Original Research Paper
Photogrammetry
H. Salih Mahdi; E. Ghnabari Parmehr; S. A. Anvari
Abstract
Background and Objectives: The urbanization occurs everywhere, especially in developing countries and is the process of changing the social order and transforming the landscape of a city. However, urbanization always leads to the growth of slums or informal settlements. The development of urban areas ...
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Background and Objectives: The urbanization occurs everywhere, especially in developing countries and is the process of changing the social order and transforming the landscape of a city. However, urbanization always leads to the growth of slums or informal settlements. The development of urban areas with dense and complex slum areas requires extensive planning and very accurate and reliable information. The process of collecting data using traditional methods is time-consuming and expensive.Methods: Remote sensing is used to identify, identify and monitor slum settlements in space and time and better understand the physical effects of slums. But due to the complexities of the slum areas and the spatial resolution of satellite images, it is not possible to use satellite images to prepare accurate maps with high details. With the emergence of unmanned aerial vehicle (UAV) as an imaging platform and the use of these images for aerial photogrammetric mapping of UAVs, its applications in various fields have increased day by day. Due to their portability, accuracy, low cost, and high imaging speed, UAVs have attracted attention in many research fields to obtain the latest information about target areas. Due to the use of non-metric cameras in UAV photogrammetry, camera calibration is necessary is carried out in the UAV image processing software using the bundle adjustment technique. However, the conventional aerial photogrammetry imaging structure, i.e. obtaining vertical images with overlaps, due to the dependence between the camera calibration parameters and the external orientation parameters of the camera, cannot achieve high accuracy in the 3D maps. In addition, due to the low height of UAV images, more hidden areas are created in the 3D photogrammetric model. In this research, vertical and oblique UAV images with angles of 30 and 45 degrees were used to prepare a three-dimensional map of the slum area with high density and complexity, and the accuracy of the vertical and oblique images was evaluated using control points in the study area.Findings: The high resolution of UAV images and the generated orthomosaic makes it possible to recognize details and provides a better understanding of the earth's features. For example, walls with a thickness of ten centimeters and power lines with a thickness of two centimeters can be seen. As a result, urban planners can determine the boundaries of buildings with high accuracy and produce cadastral maps with high accuracy. Oblique images are distinguished by a wider field of view than vertical images. It is also possible to see areas hidden under obstacles such as plants, buildings and narrow alleys. This feature provides high accuracy that can be used in projects that require detailed descriptions, such as cultural heritage protection projects and urban projects that require details such as building facades and height estimates.Conclusion: In this research, vertical and oblique UAV images were used to prepare a 3D map of the slum area, and based on the results, the total error of oblique images is 6.2 and 8.3 cm for oblique images with angles of 30 and 45 degrees, respectively. While the total error of vertical images is equal to 16.1 cm. This comparison shows the superiority of the accuracy of oblique images compared to vertical images.
Original Research Paper
Remote Sensing
H. Babaeifard; S. Sadeghian
Abstract
Background and Objectives: This research aims to present a novel approach for retrieving soil parameters from the combination of Sentinel-1 satellite data and the OH model. This information can aid in improving land management and increasing agricultural productivity. Accurate determination of soil parameters ...
