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.
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.
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.
Extraction, processing, production and display of geographic data
Seyed Hossein Mirmousavi
Abstract
Extended Abstract
Introduction
In hydrological drought, water scarcity spreads through the hydrological cycle and can subsequently reduce groundwater levels, surface water and lake levels, and this means that hydrological drought dominates those areas, leading to long-term effects. In addition, due ...
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Extended Abstract
Introduction
In hydrological drought, water scarcity spreads through the hydrological cycle and can subsequently reduce groundwater levels, surface water and lake levels, and this means that hydrological drought dominates those areas, leading to long-term effects. In addition, due to climate changes and rainfall and temperature anomalies, droughts have increased in frequency and severity in many regions of the world. The predicted changes for the coming years show that climate variables will not have uniform changes in all regions and regional changes in the amount of precipitation may lead to the creation of hydrological patterns much different from the current conditions.
The present study was also carried out with the aim of spatial analysis of drought effects on water level changes in the catchment area of Bakhtegan, Tashk and Maharlo lakes. In this research, an attempt has been made to identify temporal and spatial patterns of changes in the level of this lakes by using satellite images and spatial analysis models.
Materials and Methods
In the present study, Landsat 5(TM), 7(ETM+) and Landsat 8(OLI) satellite images with a resolution of 30 meters have been used in the period of 2000-2021 to investigate water level changes. Due to the fact that the water level of the studied lakes changes drastically with the rainfall of different months, therefore, it is difficult to determine the amount of water cover for a year without considering the fact that a part of this cover is seasonal and when the rainfall decreases, a part of the lake Dry may not provide accurate results. Based on this, in the present study, one image was used for each month for each year studied to evaluate the changes in the water level of the lakes in all months of the year.
Conclusion and Discussion
The investigation of the changes in the water level of Maharlo Lake shows that in the drought of 2108 and 2017, the permanent water level of the lake has decreased to 1.8 square kilometers. Meanwhile, in the severe and very severe drought of 2005 and 2004, the permanent water level reached 170.4 square kilometers. Examining the changes in the area of Tashek Lake in 15 years of drought shows that the area of the waterless part of this lake has increased more than the seasonal and permanent water. The highest amount in this field was in 2021 with a very severe drought, which shows that this lake has more critical conditions in terms of permanent dryness than Maharlo Lake. This lake has been in a terrible state for 5 years. Comparing the changes in the area of Bakhtegan lake in different years shows that this lake has a more critical situation than its neighboring lakes (Maharlo and Tashk), so that in a significant number of years (12 years) the lake lacked permanent water and only With monthly or seasonal rains, some water has been temporarily collected on its surface, but it has a short shelf life between 2 to 6 months (November to May).
Results
The results of the evaluation and analysis of the role of drought in the water level changes of the Bakhtegan, Tashk and Maharlo catchment lakes showed that the area of these lakes has decreased significantly during the studied period, so that over time the area of the water area has decreased. It has been permanently reduced and added to the dry and waterless area. The maximum decrease in the water level of all three investigated lakes occurred during a 6-year drought between 2008 and 2013, in such a way that the area of the part with permanent water was greatly reduced and the area of the dry part of the lakes was increased.
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.
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.
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.
Extraction, processing, production and display of geographic data
Misagh Sepehry amin; Hassan Emami
Abstract
Extended AbstractIntroductionA digital orthophoto is a reliable, accurate, and low-cost map for acquiring knowledge, including geolocation, distance, area, and changes in imagery features. It is now considered one of the most widely used and sophisticated digital photogrammetry products. Orthophoto map ...
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Extended AbstractIntroductionA digital orthophoto is a reliable, accurate, and low-cost map for acquiring knowledge, including geolocation, distance, area, and changes in imagery features. It is now considered one of the most widely used and sophisticated digital photogrammetry products. Orthophoto map creation is substantially faster than traditional topographic map production because of the development of powerful algorithms for processing aerial, drone, ground, and satellite imagery. To begin, orthophoto is a result of photogrammetry processing that employs the Digital Terrain Model (DTM), which is commonly observed in classic aerial photogrammetry. In orthophotos, you will frequently notice an effect in which the terrain representation is very accurate, but there is a tilt in the buildings and other tall structures, which is caused by the use of DTM, which only maps the natural shape of the earth, excluding vegetation and all man-made objects and structures. A true orthophoto map provides a vertical view of the earth's surface, eliminating building tilting and providing access to practically any location on the ground. Traditionally, measuring digital surface models has been highly complex and costly. It is generally accomplished through the use of LiDAR or ground measurements. The end result of drone photogrammetry is known as an orthomosaic. In actuality, an orthomosaic is comparable to a true orthophoto (since it is formed using a digital surface model), but it is often not based on a metric camera with accurate focal length and internal dimensions, as they are expensive and not readily accessible for UAVs. Furthermore, orthomosaics may be generated using both nadir and oblique images. Drone-based orthomosaics are created based on the digital surface model rather than as a separate survey like traditional aerial photogrammetry. The DSM is produced by drone photogrammetry based on the 3D point cloud, which is the initial output of data processing. Materials & MethodsThe huge success of online services like Google Earth, Google Maps, Bing Maps, and so on increased demand for orthophotos, resulting in the development of new algorithms and sensors. It is commonly understood that orthophoto quality is determined by image resolution, camera calibration, orientation accuracy, and DTM accuracy. Because digital cameras produce high-resolution imagery, one of the most important consequences in orthophoto generation is the spatial resolution of the DTM: standing objects, such as buildings, plants, and so on, exhibit radial displacement in the final orthophoto. In practical applications, orthophotos are utilized as small and medium scale maps; updated earth surface maps; three-dimensional urban scene reconstruction; village surveying; land planning; precision agriculture; desertification monitoring; land use surveying; and other sectors. True orthophotos are orthophotos that have been improved to minimize tilt inaccuracy and projection discrepancies. The true orthophoto is exceedingly stringent with the original image; the heading overlap and side overlap are at least 80% and 60% overlap, respectively. Due to the reduction of displacements produced by camera tilt and height difference, the use of orthophoto as a spatial data format with high geometric accuracy has found growing applications in recent years. With the growing relevance of geographic information systems, particularly in metropolitan areas, the use of orthophoto in conjunction with spatial data has grown. Because orthophoto contains correct spatial and textural information about complications, it is feasible to produce virtual reality by integrating it with 3D models, where it is able to properly quantify the height and plane location of complications during 3D viewing. In this research, a novel approach for generating orthophotos from Google Earth imagery for specific purposes was developed and qualitatively and quantitatively compared to orthophotos created from UAV images.Results and discussionThe result demonstrated the total error of orthomosaic generation from Google Earth imagery and UAV data to be 0.124 and 0.059 m/pixel, respectively. Moreover, the visual findings reveal that the edges of low-height barriers in the orthophoto generated from Google Earth images are superior to those in the orthophoto generated from drone imagery, but the edges of high-height obstacles, particularly those with noticeable shadows, are of poor quality. The findings of statistical parameters in quantitative surveys using randomly selected points in non-building regions revealed that the errors in the orthophoto derived from Google Earth data are 1.10 meters and 1.34 meters in terms of mean error and root mean square error (RMSE), respectively. In addition, the orthophoto generated from UAV data and Google Earth showed a 95% correlation and a 91% determination coefficient. In contrast, in building regions, the average height error and average square root error in the orthophoto generated from Google Earth data compared to UAV data were around 9 meters and 5 meters, respectively. Statistical metrics in these locations also revealed a low correlation of 80% and a determination coefficient of 65%.ConclusionsIn this research, a novel approach for generating orthophotos from Google Earth imagery for specific purposes was developed and qualitatively and quantitatively compared to orthophotos created from UAV images. As a result, as the height of the obstacles and the presence of lengthy shadows increase, so does the inaccuracy of the height component of the orthophoto derived from Google Earth imagery. Therefore, it is advised that orthophotos for special applications, flat regions, and hills be created using Google Earth images. Additionally, Google Earth data offers the following advantages: free of charge; the utilization of historical imagery to generate orthophotos; and nearly four times less processing time and volume.
