Remote Sensing (RS)
Hosein Nesari; Reza Shah-Hosseini; Amirreza Goodarzi; Soheil Sobhan Ardakani; Saeed Farzaneh
Abstract
Extended Abstract
Introduction
Atmospheric aerosols are a colloid of solid particles or liquid droplets suspended in the atmosphere. Their diameter is between 10-2 to 10-3 micrometers. They directly and indirectly affect the global climate by absorbing and scattering solar radiation, and they also ...
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Extended Abstract
Introduction
Atmospheric aerosols are a colloid of solid particles or liquid droplets suspended in the atmosphere. Their diameter is between 10-2 to 10-3 micrometers. They directly and indirectly affect the global climate by absorbing and scattering solar radiation, and they also have a serious impact on human health by emitting harmful substances. In addition, high concentrations of aerosols on a local scale due to natural or human activities have adverse effects on human health, including cancers, pulmonary inflammation, and cardiopulmonary mortality. Monitoring the temporal and spatial variability of high concentrations of aerosols requires regular measurement of their optical properties such as aerosol optical depth (AOD).
Materials & Methods
Algeria is a large country with little knowledge of the spatial and temporal diversity of AOD, and the low spatial resolution of existing products makes it very difficult to predict aerosols (airborne particles) at the local scale, especially in arid southern regions. As a result, AOD recovery with data with higher spatial resolution is crucial for determining air pollution and air quality information. Several AERONET stations have been installed in Algeria. The Tamanrasset_INM station has been selected based on its location and the availability of historical AOD data for the period (2015-2016).
In this study, Landsat-8 / OLI image from tile 192/44 was used for satellite images. To this end, 23 TOA-corrected L1G-level Landsat-8 / OLI cloudless scenes were downloaded from January 2015 to December 2016 in the study area. DN values are converted to TOA reflections using the scaling factor coefficients in the OLI Landsat-8 metadata file. In this study, the minimum monthly reflectance technique was used to recover AOD in this area. As a result, LSR images were used in the recovery process in different months of 2015 and 2016. The process of selecting reference LSRs was initially based on the selection of clear, foggy / cloudless sky images. The selected images were then used to construct artificial images in which each pixel corresponds to the second lowest surface reflection of all selected monthly images to be the LSR pixel for the respective month. The AOD retrieval method developed in this study is based on a LUT, using the 6S radiative transfer model. The advantage of using the 6S model is its ability to estimate direct components and scattering using a limited number of inputs for each spectral band in the entire solar domain. The effect of the viewing angle is limited because Landsat data are usually obtained with a fixed viewing angle. Surface reflectance can be estimated from a pre-calculated LSR database. The accuracy of AOD recovery depends on the use of the appropriate aerosol model. A continental model was selected from the available aerosol models. Other atmospheric parameters such as ozone, carbon dioxide, carbon monoxide and water vapor are considered by default. The AOD values used to make LUT are set as follows: 0.0, 0.05, 0.1, 1.5, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.2 and 1.5. The zenith angles of the sun and the sensor range from 0 to 70 degrees with a step of 5 degrees and the range of azimuth angles from 0 to 180 degrees with a step of 12 degrees. Using these parameters, the radiative transfer equation was run in forward to obtain the TOA reflection. Different combinations of input and TOA output parameters are stored in LUT. AOD retrieval is based on a comparison between the TOAs estimated with the model and the observed items using the best fit approach. Using such an approach, the estimated AODs are simulated in accordance with those used in the production of TOAs, using a competency function that minimizes the distance.
Results & Discussion
In this study, the AODs recovered at 550 nm in a 5-by-5-pixel window around the AERONET site were averaged. The considered AERONET values are the average of all measurements taken within ± 30 minutes of image acquisition time. Observation regression results (AOD from Landsat 8 images and AERONET stations) showed that the correlation coefficient is about 84%. This study shows a good fit of the model on the research data and shows the high capability of the model. This study showed a strong recovery of AOD against AERONET data of more than 70% at . The differences can be attributed to a limited number of points or hypotheses related to the aerosol model used in this study. The assumption of using a pre-calculated LSR does not limit the accuracy of this method because we have shown that in arid regions where the change in land cover in different months of the year is small, a pre-calculated LSR image can be representation used the share of surface reflection in the radiative transfer model throughout the month.
Conclusion
In this study, an AOD derived from a high-resolution satellite at an urban scale was produced in the city of Tamanrasset, Algeria. The developed method assumes that the change in land cover is minimal and the temporal change in LSR is not significant. A pre-calculated LSR image is created to show the surface reflection in the retrieval process. Based on the 6S radiative transfer model, an LUT was constructed to simulate the TOA reflection of the built-in LSRs and a set of geometric and atmospheric parameters. The retrieved AODs were compared with the AERONET ground data. The results show that this approach can achieve reasonable accuracy in AOD recovery, which reaches about 70.9% at . In addition, this approach is suitable for estimating AOD in urban areas compared to existing AOD products with low spatial resolution. The results of this study show a 4% improvement compared to the results of Omari et al. (2019). The results of this study showed that ignoring the monthly changes in LSR values leads to good results in AOD recovery.
Remote Sensing (RS)
Somayeh Aslani Katouli; Reza Shah-Hosseini; Hamid Bagheri
Abstract
Extended Abstract
Introduction
A flood is a widespread and dramatic natural disaster that affects the life, infrastructure, economy, and local ecosystems of the world. In this paper, a method for flood detection in urban (and suburban) environments using the intensity and coherence of SAR based on ...
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Extended Abstract
Introduction
A flood is a widespread and dramatic natural disaster that affects the life, infrastructure, economy, and local ecosystems of the world. In this paper, a method for flood detection in urban (and suburban) environments using the intensity and coherence of SAR based on a convolutional neural network is introduced, and from the time series of SAR intensity and coherence to draw flood without obstruction (e.g. Flooded bare soils and short vegetation) are used. Non-cohesive areas blocked by floods (e.g., flooded vegetation) and cohesive areas with flood-blocked areas (e.g., frequently constructed flooded areas) are distinguished.
This method is flexible according to the time period of the data sequences (at least one pair of pre-event and event intensities and one pair of pre-event and in-event coherence are required). The increasing number of SAR missions in orbit that have a fixed viewing scenario with a short retry time increases the chances of seeing a flood event, while also having a good pre-event scene achieved by the same sensor. This makes this method desirable for operational emergency responses.
Materials & Methods
CNN algorithm is a multilayer perceptron that is designed to identify two-dimensional information of images and includes: input layer, convolution layer, sample layer, and output layer. The CNN algorithm has two main processes: collection and sampling.
The convolution process involves the use of a trainable Fx filter, deconvolution of the input image (the first step of image input, input after image convolution, is the feature of each layer called Feature Map), then by adding bx can be hand convolution of the CX layer Found. Sampling process: n pixels are collected from each neighborhood to form a pixel, then weighted with a scalar weight of Wx + 1 and a bx + 1 bias is added, then a map of The Narrow n times feature map properties are generated.
Three images of Sentinel-1A VV polarization, wide width interference (IW), and mode (SLC) data were used in this study. Intensity images were pre-processed with radiometric calibration, noise reduced with a spell-filter (window size 5.5 pixels), and converted from linear units to decibels. Coherent images were obtained with a pair of consecutive images with a window of 7.28 (range _ azimuth). Validation data set due to the lack of other data in two separate sections of ground data in the urban area of GonbadKavous that have been collected to identify homes damaged by floods and terrestrial reality data from gamma image thresholds for output validation were extracted.
Results & Discussion
In this section, the results of the study are qualitatively and quantitatively analyzed. Because the simultaneous display of SAR data over time in the form of RGB compounds is widely used in the qualitative interpretation of land cover and surface dynamics, RGB compounds are used to provide evidence of flood magnitude in terms of intensity and coherence. For both cases, the results of combining intensity and coherence and intensity alone and coherence alone are quantitatively analyzed. Overall accuracy (OA), kappa correlation coefficient, false-positive rate (FPR), precision (e.g., correctly predicted positive patterns out of the total predicted patterns in a positive class), recall (e.g., a fraction of properly classified positive patterns), and an F1 score (ie the harmonic mean between precision and recall). Flood reference and ground data are mentioned and reported based on the reference.
Conclusion
In this paper, a method for mapping floods in urban environments based on SAR intensity and interferometry coherence was introduced. A combination of intensity and coherence extracts flood information in different types of land cover and outlet. This method was tested on the KavousGonbad flood incident obtained by various SAR sensors and the flood maps were confirmed by the flood reference resulting from thresholding and ground harvesting and satisfactory results were shown in this case study. The findings of this experiment show that the shared use of SAR intensity and coherence provides more reliable information than the use of SAR intensity and coherence alone in urban areas with different landscapes. In particular, flood detection in less cohesive / non-cohesive areas (e.g., bare soils, vegetation, vegetated areas) relies heavily on multi-temporality, while multi-temporal coherence provides more comprehensive flood information in areas Create coherence (e.g., mostly built-up areas). However, some flood-specific situations, such as flooded parking lots and flooded dense building blocks, are still challenging in terms of intensity and coherence. Also, since the proposed method is sensor and scene independent, with very frequent and regular observations of SAR missions such as Sentinel-1 and RADARSAT (RCM), there are opportunities to map global floods on a global scale, especially in small countries. Provides income.
Extraction, processing, production and display of geographic data
Heshmat Karami; Zahra Sayadi
Abstract
Extended Abstract
Introduction
Environmental changes are one of the most critical challenges to achieving sustainable development. Wetlands are part of the earth's structure and as one of the important ecosystems consisting of water, vegetation, soil and microorganisms. Monitoring, management and assistance ...
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Extended Abstract
Introduction
Environmental changes are one of the most critical challenges to achieving sustainable development. Wetlands are part of the earth's structure and as one of the important ecosystems consisting of water, vegetation, soil and microorganisms. Monitoring, management and assistance in decision-making and policy-making of surface water changes can be done according to the availability of satellite data. The availability of Landsat data helps a lot in preparing a high-quality map to show the land surface changes. Although remote sensing is superior to traditional methods in terms of time, speed, and cost, these methods require the use of powerful and practical systems that include complex analysis. The use of data and images on the web is a solution that can be used to solve the mentioned problem, which studies can be done with high accuracy and speed without the need for a strong hardware and software system. The Google Earth Engine system creates suitable conditions for processing satellite images for environmental monitoring and analysis. The purpose of this research is to monitor the dynamic changes in the Miangaran wetland sub-basin in the period (2013-2022).
Materials & Methods
Miangaran wetland with an average area of 2500 hectares is located at a distance of one and a half kilometers from Izeh city, in the northeast of Khuzestan province. Time series analysis is one of the most common operations in remote sensing that helps to understand and model seasonal patterns as well as monitor changes. In this research, 421 images from the ee.ImageCollection ("LANDSAT/LC08/C02/T1_L2") data set were used for the period from 2013 to 2022. The construction of a harmonic model was used in this research due to its flexibility in cyclic calculation with simple and repeatable forms. The normalized differential water index is an index for drawing and monitoring content changes in surface waters. Also, the Normalized Difference Vegetation Index (NDVI) is one of the most common remote sensing indices. Harmonic time series of water body and vegetation cover were extracted using NDWI and NDVI indices in Google Earth Engine platform, and Mann-Kendall's non-parametric test was performed using time series data output with XLSTAT extension in Excel software. Finally, global water data was used to confirm and complete the results of time series analysis.
Results, discussion and conclusion
The results of the harmonic time series of the water body showed a decreasing and negative trend and more changes in the sub-basin. Kendall's statistical test confirmed the decreasing and negative trend of the water body. Accordingly, since the calculated p-value (<0.0001) is lower than the alpha significance level (0.05), the null hypothesis should be rejected and its alternative hypothesis, the existence of a trend in the time series, should be accepted. The value of Kendall's tau also confirmed a negative value (-0.245) and a decrease. Due to the negative sen's slope statistic for the water area (-0.002), changes are more in the Miangaran Wetland sub-basin. The results of the Mann-Kendall test for the observed vegetation data showed the absence of a trend in the harmonic time series. Since the calculated p-value (0.064) is higher than the significance level of alpha (0.05), the null hypothesis (absence of trend) cannot be rejected. The risk of rejecting the null hypothesis (while true) is 43.6%. Kendall's tau statistic showed a negative value (-0.060) and a non-significant decrease. Therefore, accepting the null hypothesis (absence of trend) indicates that vegetation changes in the harmonic time series were not significantly different from each other. Also, the negative sen's slope statistic for vegetation (-0.026) indicates more changes in the sub-basin of Miangaran Wetland. By comparing with the results and analysis of other researches, it seems that human intervention and change of land use can be the cause of the lack of trend in the Miangaran Wetland sub-basin. Also, according to the negative value of Man-Kendall's vegetation cover which showed a non-significant decreasing trend, it seems that climate change and drought have also played a major role in the changes under the Miangaran wetland basin. The study of the global water data also showed that the water occurrence in terms of space-time is decreasing and the intensity of the change of water occurrence is critical under the basin of Miangaran wetland. The marginal parts of Miangaran Wetland show seasonal water loss, most of these changes occur during the period. This research confirmed the use of harmonic time series in monitoring wetland dynamic changes. Finally, the allocation of water rights, the establishment of laws and the determination of the limit of the ecological bed, and the use of Google Earth Engine capabilities to monitor environmental changes (use, temperature, precipitation, evaporation, etc.) of the Miangaran Wetland sub-basin were suggested.