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Background and Objectives: This research aims to present a novel approach for retrieving soil parameters from the combination of Sentinel-1 satellite data and the OH model. This information can aid in improving land management and increasing agricultural productivity. Accurate determination of soil parameters such as roughness and moisture is crucial for efficient land agriculture management and decision-making. Conventional ground-based methods for obtaining these parameters are often limited in spatial coverage and are frequently found to be time-consuming and costly. On the other hand, remote sensing techniques, especially those utilizing SAR satellite data, offer the potential for a more effective and comprehensive solution for monitoring soil conditions in vast areas. This study focuses on three main questions related to soil roughness and moisture parameters, emphasizing their significance for agriculture and their impact on soil science and agricultural processes. It also underscores the potential of remote sensing techniques, particularly the acquisition of satellite data, in providing effective and comprehensive solutions for monitoring soil conditions in extensive areas. Soil roughness and moisture are highly important for agriculture and can have significant impacts on crop growth.Methods: This research is focused on investigating and analyzing the soil moisture and roughness parameters of an agricultural land in Nazarabad County. The process includes data collection, preprocessing, radar data calibration, and validation. Radar data for this study is obtained from the Sentinel-1 satellite. The use of radar data from this satellite for monitoring agricultural lands day and night and conducting comprehensive research on the subject is highly valuable. The input data underwent preprocessing in the SNAP software, involving the use of filters to remove noise spots and geometric corrections. The necessary inputs for solving the OH model equations from polarized images, especially HH and VV, were obtained after the aforementioned settings using SNAP software. Statistical analysis involves extracting vital information such as Sigma Naught (σ) and incidence angle (θ) for each pixel, which are crucial for the OH model. Polarized images, after adjustments, were further used for analysis. Next, the equations written for each pixel were individually solved in MATLAB programming software, and the values of the root mean square height (s) for obtaining roughness and the dielectric constant (ε) - a key parameter for estimating soil moisture content, i.e., soil moisture (mv), for all pixels were obtained. Finally, matrices related to these values were transformed into the output image, generating a map displaying information on soil moisture and roughness.Findings: Based on the results obtained, it has been demonstrated that the values of dielectric constant, roughness, and humidity are very sensitive to the initial solver parameters. In particular, the dielectric constant exhibits significant sensitivity, which may be reduced by improving the solution method. Roughness profile analysis shows that the rms height varies in different regions and increases the scattering with the increase of roughness. Additionally, moisture content analysis indicates that the humidity is relatively uniform throughout the area.Conclusion: This study demonstrates that the use of Sentinel-1 satellite data in conjunction with the OH model leads to a significant improvement in access to reliable information for enhancing agricultural management. This approach has the capability to analyze spatial and temporal variations in soil roughness and moisture, providing vital information for optimizing agricultural practices. Substantial soil condition improvements lead to more precise monitoring and better productivity in agriculture, offering the potential for more accurate monitoring of soil conditions and enhanced productivity in the agricultural domain. These assessments can provide valuable insights for agricultural land management and decision-making processes, contributing to increased efficiency and environmental conservation.
Original Research Paper
Remote Sensing
A. Ghandian; Ni. Mostofi; A. Majidizadeh; H. Motieyan
Abstract
Background and Objectives: Nowadays, the development of urbanization and the increase of urban population have caused the air to heat up more than in the past and create urban heat islands. Urban heat islands are a phenomenon caused by the urbanization effects, due to which the temperature in the urban ...
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Background and Objectives: Nowadays, the development of urbanization and the increase of urban population have caused the air to heat up more than in the past and create urban heat islands. Urban heat islands are a phenomenon caused by the urbanization effects, due to which the temperature in the urban environment rises higher than in the suburbs. This phenomenon can cause irreparable damage due to the increasing atmospheric and environmental temperature, such as biological pollution, greenhouse gas emissions, diseases caused by heat, and impact on water quality brought to communities and the environment. This research proposes an effective and efficient approach with the help of remote sensing and optimization algorithms based on replacing the roof covering of an area with less heat-absorbing coverings to reduce the temperature and try to eliminate the heat island phenomenon. In this research, we are trying to reduce the urban heat island effect based on algorithms and statistical parameters affecting the ambient temperature, which has had few studies in past research. Also, using the intelligent optimization method in this field can cause innovation and create better and more accurate results. The new way that this study examines is to change the roof covering of an area with other functional coverings that reduce the air temperature in that area. The coverings that we considered to replace the covering of the roofs to moderate and cool the temperature of the studied area are two types of coverings: soil and vegetation.Methods: The proposed approach of this research is to use two optimization algorithms of genetic and particle swarm, and the parameters that form the objective function of these two algorithms are the temperature standard deviation and the average financial cost of the coverage changing of each building parcel. The research dataset is Landsat 8 satellite images of Andisheh neighborhood in Tehran. This research uses satellite images for purposes such as preparing color images, mapping the vegetation and non-vegetation indices of the study area, and calculating the earth's surface temperature and urban heat islands.Findings: The results indicate that both optimization algorithms have provided good performance and improved the problem parameters, but the genetic optimization algorithm obtained a better result in less time and iteration. In comparing the two algorithms, the genetic optimization algorithm reduced the standard deviation by 19%, bringing its value to 0.42. On the other hand, the particle swarm optimization algorithm for a longer time, reduced the standard deviation by 14%, bringing its value to 0.44.Conclusion: The genetic algorithm in optimizing the building roofs obtained excellent results with a total cost of 4678 and a standard deviation of 0.4177. It converged quickly with the 12100 number of objective function evaluations and significantly reduced both the cost function parameters (The genetic algorithm has reached the best possible answer). The particle swarm optimization algorithm also failed to achieve an answer as good as the genetic algorithm with a total cost of 4965, a standard deviation of 0.4430, and a 20100 number of objective function evaluations. About the comparison between these two algorithms, the genetic, with less than 3000 objective function evaluations, was able to experience the most optimal solution that particle swarm algorithm reached with the 20100 number of function evaluations. The use of metaheuristic algorithms in practical problem optimizations, which we frequently encounter in various industries today, can be very efficient. The results of these algorithms are very suitable despite the differences in the outputs, and it will be impossible to reach such answers to different problems without using such algorithms. In future work, based on what we obtained in this research, we suggest using other optimization algorithms or even powerful modeling algorithms such as artificial neural networks. Also, it is possible to study the change in building roof covers and the use of newer coverings in moderating the temperature by adopting new parameters from the cost function in optimization and deep learning algorithms.