Extraction, processing, production and display of geographic data
Ali Hasankhani; Mahdi Modiri; Ahmad Naghavi
Abstract
Extended AbstractIntroductionUnfortunately, seismic data recorded globally during the last fifty years does not include every type of wave propagation conditions in the environment, types of construction, the rupture process on the fault, and the geometrical relationship between the construction and ...
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Extended AbstractIntroductionUnfortunately, seismic data recorded globally during the last fifty years does not include every type of wave propagation conditions in the environment, types of construction, the rupture process on the fault, and the geometrical relationship between the construction and the fault. This is especially seen in near-field regions. Before the 1999 Chi-chi earthquake in Taiwan and the 1999 Izmit earthquake in Turkey, there were only about 20 records of earthquakes with a magnitude greater than 7 at a distance of less than 20 kilometers from the fault.The Turkish earthquake added 5 records and the Thai earthquake added 65 records to this collection, but only two fault rupture scenarios were added to our knowledge, while thousands of other possible scenarios may occur. Thus, seismologists and earthquake engineers have tried to estimate parameters related to strong near-field motions of the earth with an acceptable confidence using various experimental and theoretical simulation methods. MethodsIn earthquake engineering and seismology, earthquake phenomenon and the resulting movements are generally investigated and analyzed using dynamic and kinematic methods. Seismological models and problems are thus divided into two categories: Kinematic models which are based on slip distribution and do not take the state of stress on the fault into account. Dynamic models deal with the physics of fault rupture and its causes. Simulation methods are also divided into three main categories: deterministic (low frequencies), stochastic (high frequencies) and hybrid (broad band) methods.Generally speaking, simulating strong ground motion plays an important role in the estimation of related parameters especially in regions lacking such data. Accelerographs are used to simulate strong ground motions. The present study has introduced, investigated and validated two methods: decisive simulation models (Discrete-Wave Number and Finite Fault) and Finite Fault models. It also explains how the simulated recording are produced for near-field (less than 20 km to a seismogenic fault) and far-field events, presents attenuation relationships for the Zagros seismotectonics region, and predicts parameters of strong ground motions.Results & DiscussionDue to the special geological conditions and the existence of many active faults in Iran, our country is considered to be located in an earthquake-prone region. Zagros region is considered to be the most earthquake-prone region of Iran. Finite fault modeling combines various aspects of plate source with the ground motion model based on point source. Since previously mentioned limitations are not naturally present in finite fault modelling, the method takes geometry of the fault and the directivity effect into account. Time delay method and the sum of accelerations recorded in maps of a two-dimensional network are used for simulation in finite fault model. The fault plate is divided into various elements and a minor event is simulated for each one. The overall seismic acceleration equals the sum of the effects of these minor events. The strong ground motions in each micro-fault are calculated using the random point source method and then summed up at the desired point with an appropriate time delay to obtain the ground motion of the entire fault.Previous geological and seismic studies of each seismic region are used to determine the key parameters of the simulation input. To produce a comprehensive database, a significant number of stations are taken into account around the fault based on different hypotheses and artificial accelerograms are produced in accordance with the seismological parameters of the region. A suitable function is then selected and an attenuation relationship is fitted. The simulation results and the resulting attenuation relationship are then compared with valid global attenuation relationships and their consistency (compliance percentage) is investigated. ConclusionThe present study has produced a wide range of simulated records (about 20 thousand records) for Zagros seismotectonics region. Thus, the resulting relationships will hopefully have sufficient accuracy and efficiency to be used in structure designing and urban development. It worth noting that the regression correlation coefficient (R-Square) was above 0.95 in all fits.These attenuation relationships can provide a new perspective on site selection, and help us in understanding the dynamic behavior of structures, and the development of various infrastructure. They also help urban managers to predict and reduce earthquake damages.
Extraction, processing, production and display of geographic data
Mohammadhasan KorkiNezhad; Aliakbar Shamsipour; Kyoumars Habibi
Abstract
Extended AbstractIntroductionCity is a living, dynamic being evolving over time in the context of physical and anthropogenic components and complex relationships between them. It is the reflection of the role and attitude of man-kind influenced by social, economic, political, cultural and geographical ...
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Extended AbstractIntroductionCity is a living, dynamic being evolving over time in the context of physical and anthropogenic components and complex relationships between them. It is the reflection of the role and attitude of man-kind influenced by social, economic, political, cultural and geographical factors and conditions. Increased population and density in urban areas have far-reaching consequences, such as increased consumption of natural resources, land-use changes, climate change, and disruptions in the exchange of material and energy. Consequently, cities face many issues and problems, the most important of which are issues related to urban design. These include poor ventilation, high heat load, air pollution caused by the physical characteristics of cities, and insufficient attention to the capabilities, natural characteristics and climate of the region and the city.Data and MethodsThe present study seeks to prepare an urban climate analysis map to study and analyze spatial and climatic information collected from Tehran. Urban Climate Map (UCMap) is an information-based and analytical tool that combines factors of urban climate with urban planning factors and some environmental conditions to provide an image of urban climate issues in a two-dimensional environment. Urban climate map consists of an urban climate analysis maps (UCAnMap) and an urban climate recommendation map (UCReMap). Urban climate analysis maps apply various spatial information layers of heat load maps such as building volume, urban topography and green space along with layers of land cover, natural landscape, and proximity to open spaces in dynamic capacity maps. The proposed model is generally based on the evaluation and analysis of variables affecting climatic conditions. Based on six layers of building volume, land cover, topography, proximity to open spaces, green space, and natural landscape, maps were prepared in Arc/GIS10.4.1 environment for Tehran urban area. To eliminate the unit and reach comparability and overlap, the layers were standardized and used to prepare maps of ambient heat load and dynamic capacity.Results and DiscussionThree layers of building volume, topography, and green space were weighted and combined to create a heat load map. The other three layers of land cover, natural landscape, and proximity to open spaces were also combined to create a dynamic capacity map. Afterwards, these two maps were combined to create an UCAnMap. The resulting map was close to the on the ground realities. For example, building volume has a negative effect and increases heat load in urban areas. On the other hands, green space reduces heat load and has a positive effect. The central and southwestern parts of the city have a high heat load and core areas of the urban heat island have been calculated and obtained in these areas. The resulting map was classified into 8 categories to create urban climate analysis map of Tehran.ConclusionResults indicated that 59% of the urban area in Tehran, mostly located in the northern part of the city, has a good cooling and ventilation condition while 19% of the study area, mainly in the central, southern, and southwestern parts, faces heat stress and lacks an appropriate air ventilation condition. 22% of the study area, scattered all over the city but mostly located in the northern, western and eastern parts, faces an intermediate condition. According to the calculated heat load map, the central, southern, and western parts (in region 21) of the study area face a high and unfavorable ambient heat load. And many parts of the 4th, 1st, 2nd, 5th, and 22nd urban districts are characterized with low ambient heat load and favorable climatic conditions.
