علمی - پژوهشی
Seyed Yousef Sajjadi; Saeid Parsian
Volume 10, Issue 2 , September 2018, Pages 1-14
Abstract
In this study, the fusion of hyperspectral and LiDAR data was used to propose a new method to detectbuildings using the machine learning algorithm. The data sets provided by the National ScienceFoundation (NSF) - funded by Centre for Airborne Laser Mapping (NCALM)- over the University ofHouston campus ...
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In this study, the fusion of hyperspectral and LiDAR data was used to propose a new method to detectbuildings using the machine learning algorithm. The data sets provided by the National ScienceFoundation (NSF) - funded by Centre for Airborne Laser Mapping (NCALM)- over the University ofHouston campus and the neighboring urban area, were used. The objectives of this study were: 1)automatic buildings extracting using the hyperspectral and LiDAR fused data (automation), 2)detecting of the maximum number of listed buildings on the study area (completeness), and 3)achieving the high accuracy in building detection throughout the classification procedure (accuracyand precision). After classification of the buildings, a comparison was made between the resultsobtained by the proposed method and the reference method in this field. Our proposed methodshowed a better accuracy for buildings detection in a much shorter time compared to the referencemethod. The accuracy of the classification was assessed by four parameters of Precision,Completeness, Overall Accuracy and Kappa Coefficient, and the values of 96%, 100%, 99% and 0.94were obtained, respectively.
علمی - پژوهشی
Hamidreza Matinfar; H Mahmodzadeh; A Fariabi
Volume 10, Issue 2 , September 2018, Pages 15-32
Abstract
Soil organic matter is one of the most important Physical and chemical properties of soil that it iscritical in determining the quality and management of soils. Quantify of soil organic carbon due to thehigh spatial variability and changes over time is difficult. Near-infrared-visible spectroscopy is ...
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Soil organic matter is one of the most important Physical and chemical properties of soil that it iscritical in determining the quality and management of soils. Quantify of soil organic carbon due to thehigh spatial variability and changes over time is difficult. Near-infrared-visible spectroscopy is afeasible method to reduce the time and cost to check the organic carbon. The aim of this study was toevaluate soil organic carbon through near-infrared and visible spectroscopy with the statistical modelsPLSR and PCR. For this purpose, 40 soil samples from depths of 0 to 30 cm were collected bysystematic random method based on previous studies and determination of different classes of soils inthe region. Chemical analysis of soils was performed according to standard methods. Spectralreflectance of soil samples in the range of 350 to 2500 nm was measured then after applying thepreprocessing methods such as Savitzky and Golay filter, Soil organic carbon were calculated byprincipal component analysis (PCA), regression partial least squares (PLSR) and principal componentregression (PCR) models. The results of this study showed that the Savitzky and Golay filter was thestrongest preprocessing method for spectral data. Coefficients of determination (R2), root mean squareerror of Prediction (RMSE) and ratio of prediction to deviation (RPD) in the calibration andvalidation to predict organic matter, respectively, 0.97, 0.05, 5.09 and 0.85, 0.14, 2.78 respectively.Therefore, for dry and semi-arid soils of the PLSR model, it is more efficient to predict the organiccarbon of the soil. The results showed that the PLSR model has better performance than the PCRmodel in soil organic carbon estimation.
علمی - پژوهشی
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, ...
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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.
علمی - پژوهشی
Arash bihamta; Hamid Goharnejad; saber moazami
Volume 10, Issue 2 , September 2018, Pages 45-60
Abstract
Rainfall is the most important factor directly involved in the hydrological cycle. Obtaining accuraterainfall data is essential for analyzing various hydrological phenomena and climate change. The aimof this study was to investigate the accuracy of the rainfall data of two GPM satellites with IMERGand ...
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Rainfall is the most important factor directly involved in the hydrological cycle. Obtaining accuraterainfall data is essential for analyzing various hydrological phenomena and climate change. The aimof this study was to investigate the accuracy of the rainfall data of two GPM satellites with IMERGand TRMM with 3B42 product at four synoptic stations in Tehran on daily, monthly and seasonalscales. In comparative comparison between satellite data and rainfall observation data CorrelationCoefficient (R), Bias, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), with the aimof validating data, and Probability of Detection (POD), False Alarm Ratio (FAR) and Critical SuccessIndex (CSI) verification of the data were investigated. The results showed that the correlation betweenIMERG data and rain observation data at station is higher than 3B42 data. In addition, the Bias, MAEand RMSE values confirmed that both the 3B42 and IMERG products had the lowest error rates withobservation data. Also, In the evaluation of the IMERG product with rainfall values of the Shemiranstation, the correlation at this station on the daily, monthly and seasonal scale was 57%, 83% and87%, respectively. In general, considering to its superior technology, IMERG has a high precision anda good tool for hydrological forecasting.