Original Research Paper
Remote Sensing
S. T. Mansouri; E. Zarghami
Abstract
Background and Objectives: Today, urbanization is expanding and it is predicted that by 2030, more than two-thirds of the world's population will live in cities. This population needs spaces such as residential, business, leisure, etc. to live. This has led to changes in the natural environment to create ...
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Background and Objectives: Today, urbanization is expanding and it is predicted that by 2030, more than two-thirds of the world's population will live in cities. This population needs spaces such as residential, business, leisure, etc. to live. This has led to changes in the natural environment to create the said uses. These changes have various consequences on the environment and human life, which can be mentioned as the increase of impervious levels in the city and the reduction of green space. Based on this, the city environment acts as a heat collector and creates heat islands due to the production of more heat due to the consumption of fossil fuels as well as the presence of impermeable surfaces and tall buildings. The main reason for the formation and intensification of urban heat islands is the change of the land surface due to the uneven development of the city. Today, detailed and comprehensive investigation of urban thermal islands, which is related to the growth of the city, has been noticed by city managers. Remote sensing is one of the best tools to detect this phenomenon. This article examines the influence of urban environment structure on thermal changes in Tehran.Methods: To achieve this goal of the research, to determine the trend of temperature changes in 22 regions of Tehran in the period from January 1, 2013 to January 1, 2023, coding was done in Google Earth Engine. For this purpose, the shape file of Tehran city was prepared and after calling the shape file in Google Earth Engine, remote sensing images of MODIS 11A2 006 Terra satellite were extracted. These images were 460, which were converted into much smaller and higher resolution images by the reducer of the Google Earth Engine system. Then, according to the required data received from the MODIS 11A2 006 Terra satellite, the average ground surface temperature trend at night, the ground surface temperature change trend, the ground surface temperature transect trend and the average ground surface temperature change trend at night for the 22 regions of Tehran in the interval The period from January 1, 2013 to January 1, 2023 was examined.Findings: After measuring the data, areas 10, 11, and 12 in the center of Tehran had the least, and areas 1, 3, and 4 in the northeast of Tehran and areas 21 and 22 in the northwest of Tehran had the most thermal changes in time. The temperature of the ground surface in areas 1, 3, 4, 21 and 22 with an average of 288.6 K, were the hottest areas in Tehran.Conclusion: The results showed that the urban heat islands created in Tehran are different based on the factors that cause temperature changes. This difference is primarily due to land use and land cover in the disproportionate and unbalanced development of the city and indicates the close relationship between land cover and surface temperature. Also, the correlation study between land cover and land surface temperature showed that there is an inverse relationship between these two parameters and there is no direct relationship between population density and land surface temperature in some areas. Considering the nature of the research, this research can be effective in reducing the intensity and expansion of urban heat islands with proper planning for better and more use of water and green space.
Original Research Paper
Geo-spatial Information System
M. Minaei; M.H. Vahidnia; Z. Rezaei
Abstract
Background and Objectives: Spatial data mining techniques offer optimal efficiency in scenarios demanding thorough examination and extraction of results from extensive data sources. Emergency calls, due to their gravity and the involvement of rescue and emergency forces, present a scenario well-suited ...