Extraction, processing, production and display of geographic data
Qadir Ashournejad
Abstract
Extended AbstractIntroductionRemote sensing is considered as the most important source of spatial data in the current era, which we witness its increasing development in different dimensions. The release of global products of these data in recent years with the aim of easier access and use by experts ...
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Extended AbstractIntroductionRemote sensing is considered as the most important source of spatial data in the current era, which we witness its increasing development in different dimensions. The release of global products of these data in recent years with the aim of easier access and use by experts in geospatial science is one of the dimensions of this development. The land cover product is one of these products that is used more than other remote sensing products. When presenting these products, their qualitative and quantitative characteristics, including their global accuracy, are also published. Expressing the accuracy of these products globally makes it necessary and necessary to re-evaluate their accuracy regionally for the users of these products in different regions of the world.Materials & MethodsIn this research, the accuracy of the European Space Agency's Copernicus Global Land Service (CGLS), GlobeLand30 and Esri's land cover product were evaluated for regional use in the north of Iran - Mazandaran province. After calculating the area of the classes for each of the land cover products, Pearson's correlation coefficient was used to calculate the correlation between them. For quantitative evaluation, the error matrix was used as one of the most common ways to evaluate the accuracy of land cover products. This method is based on the comparison of classified data and ground reality data. Also, the categorized random sampling method was used to select 1329 evaluation samples in Mazandaran province. For visual evaluation, three areas with dimensions of 6 x 6 km were selected.Results & DiscussionThe regional accuracy evaluation of the studied products shows opposite results compared to the global accuracy of these products. Based on the global accuracy reported for the studied products, the highest accuracy is calculated for the Esri product at 86%, followed by GlobeLand30 and CGLS at 83-85 and 80%. Meanwhile, based on the regional accuracy obtained from the results of this research, the highest regional accuracy for the CGLS product has been calculated at 84% and then for GlobeLand30 and Esri products at 81 and 75%. In evaluating the regional accuracy of the classes, all three studied products (CGLS, GlobeLand30 and Esri) have acceptable accuracy (above 90%) in the classes of snow and ice (100, 100 and 100%), forest (90, 95 and 98 percent), water (96, 94 and 90 percent) and impervious surface (94, 91 and 90 percent). For the agricultural class, accuracy equal to 92, 69 and 84% was obtained for CGLS, GlobeLand30 and Esri land covers.In the 3 classes of shrubland, Impervious surface and wetland, the accuracy results are less than other classes for all three land cover products and in the amount of (29, 0 and 13 percent), (65, 66 and 42 percent) and (67, 38 and 0 percent).Conclusion By evaluating and comparing the regional accuracy of three CGLS products, GlobeLand30 and Esri, this research answered the question of whether the accuracy stated in global land cover products can be trusted for regional studies and planning. The results show that the regional accuracy of CGLS, GlobeLand30, and Esri are 84, 81, and 75 percent, respectively, compared to their global accuracy (80, 83, 85, and 86 percent). These results show the difference obtained for the Esri product more than the two products CGLS and GlobeLand30. Meanwhile, the remote sensing data used for the Esri product (Sentinel-2 data) and its pixel size (10 meters) are of higher quality and quantity than the other two products. In fact, these results show that only paying attention to the type of data used and the global accuracy is not enough to use products in regional scales and requires evaluations before using them.In addition, by evaluating the classes of each product and comparing them, the need for this evaluation before using these products seems necessary. The results showed that in the evaluation of the regional accuracy of the classes, all three studied products had an accuracy of over 90% in the classes of snow and ice, forest, water areas and human construction. For the agricultural land class, accuracy equal to 92, 69 and 84% was obtained for CGLS, GlobeLand30 and Esri land covers. In the 3 classes of shrubland, herbaceous cover and wetland, the results show lower accuracy than other classes for all three land cover products. Significant results were also obtained in the visual evaluation, and it seems necessary to pay attention to this evaluation before the applications where it is important to pay attention to a particular class.
Extraction, processing, production and display of geographic data
Seyed Hossein Mirmousavi
Abstract
Extended AbstractIntroductionThe planetary boundary layer (PBL) as the lowest part of the troposphere is the most dynamic part of the atmosphere that is directly affected by the interactions of the atmosphere and the surface of the Earth (Stell, 2012 and Gert, 1992). These atmospheric surface interactions ...
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Extended AbstractIntroductionThe planetary boundary layer (PBL) as the lowest part of the troposphere is the most dynamic part of the atmosphere that is directly affected by the interactions of the atmosphere and the surface of the Earth (Stell, 2012 and Gert, 1992). These atmospheric surface interactions occur in short periods of time and play an important role in the development of the boundary layer. The height of this layer is also influenced by atmospheric conditions, topography characteristics, and type of land cover, and is an important parameter for many meteorological phenomena that have various applications such as monitoring air quality, cloud formation and evolution, surface fluids, and atmospheric hydrological cycles (Garrett 1994). Since the height of the boundary layer indicates the depth of turbulent vertical mixing, it is very effective in increasing or decreasing the concentration of pollutants near the surface and is considered as an essential parameter in air quality monitoring (Su and Khan, 2018). In addition, the height of this layer is a key factor in numerical weather forecasts. Since the height of the base of clouds is usually close to the height of the boundary layer, this layer determines the extent of cloud development and causes the transition from shallow convection to deep in the clouds. MaterialsThe data used in this study included re-analysis data on the monthly time scale of the planetary boundary layer height for the entire Iranian region with a resolution of 0.25×0.25 which was obtained from the ERA5 version of ECMWF site during the period 1959-2021. In order to analyze the relationship between different climatic variables (mean temperature, mean relative humidity and air pressure), the meteorological data of 187 synoptic weather stations during the statistical period 2000-2022 has been used.MethodsIn this study, in order to prepare the data using programming capabilities in MATLAB software, maps with an average of 62 years old have been prepared and then using ARC GIS software to map the monthly average height of the boundary layer in Iran. In the next step, spatial statistics index of Getis-Ord Gi* was used to analyze the spatial changes in the height of the boundary layer in different months. In order to analyze the effective variables in elevation changes in the boundary layer temperature, relative humidity, soil moisture, etc. Multivariate standard regression method was used.Conclusion and DiscussionThe annual average elevation map of the boundary layer also shows that the maximum height of this layer in Iran is 1600 m which is located in the south of Iran in Kerman province and south of Sistan and Baluchestan province and in general, the southern half of Iran with the exception of a narrow strip of southern coasts is higher than the northern half. The lowest elevation between 520 and 1000 meters is mainly located in the northern half, the eastern part and a narrow strip of southern coast. The average height of the entire boundary layer of Iran during the year is 1131 meters. The height of the boundary layer in different months of the year has significant changes in Iran and in terms of spatial changes it follows severe cluster patterns. Analysis of hot and cold spots showed that the spatial distribution of the height of the boundary layer has completely homogeneous spatial patterns so that the northern half of the country, especially the northwest and northeastern regions of the country, have a high significance as cold spots in most months of the year.ResultsThe results of this study showed that the elevation of the boundary layer in Iran during the year has a lot of spatial and temporal changes due to geographical diversity and climatic characteristics in different regions of the country. The existence of diverse topography, expansion in latitude, large differences in relative moisture content and soil moisture content are among the factors that have caused significant changes in the height of the boundary layer at different times and places. The results of multivariate regression analysis showed that the height of this layer is mainly affected by six parameters in particular, temperature and relative humidity.