Aerial photography
Alireza Afary
Abstract
Extended Abstract
Introduction
3D similar transformation is used in various applications such as photogrammetry, geodesy, robotics and machine vision. Calculating the parameters of this transformation using the least squares method requires determining the initial values as close as to the final ...
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Extended Abstract
Introduction
3D similar transformation is used in various applications such as photogrammetry, geodesy, robotics and machine vision. Calculating the parameters of this transformation using the least squares method requires determining the initial values as close as to the final values. If the initial values used are not close to the final values, especially in the case that the rotation angles related to this transformation have large values, the least squares method will either not converge or converge to a wrong solution. In this paper, a direct and new closed-form method for determining the parameters of this transformation is presented. This method is able to determine 3D similar transmission parameters by using at least three corresponding points in both model and ground coordinate systems. In general, direct and non-iterative methods are faster and have lower computational cost, and most importantly, they do not require initial values. In contrast to these advantages, these methods are sensitive to noise in observations and outliers and have less accuracy than iterative methods. Iterative methods, although they have better accuracy, on the other hand, have more computational cost and their speed is low. Most importantly, these methods require initial values and if the initial values used in these methods are not close enough to the final values of the parameters, these methods will either not converge to the correct solution or converge to a wrong solution.
Methods and Materials
The method presented in this article is based on one of the characteristics of 3D similar transformation, i.e., establishing the same 3D similar transformation relationship between the gravity centers of corresponding points. By transferring the origin of the coordinate systems of the corresponding points to the gravity center points, the 3D similar transformation parameters between these two sets of points can be calculated in a closed-form manner, with the presented method. Two datasets were used to show the effectiveness of the presented method. The first dataset was created by simulation with large rotation angles and four times scale factor and with the minimum number of required points, i.e., three points. To simulate the real state in this dataset, random errors with normal distribution were added to each set of the corresponding points. The second dataset was selected from the real data obtained from LiDAR operations.
Results and discussion
The results of the method presented in this article were compared and evaluated with the results of the least squares method and two other closed-form and direct methods, i.e., the SVD method and the dual quaternion method. The results of the method presented in this article are close to the final values of these parameters and the values obtained from other methods. Tables (6) and (8), respectively, show the difference values of 3D similar transmission parameters between the results of using direct and closed-form methods with the least squares method for simulated dataset and real LiDAR dataset.
The data in Tables (5) and (8) show that the presented closed-form method in this paper provides similar 3D transmission parameters for both simulated data sets and real data with a slight difference of about 0.02° for rotational parameters and with a slight difference of less than 0.2m in the displacement vector parameters and with a slight difference of less than 0.002 in the scale parameter.
Conclusions
As can be seen from the obtained results, the accuracy of the values calculated by the presented method in this article is to the extent that it can be used directly for most applications, especially in online applications. On the other hand, the lower volume of calculations of the method presented in this article, compared to the SVD and dual quaternion methods as well as the iterative least squares method, justifies the use of this method for online applications. Also, the results of this method can be used as accurate initial values for the least squares method, in Close-range and UAV photogrammetry applications, where the rotational angular parameters can have large values.
Extraction, processing, production and display of geographic data
Hossein Asakereh; Fatemeh Motevali Meydanshah; Leila Ahadi
Abstract
Extended Abstract
Introduction
Temperature is a significant atmospheric element that manifests climate change, specifically global warming resulting from an increase in greenhouse gas concentration. Atmospheric simulation is a critical tool in studying changes in atmospheric-climatic elements, particularly ...
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Extended Abstract
Introduction
Temperature is a significant atmospheric element that manifests climate change, specifically global warming resulting from an increase in greenhouse gas concentration. Atmospheric simulation is a critical tool in studying changes in atmospheric-climatic elements, particularly temperature.
The most commonly used tool for simulating the responses of the climate to greenhouse gas increases and examining future temperature changes is the use of climate variables simulated by coupled atmosphere-ocean models (AOGCMs). General circulation models (GCMs) are powerful tools aimed at generating climate scenarios. However, GCMs cannot provide effective information on climate simulation at local and regional scales. Therefore, the downscaling method is used to bridge the gap between local and global scales.
The current research aims to simulate maximum temperature using an artificial neural network model that adopts data from the atmospheric general circulation model (HadCM3) under RCP8.5, RCP4.5, and RCP2.6 scenarios for the Yazd synoptic station from 2006 to 2095. The independent variable, as the input to the artificial neural network, was selected for statistical downscaling using four statistical criteria: Percentile Reduction, Backward Variable Elimination, Forward Variable Selection, and Stepwise Variable Entry. Finally, the maximum temperature of the Yazd synoptic station for the next century was simulated.
Data and Methodology
The present study aims to investigate the maximum temperature of Yazd's synoptic station in the context of climate change based on valid scenarios until 2095. To achieve this, three sets of data were used: average daily maximum temperature data from Yazd's synoptic station, observed atmospheric data for the period of 1961 to 2005 (NCEP data), and simulated data from 2006 to 2095 based on release RCP scenarios. The NCEP data from 1961 to 2005 included 26 atmospheric variables that will be used as independent or predictor variables.
Modeling, simulating, and forecasting temperature based on nonlinear and chaotic time series is a challenging task. Prior studies have shown that artificial neural networks (ANNs) are suitable for simulating and predicting basic processes that are not well known. It is crucial to select the correct input variables intelligently and according to the purpose of the artificial neural network's design for prediction and simulation. Accordingly, in this study, the most suitable atmospheric parameters as the input of the artificial neural network were selected by pre-processing and selecting the atmospheric variables for the base period (1961-2005) to simulate with four statistical criteria (Percentile Reduction, Backward Variable Elimination, Forward Variable Selection, and Stepwise Variable Entry). The resulting mean square error (MSE) obtained from the statistical criteria was compared, and the correlation coefficient and the similarity of the monthly time series trend of the simulated values with the target values were also analyzed. The best network architecture was selected to simulate the maximum temperature of Yazd's synoptic station from 2006 to 2095 under different RCP emission scenarios.
Discussion
The selection of explanatory variables for downscaling was based on four statistical methods: Percentile Reduction, Backward Variable Elimination, Forward Variable Selection, and Stepwise Variable Entry. After analyzing the mean square error (MSE), correlation coefficient, monthly average values of the maximum temperature of Yazd station, and estimated values from 1961 to 2005, the probability density function, cumulative probability function, and monthly time series trend obtained from all four methods, the explanatory variables were selected. These variables include mean sea level pressure, the divergence of 1000 hPa, zonal wind component, zonal wind intensity of 850 and 500 hPa, altitude and vorticity of 500 hPa, average temperature, and relative humidity at a 2 m height.
The structure and architecture of the neural network were designed based on these selected variables. The network consisted of a two-layer feedforward, with a sigmoid transfer function in the hidden layer, a linear function in the output layer, an input layer with eight variables, eight neurons, and the Lunberg-Marquardt training algorithm. This architecture was used to simulate the maximum temperature of Yazd's synoptic station under RCP2.6, RCP4.5, and RCP8.5 scenarios for two periods of 2050-2006 and 2095-2051.
Comparing the monthly average values of RCPs (RCP2.6, RCP4.5, and RCP8.5) in the first statistical period (2050-2006) with the base period (1961-2005), the maximum temperature of Yazd station indicates an increase in temperature in winter, spring, and summer, and a decrease in the autumn season under all three RCPs.
Comparing the monthly mean values of RCPs (RCP2.6, RCP4.5, and RCP8.5) of the second period (2051-1995) with measured mean maximum temperature (2005-1961) showed that temperature will increase the most in winter, spring, and summer, similar to the first period of the RCP8.5 scenario. In this scenario, unlike the other scenarios, the increase in temperature is evident in both subperiods for the autumn season. Finally, in the second period (2051-1995), the increase in the average maximum temperature of Yazd station in winter, spring, and summer, and the decrease in the average maximum temperature in autumn will be more significant.
Conclusion
The increase in greenhouse gas concentration resulting from human industrial activities is expected to cause global and regional warming in the future. The current study's findings indicate that the average maximum temperature of Yazd station will rise between 0.4 to 6.9 in winter, 0.2 to 8.1 in spring, and 1.1 to 7.7 in summer from 2006 to 2095. However, a decrease in the maximum temperature between 0.6 and 1.4 is expected in autumn. These results are consistent with those of other researchers.
Remote Sensing (RS)
Kolsoom Shokrilahizadeh; Hamed Naghavi; Morteza Ghobadi; Rahim Maleknia
Abstract
Extended Abstract
Introduction:
Urban green spaces constitute a pivotal component of urban ecosystems, offering a plethora of ecological benefits and services to cities. Augmenting these green patches within urban landscapes and establishing interconnected ecological networks therein represent viable ...
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Extended Abstract
Introduction:
Urban green spaces constitute a pivotal component of urban ecosystems, offering a plethora of ecological benefits and services to cities. Augmenting these green patches within urban landscapes and establishing interconnected ecological networks therein represent viable strategies to mitigate the adverse repercussions of inadequate urban development while bolstering urban environment resilience. In the past few decades, the landscape ecology paradigm has introduced innovative methodologies aimed at comprehending the intricacies of urban green space dynamics and how landscape configurations wield influence over the environmental processes within cities. This research, consequently, sets out with the intention of quantitatively assessing and dissecting the transformations transpiring within Khorramabad's urban green spaces. It does so by harnessing remote sensing data and leveraging landscape metrics to gain deeper insights into the urban landscape's evolution.
Materials & Methods:
The focus of this research centers on Khorramabad city, which serves as the capital of Lorestan province and holds the distinction of being the province's largest city in terms of both population and geographical expanse. Municipally zoned into three distinct regions, the study unfolds across two main phases. Initially, the endeavor involved the creation of comprehensive synoptic maps capturing Khorramabad city's green spaces. This process relied on satellite imagery, followed by a subsequent phase of scrutinizing these maps through the application of landscape metrics.
To execute this, satellite images from various sensors—namely TM, ETM+, and OLI on Landsat 5, 7, and 8 satellites—were harnessed for the years 1987, 2003, and 2019, respectively. These images underwent meticulous preprocessing, culminating in their classification using the maximum likelihood method within the ENVI software environment. To validate the accuracy of the resultant maps, an error matrix was employed. In order to model the quantitative alterations and patterns within Khorramabad's urban green spaces, landscape metrics were harnessed. Notably, the Fragstat software facilitated the analysis of selected landscape metrics, which encompassed four key measures: class area (CA), number of patches (NP), percent of landscape (PLAND), and mean Euclidean nearest neighbor distance (ENN-MN).
Results:
The analysis of spatial-temporal changes in Khorramabad city's green spaces reveals an evident declining trend in their overall pattern. The outcomes underscore a substantial reduction both in the quantity of green patches and the area they encompass, dwindling from 703.8 hectares in 1987 to 629.88 hectares in 2019. Additionally, the investigation into landscape metrics' composition and distribution underscores an absence of cohesive dispersion on the city-wide scale. Within Khorramabad city, regions 1 and 3 exhibit inadequate green space composition and distribution. The computed metric for Class Area (CA) reflects a decrease from 195.66 hectares in 1987 to 191.63 hectares in 2003, further diminishing to 170.145 hectares by 2019. Correspondingly, the metric for Number of Patches (NP) indicates the lowest count of patches (33) in 1987, which escalated to 122 patches in 2003, and ultimately reaching 183 patches by 2019. Moreover, Proportion of Landscape (PLAND) data highlights that regions 3 and 2 demonstrate the highest (19.45%) and lowest (7.18%) green area proportions, respectively. Notably, the PLAND metric underwent modification from 229.81 meters in 1987 to 88.47 meters in 2003, further diminishing to 78.65 meters in 2019. The findings underscore deficiencies in Khorramabad city's urban green spaces, indicating a lack of favorable conditions for their development.
Conclusion:
The research conducted an assessment of urban green spaces within the urban areas of Khorramabad, utilizing remote sensing data and landscape metrics. The findings indicated a consistent downward trend in the overall extent of green spaces in Khorramabad city over various years. The distribution of green patches within the city was deemed relatively inappropriate, lacking an optimal arrangement. To enhance the status of green spaces, there is a need to establish continuity between discrete green patches and smaller green areas. This study underscores the significance of prioritizing sustainable management for Khorramabad's urban green space, aiming to prevent its degradation. The study's limitation lies in its reliance on medium-resolution Landsat image data. Overcoming this constraint through the incorporation of high-resolution data holds promise, particularly for fragmented green spaces in urban areas.