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Background and Objectives: Spatial data mining techniques offer optimal efficiency in scenarios demanding thorough examination and extraction of results from extensive data sources. Emergency calls, due to their gravity and the involvement of rescue and emergency forces, present a scenario well-suited for geographical data mining. Typically, environmental science and geography researchers employ models such as ordinary least squares (OLS) regression to understand spatial relationships between variables. However, OLS has limitations, particularly at the local scale, prompting the utilization of Geographically Weighted Regression (GWR) in this study to address these shortcomings.Methods: This study employs OLS and GWR methods to analyze the relationship between the high volume of emergency calls in Dallas, USA, and the influencing factors. Various statistical tests were employed for evaluation. Dependent variables include the number and dispersion of emergency calls, while independent variables encompass population, education levels, peak call hours, and distance from the city center. Spatial-statistical analysis and mapping were conducted using ArcGIS Pro software.Findings: Results indicate that population, education levels, distance from the city center, and peak call time respectively exert the greatest influence on the occurrence of emergency calls. In the OLS method, Koenker and Jarque-Bera indices, assessing model stationarity and residual normality respectively, did not yield satisfactory results. Evaluation of both OLS and GWR models revealed an R^2 value of approximately 0.61 for GWR and 0.41 for OLS, suggesting greater proximity to reality in the GWR model. Spatially, the weight of population parameter is higher in central city areas, while the weight of peak call time parameter is more pronounced in northern, southern, and western regions. Additionally, the weight of education level parameter is higher in southern parts of the city.Conclusion: Collectively, the identified factors exhibit a cumulative effect on the occurrence of emergency calls, enabling prediction of future occurrences. Leveraging these insights, appropriate tools can be devised for optimal management and control of regional issues.
Original Research Paper
Geo-spatial Information System
M. Rahmati; H. Aghamohammadi; S. behzadi; A.A. Alesheikh
Abstract
Background and Objectives: Traffic accidents are a major public health concern worldwide, causing significant loss of life and property damage. To reduce the number of traffic accidents, it is crucial to identify where and when recurrent accidents occur. These accidents often follow specific spatial ...
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Background and Objectives: Traffic accidents are a major public health concern worldwide, causing significant loss of life and property damage. To reduce the number of traffic accidents, it is crucial to identify where and when recurrent accidents occur. These accidents often follow specific spatial and temporal patterns and may form clusters, representing areas of concentrated accidents within a geographical space. Therefore, analyzing traffic accidents in both spatial and temporal dimensions is essential for determining the most effective and sustainable solutions. Isfahan province is among the provinces in the country with high accident rates in Suburban areas. Previous research conducted in Isfahan province has predominantly relied on statistical methods and has not adequately addressed the spatial and temporal aspects of accidents.This study aims to address the gaps in previous research by determining the spatial and temporal patterns of urban traffic accidents in Isfahan province and visualizing these patterns using spatial statistical methods in a GIS environment. The novel aspect of this research lies in utilizing spatial statistical techniques to identify and analyze the spatiotemporal patterns of urban accidents in Isfahan province at different time intervals and intensity levels.Methods: The spatial and temporal patterns of traffic accidents in Isfahan Province were investigated using suburban traffic accident data from March 2017 to March 2019. After collecting the relevant data, performing necessary preprocessing, and preparing the data, the spatial and temporal patterns of traffic accidents occurring on the main roads, highways, and freeways of the study area were analyzed and identified using spatial statistical methods such as the Average Nearest Neighbor test, Spatial Autocorrelation (Global Moran's I), and Optimized Hot Spot Analysis (Getis-Ord Gi* technique) at different levels in a GIS environment.Findings: Since the aim of this study is to identify the spatial-temporal patterns of suburban traffic accidents in Isfahan Province, the spatial distribution pattern of accident events was first examined using the Average Nearest Neighbor and Spatial Autocorrelation (Global Moran's I) methods. The results indicated the presence of a strong clustering pattern in the traffic accident data during the study years in Isfahan Province. Then, an optimized Hot Spot Analysis was performed on the entire dataset of accidents using the Getis-Ord Gi* method. Subsequently, the analysis was conducted on the dataset of each level separately, considering different levels such as time of day, day of week, month, year, and accident severity level. The results of the Getis-Ord Gi* analysis at different levels showed that the majority of hot spots with a 99% confidence level are located on the routes leading to the provincial center, namely Isfahan City, as well as the neighboring populous cities. These areas experience the highest volume of traffic and congestion, and the accident density decreases significantly with increasing distance from the provincial center.Conclusion: Based on the results of the Spatial Autocorrelation analysis of accidents and the hot spot maps at the studied levels, the results showed that accidents are clustered in some areas of Isfahan Province. Proximity to the provincial center and major populated cities has a significant impact on the concentration of traffic accidents in this region. The frequency of accidents decreases with distance from major urban centers. The results of this study and the insights gained about the spatial and temporal patterns of traffic accidents can be used to develop new strategies, guide transportation managers and stakeholders, make decisions, and take suitable proceedings to effectively improve the safety of accident-prone areas.