Extraction, processing, production and display of geographic data
Mahdi Ebrahimi Boozani; Asghar Norouzi; Hengameh Khaksar
Abstract
Extended AbstractIntroductionPassive defense refers to a set of non-armed actions and activities which reduces the vulnerability of buildings, manpower, facilities, equipment, funds and vital arteries of the country against destructive and hostile operations of the enemy as well as natural disasters ...
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Extended AbstractIntroductionPassive defense refers to a set of non-armed actions and activities which reduces the vulnerability of buildings, manpower, facilities, equipment, funds and vital arteries of the country against destructive and hostile operations of the enemy as well as natural disasters such as earthquakes and floods. The Including passive defense policies in most countries of world and especially Iran is Building public shelters to protect and maintain of Citizens' lives, also minimizing possible damages in the human domain. But what is important in Proportion with the proposed policy, location and choice of place is suitable for the construction of urban shelters. That People can take refuge to these shelters during enemy attacks or during natural crises. For this end, the aim of doing this study is locating potential areas of urban shelters based on passive defense principles in Ilam city. Materials & MethodsPresent research In terms of the purpose, is of the type applied research and in terms of the nature and method of investigation, is of the type descriptive-analytical. Also, in terms of the data collection method, is included in the category of documentary and field research. The statistical population studied is including experts, professors and experts working in academic centers and higher education institutions. Sample size for research based on pairwise comparisons it's limited According to Saati (2002) and are selected Minimum 5 and maximum 15 people for this type of studies. Therefore, in this research, 15 university professors and experts were selected by available sampling method. To weight to information layers used from Saati's 9-option spectrum (superiority of one criterion over another) in the form of a questionnaire and a plan of language expressions. In this Research selected 10 location index of urban shelters (Distance from densely populated places, Distance from the centers of population, Distance from the canal, river and surface water, slope of the land, Distance from vulnerable areas and worn tissue, Distance from main roads for access and movement, Distance from historical and cultural monuments, Distance from industrial centers and hazardous products, The distance from the target centers of enemy and Distance from centers with support functions in times of crisis) in the form of four general criteria (Demographic, functional, physical and natural-environmental). In the next step was determined Coefficients of importance of indicators and criteria using the network analysis process technique (ANP), Eventually has been identified the most preferred places In proportion to the purpose through overlapping layers of information and applying the obtained coefficients. Data analysis has been done in a descriptive-analytical way and Using Analytical Network Process (ANP) and also by using Excel, Super Decisions and GIS software.Results & DiscussionThe research results show that: Among the general criteria studied, two demographic and functional criteria in order with weights 0.427 and 0.305 and among the studied indicators, two indicators Proximity to densely populated places and Establishing at a suitable distance from the enemy's targets in order with weights 0.303 and 0.236 have been highest coefficients of importance. In the following Results of combined analysis GIS- ANP showed that: All four urban areas of Ilam (including Haniwan, Ostandari, Markazi, Banborz, Sabzi Abad, Nowruz Abad, Janbazan and Razmandegan districts) is prone to shelter construction, But is in priority Respectively Region 2 (Banborz and Sabzi Abad districts), Region 1 (Haniwan and Ostandari) and Region 3 (Nowruz Abad).ConclusionExamining the first question based on Current status of urban shelters in Ilam city show that, most urban shelters located in the average status from the aspect of spatial distribution. The result of second question based on identification most important indicators affecting on location of urban shelters show that, two demographic and natural-environmental criteria identified as the most important and least important effective criterion in Location of urban shelter respectively with weights of 0.427 and 0.056. Eventually the results of third question based on identification best places to build urban shelters in Ilam city show that, most suitable place to build urban shelter situated in Haniwan, Ostandari and central districts of region 1, Banborz and Sabzi Abad districts of region 2, Nowruz Abad district of region 3 and Janbazan and Razmandegan districts of region 4.
Geographic Data
Majid Goodarzi; Farkhondeh Hashemi Ghandali
Abstract
Extended AbstractIntroductionUrbanization is a developing phenomenon, and the analysis of the appropriate location and the geographical distribution of urban green space plays a significant role in the development and future of the city. Although in the past, green spaces were primarily manifested in ...
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Extended AbstractIntroductionUrbanization is a developing phenomenon, and the analysis of the appropriate location and the geographical distribution of urban green space plays a significant role in the development and future of the city. Although in the past, green spaces were primarily manifested in the beautification and appearance of urban areas, nowadays, for several reasons, it is considered as a breathing space of the cities. The growth of industry and the increase in population in cities have led to speculative constructions that do not pay enough attention to health issues, provision of sufficient light and healthy air, and leisure spaces in buildings. Moreover, the necessity of creating new urban land use to meet the ever-increasing needs of urban dwellers has gradually reduced the share of urban green space, which is the consequence of limiting the access of urban dwellers to nature. But for some reason, at the beginning of the 20th century, the urban man showed a renewed attention to nature and green spaces, which manifests itself in creating functional gardens instead of recreational gardens that respond to the new needs of citizens. The present study aims to Rank the influencing factors to locate urban green spaces in Masjed Soleyman city. Materials & Methods The present applied study employed an analytical-descriptive method. Reliable internal and external sources related to the subject were reviewed, and in some cases, field studies and referrals to related organizations were conducted for data collection. In this research, the DEMATEL-ANP-integrated approach was employed, and the criteria weights were calculated. Then, the layer of each weight was entered into the Arc GIS software.Results & DiscussionAs the research findings show, 14 criteria are involved in the optimal location of urban green spaces in Masjid Suleiman, distance to commercial centers, distance to waste and empty lands, distance to administrative centers, distance to medical centers, distance to educational centers, distance to existing green spaces, distance to industrial centers, distance to urban facilities and equipment, distance to military centers, distance to religious centers, distance to communication paths, and density.ConclusionThe results of this study showed the priority of the mentioned 14 indicators in order from low to high: proximity to residential centers (0.09263, rank 1), proximity to educational centers (0.07428, rank 2), proximity to cultural centers (0.07268, rank 3), population density (0.07154 and rank 4), proximity to communication ways (0.07092, rank 5), proximity to religious centers (0.06979, rank 6), proximity to existing green spaces (0.06967, rank 7), proximity to medical centers (0.06934, rank 8), proximity to commercial centers (0.06923, rank 9), proximity to urban facilities and equipment (0.06902, rank 10), proximity to military centers (0.06874, rank 11), proximity to administrative centers (0.06761, rank 12), proximity to industrial centers (0.06729, rank 13) and proximity to empty and barren land (0.06726, rank 14).
Extraction, processing, production and display of geographic data
Masoud Eshghizadeh
Abstract
Extended Abstract Introduction The best and most effective way to control wind erosion is to increase vegetation to cover the land surface. The roughness of the land surface is increased by vegetation. Because it increases the friction that causes a decrease in wind speed on the surface of the ground ...