Geodesy
Seyyed Reza Ghaffari-Razin; Navid Hooshangi
Abstract
Extended Abstract
Introduction
In geodesy, three levels are considered: the physical surface of the earth on which mapping measurements are made, the ellipsoidal reference surface (geometric datum) which is the basis of mathematical calculations, the geoid physical surface (physical datum) which is ...
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Extended Abstract
Introduction
In geodesy, three levels are considered: the physical surface of the earth on which mapping measurements are made, the ellipsoidal reference surface (geometric datum) which is the basis of mathematical calculations, the geoid physical surface (physical datum) which is the basis for measuring heights. Satellite positioning systems measure the height of points relative to the ellipsoid surface. The geoid is one of the equipotential surfaces of the earth's gravity field, which approximates the mean sea level (MSL) by least squares. Geoid is very important in geodesy as a representative of the physical space or the space of observations made on the earth and also as the base level of elevations. The separation between the geoid and the geocentric reference ellipse is called geoid height (N). Although there is only one equipotential surface called geoid, various methods are used to determine it. These methods include: geometric method, geoid determination by satellite method, Gravimetric methods and geoid determination using GPS/leveling.
Materials and Methods
In this paper, the aim is to estimate the height of the local geoid using machine learning models. To do this, artificial neural network (ANN), adaptive neuro-fuzzy inference model (ANFIS), support vector regression (SVR) and general regression neural network (GRNN) models are used. The geodetic coordinates of 26 GPS stations in the north-west of Iran along with their orthometric height (H0) and normal height (h) were obtained from the national cartographic center of Iran. In all stations, the difference of orthometric height and normal height is considered as geoid height (N). Therefore, the geodetic longitude and latitude of the GPS stations are considered as the input of the machine learning models, and the corresponding geoid height was considered as the output. In order to test the results of machine learning models, two modes of 4 and 7 test stations are considered. Also, the output of the models is compared with the local geoid model IRG2016 presented by Saadat et al. for the Iranian region and also the global geoid model EGM2008.
Results and Discussion
Due to the availability of a complete set of observations of GPS stations along with orthometric height obtained from leveling in the north-west region of Iran, the study and evaluation of the models proposed in the paper has been carried out in this region. Observations of 26 GPS stations of North-west of Iran were prepared from the national cartographic center (https://www.ncc.gov.ir/). Two modes are considered for training and testing of ANN, ANFIS, SVR and GRNN models. In the first case, the number of training stations is 22 and the number of test stations is 4. But in the second case, by increasing the number of test stations to 7 stations, the error evaluation of the models has been done. It should be noted that the distribution of training and test stations is completely random.
After the training step of machine learning models and choosing the optimal structure, the test step is performed in two different modes (4 and 7 stations). At this step, the value of the geoid height in the test stations is estimated and compared with the value obtained from the difference of orthometric height and normal height as a basis. Two statistical indices of relative error in percentage and RMSE in centimeters were calculated for all models and presented in Table (1) for the first case.
Table 1. Relative error (%) of ANN, ANFIS, SVR, GRNN and IRG2016 models in the test stations considered for the first case
According to the results of Table (1) and comparing the relative error values of all models in the test stations, it shows that the ANFIS model was more accurate than other models. After ANFIS model, IRG2016 model has higher accuracy than ANN, SVR and GRNN models. It should be noted that the IRG2016 local model uses the observations of all Iranian plateau stations to model the local geoid, and therefore it is expected that this model will be more accurate in the study area than other models.
Conclusion
The evaluations show that in the case of 22 training stations and 4 test stations, the RMSE of ANN, ANFIS, SVR, GRNN and IRG2016 models in the test step are 37.32, 19.83, 49.34, 53.82 and 29.65 cm, respectively. However, in the case of 19 training stations and 7 test stations, the error values of the models are 36.63, 58.31, 39.64, 41.29 and 24.68 cm, respectively. Comparison of RMSE shows that ANN model with less number of training stations provides higher accuracy than ANFIS, SVR and GRNN models. The results of this paper show that by using ANN and ANFIS models, geoid height can be estimated and used with high accuracy locally in civil and surveying applications.
Issues of the border regions of the country
Mehdi Safari Namivandi; Seyed Ali Ebadinejad; Mehdi Kazemi; Yahya Ghobadi; Sadegh Yasami
Abstract
Extended Abstract
Introduction
Protecting the borders and establishing security and defense in the border and internal areas of every country has been the concern of the rulers of their time. To some extent, all countries throughout history have faced insecurity, chaos, crisis and war at the borders ...
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Extended Abstract
Introduction
Protecting the borders and establishing security and defense in the border and internal areas of every country has been the concern of the rulers of their time. To some extent, all countries throughout history have faced insecurity, chaos, crisis and war at the borders to stabilize their country. In the past years, many ethnic, racial and religious groups have lived side by side in unstable political and social frameworks and have lived on the borders. Throughout history, the country's border strips have seen the most significant conflicts and confrontations between governments and nations; therefore, the issue of borders has always been one of the basic and important issues of governments, so borders have a special place in every country. One of the most difficult tasks of any government is the control and security of political borders, and if this issue is not paid attention to and there are no appropriate plans and solutions, the country's political sovereignty will be threatened. In order to turn threats into opportunities and benefit from conditions and situations in order to maintain security and secure national interests, we must have a deep and comprehensive understanding of the level of border areas and its surrounding spaces. In the meantime, one of the most important measures is planning according to the geomorphological capabilities of the border areas. One of the measures that increase security in the border strip is to identify areas prone to establishing military bases and surveillance centers. Considering that the border strip of Kurdistan province, including Marivan city, has a sensitive location, therefore, in this research, the areas prone to the construction of military bases and observation centers in this city have been identified.
Materials and methods
In this research, according to the subject and objectives, library information, SRTM 30 meters high digital model and digital layers of information have been used as research data. The most important research tools are ArcGIS (for the purpose of preparing necessary maps) and Expert Choice (for the implementation of the AHP model). In this research, in order to identify the vulnerable areas of Marivan city against the enemy's influence, as well as areas susceptible to the construction of military bases and observation centers, the integrated model of fuzzy logic and AHP has been used. This research has been done in several stages. In the first step, the parameters used are identified. In the second step, the used parameters are fuzzification. In the third step, using the Analytical Hierarchy Model (AHP) and based on the opinions of experts, weight has been given to the information layers and then the obtained weight has been applied to the information layers. In the fourth stage, the layers of information are combined and finally, a map of areas prone to enemy infiltration and areas prone to establishing military bases and observation centers is prepared.
Discussion and results
In this research, in order to identify vulnerable areas against enemy infiltration, 6 parameters of height, slope, field of view, distance from the river, distance from urban areas and distance from military bases have been used. Also, considering that the study area corresponds to the border strip, therefore monitoring and controlling these areas is very important. One of the ways to control and monitor the border areas is to create military bases and observation centers, which has been addressed in this research using 6 parameters. Investigations have shown that Marivan city has a lot of diversity in terms of geomorphology, and this problem has caused its different parts to have different potentials for creating military objectives. Considering the importance of creating security in the border strip, it is necessary to pay attention to the geomorphological capabilities of this city in order to create security in the region. In this research, based on the geomorphological capabilities of Marivan city, vulnerable areas were identified against the enemy's influence, as well as areas susceptible to the construction of military bases and observation centers.
Conclusion
According to the results, due to the geomorphological situation of Marivan city, parts of this city lack sufficient visibility and are far from the monitoring and control of military bases, and this problem has caused these areas, which mainly include the southwestern and southern regions. Is a city, have a high vulnerability potential. Also, the results of this research have shown that parts of Marivan city, which mostly correspond to the central areas of the city, have a high potential for building military bases and observation centers. Due to high altitude, low slope, western directions, wide field of view, proximity to main roads and being far from military bases, these areas have high potential for the desired goals. The total results of this research have shown that in locating military bases in Marivan city, geomorphological factors have not been given much attention and it is necessary to establish security in this region, military bases and observation centers. He built a new building in accordance with the geomorphological strength of the region.
Extraction, processing, production and display of geographic data
Zahra Rabiee Gaffar; Hossein Asakereh; Uones Khosravi
Abstract
Extended Abstract Introduction The Intergovernmental Panel on Climate Change (IPCC) has reported that climate change results in anomalies, fluctuations or trends in climatic elements, such as precipitation and temperature. In this study, we aim to investigate the decadal changes in the probability ...
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Extended Abstract Introduction The Intergovernmental Panel on Climate Change (IPCC) has reported that climate change results in anomalies, fluctuations or trends in climatic elements, such as precipitation and temperature. In this study, we aim to investigate the decadal changes in the probability of different durations of precipitation in Iran over the past four decades (1977-2016). To achieve this goal, we used the third version of the Asfazari database. We defined a rainy day as a day when the precipitation is more than the average precipitation in a given place. The Markov chain method was employed to estimate the probability of precipitation duration from 1971 through 2016.Materials and MethodsWe adopted the daily data of 2188 stations under the supervision of Iran’s Meteorological Organization for the period 1971 through 2016. Accordingly, we estimated the probability of precipitation duration for 1-7 days for the entire period. We investigated the decadal changes in the probability of precipitation duration for the four study decades and compared them to the whole period under investigation. To understand the spatial features of these changes, we estimated the relationship between changes in the probability of precipitation duration for 1-7 days and spatial factors using multivariate regression models.Results and DiscussionOur findings revealed that as the duration of rainy days increased, the area affected by precipitation decreased. Therefore, the spatial distribution of the probability of precipitation duration for more than 7 days indicated the smallest area that received precipitation. The probability duration of precipitation lasting 4 days or more throughout Iran was very small, which can be attributed to the effects of local features on precipitation formation. The probability of 1-day precipitation for most regions of Iran was higher than other durations; however, there was only a probability of 1-day precipitation in half of Iran. The highest probability of precipitation duration occurred in the Caspian region, the only region that experienced all durations of precipitation, indicating the presence of various precipitation mechanisms in this area. The greatest probability of decadal changes was observed in the 1-7 day duration in the northern part of Iran, including the northwest to the east of the Caspian Sea and in the south of Alborz Mountain range. Additionally, the most changes in the probability durations of 1-7 days of precipitation in the south have been seen in Sistan and Baluchistan. The lowest probability of decadal changes was shown in large areas of the regions from the east, southeast, and southwest. Therefore, the changes in precipitation durations in the southern half of the regions were generally low; however, in the northern half, the changes were relatively significant.In general, during the four study decades, the relationship between changes in the probability of 1-7 day precipitation durations and spatial factors, particularly latitude, was positive. Thus, decreasing latitudes resulted in an increasing probability of 1-7 day precipitation.ConclusionThe most likely changes in precipitation duration were related to the western and eastern coast of the Caspian Sea and the northwestern region of Iran, as well as southern Alborz, where the probability of changes decreased. The least amount of possible changes was related to the south of Iran, where only two provinces, Sistan and Baluchistan, and Hormozgan, experienced the greatest change in the probability of one to seven days of precipitation. Thus, the possible changes in the spatial continuation of precipitation in the southern half of the country were primarily low. However, in the northern half, the possible changes in the duration of precipitation were more significant. changes in the duration of precipitation, along with changes in the intensity and frequency of precipitation, can have significant consequences in extreme events such as droughts and floods. Accurately depicting changes in precipitation duration can be helpful in addressing problems concerning precipitation.
Geographic Data
Seyed Asadallah Hejazi; Fariba Karami; Saye Habibzadeh
Abstract
Extended AbstractIntroductionIn recent decades, cities have provided the prelude to widespread urban growth and development as the most important human settlements, due to the increasing degree of urbanization and the increase in urban population, which is one of the most important aspects of global ...