Original Research Paper
Remote Sensing
J. Piri; E. Javadnia
Abstract
Background and Objectives: Land subsidence is recognized as one of the most perilous natural occurrences, often resulting from human negligence in water extraction, underground mining, and various other factors. This phenomenon poses a significant threat, specifically in sensitive areas such as railway ...
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Background and Objectives: Land subsidence is recognized as one of the most perilous natural occurrences, often resulting from human negligence in water extraction, underground mining, and various other factors. This phenomenon poses a significant threat, specifically in sensitive areas such as railway systems, where irreparable damage can transpire. Notably, subsidence-induced cracks have emerged along several railway routes, including Tehran-Mashhad, Tehran-Varamin, and Isfahan to Shiraz, jeopardizing the integrity of these lines. Consequently, comprehensive monitoring of subsidence and deformation in both temporal and spatial dimensions becomes imperative for effective event management. To accurately assess the deformation patterns of such phenomena, a thorough analysis of the instantaneous time series within the study region is essential. In recent times, Synthetic Aperture Radar Interferometry (InSAR) has emerged as a widely adopted technique for precisely measuring crustal deformation.Methods: This study focuses on examining the rate of land subsidence along the railway lines in the Tehran region, utilizing InSAR and Sentinel-1 satellite imagery spanning the period from 2017 to 2020. The analysis involved processing a total of 46 images and generating 158 interferograms through the application of time series analysis and employing the Small Baseline Subset (SBAS) technique. GMTSAR software was used to create a time series and a displacement map from the interferograms. To ensure the credibility and comprehension of the research findings, diverse datasets were utilized, including the Iranian Permanent GPS Network for Geodynamics (IPGN) data, the data sourced from the Shamim network of the Land Registry Organization, the measurements from piezometric wells, and the soil characteristics derived from drilling boreholes.Findings: The analysis of the interferometry time series reveals the occurrence of subsidence in specific areas within the case study. The most significant subsidence was observed along the Karaj-Kordan and Maleki-Aprin railway lines, with a deformation rate of approximately 139 mm/year along the line of sight (LOS). Notably, the validation process considering the errors associated with each method yielded relatively satisfactory results. Furthermore, an investigation was conducted to explore the relationship between subsidence, groundwater withdrawal, and soil type. This investigation utilized data from 12 piezometric wells located in the Tehran and Karaj plains, as well as information gathered from drilling boreholes in the study region. The overall findings indicate that the primary cause of subsidence in the region is attributed to a decline in groundwater withdrawal.Conclusion: Upon analyzing the relationship between annual water loss, subsidence, and the soil characteristics within the region, it was determined that the primary cause of subsidence is the withdrawal of groundwater in areas characterized by thick deposits of fine-grained sediments. The proposed approach in this study highlights the effectiveness of utilizing the InSAR technique for initial evaluations of subsidence along linear infrastructures like railway lines. However, it is advised to employ more precise methods in subsidence-affected regions. Given the relatively limited resolution of Sentinel-1 imagery, it is recommended to utilize images with smaller pixel sizes when assessing linear structures such as roads or railways. Additionally, accurate leveling techniques can be employed to enhance the precision and identification of subsidence areas.
Original Research Paper
Remote Sensing
H. Joulaei; A. R. Vafaeinajad
Abstract
Background and Objectives: The issue of urbanization and monitoring of urban expansion and land use changes using satellite images has become a basic focus in the society. Easy and stable access to satellite data has made it possible to monitor and monitor land changes more accurately; But for optimal ...