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Extended Abstract Introduction The best and most effective way to control wind erosion is to increase vegetation to cover the land surface. The roughness of the land surface is increased by vegetation. Because it increases the friction that causes a decrease in wind speed on the surface of the ground and the carrying capacity of sediments by the wind. By determining resistant species and more adapted to dry conditions, it will be possible to establish vegetation in these areas in different non-desertification projects to control and reduce wind erosion.Materials & Methods In this research, in one of the critical centers of wind erosion in Gonabad County in northeastern Iran, investigated the performance of a biological project of non-desertification operations with Haloxylon aphyllum, Haloxylon persicum, Seidlitzia rosmarinus, Nitraria schoberi, Atriplex canescens and annual plants in different intensities of the drought for 2004 to 2021. At first, using the RDI index, drought intensities were determined in March, April, and May in the studied period. In the next step, the maximum, average, and minimum values of NDVI, TDVI, SAVI, and EVI indices were calculated using Landsat satellite images and data processing ENVI 5.3 software in each of the covered areas by desired specie in the studied months. In the final stage, the values of these vegetation indices were compared and analyzed for drought intensities in the areas and months.Results Based on the results, in all the indices, the area covered by Seidlitzia rosmarinus had a better condition than in other areas in the very dry drought intensity and with the highest value of 0.341 in the EVI index. But in the medium and mild drought intensities, the area covered with the Haloxylon sp had a better condition than in other areas and with the highest value of 0.456 in the TDVI index. However, all studied vegetation indicators did not show any significant difference between the planted areas. In March with the very dry condition, vegetation was more dependent on the intensity of dry conditions in February. The severity of the drought in February caused the values of all vegetation indicators in March in the studied areas to be negative, except in the annual species area. In March, the SAVI index, in April TDVI index, and in May TDVI and EVI indices had better ability to distinguish vegetation cover. The results of the Kruskal-Wallis test showed that in March, there was a significant difference between high, medium, and mild dry conditions only for the TDVI index at the level of 5% and the SAVI index at the level of 1%. In April, the NDVI and SAVI indices at the level of 1% and the EVI index at the level of 5% showed a significant difference between the three dry conditions. The results of the Mann-Whitney test showed that in May, only the SAVI index had a significant difference at the level of 1% between the moderate and mild dry conditions.Discussion & Conclusion The results confirmed the ability of vegetation indices obtained from Landsat satellite imagery to monitor the vegetation changes due to the drought. All the indices showed changes in the vegetation in the drought conditions, but no difference was seen between the vegetation areas. The resistance of the species to drought was one of the main reasons that caused there to be no significant difference between the vegetation areas, but the difference between the drought conditions was significant. Due to the adaptation and resistance of desert species to drought conditions, their sensitivity to drought in dry and desert areas is lower than in humid areas. In the condition that February is affected by drought, the cover conditions of annual plant species in the studied area in March were better than in other areas. But in March with very dry or moderate drought conditions, the cover conditions of Seidlitzia rosmarinus species were better coverage than in other areas. Based on the results, in the continuation and occurrence of moderate to high drought in April and May, the area of Seidlitzia rosmarinus showed a better cover than in other areas. In the condition of continued drought in March, annual plants do not have a chance to grow and the species that can use the moisture reserve in the deeper soil will have more opportunity to cover the surface of the ground, which this research showed that among the species in this area, Seidlitzia rosmarinus has more ability. Therefore, the principle of mixed planting and preventing single planting in the desert restoration and non-desertification projects should be emphasized and implemented.
Extraction, processing, production and display of geographic data
Fatemeh Ahmadi; Yasser Ebrahimian Ghajari; Abbas Kiani
Abstract
Extended AbstractIntroductionUrbanization can be defined as social and economic development and thus, urban planners need timely information for service provision and management in urban areas. Due to the difficulties of traditional methods, automatic and semi-automatic methods have gained special importance. ...
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Extended AbstractIntroductionUrbanization can be defined as social and economic development and thus, urban planners need timely information for service provision and management in urban areas. Due to the difficulties of traditional methods, automatic and semi-automatic methods have gained special importance. Therefore, remote sensing data and image classification techniques have been used to help identify different types of land use. Nighttime light emission data can help researchers effectively identify human activities and urban areas. These satellite images are collected from the surface of the earth at night and can clearly separate nighttime light emission of urban areas from the surrounding dark areas. Thus, it can be concluded that various types of data with different nature and capabilities (spectral, nighttime light, etc.) are available for any specific area each of which has its own advantages and limitations. As a result, using a combination of these data types will increase accuracy and reduce uncertainty. Algorithms and scientific methods enabling this combination are thus of great importance. The present study applies a combination of nighttime light emission and daytime multispectral images to produce automatic and high-quality optimal training samples and locate built-up areas.Material & MethodsTwo study areas (in Babol and Kerman) with two different climates have been investigated in the present study. Also, DMSP and VIIRS nighttime light emission images and Landsat 5, 7 and 8 images collected during the statistical period have been used.Research MethodsThe present study has proposed an approach consisting of four main phases of pre-processing, feature extraction and production of initial training samples, selection of optimal training samples and finally classification and evaluation. Nighttime light emission images were corrected and primary samples including two classes of built and unbuilt areas were produced using the limit of automatic thresholds. Nighttime light emission is generally related with human activities, and thus, built-up areas usually have a higher nighttime light emission value compared with unbuilt areas which have a lower or zero value. Due to the saturation and blooming problems occurring in DMSP images and the relatively low spatial resolution of nighttime light emission data, training samples extracted from built areas using these data still include unbuilt areas such as water bodies and vegetation cover. Therefore, an index has been developed using features extracted from nighttime light emission and Landsat images. Considering the inverse relationship between various features of urban and rural areas (vegetation cover and soil) in LST images obtained from the thermal band of Landsat images and the NDVI vegetation index obtained from Landsat and features of urban areas in nighttime light emission image, an index was provided which maintains the main characteristics of urban areas in nighttime light emission images while minimizing saturation and blooming. Finally, time series of classified images was investigated and urban expansion was analyzed.Result & DiscussionFollowing nighttime light emission data correction, an upward trend was observed for the values of pixels collected from each city which verifies the pre-processing stage. Then, an appropriate automatic threshold limit was selected in accordance with the features of each nighttime image and applied to produce the initial training samples. Nighttime light emission images were corrected using the introduced index to minimize saturation and blooming in urban and suburban areas. Training samples thus optimized were used for final classification. Due to the low quality of initial training samples, classified pixels obtained from urban areas did not confirm to reality. Thus, classification faded in Kerman city in some years and practically no classification was performed which shows the low quality of initial training samples. Due to the low spatial resolution of nighttime light emission images, the size of samples collected from built-up areas was falsely detected to be large, and thus, there were definitely samples related to vegetation, soil, and etc. in the specified range. In the next step, classification was performed using optimal training samples in which built-up regions were modified. In this way, results got closer to the reference data and reality. In fact, using a combination of nighttime light emission and Landsat data can overcome the limitations of both methods.Conclusion Selection of training samples is considered to be the main and fundamental challenge of classification. With a valid training sample, classification is precisely performed. Since, traditional and manual methods of obtaining training samples are costly and time-consuming, automatic and semi-automatic methods have become specifically important. Therefore, the present study has classified and extracted built-up areas using satellite images. The initial training samples can be obtained automatically from nighttime light emission images, however high saturation and blooming of these images have reduced their quality. To solve this problem, a nighttime light index has been developed based on the relationship between the characteristics of urban areas in optical images and nighttime light emission images which has minimized related problems in both study areas with two different climates to a great extent. This shows the flexibility and effectiveness of the proposed method. High-quality training samples thus obtained were highly effective in the final classification phase. Investigating urban expansion time series has shown that urban growth and expansion have generally occurred around the city.
Extraction, processing, production and display of geographic data
Shokoufeh Farhadi; Nazila Mohammadi; Amin Sedaghat
Abstract
Extended AbstractIntroductionReconstruction of 3D models and their use in photogrammetry and remote sensing has been considered as the most important and challenging topics in recent years. With the development of laser scanner technology and obtaining spatial data of the environment and objects, the ...