علمی - پژوهشی
H Emami; M Jafary; A Nazari Samani; A Malekian
Volume 10, Issue 2 , September 2018, Pages 61-74
Abstract
Spatial modeling of the groundwater springs occurrences allowed the identification of new springs fordrinking, agriculture and industry. The objective of this study was spatial modeling of thegroundwater springs occurrences using the geomorphometry indexes affecting the groundwatersprings occurrences ...
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Spatial modeling of the groundwater springs occurrences allowed the identification of new springs fordrinking, agriculture and industry. The objective of this study was spatial modeling of thegroundwater springs occurrences using the geomorphometry indexes affecting the groundwatersprings occurrences and Weights-of-evidence control model and evaluating this model in CentralAlborz. Generally, 584 springs were identified in the study area that 409 (70%) of them were utilizedfor training and 175 (30%) springs for validation of Weights-of-evidence control model. 14 importantgeomorphometry indexes includin elevation, degree of slope, aspect, plan curvature, profilecurvature, topographic wetnes index, stream power index, slope length, topography position index,lithology, distance of faults, fault density, distance from rivers and drainage density were chosen inthe form of Weights-of-evidence control model for spatial modeling of the groundwater springsoccurrences. According to Weights-of-evidence control model, aspect and topographic wetness indexhad the lowest and the highest impact on the ground water springs occurrences respectively. The mapresulted from spatial modeling of groundwater springs occurrences were classified into 4 classesincluding the low, middle, high, and very high potential occurrences. The model was validated usingROC method, which the area under the curve was 0.866. This means the weights-of-evidence controlmodel was accurate enough for estimating the spatial modeling of groundwater springs occurrences inCentral Alborz.
علمی - پژوهشی
K Borna; F Fathi
Volume 10, Issue 2 , September 2018, Pages 75-94
Abstract
Repairing incorrect polygons for use in GIS software is semi-automated and time-consuming.Automatic polygon repair, interpretation of obscure polygons, and elimination of all existing bugsbased on definitions and global standards that have many uses in software related to GIS. Due to thecomplexity of ...
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Repairing incorrect polygons for use in GIS software is semi-automated and time-consuming.Automatic polygon repair, interpretation of obscure polygons, and elimination of all existing bugsbased on definitions and global standards that have many uses in software related to GIS. Due to thecomplexity of the computation and data volumes in working with big data, there is always acompetition between the speed and the amount of memory used. In this paper, while introducing thestandard of the characteristics of simple complications in polygons, using Delaunay Triangulation andGTS functions in Java and with the help of the H2 database, a method is presented that receivespolygons in the form of a file in the CSV format and applies several effective algorithms toautomatically repair them. The polygons in the spatial data set are automated at optimal time and withminimal memory consumption and are repaired if necessary. The results show that this method,compared with the previous ones, our method leads to relative improvement in execution speed andprovides more than 50 percent saving (in average) in the main memory while working with big data.
علمی - پژوهشی
Faraham Ahmadzadeh; Negar Amiri; Elham Ebrahimi
Volume 10, Issue 2 , September 2018, Pages 95-108
Abstract
Today, it is well-known that predicting the distribution potential of endangered species by usingspatial modeling methods is highly beneficial and using these methods can greatly contribute toecological conservation and management. Rana pesudodalmatina is one of the Iranian endemicamphibian species of ...
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Today, it is well-known that predicting the distribution potential of endangered species by usingspatial modeling methods is highly beneficial and using these methods can greatly contribute toecological conservation and management. Rana pesudodalmatina is one of the Iranian endemicamphibian species of Iran. In order to predict the potential geographic distribution of the species itsoccurrence points were collected through field work and 19 so-called bioclim climate variables asspatial environmental predictors were extracted from the Worldclim database. By applying Pearsoncorrelation test, the highly correlated variables with correlation coefficient of 0.75 were eliminated.Species distribution modeling was done using newly published R package which includes GLM,GAM, RF, MARS, CART, FDA, BRT and SVM models. All individual models were compound as anensemble to reduce the uncertainty which increase the accuracy and predictive power. The resultsrevealed that the long-legged wood frog has maximum distribution potential in Hyrcanian forest ofIran. Also, the results of the valuation of the models showed that the AUC and TSS had better statusand the SVM model was the most credible. In addition, the results of measuring the importance ofeach of the variables showed that BIO6 had the highest and BIO19 had the least importance for this.