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Background and Objectives: The issue of urbanization and monitoring of urban expansion and land use changes using satellite images has become a basic focus in the society. Easy and stable access to satellite data has made it possible to monitor and monitor land changes more accurately; But for optimal use of these images, it is necessary to collect samples of images and then classify their pixels based on regional features and characteristics. This process faces challenges such as data dispersion, which can be solved by using appropriate classification methods. In this study, in order to evaluate the area of land uses in cities, various methods of machine learning have been used. Instead of using a fixed and absolute method for classifying pixels, four different machine learning methods are investigated separately for each image. These diverse methods of machine learning provide the possibility of choosing the best and most efficient method for each image, thus improving the ability to detect and classify pixels for land use areas in cities and increasing accuracy and efficiency.Methods: In this research, the Landsat 9 satellite image has been used to study and analyze different areas of Tehran in 2023. First, the desired image was subjected to the necessary corrections and then four appropriate machine learning algorithms (which included K-nearest neighbor, support vector machine, random forest and maximum likelihood) were used to classify Landsat 9 satellite images related to four different areas of Tehran (including 2, 5 , 21, 22) were used. To evaluate the accuracy of the results, more than 200 check points were created on the image using the Stratified Random method, and then Google Earth Pro was used to check the check points. The overall classification accuracy and kappa coefficient were evaluated as evaluation criteria for the best classification method of image pixels. In the next step, the studied area was divided into equal blocks in order to better understand the area of land uses in that area. Then, using Zonal Statistics, the amount of land use area in each block was investigated.Findings: Based on the methods used, the performance of the SVM method in this study achieved the highest possible accuracy, which is equal to 95%, and the Kappa coefficient, which is 89%. These results may be justified due to the non-uniformity of pixel areas in dense urban environments. In addition, different areas of land, including green areas with an area of 12 square kilometers, barren lands with an area of 64 square kilometers, and built-up areas with an area of 137 square kilometers were also examined in this analysis.Conclusion: Through this approach, we have presented a highly accurate classification method for the analysis of satellite images related to the Landsat 9 satellite. This method enables a more accurate assessment of the area of land uses and provides urban decision makers and policy makers with a direct link with valuable insights for sustainable development in cities. This can play an effective role in the process of facilitating development plans to improve cities and citizens' lives, because it provides accurate and reliable information that helps strategic decisions in the field of urban development and enables more effective and targeted changes in urban policies and programs.
Original Research Paper
Remote Sensing
Sh. Felegari; K. Moravej; A. Sharifi; A. Golchin; P. Karami
Abstract
Background and Objectives: Every country relies on soil as a vital natural resource that significantly contributes to environmental conservation and food production. Preparation of soil nutrient distribution map serves as a valuable tool for managers to make decisions. Due to the time-consuming and expensive ...
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Background and Objectives: Every country relies on soil as a vital natural resource that significantly contributes to environmental conservation and food production. Preparation of soil nutrient distribution map serves as a valuable tool for managers to make decisions. Due to the time-consuming and expensive nature of laboratory analysis for these variables on a large scale, efforts have been made to explore soil nitrogen through remote sensing. The current research deals with the application of remote sensing methods along with regression and random forest models to predict total soil nitrogen in Gilan province. This study aimed to answer two main questions: (1) Can SAR data be used to quantify total soil nitrogen (TSN) (2) How do SVM, BRT and RF algorithms perform in predicting soil nitrogen content?Methods: This study focused on evaluating the data capabilities of Landsat-9 and Sentinel-1 satellites individually and in combination, using advanced algorithms such as Support Vector Machine (SVM), Boosted Regression Tree (BRT), and Random Forest (RF). The purpose of this evaluation was strategic, aiming to showcase the diverse conditions of the study area based on land cover/land use, climatic, and topographical parameters. Various variables, including climate parameters, topographic components, and remote sensing subscale indices, were investigated in conjunction with SAR data and optical images. Nonlinear machine learning algorithms, specifically SVM, RF, and BRT, were employed to predict total soil nitrogen status by modeling complex relationships between soil properties and environmental variables. R software, utilizing the CARET package for parameter input, was employed to implement the algorithm.Findings: The results indicated the following: RF and BRT algorithms outperformed SVM and were effective in monitoring total soil nitrogen values. Multi-temporal SAR images showed higher accuracy in monitoring total soil nitrogen content compared to optical remote sensing data, facilitating more realistic predictions in paddy soils. The integration of environmental variables led to an increase in the accuracy of algorithms, where remote sensing variables played a crucial role, contributing to 61% and 51% effects in RF and BRT algorithms, respectively. The comparison of SVM and RF algorithms revealed that RF ranked second after the BRT algorithm, and the accuracy of total soil nitrogen estimation was not achieved with the SVM algorithm. However, both BRT and RF algorithms were able to monitor changes in total soil nitrogen. BRT performed better, accurately recording 58% of changes, as evidenced by a higher R2 value (0.58) and lower RMSE (0.25 mg/kg) and MAE (0.19 mg/kg) values.Conclusion: In conclusion, the following key points were extracted from this research: 1) RF and BRT algorithms outperformed SVM in effectively monitoring total soil nitrogen levels; 2) multi-temporal SAR images demonstrated higher accuracy in tracking total soil nitrogen compared to optical remote sensing, enabling precise predictions in paddy soils; 3) the incorporation of environmental variables enhanced algorithmic accuracy; and 4) remote sensing variables contributed 61% and 51% to RF and BRT algorithms, respectively.