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Extended AbstractIntroductionIn recent decades, cities have provided the prelude to widespread urban growth and development as the most important human settlements, due to the increasing degree of urbanization and the increase in urban population, which is one of the most important aspects of global transformation. In recent decades, following the growing expansion of urbanization and urbanization, as well as the continuous increase in population, many cities in the country have faced significant physical development, which has left planners and city managers with the problem of determining the right axes. And the boundaries of future physical growth of cities have faced. Maku is one of the cities that experienced an annual growth of 3.7 percent between 1996 and 2016, with a population of 46,581. Given the forecast of the increase in the population of the city in the coming years, the identification of suitable land for its physical development is an inevitable necessity. Several factors, including geomorphological features, climatic conditions, geological features, are effective in choosing the location of cities. The study evaluated the role of geomorphology as one of the factors influencing the location and physical development of the city of Maku.Materials and MethodsThe research method is of a descriptive-analytical type with a functional purpose. In this study, raw data was collected through documented and field studies. This study examines the geomorphological factors influencing the physical-physical development of the city of Maku. To evaluate the optimal development of urban land, the components of lithology, soil, slope, distance from the river, direction of slope, height, land use, distance from fault and Road in the area of the surrounding city of Maku were used. To analyze data and select the optimal location, a combination of two phase - electro and Shannon entropy models has been used. To prepare the ground fit layer, the layers in question are standardized and phased in the ArcGis environment using the Phase model and by the calculator instrument and in the form of a raster in the form of a value of zero to one. Finally, the coating of layers using phase logic (gamma) to optimize the development of the city of Maku was determined, and then the development path of the city of Maku was classified into five groups: completely appropriate, relatively appropriate, appropriate, inappropriate and very inappropriate.DiscussionAfter determining the effective criteria in locating and detecting the weight of the criteria, the information layers should be combined with the appropriate method. The composition of the map is obtained by overlapping weighted maps. Merging and combining different spatial layers from different sources together is the main goal of GIS projects and its unique feature, so that the interactions are described and analyzed with the help of predictive models to support decision-makers. The final map of the development potential of the city of Maku was prepared by combining different layers of information and classified according to the Likert scale. In this classification, land was considered suitable for urban development in 5 groups of lands with very low, low, medium, high and very high development potential. According to the above map, most of the city's immediate land is located in the eastern and western parts of the city for Urban Development. The southern and northern lands of the kalbdi District of Maku are also small or very small for the future development of the city. The proximity to the epicenter of earthquakes, the short distance from the river and the location of the flood path are the main reasons for the inadequacy of the above land for the physical development of the city of Maku. The lands located east and West at the entrance of the city from the shout and merchant side are the only immediate areas of the city that are very suitable for the future development of the city.ConclusionAmong the seven geomorphological factors studied, the two factors "altitude" and "lithology" are the highest coefficients of importance, and the factors "slope direction" and "distance from the river" are also the least important. The results of comparative analysis of the eight geographical directions in terms of geomorphological factors also show that in terms of the litholysis factor, the east, west and northwest directions are more desirable compared to other options. In terms of the elevation factor, the Northeast and East Directions are more suitable, and in terms of the distance factor, the West and northwest directions are more preferred. Comparing options in terms of soil factor also indicates a greater favorability of the Northeast and northwest directions. Distance from the river was another component that, based on the analyses, the East and Southeast directions, identified more favorable areas for urban development in terms of this component; and finally, in terms of the slope direction criterion, the lands located in the southeast of the City face greater desirability. After determining the coefficient of importance of the criteria and the relative score of the options in terms of each of the factors studied the coefficients of importance of the criteria and the relative weight of the options were calculated within the framework of the method of the process of hierarchical analysis of the integration and score of each of the eight geographical directions as follows the East was calculated with a gradient of 5 West 5 southwest 1 northeast 2 North 0 south 0 Southeast 4 Northwest 0 thus in terms of geomorphological factors the study word in the ین research orientations east west and the southeast is proposed as a priority for the future development of the city of Maku.
Geographic Data
Elham Forootan
Abstract
Extended Abstract
Introduction. In recent years, the population growth, the increase in irrigated land and economic development have caused the increase in the demand for groundwater resources all over the world. In arid and semi-arid regions where surface water does not have a significant amount ...
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Extended Abstract
Introduction. In recent years, the population growth, the increase in irrigated land and economic development have caused the increase in the demand for groundwater resources all over the world. In arid and semi-arid regions where surface water does not have a significant amount due to low rainfall and high evapotranspiration, people lives mainly depend on groundwater. As a result, it is necessary to identify the groundwater potential areas and determine its recharge areas using accurate technologies. So, the aim of this research is to compare the method of multi- influencing factors with the fuzzy method for determining the potential of groundwater in a part of Kebar-Fordo watershed, Qom city, Iran.
Materials & Methods. For this purpose, a part of Kebar-Fordo watershed located in Qom province was selected. Six factors layer, viz. slope, annual rainfall, distance from river, geology, soil, and landuse were considered and classified based on groundwater potential susceptibility in different scales. Multi-influencing factor method can determine the groundwater potential of the region by assigning appropriate weight to different effective factors. In this approach, the layers were combined in Arc-GIS after determining the weight of the layers. In the fuzzy method, the layers of six factors were converted to fuzzy based on the linear function, and then the layers were incorporated using the gamma function. Finally, the statistics of observation points and accuracy index were used in order to evaluate the models,
Results & Discussion. The slope map represents that most part of the studied area (78.56%) has a "0-1" class while "1-3", "3-9" and "9-25" slope classes could be observed in 19.97, 1.29 and 0.18% of the total area, respectively. The soil texture has a significant effect on the infiltration and percolation of the surface water movement towards the groundwater. Therefore, in this research, the soil factor has been investigated as one of the input factors to the models. Soils with high permeability are more suitable for groundwater recharge and vice versa. The soil texture of the area consists of sandy loam, loam, sandy clay loam, and clay loam textures, which cover 3.73, 90.72, 0.23, and 5.32% of the total area, respectively, with a rank of four to one for groundwater potential. In this study, geology map showed that Qft2 formation has the largest area (88.98%) and Plc formation is in the second rank (4.9%). Qft1, Qs.d and Mur units have an area of 2.22, 2.12 and 1.10% and the smallest area belongs to OMq formation (0.68%). Also, different types of land use in the study area were agriculture, garden, rangeland, bareland, and resendential area. The land use map showed that the largest area of this area was ariculture landuse (77.18%), while garden and rangeland covered 0.07 and 6.5% of the total area, respectively. Bareland and residential area comprise 2.94%, 13.31% of the total area, respectively. Among the different landuses, agriculture and residential area have the highest and lowest ranks in groundwater recharge. The rainfall map was categorized with four classes. The classes of 140-156, 156-168, 168-182, and 182-203 mm layers include 14.15, 48.92, 21.84 and 15.09% of the total area with the rank of one to four for groundwater recharge, respectively. The map of distance from the stream was divided into four categories: "0-659", "659-1480", "1480-2675" and "2675-4939" meters, which comprise 46.33%, 34.15%, 15.72% and 3.8% of the total area, respectively. In the method of multi influencing factor, the distance from the stream (8.33%) and the geological factor (25%) were the lowest and highest weights. In this regard, the factors of rainfall, slope, soil, landuse have 20.83%, 16.67%, 16.67% and 12.5% weights, respectively. Then, the groundwater potential map was prepared through overlaying in ArcGIS and the studied area was classified into suitable and unsuitable classes. The suitable class covers 75.15% of the studied area and the unsuitable class covers 24.85% of the total area. In the fuzzy method, the unsuitable class comprises 43.63% and suitable class covers 56.37% of the area. In order to evaluate the models, the statistics of the observation points were applied which the accuracy of the multi- influencing factor and fuzzy models was calculated as 71.42 and 78.57%, respectively.
Conclusion. Preparation of groundwater potential map is necessary to adopt management measures of rainfall storage and groundwater recharge in arid and semi-arid regions and it can be used for sustainable management of groundwater resources. The findings of this research revealed both model's accuracy in the studied area.
Geographic Data
Nargas Shokohi; Reza Zakerinejad
Abstract
Extended Abstract Introduction Today, tourism is one of the main sources of income for developed countries and some developing countries and the countries of the world, especially the developing ones, are seeking to advance and develop their tourism industry with well-codified plans. One of the ...
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Extended Abstract Introduction Today, tourism is one of the main sources of income for developed countries and some developing countries and the countries of the world, especially the developing ones, are seeking to advance and develop their tourism industry with well-codified plans. One of the types of tourism that has received more attention in recent decades is ecotourism or nature tourism. Iran has a very high potential in attracting tourists in terms of various ecological conditions and characteristics. The northern regions leading to the Caspian Sea are among the regions of Iran that at the same time, it has three important ecological zones (foothills, plains and coasts). Namka Abroud tourist town is one of the areas that has improved in recent years in attracting tourists and ecotourism. Studies on ecotourism and its development have been conducted. These studies can be divided into three main categories. Studies have identified ecological factors that determine ecotourism areas, most of which have focused on factors such as land slope, slope direction, altitude, water resources, access to roads, rainfall, and annual temperature. Other studies emphasize the methodological identification of ecotourism areas. The main methods emphasized are hierarchical analysis methods and geographical systems. The third group of studies focused on how regions were developed. Among the points emphasized by these studies measured to attract ecotourism to preserve its resources and its sustainability, and finally to develop the economic situation of the region.Materials and Methods Data and research method: The study data were collected from the Meteorological Organization of Iran, the National Mapping Organization and the Road Engineering Company. Point temperature and precipitation data were obtained using the Kriging spatial interpolation method for the town surface. In this study, the ecological capabilities of Namak Abroud tourist town have been tried by AHP hierarchical analysis method and GIS geographical analysis system of eight layers of ecological slope and slope direction, altitude, annual rainfall, distance from road network, annual temperature, Land use and catchments should be investigated. The hierarchical analysis process is a flexible, simple, and robust method used to make decisions in situations where conflicting decision criteria make it difficult to select options. This method was developed in 1980 by El Thomas Saati. It has been proposed and has had several applications in various sciences. A basic method for testing the AHP method is the pairwise comparison method. This method significantly reduces the conceptual complexity of decision making. Because only two components are examined at a time.Results and discussion he results showed that the importance of land slope compared to the slope direction layer is quadruple, furthermore the importance of land slope compared to altitude is sextuple, the average annual rainfall triple, the distance from the road network five times, Moderate annual temperature Eight times, land use is sextuple, and the catchments of the region are double. In other words, the slope is the most influential factor in choosing the Eco-tourism locations of Namkabroud town. After that, three-factor of slope direction, altitude, and annual rainfall is influential in selecting and finding Ecotourism places in Namkabroud town, respectively. Then, using overlapping layers, the important Eco-tourism places of the town were identified, in which four places of Ecotourism importance could be trusted and studied. Two of these four places are parts of the coastal forests of violet and boxwood. Areas that remain almost intact. Therefore, codified management plans can be designed and implemented based on the preservation of Ecotourism attractions on them. But the important point is in a part of the important southern place of Banafsheh Park. Contradictory constructions have been carried out in this area while preserving Ecotourism areas. Most of these constructions are places of entertainment and tourism, including a flight site, restaurant, coffee shop, karting, and shooting club, each of which can cause damage to its Ecotourism features. Government places such as the municipality and the fire brigade are also located in this area, which has no justification for maintaining strategic Ecotourism sites. Important Ecotourism sites located in the east and southeast of the town have become inefficient with unprofessional and irresponsible constructions and have been turned into residential neighborhoods. The last important Ecotourism place in the town is Madoban base mountain, which is known as the cable car area. This place, which is located in the 5th residential neighborhood of town, has created several tourist entertainments sites, such as lines one and two, cable cars, sleds, a cinema, and a smashing machine. In addition, the construction of commercial and residential sites has caused irreparable damage to its Ecotourism importance.Conclusion The following results were obtained in the study of important places in Namakabroud town. Only the places located in the two coastal forests of Banafsheh and Shamshad are almost safe from the bite of unprofessional constructions, while other places have either been inhabited or government and recreational places have been created in them. It is not in line with the goals of preserving sustainable Ecotourism places. Therefore, the proper management of ecotourism areas was evaluated. To be able to properly manage the preservation and sustainability of Namak Abroud Ecotourism vulnerable places are to be proposed. The results of the studies in this section showed that the coastal and foothill areas of the town have the potential for extensive recreation and other important places can develop central recreation, which planners and investors in the tourism sector should implement and Consider constructing tourist sites.
Geographic Data
Atikeh Afzali; Masoud Moghnee Tabari
Abstract
Extended Abstract IntroductionIncreasing population and urban development, increasing use of cars, increasing the number of private cars, Also the narrow width of the streets and the lack of supply of marginal park space, especially in the central parts, have caused many problems for large and densely ...
