city. In the proposed model, the combination of subjective and objective weighting methods has been used globally and locally on the basis of neighboring states to determine the relative importance of mentioned criteria and the Markov model is used for generating the transition rules. In addition to the two parameters of the criteria and the weight of each criterion, the risk-factor parameter is also considered for the mapping of the physical expansion of the city. In order to determine the degree of risk and the optimal weighting method, each of simulated built-up area maps was compared with a real built-up area map. The results obtained for the study area show that the optimum ORness values in local and global strategies for generating suitability map are 0.3 and 0.7, respectively. Moreover, the average overall accuracy for the local and global weighting methods at different levels of risk is 87.6 and 86.8, respectively. This means that the local weighting method is more accurate than the global method for generating the suitability map.
Urban physical expansion, simulation model, multi-criteria spatial decision making, risk.
http://jgit.kntu.ac.ir/article-1-745-en.html
http://jgit.kntu.ac.ir/article-1-745-en.pdf
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
7
3
2019
12
1
Discrimination of Geological Top-Formations by their Morphology through SAR Images and via Fractal Geometry implementation in IEM Backscattering Model(Case Study: Zagros Thrust Belt)
137
157
FA
Ali
Ghafouri
University of Tehran
ali.ghafouri@ut.ac.ir
Y
Jalal
Amini
University of Tehran
N
Mojtaba
Dehmollaian
University of Tehran
N
Mohammad Ali
Kavoosi
Islamic Azad Shahrood University
N
10.29252/jgit.7.3.137
Morphological discrimination of geological top-formations is the supplemental procedure of geological mapping; so in situ measurements to register geomorphological data are unavoidable; though due to the impassable and fault cliffs field operations to visit all areas within a geological map is almost impossible. Microwave or radar remote sensing, via synthetic aperture radar (SAR) images is capable to obtain the surface morphology and alteration zones discrimination on the basis of lithology texture. For this purpose, it is necessary to model the surface roughness against microwave signal backscattering; among available models, Integral Equation Model (IEM) is the most famous one, in which surface roughness is calculable via roughness height statistical parameter (RMS-height). Whereas, this parameter is not capable enough to measure, since it measures the surface roughness merely in vertical direction, and roughness dispersion on the surface is not included. To apply the proposed method of geomorphological mapping, the roughness map for the area of concern which is the northern part of Anaran anticline (located between Dehloran and Ilam cities in Iran) using ALOS-PALSAR and TerraSAR images is computed. Field micro-topography measurement is performed on three different sites containing the main lithologies of the case study, using surveying total station. It is clarified in comparison of roughness map with the ground truth, that using fractal geometry parameters in IEM model computation, the standard deviation had more than 10% of decrease, in comparison with conventional IEM calculations. In addition, in this paper, a comparison is made between the results obtained with another article from the authors to the results gained by the method of this article, which shows a 10 to 15 percent advantage of this paper method.
Geology Mapping, Synthetic Aperture Radar, Integral Equation Model (SimWeight), Mazandaran province.
http://jgit.kntu.ac.ir/article-1-746-en.html
http://jgit.kntu.ac.ir/article-1-746-en.pdf
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
7
3
2019
12
1
Comparison of the Performance of Geographically Weighted Regression and Ordinary Least Squares for modeling of Sea surface temperature in Oman Sea
159
172
FA
Ali
Bahri
University of Zanjan
N
Younes
Khosravi
University of Zanjan
khosravi@znu.ac.ir
Y
Azadeh
Tavakoli
University of Zanjan
N
10.29252/jgit.7.3.159
In Marine discussions, the study of sea surface temperature (SST) and study of its spatial relationships with other ocean parameters are of particular importance, in such a way that the accurate recognition of the SST relationships with other parameters allows the study of many ocean and atmospheric processes. Therefore, in this study, spatial relations modeling of SST with Surface Wind Speed (SWS), Chlorophyll a Concentration, latitude and longitude in Oman Sea between 2003 to 2016 was performed by Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) method available in ArcGIS software and the outputs of the two methods were compared. The results of the OLS method showed that the Surface Wind Speed variable had the most effect on estimating SST values in the Oman Sea, and other variables had shown a low effect on the SST estimation. But in the GWR model, it was found that the longitude variable had the most effect in the estimation of SST values and had a positive relation with SST. In this model, the SWS variable has a positive relationship with SST, but its impact is less in compared with OLS model. Other variables also have a negative relationship with SST. Subsequently, using the local explanation coefficient (R2), it was determined that the GWR model had a higher accuracy than the OLS model for estimating SST values in the Oman Sea, so that the GWR model justify 85% of SST spatial changes in the Oman Sea, but the OLS model justifies only 55% of spatial variations of this parameter. The higher accuracy of the GWR model in the estimation of SST values was found in the eastern and western parts of the Oman Sea and this model was less accurate in the central part of the sea.