Original Research Paper
Geo-spatial Information System
H. Babaeifard; S. Sadeghian; A. Gharagozlo
Abstract
Background and Objectives: Unauthorized construction activities in forest lands have raised significant concerns. Environmental cadastre is regarded as a fundamental tool for providing precise spatial, legal, and environmental information, enabling close monitoring, land management, and sustainable development.Methods: ...
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Background and Objectives: Unauthorized construction activities in forest lands have raised significant concerns. Environmental cadastre is regarded as a fundamental tool for providing precise spatial, legal, and environmental information, enabling close monitoring, land management, and sustainable development.Methods: In this study, a combination of qualitative and quantitative research methods has been employed. The qualitative approach included the analysis of the legal and supervisory framework of the cadastre and the examination of institutional arrangements and stakeholder participation. On the other hand, the quantitative approach involved spatial data analysis and cadastre implementation to assess changes in land use and villa construction in forest lands. To identify villa constructions in the forest regions of Laloon village, data collection tools such as Google Earth images were utilized. Subsequently, using supervised maximum likelihood algorithms, classified images were generated and a land use map was prepared. Finally, a 1:5000 cadastral map of Laloon village was created and the implementation idea of an environmental cadastre in this area was examined.Findings: Significant changes were observed in land use patterns in the village of Laloon between the years 2011 and 2024. The increase in residential areas on forest lands indicates undesirable environmental impacts. These changes have endangered the ecosystem and biodiversity of the region and led to population growth. In this study, the Kappa coefficient for the years 2011 at 0.902 and for 2024 at 0.945, has been evaluated as an indicator of land use differences in Laloon village. Furthermore, the overall accuracy of the research was calculated to be 92.703% for 2011 and 96.405% for 2024. The results indicate the high importance and accuracy in detecting land use changes in Laloon village. The assessment of the implementation of environmental cadaster for villa construction in forest areas of Laloon village shows the need for effective land management strategies, and the performance of the environmental cadaster system is capable of detecting land changes and conservation planning. This research emphasizes the importance of incorporating environmental considerations into cadastral mapping processes.Conclusion: Based on the findings of this study, optimal use of environmental cadastre in Iran for the protection of natural resources and preservation of habitat sustainability is considered as a very vital action. Adhering to environmental laws and regulations during the execution of development processes and construction of structures is universal and undeniable for everyone. Implementing environmental cadastre requires collaboration among governmental organizations, technical expertise, utilization of advanced technologies, and community participation. This research indicates that implementing environmental cadastre plays a significant role in maintaining a balance between development and environmental protection. To enhance the efficiency of the environmental cadastre system in Iran, it is suggested that actions such as strengthening the legal framework, improving data management, upgrading technology, increasing stakeholder participation, and ensuring sustainable financial support be taken. These actions include drafting specific laws and regulations, establishing a centralized and powerful database, forming a multi-stakeholder advisory committee with the participation of various entities, and allocating sustainable financial resources for the advancement and development of the environmental cadastre.0.
Original Research Paper
Remote Sensing
H. Ashoori
Abstract
Background and Objectives: Texture quantization is a useful method for extracting spatial relevance between pixels, which is used in the human brain for image interpretation. Aside from spectral bands, textural features of high spatial resolution image can be used to improve classification accuracy. ...