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Extended Abstract IntroductionIncreasing population and urban development, increasing use of cars, increasing the number of private cars, Also the narrow width of the streets and the lack of supply of marginal park space, especially in the central parts, have caused many problems for large and densely populated cities. Parking lots as one of the most important urban infrastructures play a major role in reducing these problems. Today, in large cities, with the correct location of public parking lots, optimally manage of urban traffic is possible by increasing the provision of services to a large number of vehicles. Materials and MethodsIn this research, first, effective criteria were extracted according to the opinion of Babol Municipality experts. The information layers of each criterion were prepared in the GIS environment and to equalize the layers, each criterion was classified, Then, the identified criteria were weighted using ANP technique and with the help of Super Decision software, and then pairwise comparisons were done. Weighted layers were combined by ANP method, were placed on top of each other in the GIS environment based on the influence of each layer (relative weight) and the map of the optimal areas for the construction of public parking lots was obtained. Results and DiscussionIn this research, the inconsistency coefficient obtained was 0.07. Criteria prioritization showed that, "Distance from the road", "Distance from offices use" and "Distance from business use" criteria with a significance coefficient of 0.25, 0.24 and 0.15 respectively had the highest weights. Arc GIS software was used to prepare the final maps. Finally, suitable places for creating public parking lots were determined by applying the final weight of the criteria and overlapping the layers. According to the final location map, areas with very high potential, areas with high capability and areas with medium capability each have 3.11, 55.75 and 36.56 percent of urban lands, respectively. Areas with very high and high potential are mostly located in the northwestern, northeastern, southwestern and central parts of the city. ConclusionThe results indicate that the spatial distribution of existing parking lots in the city is not related to the effective criteria in these parking lots. According to the residential use map and population density and due to the epidemic of private car use, there is no match between the number of parking spaces in the city and their spatial distribution and population density. The result of urban parking location zoning using the ANP model and comparing it with existing parking lots in the city shows that The class of areas with high capacity for parking lots is located in parts of the city where there are no parking areas and there is an urgent need to create these types of users. This factor indicates that the location of existing parking lots in Babol city has been done without considering effective factors and criteria. All parking lots are located in one part of the city and at a close distance from each other. There is a need to create many public parking lots in other parts of the city, according to the urban population, until the per capita is closer to the reality. Considering the population of Babol city, which is 250,217 people, and the number of public parking lots, which is 6, this amount per capita is very low.
Geographic Data
Taraneh Karimi; Ali Mohammad Safania; Rahim Sarvar; Salaheddin Naghshbandi
Abstract
Extended Abstract IntroductionThe tourism industry is the world's largest industry in terms of income and cultural exchanges. Progress in the field of sports is one of the strategic priorities of planners in countries. Sports tourism is one of the most important developed sectors of the tourism ...
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Extended Abstract IntroductionThe tourism industry is the world's largest industry in terms of income and cultural exchanges. Progress in the field of sports is one of the strategic priorities of planners in countries. Sports tourism is one of the most important developed sectors of the tourism industry. In this regard, the Ministry of Sports and Youth, the National Olympic and Paralympic Committee, the National Olympic and Paralympic Academy, sports federations, physical education, the Ministry of Science, Research and Technology Sports and Education Federation, Ministry of Education and Culture, Sports and Youth Fraction of the Islamic Council, Provincial Sports and Youth Departments, Sports Associations, each of which is a decision-making body in the field are sports The existence of multiple, as well as the simultaneous existence of policy-making and implementation in the Ministry of Sports and Youth, have created inadequacies in these sectors, which, of course, have denied the possibility of competition from the private sector. It seems that the general spirit governing the constitution of the Ministry of Cultural Heritage and Tourism, like the provisions related to tourism in the country's five-year development laws, is a reductionist attitude towards the tourism industry. Due to the uncertainty of the coordination authority in the field of sports tourism decision-making and policy-making, the lack of legal commitment and the need for coordination between sports executive bodies, the multiplicity and diversity of sports tourism tasks. Institutions in charge, the emphasis and clarity of Article 100 of the Law of the Sixth Five-Year Development Plan, based on integrated management, the lack of attention of upstream laws and documents to the priority of sports tourism, the lack of cooperation and the establishment of coherent relations between the government, government and private institutions in the sports tourism industry, preparation An integrated model is important in the sports tourism industry in the country. Considering the number of related organizations in sports tourism and the lack of clarity about the interaction of these organizations with each other, the present research aims to investigate the integrated management of related institutions in the sports tourism industry. This research can play an important role in optimizing management planning in the tourism industry by identifying the effective factors of integrated management of Iran's sports tourism industry. Therefore, the current research aims to provide an integrated management model in Iran's sports tourism industry and seeks to answer this question: What are the effective factors of integrated management in Iran's sports tourism industry?Research methodology and FndingsThe current research has a mixed (qualitative-quantitative) approach based on data search. In the qualitative part, the foundational data approach (Glaser method) has been used to present and develop the theory in the study area of the research. The statistical (qualitative) community includes all the experts in the field of tourism, sports management as well as sports tourism. In this research, the purposeful sampling method was used. Sampling continued until reaching a point where adding a new sample no longer has an effect on the development of research theory, theoretical saturation was achieved after the 20th interview. The main method of data collection was semi-structured in-depth interviews. In the process of qualitative data analysis and performing three stages of coding (open, central and selective), a number of 1167 open codes were obtained, which were reduced to 377 unique open codes after merging and removing commonalities, then the open codes were categorized into 29 central codes and finally In selective coding, they were divided into 8 groups: program policy, task, management, legal, institutional, structural, beneficiaries, tools and resources. In this way, a questionnaire based on the main factors and sub-criteria was compiled on a five-point Likert scale with 119 questions. The validity and reliability of the questionnaire was checked with exploratory confirmatory factor analysis and Cronbach's alpha (96%). The statistical population of the quantitative part included 600 experts and managers of the ministries of cultural heritage, sports and youth, as well as the Environmental Protection Organization, the Civil Aviation Organization, the National Olympic and Paralympic Committee. , travel service offices and private sector activists as well as sports management doctoral students and professors. Frequency, mean and standard deviation methods were used to analyze the information in the descriptive part. The normality of the data was checked with the Kolomogorov-Spirov test (<0.05). In the inferential statistics section of the structural equation modeling test, Friedman's test (P=0.001) and Pearson's correlation coefficient, which showed a significant correlation at the 99% level between the main research criteria; It was used with the help of AMOS and SPSS software.DiscussionUndoubtedly, the existence of various institutional, legal, structural and managerial differences and the involvement of various organizations and institutions in the tourism cause create complicated problems. The current research by observing the situation of sports tourism in Iran, as a strategy for development; He also carefully specified documents and strategic plans at the macro levels; What is observed in the sports tourism industry is the presence of various actors with unequal powers, which has become a fundamental issue in this field of tourism. Despite the emphasis in the general policies of the sixth development plan and the importance of various aspects of development, the laws of some ministries and organizations related to sports tourism do not have the necessary overlap. In this way, it is necessary for the officials and legislators to act transparently, to remove the dispersal of the same laws and the uniform and unsavory implementation of the laws. It should also be noted that; Among the different components of the sports tourism system, management activities and tasks are distributed disproportionately and have led to functional, institutional and managerial differences. The use of tasteful management instead of scientific management and the incompatibility of the actions taken by the previous managers with the attitude of the new managers are among the many problems in the division of the field of sports tourism. It is included in the development of the country.Therefore, integrated management in this field, taking into account the indicators of the current research, is a balance between barriers and facilitating factors of costs and benefits. Considering the participation of various elements to play a role in sports tourism; This integration leads to the creation of a network of actors with different powers, the imbalance in the power of the beneficiaries in the field of sports tourism will lead to forward movement if there is inter-sectoral coordination and systematic dialogue between the relevant institutions. According to the obtained results, in order to achieve the integrated management of sports tourism in Iran, the following solutions can be presented to lay the groundwork and facilitate the implementation of integrated management.Revision of the rules and regulations proposed for tourism-sports in terms of having a guarantee of implementation.Formation of the tourism-sports executive committee with the aim of strengthening the monitoring and control approach.Creating the capacity to plan and manage sports tourism resources, in order to participate and improve local incomes.Establishing an efficient and transparent tax system for the benefit of sports tourism.
Extraction, processing, production and display of geographic data
Hossein Asakereh; Somayeh Taheri Alam; Nosrat Farhadi
Abstract
Extended Abstract
Introduction
Climate changes manifested in different ways and time scales (short-term fluctuations and long-term changes) effects. The consequences of such changes can be traced to various parts of the environment. One of the climate change manifestations is the change in biological ...
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Extended Abstract
Introduction
Climate changes manifested in different ways and time scales (short-term fluctuations and long-term changes) effects. The consequences of such changes can be traced to various parts of the environment. One of the climate change manifestations is the change in biological phenomena, primarily vegetation, which reflects an intricate pattern of changes in climatic elements, particularly temperature, and precipitation. Although the substantial role of climatic elements on the density and geographical distribution of vegetation has been confirmed, it is arduous to estimate the relationship between climate changes and vegetation due to the complexity of the mechanism of different characteristics of climatic elements (such as the amount, type, intensity, season, continuity, etc.), feedback processes, and also the response time of the vegetation to climatic changes.
Materials and Methods
In the current research, the gridded data of the Normalized Difference Vegetation Index (NDVI), a product of the MODIS terra, was used from 2001 through 2016. The data were extracted from a GIOVANNI website. In the present study, Iran's vegetation density classes were determined based on quantitative methods, and the geographical distribution of two-half parts of the understudy periods was compared.
Results and Discussion
The long-term average and changes in Iran's NDVI were examined using NDVI grid data. The finding revealed that the NDVI has a direct relationship with the precipitation. Accordingly, the northern, northwestern, and western regions, as wet regions in Iran and comprise proper soil, included high NDVI.
Dividing NDVI data into two 8-year periods revealed that in the first 8 - year, despite the high amount of precipitation, the NDVI was lower approximated to the second 8 - years. This difference can be attributed to the lag - time in reactions of NDVI to climate changes. It takes several decades for most tree species to react to climate change. In addition, the increase in cultivated area and, consequently, the excessive use of underground water has a noticeable role in increasing trends of the NDVI values.
Conclusion
The long-term average and changes in Iran's NDVI were examined using NDVI grid data. Our finding showed that the spatial distribution of NDVI has a direct relationship with the precipitation. Comparing two - half of understudy data showed despite the high amount of precipitation, the NDVI in the first half was lower approximated to the second 8 - years. This difference can be attributed to the lag - time in reactions of NDVI to climate changes. It takes several decades for most tree species to react to climate change. In addition, the increase in cultivated area and, consequently, the excessive use of underground water has a noticeable role in increasing trends of the NDVI values.
Remote Sensing (RS)
Nastaran Nazariani; Asghar Fallah; Hava Hasanvand; Hassan Akbari
Abstract
Extended Abstract
Introduction
The traditional method of chemical analysis has high accuracy and precision. However, it is time-consuming and laborious, and it is not possible to obtain continuous information about the pollutant status over a large area. Therefore, there is an urgent need for a reliable ...
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Extended Abstract
Introduction
The traditional method of chemical analysis has high accuracy and precision. However, it is time-consuming and laborious, and it is not possible to obtain continuous information about the pollutant status over a large area. Therefore, there is an urgent need for a reliable and environmentally friendly method to quickly identify and investigate the distribution of heavy metals in soil and thus identify suspected contaminated areas (Scheuber & Köhl, 2003:33). Remote sensing is one of the ways that can provide a cost-effective and quick solution to investigate the distribution of heavy metals on a large scale using spectroscopic techniques (Bi et al., 2009:16). Habibi et al. (2023:4) also measured and evaluated the concentration of heavy metals in the aerial parts and soil of the tree species of Bandar Abbas city and also identified the species that has the highest potential for absorbing heavy metals. The results showed that the pattern of heavy metals in soil and leaves of tree species was Mn>Zn>Pb>Cd. (Nikolaevich, 2023:30) they addressed the modeling of heavy metal pollution in Central Russia based on satellite images and machine learning. Al, Fe, and Sb contamination were predicted for 3000 and 12100 grid nodes in an area of 500 km2 for the Central Russian region for 2019 and 2020. Estimating the amount of this pollution requires time and high cost. Considering the traffic on the Aleshtar -Khorramabad highway near Kakareza forests and the effect of heavy metal concentration in the soil and leaves of the oak species which can be caused by natural and human pollution, the accumulation of heavy metals in the species Iranian oak is a serious threat to this forest. Therefore, it is necessary to study and discuss pollutants and their effects on the environmental cycle. In this regard, considering the cost and time-consuming nature of traditional methods and since remote sensing methods are a suitable complement to traditional methods; the aim of the present research is to use remote sensing techniques and spectral analyses to evaluate and model the accumulation of heavy metals in Iranian oak species.
Materials and Methods
The present study is located on the road of Aleshtar -Khorramabad, 20 kilometers northwest of Khorramabad. For this purpose, five transects were created at distances adjacent to the road, 500 and 1000 meters on both sides of the road, and 10 x 10 m sample pieces were planted. Inside the sample plots, 30 soil samples were randomly collected and 30 leaf samples were collected from trees in all directions of the crown. To extract heavy metals from soil samples and plant samples, the acid digestion method was used and the physical characteristics of the soil were measured using standard methods. After preparing the samples, the concentration of Pb, Cu, and zinc heavy metals in soil and leaves was measured and the index of biological concentration of heavy metals from soil to leaves was calculated. Then the relationship between the concentration of heavy elements measured and the reflectance in different bands or band ratios at the corresponding sampling points was obtained. Non-parametric methods and generalized multiple linear regression models were used in order to model quantitative variables and spectral values corresponding to sample parts in satellite data. ArcGIS software was used to implement sample parts on the image, ENVI software was used for image processing, and STATISTICA software was used for modeling.