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Extended AbstractIntroductionReconstruction of 3D models and their use in photogrammetry and remote sensing has been considered as the most important and challenging topics in recent years. With the development of laser scanner technology and obtaining spatial data of the environment and objects, the use of this technology has increased nowadays. This technology extracts points from the external surfaces of the environment or objects in high volume, in a short time, which is called point cloud.Due to laser scanners’ easy placement, point clouds are usually taken from different angles, so they define in the different coordinate systems, which must be unified to give a complete 3d view of the object. The process is considered as “registration”.For this purpose, first, the corresponding pairs of points in each point cloud must be determined and then they must be matched correctly.after all a three-dimensional model is created.Finding the best pair of corresponding points in the Point clouds as well as estimating the optimal error metric and the displacement between pairs of corresponding points is one of the most important and challenging steps of three-dimensional reconstruction.Three-dimensional descriptors are one of the most suitable tools for determining the corresponding pairs of points in Point cloud. These descriptors create a set of information for every single point to determine the corresponding points in each Point cloud. Defining a three-dimensional descriptor whose computation complexity is low but its descriptive is high, can help to find the correct pair of points for 3d registration and modeling.Materials & Methods The main purpose of the present study is to define a strong three-dimensional descriptor to find the best corresponding pair of points to reconstruct the three-dimensional model.The descriptor proposed in this study consists of two single local three-dimensional descriptors based on the spatial and geometric properties of the Point cloud, which combine to form a strong descriptor to determine corresponding points in the Point cloud.Laser scanners extract a large volume of points from surfaces in a short period of time, which due to the reflection of laser beams, Point cloud may contain noise and mistakes. In the process of analyzing and using the data, these mistakes cause problems and should be removed in the pre-processing phase. To define the desired descriptor, in the pre-processing phase the Point cloud gets ready to extract the required properties.The Statistical removal filter method is used to remove the noise and the voxel grid filter method is used to improve the speed of future preprocessing.Each point in the neighborhood of Query Point provides a lot of that can be used to create the desired descriptor.In the present study, by determining the appropriate neighborhood radius and Nearest Neighbor Search (NNS) method, using the k-dimensional tree, correct and efficient neighborhoods are determined for each point.In the first step, a spatial descriptor is formed for each point. This descriptor is defined in the form of a histogram based on two distances for the point in its neighborhood. In the second step, the angles of the normal vectors of the Point cloud in different states are used to create a descriptor based on geometric information. In this research, two features called and have been used, which for each descriptor is formed in the form of a histogram. Then the spatial descriptor is combined with each of the descriptors based on the geometric feature and forms two desired descriptors.To ensure the accuracy of the matching process based on the proposed descriptor, by assigning a suitable threshold for the basis of the distance between the Query point and its neighborhood, with the corresponding point of the Query point and its neighborhood in the second Point Cloud, incorrect correspondences are detected and removed. Next, the remained correct corresponding pairs of points are used to reconstruct the three-dimensional model.Results & DiscussionIn this research, two sets of Point cloud have been used to evaluate the proposed process. These two data sets are obtained in such a way that in the first data set the perspective and angle of view and in the second data set the position and arrangement of objects are changed.By forming descriptors based on spatial and geometric features in different neighborhood radii and then forming a proposed combination descriptor based on what has been mentioned, it can be considered that combining the geometric descriptors with spatial descriptors, in cases where The two datasets have less relative overlap or more relative rotation than each other, in contrast to the position shift, leading to improved descriptor performance and increased matching accuracy.Considering the results obtained from the comparison of the proposed descriptors, it can be said that because of the existence of two different radii in each part of descriptors based on spatial and geometric relations in the proposed descriptors, it turns out that the required descriptor is high quality.On the other hand, the properties used in these descriptors are also resistant to changing the position of objects and have high efficiency in mentioned category. Also, the process of identifying and eliminating incorrect correspondences improves the matching process and increases the matching percentage of similar points up to 25% in the study data set.ConclusionThe results of comparing the set of Point Cloud studied using the proposed descriptor indicate that this descriptor is more efficient in cases where two data sets rotate relative to each other, compared to cases where the location of the data pair has changed relative to each other. And the accuracy of the comparison obtained from the proposed method, in this case, increases compared to other data pair placement modes.
Extraction, processing, production and display of geographic data
Khalil Valizadeh Kamran; Maryam Sadeghi; Asadollah Hejazi
Abstract
Extended Abstract:Introduction Monitoring and investigation of land use changes in forest areas provides acceptable information for efficient management of these resources. Also, taking care protecting natural resources requires awareness of the conditions and how to change different land uses. ...
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Extended Abstract:Introduction Monitoring and investigation of land use changes in forest areas provides acceptable information for efficient management of these resources. Also, taking care protecting natural resources requires awareness of the conditions and how to change different land uses. Therefore, the purpose of this research is to evaluate the change of forest use in the forest area of Fandoqlu from 2010 to 2019 by using Landsat 5, 8 images and integrating them with Sentinel 2, Ester images. After preparing images from the years 2010, 2015 and 2019, geometrical, radiometric and atmospheric corrections of the images were done and the classification accuracy using kappa accuracy was% 93, %83, 91% respectively. The land use of Fandoqlu forest area was To model the change of use for 2025 with the Geomod model, it is necessary to prepare a suitability map of the area, which is prepared using the Fuzzy ANP method and the incompatibility coefficient is less than 0.06. In order to prepare a suitability map of four general factors: human, biological, topographic and climatic, and 12 sub-criteria were obtained with Boolean functions, and Boolean land use maps (forest and non-forest) 2010 and 2015 were modeled for 2019 and for modeling Land use for 2025 was done from the base map of 2019 and the transition matrix of Markov chain of land use in 2025 with the CA-Markov model And the result of location changes for 2025 was obtained. To evaluate the accuracy of the model, the agreement and non-agreement of pixels with Klocation and Standard were done with 98 and 95 accuracy, respectively. Modeling results for the year 2025 changes in a decreasing manner; The increase of non-utilized covers and the reduction of forest use, which will decrease from 3204.18 hectares in 2010 to 3070.55 hectares in 2019; According to the results of the human criterion and the sub-criteria of land use and distance from the road, the tourism potential of this area and the attraction of tourists as well as the interference of local residents can have a direct effect on this forest reduction process.Materials & Methods: organizations, people and local, is the only way to protect the forests of this region. In this study, remote sensing data such as satellite images of Landsat8,5, ASTER and Sentinel 2A were used to prepare the baseline map. Climatic data of all parameters up to 1396 were received from the synoptic station of Ardabil province. The digital model of 12.5 altitude was prepared from NASA website to prepare slope maps, slope direction, border layer of the study area and vegetation layer from Ardabil Natural Resources Organization. The research used Arc GIS, ENVI 5.3, TerrSet, eCognition 9 Google Earth pro and SUPER DECITION software. then based on the value and purpose of Reclassify and layer fuzzy. to predict the future conditions of forest cover changes by GEOMOD method, a time map of the start of the modeling process and a map of change appropriateness are needed. Geomod is used to model spatial patterns, forecast and probability of change. GEOMOD is used to simulate patterns of spatial change of use or change between two categories of use (forest and non-forest).Results and Discussion: In order to implement the GEOMOD model, a fit map prepared from the study area is required. Fuzzy ANP method was used to prepare the appropriateness map of the study area, which has four criteria: human (distance from the road, distance from the village, population), topography (slope, direction, height) and biological (land use, lithology, soil), criteria. And the following criteria are used in the map. Climatic parameter (average annual rainfall, temperature, altitude, slope, direction of slope, waterway) was used. 2025 user is required, so using 2015 and 2019 user with CA Markov model for 2025 was modeled. Decreased accuracy was associated. The results of predicting forest spatial changes for 2025 were used from the 2019 Boolean user map and the CA Markov modeled user map. Conclusion: To implement the GEOMOD model, we need a fit map for spatial modeling of changes. In this study, four criteria and 12 sub-criteria discussed in Chapters 3 and 4 were used to prepare a fit map of the region. They have acquired Super Decision software.Conclusion: Using the Boolean forest and non-forest boards and the 2015 and 2019 land use maps with the CA Markov model for 2025, it was modeled. Human, climate and biological have weights of 0.358, 0.258, 0.203 and 0.165, respectively, which the topography achieved the highest weight in Super Decision software. Among the sub-criteria, the type of land use has a high impact on changes in the region. The final output of the fit map was prepared by applying the OR function after applying the weights, which had a better result than the other functions. Finally, using the 2015 and 2025 user maps for 2025, forest spatial changes were made. To evaluate the accuracy of the model, the agreement and non-agreement of spatial pixels were used, which was modeled with Kappa 98% for 2019. The results of spatial change modeling show the high accuracy of the model in predicting spatial changes. GEOMOD results for 2025 will reach 3085 thousand hectares from 3151.9 hectares. Research conducted in different places. the country indicates a decline in forest areas in the coming decades.