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Background and Objectives: Texture quantization is a useful method for extracting spatial relevance between pixels, which is used in the human brain for image interpretation. Aside from spectral bands, textural features of high spatial resolution image can be used to improve classification accuracy. Finding proper textural features among available features is important for special case studies.Methods: In this paper, two methods based on genetic algorithm (GA) are introduced to choose efficient features. The first is binary GA, which improves classification accuracies through selecting the best textural features. The second one is GA with a variable number of selected features in a refined and full feature space. Results show that the best combination does not necessarily consist of features with improved individual accuracy.Findings: The proposed methods have better accuracy, less number of features, and less computational time when comparing with the simple GA. They could be used based on the number of spectral bands, number of generated features, and train and check pixel number. Second method needs more prerequisite time and could be used for images with fewer bands, train and check pixels, and generated features, because increasing these items increase computational time very much. Second method could be used in large images with more train and check pixels but led to more selected features.Conclusion: Results obtained on three datasets indicate 7.7 to 50.48 percent improvement in mean accuracy.
Original Research Paper
Geo-spatial Information System
M. Nagahi; S. Behzadi
Abstract
Background and Objectives: Drought is a persistent and critical challenge that affects many countries around the world, including Iran. This natural phenomenon can have severe economic, social, and environmental consequences, making the study and prediction of drought an important focus for researchers ...
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Background and Objectives: Drought is a persistent and critical challenge that affects many countries around the world, including Iran. This natural phenomenon can have severe economic, social, and environmental consequences, making the study and prediction of drought an important focus for researchers and experts. The primary objective of this paper is to predict drought using an expert system and to find an appropriate behavioral model for this phenomenon across all provinces of Iran. Drought is a complex and multifaceted issue that can have far-reaching impacts. In Iran, where water scarcity is a longstanding concern, drought can exacerbate existing challenges and lead to significant disruptions in various sectors, such as agriculture, water supply, and energy production. Accurate and timely prediction of drought can help policymakers and stakeholders implement effective mitigation and adaptation strategies, thereby minimizing the adverse effects of this natural disaster.Methods: This study utilized data related to drought in all provinces of Iran from 2009 to 2021. These data include various drought indices, such as precipitation, temperature, humidity, and climate change indicators. Using these data, the researchers developed monthly behavioral models of drought for each province, employing an expert system and artificial intelligence techniques. The researchers first examined the drought patterns and trends in each province to identify suitable behavioral models. This process involved analyzing the historical data and identifying the key factors that influence drought patterns in the different regions of Iran. By leveraging the expertise of domain experts and the capabilities of advanced analytical tools, the researchers were able to construct comprehensive behavioral models that capture the complexity of drought dynamics. The development of these monthly behavioral models for each province was a critical step in the research process. By modeling the drought patterns at a granular, provincial level, the researchers were able to account for the unique geographic, climatic, and socioeconomic characteristics of each region. This approach enabled the creation of tailored predictions that can be more effectively utilized by decision-makers at the local and provincial levels.Findings: The results of this study demonstrated that the use of drought data from all provinces of Iran and the development of monthly behavioral models can indeed facilitate the prediction of drought in each province on a monthly basis. The researchers were able to produce twelve behavioral models for each province, representing the probability of drought occurrence in different months. These models can serve as powerful tools in managing and planning to combat drought at both the provincial and national levels. By providing accurate and timely predictions of drought, policymakers and stakeholders can make more informed decisions regarding water resource management, agricultural planning, and disaster response strategies. The findings also highlighted the importance of an integrated, expert-driven approach to drought prediction. By leveraging the expertise of domain experts and the capabilities of advanced analytical tools, the researchers were able to develop comprehensive and reliable behavioral models that capture the nuances of drought dynamics in Iran.Conclusion: The findings of this study have significant implications for drought management and decision-making in Iran. By using an expert system and behavioral modeling of drought across all provinces, the researchers were able to achieve more accurate and timely predictions of this phenomenon. The linear model was selected as the best model, and an online web-based map was created to display the probability of drought for each province on a monthly basis. This web-based tool can serve as a valuable resource for policymakers, stakeholders, and the general public, facilitating informed decision-making and drought management at various levels. The availability of this information can lead to the reduction of the impacts and consequences of drought, enabling more effective planning and mitigation strategies to be implemented. The comprehensive and systematic approach used in this study can be replicated in other regions or countries facing similar drought-related challenges. By leveraging the power of expert systems, artificial intelligence, and behavioral modeling, researchers and policymakers can work together to develop robust drought prediction and management frameworks that enhance resilience and sustainability in the face of this critical natural phenomenon.
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