Results and Discussion
Cu and Pb in Iranian oak leaves had significant differences at different distances at the 0.05 level, but Cu did not have significant differences at different distances at the 0.05 level. Cu and Pb did not have significant differences in different soil intervals at the 0.05 level, but Cu had significant differences in different soil intervals at the 0.05 level. The bioconcentration factor was obtained as (0.2, 0.5, 0.2) mg/kg. The study of modeling of non-parametric methods using Sentinel-2 satellite data showed that the highest explanatory coefficient values (0.85, 0.88, and 0.97) were obtained for the three metals Cu, Pb, and Cu, respectively. The artificial neural network (ANN) algorithm obtained the highest accuracy. Also, according to the results of the random forest algorithm, for the three mentioned metals, PSRI, HMSSI, and PSRI indices are the most important in modeling.
Based on the findings, the concentration values of Cu and zinc were significantly different at different distances, but the Cu values were not significantly different at different distances. In this regard, Mansour concluded in 2014 that there is a significant difference between the concentration of Cu and zinc in the leaves of the species, which can be attributed to traffic density and human activities, and the high amount of zinc metal in this study is the wear of car tires؛ and stated that the concentration of Cu is caused by the production of greenhouse gases and the use of vehicles using Cu gasoline. Based on the findings, the values of Cu and zinc concentrations at different distances did not have significant differences, but the Cu values had significant differences at different distances. Sources of input of Cu element to the soil are urban, industrial, and agricultural waste, fertilizers, and chemicals that add it to the soil through liquid, solid, or mineral fertilizers. These findings are with the results of some researchers including Wu and colleagues (2010:38), Botsou et al. (2016:17) are consistent. Based on the findings obtained from the calculation of the bioconcentration index and their comparison with the classification proposed by Ma et al. (2001:25) for Iranian oak species plants in relation to Cu, zinc, and Cu metals from soil to leaves, it acts as an accumulating plant. In accordance with the results of this research, in the study of Khodakarmi et al. (2009:15), Iranian oak was included in the category of superabsorbent plants in relation to the accumulation of Cu pollutants, which has a high capacity in terms of root absorption. Also, Madejón et al. (2006:25) stated that oak leaves are more resistant than olive leaves. The concentrations of elements in leaves and fruits decrease with time and the risk of toxicity in the food web is reduced. The review and comparison of five algorithms showed that (ANN) the highest explanatory coefficient values (0.85, 0.88, and 0.97) were obtained for three metals, Cu, Zn, and Cu, respectively. Considering the importance of the PSRI synthetic band in increasing the accuracy of modeling with satellite images and the influence of the visible and near-infrared bands, the amount of reflection measured by the spectroscopic method showed that with the increase in the concentration of heavy elements, the amount of reflection in the visible and infrared range decreases (Liu et al., 2011:24).
Conclusion
The results showed that Sentinel-2 images along with artificial intelligence techniques have a relatively good ability to model the level of biological pollution index in the region. In line with the obtained results, it is suggested that the Iranian oak species is used to reduce pollution on highways because it accumulates heavy metals.
Geographic Data
Ali Sadeghi; Amir Reza Khavarian-Garmsir; Maryam Zareei
Abstract
Extended Abstract
Introduction: Cities have many challenges, but it can be said that the problem that threatens them is weak. The existence of poverty in cities leads to the occurrence of social and economic issues and causes the stability and development of these cities to be created with problems. ...
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Extended Abstract
Introduction: Cities have many challenges, but it can be said that the problem that threatens them is weak. The existence of poverty in cities leads to the occurrence of social and economic issues and causes the stability and development of these cities to be created with problems. For example, poverty can lead to unemployment, homelessness, crime, and increased disease rates. Therefore, eliminating poverty in cities plays a very important role in creating healthy and sustainable societies. Due to population growth and the influx of Afghan immigrants in recent years, some neighborhoods in District 11 of Isfahan municipality have experienced poverty due to inequality and unfair distribution of services and facilities. In order to organize the current situation and overcome the existing conditions, the spatial distribution of poverty spots must first be identified and then, with regular planning, this problem can be solved to prevent the consequences of poverty at the regional level. The aim of the current research is to analyze the spatial distribution of urban poverty indicators in the 11th district of the municipality and the social gap among the residents of this neighborhood.
Materials & Methods: The research was applied in terms of purpose and descriptive-analytical in terms of method. Based on the data of the statistical block of the 11th district of Isfahan municipality, hotspot analysis and Moran's spatial autocorrelation were performed in the GIS environment. Excel software was used for urban poverty indicators. SPS software is used for the factor analysis of the defined indicators.
Results & Discussion: The results showed that weak in the 11th region of Isfahan municipality has a cluster distribution pattern and spatial autocorrelation. According to the zoning, the parts of the center, east, northeast, and parts of the southeast and south. The west is surrounded by poor and very poor blocks, and in the north, northwest and west parts of region 11, there are very prosperous and prosperous blocks. However, in district 11 of Isfahan municipality, we see a class divide. On the other hand, I can say that having poor space in the 11th district of Isfahan city follows the characteristic pattern, in such a way that as we approach from the south to the north and from the east to the west, the poverty be decreases.
Conclusion: Some social and cultural values can perpetuate poverty and social inequality, and people in poverty may have different beliefs, attitudes, and behaviors that exacerbate their economic problems. In addition to individual and social factors, institutional factors such as housing policies, zoning laws, and land use regulations can also play a role in the spatial distribution of poverty and social inequality in urban areas. For example, discriminatory housing policies can lead to the concentration of low-income individuals in specific areas, while deprivation zoning policies can limit their access to affordable housing and employment opportunities. Today, poverty exists in various dimensions of human life and has brought with it problems and challenges. Therefore, in order to reduce poverty and implement human and sustainable development, it is essential to identify scientific and specialized methods, the geography of poverty-stricken areas, and important indicators in this field. The successful implementation of strategies and policies to reduce poverty requires the identification of all factors and needs of residents in the geographical area affected by this problem, so that programs can be developed to reduce poverty and improve conditions. This research contributes to the development of knowledge in the field of poverty and urban social planning. Its results can provide the necessary information to make decisions in addressing the urban poor problem.
Finally, the following recommendations are proposed to improve the current conditions in District 11 of Isfahan city:
Implementing neighborhood-based projects to achieve sustainable urban redevelopment with people's participation.
Establishing neighborhood development offices to identify the specific problems of each neighborhood and provide solutions.
Conducting research on poverty with the support and participation of organizations such as the Imam Khomeini Relief Committee and municipal authorities to align their results and find the best solution to address urban poverty.
Considering that the main reason for the migration of native residents of District 11 is the presence of Afghan immigrants in this area, and as a result, many social problems have arisen, it is essential to address this issue with appropriate policies; otherwise, we will face more serious problems between native residents and Afghan immigrants in the future.
Providing facilities and loans for renovation and reconstruction in the area, especially in the central, eastern, and northeast parts.
Creating social justice for the use of facilities.
Improving environmental conditions in District 11 of Isfahan, especially in the Sajjad Square neighborhood, which has an unfavorable situation. Municipal officials can address the environmental problems of this area by creating parks and green spaces, paving the streets, removing environmental pollution, collecting garbage, and organizing the vacant lands.
Creating a space for the education of working children, supporting them, and providing suitable employment opportunities for them.
Improving the physical condition of the area through redevelopment programs, and more.
Geographic Data
Hamed Asghari; Mohammad Reza Fallah Ghanbari
Abstract
Extwnded Abstract
Abstract
Introduction: How to invest and choose the right place to build a factory is one of the issues that is of vital importance for factories / companies or organizations due to its effects on factors such as performance, profitability, competitiveness, survival and various criteria ...
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Extwnded Abstract
Abstract
Introduction: How to invest and choose the right place to build a factory is one of the issues that is of vital importance for factories / companies or organizations due to its effects on factors such as performance, profitability, competitiveness, survival and various criteria such as social, economic, environmental, quality and Quantities and other goals are always noticeable to investors and managers.
Materials & Methods: Since decision-making in this field is strategic and as a result, the incomplete information of experts in conditions of uncertainty may reduce the success of future exploitation; Therefore, researchers have introduced different methods to choose the right place; D number theory as an extension of Dempster-Shafer theory in locating, while solving the deficiencies in Dempster-Shafer theory, takes into account the lack of expert information in forecasting. In this research, due to the significant amount of demand and sensitivity in the correct direction of capital resources, considering the high amount of capital required and the great importance in choosing the right place in the geography of Iran to achieve success, and that investing in this industry has always been attractive, while choosing criteria with The importance of investigating the selection of a suitable location for the construction of an edible oil refinery in thirty-one provinces of the country with the combined method of Analytical Hierarchy Process and D-Number Theory (D-AHP), due to its ability to analyze data under conditions of uncertainty that can provide a more realistic estimate , has been investigated.
Results & Discussion: the factors affecting the research problem of this research in the form of a combined method (D-AHP) and based on the consensus of the opinions of ten experts and experts have been helped with the help of brainstorming, which include: access to Raw materials, provincial demand, fixed capital costs such as land, etc. and the production capacities (factories) in the region and the frequency of consumption in the neighborhood of the province and the potential threat to the industry in case of a favorable focus are based on the behavior of consumers and political and social factors. Based on the hierarchical structure, the paired relations of D numbers for the criteria, sub-criteria (1 to 17) and options at different levels of investigation and weights have been calculated with this method, and the criteria of access to raw materials (crude oil) and provincial demand are the most important criteria. Finally, the important weights and ranks of places (provinces) in relation to the overall goal have been calculated and prioritized. Important criteria include: access to primary oil raw materials (distance from ports), fixed capital costs such as land, etc., the amount of demand in the provinces, the amount of previously created production capacities, the frequency of consumption in the neighborhood of the provinces, the lifespan of the industry in The future and political and social factors have been investigated and evaluated for 31 provinces of the country with the combined method (D-AHP) and with the consensus opinion of ten experts in the field of Iranian oil industry.
Conclusion: Therefore, the suitable place for investment in the future according to the importance coefficient of the criteria and sub-criteria and in the order of priority are as follows: provinces; Tehran (first priority), Semnan (second priority), Alborz (third priority), Central (fourth priority), Mazandaran (fifth priority), Isfahan (sixth priority), Qom (seventh priority), Fars (eighth priority), Lorestan (priority 9th), South Khorasan (10th priority), Khuzestan (11th priority), Kahkiloyeh and Boyar Ahmad (12th priority), Zanjan (13th priority), Hormozgan (14th priority), Kerman (15th priority), Yazd (16th priority), Chaharmahal and Bakhtiari (17th priority), Bushehr (18th priority), Qazvin (19th priority), East Azerbaijan (20th priority), Razavi Khorasan (21st priority), Hamadan (22nd priority), West Azerbaijan (23rd priority) ), Gilan (24th priority), Kurdistan (25th priority), North Khorasan (26th priority), Ardabil (27th priority), Sistan and Baluchistan (28th priority), Ilam (27th priority) 9th), Kermanshah (30th priority), Golestan (31st priority). Finally, the important weights and ranks of the places (provinces) have been calculated and prioritized in relation to the overall goal, which will facilitate optimal decision-making and appropriate selection for new investment and prevent waste in the consumption of capital resources and strategic planning in the long term and prevent It helps and prevents the crisis of reduction of national gross product and reduction of capacity or closure of factories, which will lead to unemployment of many employees and activists in this field and social consequences. And it shows the rational policy making to reach the desired situation.
Geographic Data
Zahra Heydari monfared; Seyed Hossein Mirmousavi; Hossein Asakereh; Koohzad Raisipour
Abstract
Extended Abstract
Introduction: Snow-cover changes and related phenomena (especially depth, snow water equivalent and snow density) have a fundamental role in mountainous environments and strongly affect water availability in downstream areas. In this way, the importance of correct and appropriate analysis ...
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Extended Abstract
Introduction: Snow-cover changes and related phenomena (especially depth, snow water equivalent and snow density) have a fundamental role in mountainous environments and strongly affect water availability in downstream areas. In this way, the importance of correct and appropriate analysis is more visible. Due to the fact that most of the rainfall falls in the form of snow in mountainous areas, the management of snow resources in these areas is very important, and knowing the different aspects of variability and geographical patterns governing the phenomenon of snow is a scientific and practical need. It is considered special in water resources and in the agricultural sector. Thus, in the current research, the spatio-temporal patterns governing the annual average of snow density in different decades and the difference of each of the decades compared to the entire time period have been estimated and analyzed using spatial statistics methods.