Extraction, processing, production and display of geographic data
Mohammad Mahdi Hosseinzadeh; Reza Esmaili; mohsen Nabizadeh Bahnamiri
Abstract
Extended Abstract IntroductionChanges in river pattern is one of the most important issues of river engineering that affects the activities and construction structures along rivers.Changes in the parameters of the meandering can be due to changes by humans, changes in the hydrological regime ...
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Extended Abstract IntroductionChanges in river pattern is one of the most important issues of river engineering that affects the activities and construction structures along rivers.Changes in the parameters of the meandering can be due to changes by humans, changes in the hydrological regime of the river, ecological changes or due to geomorphology of the region.Knowledge and understanding of the morphological changes of meandering patterns and the natural dynamics of river systems are very important for the purposes of planning, urban planning, dam construction, erosion and sedimentation, road construction and protection and reconstruction of river channels.By studying the amount of changes in river parameters, especially the length and width of the channel in a region, the future of the meandering patterns and rivers can be predicted.Channel form is a good initial guide to determine the morphology and channel form changes in alluvial rivers.In fact, the most obvious feature of a river is its planform or geometric form.A river plan is called a planform, which shows the characteristics of a channel and a floodplain in an alluvial river.The term river pattern describes the planimetric (two-dimensional) shape of rivers.In the new classifications, channel patterns are classified into straight, meandering, braided, and anastomosing.The purpose of this study is to investigate the trend of morphological changes in the Nekaroud River from the city of Neka to where the river joins the Caspian Sea for a period of 35 years between 1364 to 1399.MethodologyNeka river originates from Alborz mountains in the southern part of Gorgan city and passes through the southern part of Behshahr and flows into the Caspian Sea.The area of Neka Basin is 1902 km.The Neka River is 130 km long in the mountains and 39 km long in the coastal plains.In this study, the Neka River with a length of 39 km in the coastal plain from Neka to the Caspian Sea has been studied.At first, Nekaroud was divided into three parts: upper, middle and lower part and the changes were studied separately and together.With this method, the amount of displacement, change of dimensions and pattern of bends were determined.Using the method of historical changes, the river route map related to different periods based on aerial photographs of 1985 (National Geographical Organization of Iran) and satellite images (from Google Earth) of 2006 and 1399 has been digitized in GIS software.In order to study the changes of the river in three time periods, from three indicators including morphometry which includes (channel length, arc radius, central angle, wave amplitude, wavelength), morphology which includes (different models of lateral migration including retraction, new meander,migration, confined migration, cutoff, growth, meandering change, double heading) and morphodynamics (transect method) have been used.Finally, the changes of the left and right sides of the channel in the studied statistical period were statistically tested in SPSS software.Results and discussionThe results of the study of satellite images and aerial photographs show that the values of the curvature coefficient, the central angle, the angles of the arcs along the river have decreased from the upstream to the downstream.Based on these morphometric characteristics, the river is classified into three parts including the developed meandering pattern, the undeveloped meandering pattern and the almost meandering pattern in the third reach.Although the morphometric values of the channel show that the Neka River has changed,however, the statistical test in the section with a developed meandering pattern showed that no significant changes were observed in the right bank during two different periods.There were no significant changes in the left bank of the river during 2006 and 2016.The study of Yemani and Hosseinzadeh (2013) on the Talar River and Yemani et al. (2014) on the Babol River had similar results. In these studies, similar to the Nekarud River, at the beginning of the river's entry into the coastal plain, i.e., the alluvial fan section (the first part of the Nekarud), there is more instability and changes are still taking place during large floods; However, in the second and third periods, due to human intervention and protection, the channel deepened and finally, the main river was divided into several branches in order to transfer water to the rice agricultural lands, which caused the degree of instability and changes in the channel pattern in the four The last decade is at a minimum.ConclusionAccording to the studies, it was found that the Nekarood River has had limited changes in terms of morphology, morphometry and morphodynamics during 35 years.Changes in the bed of the Nekaroud River during 2006 to 1399 have been decreasing. The reasons for these conditions are the existence of large-scale cultivation around the river and the protection of the river bank, the creation of storage dams and water diversion in the middle part of the coastal plain and the construction of dams on this river (Golord Dam and other dams).With the construction of these dams, many changes were made in hydrological parameters such as flow velocity and discharge. Also, the amount of erosion and the amount and method of sedimentation have changed.
Extraction, processing, production and display of geographic data
Hamid Ganjaeian; Fatemeh Menbari; Afshan Ghasemi; Mozhgan Nosrati
Abstract
Extended AbstractIntroductionSubsidence risk, unlike many risks such as floods and earthquakes, is insignificant and in the long run causes a lot of damage such as cracking of buildings, sloping of high facilities, destruction of agricultural lands, subsidence, etc. So the areas at risk are facing a ...