Sea Surface Temperature, Modeling, Ordinary Least Squares, Geographically Weighted Regression, Oman Sea.
http://jgit.kntu.ac.ir/article-1-747-en.html
http://jgit.kntu.ac.ir/article-1-747-en.pdf
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
7
3
2019
12
1
Digital surface model extraction with high details using single high resolution satellite image and SRTM global DEM based on deep learning
173
198
FA
Hamed
Amini Amirkolaee
University of Tehran
hossein.arefi@ut.ac.ir
Y
Hossein
Arefi
University of Tehran
N
10.29252/jgit.7.3.173
The digital surface model (DSM) is an important product in the field of photogrammetry and remote sensing and has variety of applications in this field. Existed techniques require more than one image for DSM extraction and in this paper it is tried to investigate and analyze the probability of DSM extraction from a single satellite image. In this regard, an algorithm based on deep convolutional neural networks (CNN) is designed. In the proposed subject, firstly, some preprocessing such as dividing the satellite image into smaller images, localizing the height values and data augmentation are applied in order to prepare data to enter the network. The proposed CNN network has an encoder-decoder structure in which, different and effective features in different scales are extracted in the encoder stage and the generated features are fused to estimate height values by presenting an effective procedure in the decoding stage. Subsequently, the ground and non-ground pixels are separated and height values of the non-ground objects are extracted. The final DSM is obtained by adding the non-ground pixels with height information to the SRTM digital elevation model (DEM) with 30 meter pixel size. The proposed algorithm is evaluated using the satellite images and their corresponding DSMs. Analyzing the estimated small height images using the proposed CNN indicated 0.921, 0.221 and 2.956m on average for relative mean error (ER), logarithm mean error (EL) and root mean squared error (ERMSE), respectively. Moreover, analyzing the final seamless DSMs indicated 4.625 on average for ERMSE.
Digital Surface Model, Convolutional Neural Network, single satellite image, SRTM DEM.
http://jgit.kntu.ac.ir/article-1-748-en.html
http://jgit.kntu.ac.ir/article-1-748-en.pdf
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
7
3
2019
12
1
Optimization of RFM\'s Structure Using a New Reformulation of PSO in Case of Limited GCPs
199
212
FA
Saeed
Gholinejad
University of Isfahan
N
Amin
Alizadeh Naeini
University of Isfahan
a.alizadeh@eng.ui.ac.ir
Y
Alireza
Amiri-Simkooei
University of Isfahan
N
10.29252/jgit.7.3.199
Metaheuristic algorithms have been widely used in determining the optimum rational polynomial coefficients (RPCs). By eliminating a number of unnecessary RPCs, these algorithms increase the accuracy of geometric correction of high-resolution satellite images. To this end, these algorithms use ordinary least squares and a number of ground control points (GCPs) to determine RPCs' values. Due to the cost of GCPs collection, using limited GCPs has become an attractive topic in various researches. In this study, a new reformulation of particle swarm optimization (PSO) algorithm, namely, Discrete-Binary PSO for Rational Function Model (DBPSORFM), is presented to find the optimal number and combination of RPCs in the case of limited GCPs. Based on the fact that the maximum number of RPCs, the values of which are obtained through least squares, is twice the number of GCPs, the particle of the proposed algorithm is composed of two binary and discrete parts. The discrete part contains the number of rational coefficients that can vary from 1 to 78. In the binary section, which contains 0 and 1 values, the absence or presence of the corresponding coefficient in the discrete section is investigated. This method is not only compatible with the nature of the metaheuristic algorithms but also significantly reduces the search space. The proposed method has been tested on various types of high-resolution data. The results of the experiments indicate the superiority of the proposed method in comparison with the conventional approach in metaheuristic algorithms.
Rational Function Models (RFMs), Particle Swarm Optimization (PSO), Limited number of GCPs.
http://jgit.kntu.ac.ir/article-1-749-en.html
http://jgit.kntu.ac.ir/article-1-749-en.pdf
kntu
Engineering Journal of Geospatial Information Technology
2008-9635
7
3
2019
12
1
Estimation of Tree Biomass at Individual tree, Sample plot and Hybrid Level using Drone Images
213
230
FA
Mohammad Reza
Kargar
Tarbiat Modares University
N
Hormoz
Sohrabi
Tarbiat Modares University
hsohrabi@modares.ac.ir
Y
10.29252/jgit.7.3.213
Two-dimensional image conversion algorithms to 3D data create the hope that the structural properties of trees can be extracted through these images. In this study, the accuracy of biomass estimation in tree, plot, and hybrid levels using UAVs images was investigated. In 34.8 ha of Sisangan Forest Park, using a quadcopter, 854 images from an altitude of 100 meters above ground were acquired. SFM algorithm was applied to produce 3D data and the height of the trees was extracted. 28 samples with 30×30 m dimension were collected and the height and the diameter at the breast height were measured and the biomass was calculated based on a general allometric equation. In order to estimate the biomass at plot-level, the height metrics were extracted from the point cloud. For tree-level, the biomass of each tree was modeled based on the height value derived from CHM for each tree. In hybrid-level, sum of estimated biomass of trees in tree-level was used as the predictor in modeling. The accuracy and precision of the estimates were evaluated using relative bias (rBias), relative root mean square error (rRMSE), and adjusted r square (r2). rRMSE for biomass estimation in Buxus hyrcana, Carpinus betulus, Parottia persica, and other species were 17.56, 7.11, 14.67 and 22.73 percent, respectively. For plot level and hybrid level, rRMSE were 58 and 47 percent, respectively. Based on the result, the most precise approach for biomass estimation is hybrid level and the precision of the estimate is appropriate for overall assessment of forest stands, not for management planning.
UAV, Sisangan, Crown Height Model, Above Ground Biomass, Digital Terrain Model.
http://jgit.kntu.ac.ir/article-1-750-en.html
http://jgit.kntu.ac.ir/article-1-750-en.pdf