Materials & Methods: The studied area with an area of about 151,771.91 square kilometers is located between 34°44' to 39°25' north latitude from the equator and 44°3' to 49°52' east longitude from the Greenwich meridian. In order to investigate the spatial autocorrelation changes of the average snow density in northwest Iran during the years 1982-2022 from the data obtained from the database of the European Center for Medium-Range Atmospheric Forecasting ECMWF4/ ERA5 based on daily data, and to identify and understand the spatial patterns of density Barf, based on statistical and graphic models have been used in the geographic information system environment. In the study of temporal-spatial changes of the average snow density of the region in different time periods including 4 decades ((1982-1992), (1992-2002), (2002-2012), (2012-2022)) and the whole period of 41 years (2022) -1982)), general Moran's I and Getis-Ord Gi* statistics were used. Also, in the current research, in order to investigate the effect of changes in Extreme snow precipitation on the amount of snow density in the northwest region, it has been done to determine the snow threshold. In order to estimate snow drift, a threshold was defined. Since the station snowfall amount data has a high dispersion, values above the mean cannot be accurate for defining the threshold of freezing snow. In this way, the 99th percentile index has been used to determine the snow threshold.
Results & Discussion: The aim of the current research is to investigate the spatial autocorrelation changes of the annual mean snow density in the northwest of Iran. For this purpose, the annual snow density data during the statistical period of 1982-2022 was obtained from the ECMWF/EAR5 database with a resolution of 0.25 x 0.25 degrees, and then divided into four ten-year periods. In order to analyze spatial autocorrelation changes, global Moran indices and hot spot analysis (Gettys-RDJ) were used at the significance level of 90, 95 and 99%. Also, in order to investigate the effect of extreme precipitation on changes in the level of snow density, the 99th percentile statistical index was used, and based on this index, the freezing threshold of each synoptic station in the region was determined during the last decade (2012-2022) and the interval the entire statistical period (1982-2002) was carried out. The results of the present research showed that in the studied area, snow density has spatial autocorrelation and a strong cluster pattern. With a density threshold less than 0.10 kg/m3, from the first decade to the end of the fourth decade, the area (number of pixels) and the amount of snow density in the northwest have decreased. The results of the analysis of the changes in precipitation in the 99th percentile showed that the amount of this type of precipitation has increased significantly during the last decade of the study, and this has caused the snow density to increase relatively in the last decade compared to the first to third decades. However, in general, the amount of snow density in the entire northwest area has significantly decreased during the last four decades.
Conclusion: The evaluation of the temporal changes of snow density also strengthened the hypothesis of the occurrence of freezing snow precipitation leading to an increase in snow density in the months of cold seasons during the last decade. This point was confirmed by examining the statistical index of the 99th percentile of snowy days of each synoptic station in the region during the last decade (2009-2018) compared to the entire period of station statistics (2000-2018). The results of the analysis of the changes in precipitation in the 99th percentile showed that the amount of this type of precipitation has increased significantly in the last decade of the study and this has caused the snow density in the last decade to increase relatively compared to the first to third decades. However, in general, the amount of snow density in the entire northwest area has decreased significantly during the last four decades. Moran's statistic was used to explain the pattern governing snow density in northwest Iran. The results of Moran's index about the annual average of snow density showed that the values related to different time periods have a positive coefficient and are close to one, which indicates that the snow density data has spatial autocorrelation and has a cluster pattern. Also, the results of standard Z score and P-value confirmed the cluster significance of the spatial distribution of snow density in the northwest. Finally, the analysis of hot spots has been a clear confirmation of the continuation of concentration and clustering of snow density in northwest Iran in space with the increase of the time period, which mountainous areas have the first rank in the formation of hot clusters with a probability of 99%. have given.
Remote Sensing (RS)
Samaneh Bagheri; Mahmoud Soorghali; Hassan Emami
Abstract
Extended Abstract
1-Introduction
Monitoring vegetation changes is crucial for environmental planning and management, and satellite images offer various methods for detecting these changes, each with its own advantages and disadvantages. The use of various plant indices from remote sensing (RS) systems ...
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Extended Abstract
1-Introduction
Monitoring vegetation changes is crucial for environmental planning and management, and satellite images offer various methods for detecting these changes, each with its own advantages and disadvantages. The use of various plant indices from remote sensing (RS) systems is utilized to evaluate changes and create thematic maps for monitoring diverse plant cover. Today, RS indices are widely used in research projects in specialized fields, such as vegetation health, stress assessment, plant development rate, and plant greenness, to evaluate vegetation health, stress types, and plant illnesses. Hyperspectral imagery, particularly from the red and near-infrared bands in the electromagnetic spectrum (690-740 nm), has been widely used to derive vegetation indices. This project intends to monitor the forest risk regions of a segment of northern Iran's forests in 2020 using a combination of various indices produced by RS data and a geographic information system (GIS). Prisma hyperspectral images were used to assess the health of forests in Northern Iran's Rudsar, Ramsar, and Tonkabon forests, focusing on water stress, insufficient growth, plant pests, diseases, and greenness. Forest areas are divided into five risk-acceptance regions using RS indices, and the data is analyzed using various GIS weighting methods to determine the remaining dangerous forest regions.
2- Methodology
The study utilized twelve plant indices from three categories (greenness, growth, leaf pigments, and leaf surface moisture) and four other individual vegetation indices using various techniques. Based on this, the study selected sixteen forest risk-taking maps from five classes with varying risk-taking potential, weighted the layers using hierarchical analysis, and generated a final map based on the obtained weights. When the average results of combined and individual indices were compared with the classification map, it was discovered that the combined indices were more accurate than the individual indices. Existing composite indices are categorized into three broad groups: plant greenness, leaf pigment, and productivity of water or light usage in the vegetation canopy. The three primary characteristics each possess multiple indices that can be combined to provide crucial insights into forest health.
3- Results and discussion
The study reveals that when combined with appropriate indices, combined indices can provide high accuracy in the risk assessment of forest areas in the north of the country. In contrast, an incorrect combination can result in low-accuracy outcomes. The study found that the combined indices had a 11% error in two high-risk forest areas, while individual indices had a nearly double error of 21%. The use of composite indices significantly reduces the inaccuracy of calculating forest risk regions by 50% and enhances the accuracy of monitoring these areas. Furthermore, when the combined indices were examined independently, the findings revealed that the combination of the VCN and VCNW indices yielded the maximum accuracy. These compounds are highly effective in assessing the health of vegetation, assessing plant stress, and determining plant water content. On the other hand, the combined indexes from RC were less accurate than the previous combination, with the highest accuracy levels being SIPI, NDII, NDWI, and WBI. These synthetic substances are utilized in the fields of plant health and stress assessment. The accuracy of SIPI, NDII, NDWI, WBI1, PRI1, and RGRI is significantly reduced when combined with the NC index. The combination's low accuracy may be due to the NDVI index's limitations, as it is primarily used to detect vegetation presence or absence and does not detect plant health or stress. The study presents the first results from research on plant stress in northern Iranian forests using Prisma hyperspectral data. Hyperspectral data is chosen for its superior spatial, spectral, and radiometric resolution, making it ideal for studying dynamic ecosystems in the current research region. Hyperspectral RS allows for non-destructive monitoring of leaf pigments like chlorophyll, carotenoids, and anthocyanin content, crucial indicators of vegetation health. Therefore, the recommendation is to employ a combination of indices with diverse approaches in hyperspectral images instead of using individual indices for monitoring vegetation usage.
4- Conclusion:
Forest health monitoring is a crucial aspect of forest management programs, and utilizing RS techniques and data can be highly beneficial in this field. The study compared the accuracy of combined indices and individual indices using the classification map, revealing that combined indices were more precise. In addition, the results showed that in almost two high-risk classes of the forest area, the combined indicators have an error of 11% and the individual indicators have an error of almost twice their error, 21%. Therefore, composite indices significantly reduce forest risk area estimation errors by 50% and improve accuracy. Therefore, it's recommended to use a combination of indices with different approaches in hyperspectral images instead of individual indices for monitoring vegetation usage.
Extraction, processing, production and display of geographic data
Sara Sheshangosht; Hossein Agamohammadi; Nematollah Karimi; Zahra Azizi; Mohammad Hassan Vahidnia
Abstract
Extended Abstract
Introduction
Glaciers and their short-term and long-term elevation changes are among the most critical environmental hazard indices for monitoring climate change and evaluating geomorphology, perpetually posing risks to climbers, environmentalists, and tourists. The Alamkooh ...
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Extended Abstract
Introduction
Glaciers and their short-term and long-term elevation changes are among the most critical environmental hazard indices for monitoring climate change and evaluating geomorphology, perpetually posing risks to climbers, environmentalists, and tourists. The Alamkooh glacier’s snout is known as one of the most dynamic parts of glaciers in Takht-e-Soliman height due to the yearly advance and retreat of glacier movement causing substantial volumes of various glacial deposits to collapse into their downstream areas. Nowadays, the advancements of satellite imagery, aerial photos, and Unmanned Automated vehicles (UAV) pave the path for accurately extracting and evaluating these changes. Therefore, the objectives of this research are: (a) evaluating the use of new and cost-effective technologies (UAVs) in comparison to satellite imagery for monitoring glacier changes, (b) identifying spatiotemporal glacier elevation changes, and (c) evaluation of the elevation change rate of the Alamkooh glacier snout from 2010 to 2020 using high spatial resolution remotely sensing data. In this context, the elevation changes of the snout of Alamkooh Glacier, as the hazardous activist part of this glacier, were assessed using Digital elevation models (DEMs) differences of 2010, 2018, and 2020.
Materials and Methods
Alamkooh Glacier is located on the northern hillside of Alamkooh Summit in the Takht-e-Soliman region. The snout of this glacier is situated in a steep valley known as Lizbonak and its high activity changes the shape and morphology of this area. In this paper, spatial and temporal elevation changes of Alamkooh Snout were identified and evaluated using DEMs subtraction derived from aerial laser scanning (LiDAR) data in 2010, and from images captured by UAV in 2018 and 2020. Before elevation change analysis, the DEMs obtained through UAVs in 2018 and 2020 were carried out using approximately 40 and 20 ground control points, respectively. The resulting outputs displayed a reliable accuracy of around 15 cm for these DEMs. In addition, for assessing elevation changes precisely, the all of extracted DEMs were preprocessed and orthorectified and then subsequently subtracted pairwise. Then after, the accuracy of elevation changes was appraised based on non-glacial area elevation change. The outcomes of elevation change in this region signify a high level of accuracy in the 10-year time span. According to the results, the average and standard division elevation change of non-glacial area was ±0.05 cm and 0.34 cm respectively. Moreover, the average error assessment on the non-glacial area indicates that within eight years from 2010 to 2018 the average error was ±0.16 cm, and within two years it was ±0.11 cm from 2018 to 2020.
Result and discussion
Results of DEMs pairwise differences show significant elevation changes in this part of Alamkooh Glacier from 2010 to 2020. The average and the maximum elevation change rates in this period are -0.8 (m/yr.) and -2.31(m/yr.) respectively. The major elevation changes in the snout of Alamkooh happened in the initial period from 2010 to 2018 where the yearly and the maximum mean elevation change rates were -1.03 (m/yr.) and –2.77 (m/yr.) respectively. On the contrary, the elevation changes from 2018 to 2020 were lower than the first period whereas the yearly mean elevation change was about +0.1 (m/yr.) and the maximum elevation change rate was -1.85 (m/yr.). The positive rate of elevation change from 2018 to 2020 is due to debris and ice cubes flowing from upstream and accumulation downstream. Moreover, the Spatial analysis of elevation changes results show a heterogeneous distribution whereas the most significant elevation change in the snout of Alamkooh glacier has occurred predominantly across and along the largest existing valley rather than being evenly spread out across the entire area. The elevation change domain in this valley is between +1.3±0.05 to -23.05±0.05 and the average elevation change of in ten years from 2010 to 2020 is about -8.01 ± 0.05 meters. These changes mostly were negative with decreasing and eroding rates. In contrast, the elevation changes in other valleys only occurred at the exit area of the glacier and just the entrance of the snout area, and the margins did not show a considerable change. When considering all valleys in the snout of Alamkooh the elevation changes distribution across the snout varies between +0.45 to -13.2 (m) with an average of -7.8 (m) which is less than alongside changes at the main valley.
Conclusion
The results show elevation changes in the Almakooh snout do not have constant rate and largely fluctuate in different years and regions. The maximum elevation changes occurred from 2010 to 2018 and along with the main steepest valley. The main valley plays a vital role in elevation change analysis and flowing debris down. This area is also known as the depletion area of the Alamkooh glacier and its drastic elevation changes are caused due to ice and snow melt. The tremendous historical flood of the SardAbrood River occurred in June 2011 was created and affected by elevation changes in this area. Therefore, the tongue of Alamkooh Glacier is considered one of the most dangerous areas regarding natural hazards, and morphological change studies require precaution regarding approaching or visiting this area. This research also confirms that using time-series of remote sensing data such as UAV and Lidar images is very helpful and cost-effective data for identifying, extracting, and monitoring the spatiotemporal changes of glaciers, debris flow directions, and natural hazards.
Geographic Information System (GIS)
Abolfazl Ghanbari; Mostafa Mousapour; Habil Khorrami hossein hajloo; Hossein Anvari
Abstract
Extended AbstractIntroduction:The urban space is the most important human-made spatial structure on the planet earth. The history of urban development shows the path of human development, political system evolution and technological, technical and industrial developments. The physical development of ...
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Extended AbstractIntroduction:The urban space is the most important human-made spatial structure on the planet earth. The history of urban development shows the path of human development, political system evolution and technological, technical and industrial developments. The physical development of urban areas is one of the main drivers of global changes that have important direct and indirect effects on environmental conditions and biodiversity. In the process of physical development of the city, due to the transformation of natural and semi-natural ecosystems into impermeable surfaces, it often causes irreversible environmental changes. One of the new approaches in urban planning is the use of remote sensing techniques and geographic information system. The emergence of remote sensing and machine learning techniques offers a new and promising opportunity for accurate and efficient monitoring and analysis of urban issues in order to achieve sustainable development. The process of processing satellite images can generally be divided into two approaches: pixel-based image analysis and object-based image analysis. The pixel-based analysis technique is performed at the level of each pixel of the image and uses only the spectral information available in each pixel. On the other hand, the object-based analysis approach is performed on a homogeneous group of pixels, taking into account the spatial characteristics of the pixels. One of the basic problems in urban remote sensing is the heterogeneity of the urban physical environment. The urban environment usually includes built structures such as buildings and urban transportation networks, several different types of vegetation such as agricultural areas, gardens, as well as barren areas and water bodies. Therefore, in the pixel-based processing approach, the existence of heterogeneity in the urban biophysical environment causes spectral mixing and also spectral similarities in the classification operation of satellite images in such a way that in a place where a pixel is If the surrounding environment is different, it causes Salt and Pepper Noise. Therefore, according to the problems in the pixel-based processing approach, the aim of this research is to compare the accuracy of machine learning algorithms based on object-based processing of satellite images in extracting the physical development area of Hamedan city using Sentinel 2 satellite image.Materials & Methods: The remote sensing data used in this research is a multi-spectral satellite image with a spatial resolution of 10 meters from the Sentinel 2 satellite, including bands 2 (blue), 3 (green), 4 (red) and 8 (near infrared) related to the date is the 23 of August 2023 in the city of Hamadan. The image of the Sentinel 2 satellite was downloaded from the website of the European Space Agency. In ENVI software, the pre-processing operation was performed on the satellite image. Then, in the eCognition software, the segmentation process was performed based on the appropriate scale, shape factor, and compression factor with the aim of producing image objects. After segmenting and converting the image into image objects, using machine learning classifiers based on object-oriented processing of satellite images including Bayes classification algorithms, k-nearest neighbor, support vector machine, decision tree and random trees, the classification process was carried out and maps of urban physical development area were produced. After the segmentation operation and the production of visual objects, three classes of built-up urban land, vegetation and barren land were defined, and some of the built objects in the segmentation stage were selected as training points and some were selected as ground Truth points.Results & DiscussionAfter downloading the satellite image from the website of the European Space Organization, in order to apply the radiometric correction of the image and also with the aim of matching the value of the gray levels of the image with the value of the real pixels of the terrestrial reflection, the gray levels are converted to radiance and then, using atmospheric correction, to coefficients. They became terrestrial reflections. In order to apply radiometric correction, Radiometric Calibration tool was used, and to apply atmospheric correction, FLAASH model was used in ENVI software. In order to classify the satellite image based on machine learning algorithms based on object-based processing, eCognition software was used. The satellite image of the study area, which was pre-processed and saved in TIFF format, was called in the environment of this software and saved as a project. In order to produce visual objects, segmentation operations were performed in different scales, shape factor and compression ratio to reach the most appropriate segmentation mode. In this step, the multiple resolution segmentation method was used to segment the image. The most appropriate segmentation included the scale of 100 and the shape factor of 0.6 and the compression factor of 0.4. Because in scales higher than 100, the construction of the visual object was not done correctly, so that several distinct complications were placed in one piece, and in scales less than 100, in some cases, one complication was placed in several pieces. In order to classify the generated image objects, machine learning algorithms were defined separately and after training each algorithm, the classification operation was performed. In this step, the classification was done based on the nearest neighbor method and by selecting the average and standard deviation parameters for each image band. After producing a map of the city physical development range through machine learning classifiers based on object-based processing of satellite images, the classification accuracy of each of the used algorithms was calculated. In order to calculate the accuracy of the above algorithms in eCognition software, using selected ground Truth control points, the overall accuracy and kappa coefficient were calculated for each of the algorithms.Conclusion:Based on the results of the research, it is possible to produce a map of Hamedan's urban physical development using machine learning algorithms based on object-based processing of satellite images with acceptable accuracy. Also, among all the algorithms used in this research, k-nearest neighbor with overall accuracy of 97% and kappa coefficient of 0.96 provided more accuracy.
Territorial conditions and security of border areas
Sayed Mehdi Mousavi Shahidi; Bahador Zarei; Mehdi Oriya
Abstract
Extended AbstractIntroductionHydropolitics is the exploration of the role of water in the relations between countries on four scales: local, national, regional and global. With 26 border rivers and the dependence of about 30% of the country's population on the water of common watersheds, Iran is among ...
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Extended AbstractIntroductionHydropolitics is the exploration of the role of water in the relations between countries on four scales: local, national, regional and global. With 26 border rivers and the dependence of about 30% of the country's population on the water of common watersheds, Iran is among the countries that are heavily affected by hydropolitical developments and changes in the world. Additionally, the security of the country's border areas is greatly impacted due to their peripheral location and strong reliance on water from border rivers. Hence, this research investigates the hydropolitics of Iran's border rivers, the indicators and components that influence it, and the security consequences on the border areas. The research utilizes Qualitative method and descriptive-analytic approach, employing Delphi methods, cross-matrix analysis (MICMAC), and ArcGIS software to produce maps.Materials & MethodsBased on the purpose, this research is among the applied research and based on the method, it is among the qualitative research, with a descriptive-analytical approach and using the Delphi technique and the cross-matrix analysis method. In this research, the authors will analyze the issue by using library resources and written documents related to the topic, while describing and explaining the event, and then while studying the library resources from the questionnaire in order to identify and screen the most important Dimensions and hydropolitical indicators of Iran's border rivers, as well as the effectiveness of the indicators will be used. The statistical population of this research includes experts, custodians and elites of the country in the field of water. In this regard, due to the unlimited statistical population and the lack of official information on the number of experts and elites, it is not possible to use Cochran's formula, and the number of 20 people is considered as the statistical population in this research and they are questioned. . Due to the type of research and not knowing the full number of the statistical population, the sampling method is "targeted sampling" and the snowball sampling method. In order to analyze and analyze data and information, since this research is one of qualitative researches, in addition to the use of library sources and analysis with a descriptive-analytical approach, methods such as Delphi in order to identify dimensions and indicators, as well as the method Cross-matrix analysis will be used in future research of effective hydropolitical strategies. In this research, Arc GIS software is used for map preparation and Micmac software is used for data analysis.Results & DiscussionThe research findings reveal that more than 50 indicators affecting the hydropolitics of border rivers were identified through the use of library resources, the Delphi technique, and a questionnaire. Ultimately, 31 factors were confirmed in the second and third stages of the Delphi process. These 31 factors were categorized into five dimensions: natural factors, human factors, geopolitical factors, military factors, and geo-economic factors by forming an expert team and consulting with professors.The results of the cross-matrix analysis in MICMAC software have shown the indicators of influential, influenceable, target, independent, result indicators, and especially risk indicators in the hydropolitics of Iran's border rivers. Among these, target indicators and especially risk indicators are important strategic indicators. The indicators of the need for drinking water from the border rivers, unemployment, and migration due to water shortage in the areas of the common catchment basins, the relations of the surrounding countries affected by the catchment basins, the existence of a large population of people in the common catchment basins, the construction of dams and mines in the upstream countries, the defense and military situation of Iran's border rivers, the political and geopolitical exploitation of water by the upstream countries, and the activities of evil and terrorist groups in the upstream countries are the most important risk indicators in the hydropolitics of border rivers of Iran.ConclusionFinally, the results show that the most important security consequences of the hydropolitics of border rivers on border areas are in environmental, economic, political, social, and cultural dimensions. The most important of these include ethnic tensions on both sides of the border, smuggling of goods and drugs in the border areas, joining terrorist groups and striving for independence, migration from border areas, reduction of agriculture in border areas, growth of poverty in border areas, and as a result, the growth of crime and the increase in the cost of providing security. Other consequences include ethnic crises due to spatial and ethnic ties, conflicts over water, marginalization and increase in crime, air pollution, drying up of border wetlands, respiratory problems in border areas, the emptying of borders, and the destruction of the environment in border areas.
Geographic Information System (GIS)
Jalal Samia; Manouchehr Ranjbar Shoobi; Amer Nikpour
Abstract
Extended abstract
Introduction
Visiting Mazandaran province could be a fascinating and memorable trip due to its amazing natural touristic attractions such as Caspian Sea and mount Damavand. The three main roads naming Kandovan, Haraz and Firoozkooh can be used to access Mazandaran province. Among ...
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Extended abstract
Introduction
Visiting Mazandaran province could be a fascinating and memorable trip due to its amazing natural touristic attractions such as Caspian Sea and mount Damavand. The three main roads naming Kandovan, Haraz and Firoozkooh can be used to access Mazandaran province. Among them, passing through Kandovan road is fascinating with its beautiful natural landscapes. At the same time, this road is also known as one of the most dangerous roads of Iran due to its mountainous location and the potential occurrence of different types of climatic and geomorphologic hazards. Apart from these dangers, the occurrence of accidents in Kandovan road is one of the main concerns of tourists visiting west parts of Mazandaran province and also the local governments providing relief and rescue services and facilities to injured people. Therefore, it is crucial to identifying the dangerous sections of this road in order to minimize fatalities and socio-economic losses. The purpose of this research is to investigate the spatio-temporal density pattern of road accidents and also to identify accidents clusters along Kandovan road.
Material and methods
To this end, we used road accidents information along Kandovan road, collected by the relief and rescue bases of Red Crescent organization of Mazandaran province in the period of 2016 to 2022. Information like location, date, and the number of death and injuries in the road accidents along this road were used in this research. First, we used GIS, spatial and statistical analyses in order to get insight from road accidents distribution and statistics. In the next step, Kernel Density Estimation – a Geostatitical measure – was used to investigate the general spatial density pattern of road accidents in the period of 2016-2022 and also the spatio-temporal density pattern of road accidents in every year from 2016 to 2022. Furthermore, the hot spot analysis was implemented to the distribution of road accidents in this period in order to find out whether accidents are clustered, dispersed or randomly distributed. Both general spatial pattern and annual spatio-temporal patterns of accidents were investigated using hot spot analysis. With this, accidents clusters reflected as hot spots were identified based on the Getis-Ord Gi*index and the associated Z-score, P-value and Gi-bin statistics. In this context, the number of accident clusters, the length of road in the accident clusters and the percentage of observed accidents in the clusters were computed from 2016 to 2022.
Results and discussion
Results show that 2084 accidents were occurred in the period of 2016-2022 with 9076 injuries and 52 deaths. The most number of accidents was occurred in 2022 following the end of Corona lockdown in 2021. Also, several parts of Kandovan road indicated to contain the highest number of accidents density. Besides, the accident density pattern changes spatially and temporarily with an increasing trend in the number of accidents density from the end year of Corona disease epidemic in 2020. Results from hot spot analysis also identified several accidents clusters along this road in the period of 2016-2022. In this context, road accidents clusters were identified in Zangouleh Bridge, Majlar, Siah bisheh, Knadovan tunnel and Ushen Bridge with average Z-score value of 3.12, average P-value smaller than 0.05 and confidence interval of 90 to 99%. The total length of road in these clusters was more than 14 kilometer which contains around 60 % of the total accidents. The spatio-temporal distribution pattern of accidents clusters and also road lengths in the identified clusters change decreasingly in the period of 2016-2022. The results of this research can be used to investigate the reasons behind the occurrence of road accidents in the high accidents density sections and also in accidents clusters identified along the road. Taking proper preparation and mitigation strategies can be beneficial in proper crisis management of road accidents in order to avoid human causalities and socio-economic losses.
Conclusion
We conclude that kernel density estimation and hot spot analysis are effective geostatistical approaches to investigate the density pattern of road accidents and also to identify accidents clusters. In order to increase the safety of Kandovan road, the factors contributing to accidents occurrence in highly dense accidents sections of road and also in accidents clusters need to be identified, and with implementing proper measures, their effects can be minimized.