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Extended AbstractIntroductionSubsidence risk, unlike many risks such as floods and earthquakes, is insignificant and in the long run causes a lot of damage such as cracking of buildings, sloping of high facilities, destruction of agricultural lands, subsidence, etc. So the areas at risk are facing a lot of challenges. Among the areas that are at risk of subsidence are the plains of arid and semi-arid regions, including the plains of Iran. In fact, the location of a large part of Iran in the arid and semi-arid region has caused these areas to face a shortage of surface water resources, and this has led to overuse of groundwater resources in recent years and the occurrence of the risk has subsided. One of the areas that is at risk of subsidence is Kaboudar Ahang-Famenin plain in Hamadan province. Due to the lack of limiting geomorphological barriers, this plain has been associated with the development of many agricultural lands and due to the lack of sufficient surface water resources, the utilization of groundwater resources in this plain has been more than allowed and this has caused a decline. Extreme groundwater resources in this area and eventually the risk of subsidence. Due to the importance of the issue, in this study, the subsidence of Kaboudar Ahang-Famenin plain has been evaluated and the effective factors in its occurrence have been analyzed. Materials and methodsIn this study, in accordance with the subject and objectives, statistical information (information about 13 piezometric wells in the study area), library and video (radar images related to Sentinel 1 satellite, Landsat satellite images and also 30 m high digital model SRTM) has been used as research data. The tools used in the research include GMT software (to prepare subsidence mapping using radar interference and Russian SBAS time series method), Google Earth (to monitor area and identify subsidence) and ArcGIS (to prepare final maps). According to the objectives, this research has been done in three stages. In the first stage, using the digital model of 30 m altitude SRTM and Landsat satellite images, the geomorphological status and land use of the region have been studied. In the second stage, in the second stage, using information related to 13 piezometric wells, the groundwater depletion situation of the region was investigated and in the third stage, using Sentinel 1 radar images and SBAS time series method, the amount was evaluated. Subsidence of Kaboudar Ahang-Famenin plain has been studied. Discussion and resultsThe study of the altitude situation of the region shows that there is a very small difference in height between the cities of Kaboudar Ahang and Famenin and also there are no significant obstacles and landforms in this distance. Also, the study of the slope classes of the region shows that the area of Kaboudar Ahang-Famenin plain is less than 10% in the slope class and the region does not have steep and restrictive areas. According to the prepared maps, Kaboudar Ahang-Famenin plain, in terms of geomorphology, has no limiting obstacles for the development of agricultural lands as well as residential areas. In fact, the lack of restrictive barriers has led to the development of agricultural lands in this region, especially irrigated agricultural lands in recent years, and this has led to excessive pressure on groundwater resources in recent years. The results of the study of the decline in groundwater resources in the region indicate that the rate of decline in water levels during a period of 24 years has been between 14.7 (Hemehkasi well) to 78.1 (Einabad well) meters. Also, according to the calculations, most of the studied wells have faced an average of more than 2 meters of water level drop annually. Also, the results of the assessment of subsidence in the region indicate that the study area has had a subsidence of 29 to 216 mm during a period of 5 years (from 16/01/2015 to 14/01/2020). ConclusionThe results of studying the natural state of Kaboudar Ahang-Famenin plain have shown that this plain has a high subsidence potential due to its geomorphological and hydro-climatic conditions. In fact, in terms of geomorphological status, this plain is without limiting obstacles for the development of agricultural lands, and this issue has led to the development of irrigated agricultural lands regardless of environmental capabilities, including the hydro-climatic situation of the region. According to the above cases, the development of agricultural lands, regardless of the capacity of water resources in the region, has led to over-harvesting of groundwater resources and as a result, a sharp drop in groundwater levels Based on the results of the evaluation of 13 wells studied, most of the studied wells, with an average annual water level drop of more than 2 meters and this issue has caused the Kaboudar Ahang-Famenin plain during The 5-year period (from 16/01/2015 to 14/01/2020) should have a subsidence of 29 to 216 mm. Also, the results of the study of the spatial distribution of subsidence have shown that the highest amount of subsidence is related to the middle areas of the region and the distance between Kaboudar Ahang and Famenin cities, and considering that in these areas there was the highest level of groundwater loss, Therefore, it can be said that the main cause of subsidence in the region has been a sharp decline in groundwater resources. The sum of the results of this study has shown that Kaboudar Ahang-Famenin plain is in danger of subsidence and this issue has led to the emergence of numerous depressions in this plain which is a serious threat to human facilities and habitat.
Extraction, processing, production and display of geographic data
Maryam Kouhani; Abbas Kiani; Yasser Ebrahimian Ghajari
Abstract
Extended AbstractIntroductionVegetation has always been affected by various environmental and human factors that have directly or indirectly affected the conditions and performance of the environment over time. Consequently, monitoring and investigating the vegetation cover in the northern regions of ...
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Extended AbstractIntroductionVegetation has always been affected by various environmental and human factors that have directly or indirectly affected the conditions and performance of the environment over time. Consequently, monitoring and investigating the vegetation cover in the northern regions of Iran is also highly considered important. Research suggests that the destruction and change of vegetation cover and forests are among the most important factors influencing natural hazards such as floods, erosion, and earthquakes. In addition to processing and presenting well-known spatial data, remote sensing can also be used to improve human understanding of annual changes in vegetation cover, from a local to a global scale. In this regard, the anomaly evaluation criterion with high differentiation can separate and display anomalous areas in order to recognize the change process and reveal the areas with anomalies over time. Thus, medium-resolution images, vegetation indices, and anomaly criteria can be used to evaluate long-term vegetation changes. Therefore, a positive step in reducing the environmental effects of a region can be made by locating the urban areas that have experienced changes over time and making decisions related to future planning.Material and methodsThis study utilized a time series of Landsat 5, 7, and 8 images downloaded from the Google Earth engine. To get the best representation of the vegetation in this study, spring and summer were chosen because vegetation at this time is at its greenest. The main focus of this study was on the evaluation of vegetation changes over time quantitatively and qualitatively, using remote sensing data from Google Earth Engine to prepare a map of vegetation changes over time. The general process of implementing this research can be summarized in 7 phases. The first phase involves taking Landsat images and preparing statistical meteorological data. In the second phase, the time series images were collected according to the specific period and in the third phase, the obtained images were corrected and pre-processed. As a next step, the EVI index is extracted from all Landsat images, and then to determine the anomaly of changes, a series of statistical analyses, including the mean and standard deviation, are applied. The next step involves generating the map of anomalous time series changes and extracting the map of vegetation changes to improve understanding. The end of the process also includes evaluating the results obtained from this research. Results and DiscussionSince vegetation and drought changes are non-uniform depending on location and distance from the sea and humid areas, and vegetation is destroyed to build villas, residential areas, commercial areas, and towns, several study areas were divided into smaller pieces. Then each area was analyzed and evaluated separately for its changes. It has been observed in the first and third study areas that vegetation has generally been on the rise in the past 36 years, although sometimes there have been anomalies and fluctuations in EVI value. It was significant to see the reduced vegetation in 2008 in both regions. For example, 262.5 mm of precipitation in the first region fell this year, indicating a rain shortage. The results obtained from the second region, considered one of the coastal regions, indicate that the anomaly graph in the region during the period had a downward slope in the direction of decreasing vegetation, and EVI values reached 0.14 in 2005 and 0.09 in 2013. The 4th and 5th regions have shown a lot of fluctuations in anomalous changes and EVI values, although the trend has generally been downward. Results obtained in the 4th region show that vegetation cover peaked in 2004 and 2011. Rainfall in the 5th region, a highland region, in 2008 was deficient, with 259.8 mm reported by the meteorological station. The anomaly value in this year was -1.96. According to the Department of Meteorology in Mazandaran province, most droughts that have affected the underground water in the province have taken place in coastal and plain areas in the province's east and center, and in western cities, they have mostly affected mountainous areas.ConclusionThirty-six years of EVI time series images obtained from Landsat images were utilized in this study to investigate the changes and identify anomalies. In order to conduct a more detailed investigation, the study area was divided into several different regions, and each region was evaluated separately. The results obtained with existing meteorological statistical data were analyzed because vegetation can be affected by climatic and environmental conditions such as weather conditions. According to the results from study area )4(, vegetation cover has consistently decreased over the last three decades due to various factors like tree cutting, landslides, or land use changes. As shown in the map showing the obtained changes, there appears to be an increase in the value of the vegetation index in some northern areas of Chalus city until around 2002, indicating an improvement in greenness. While In some areas close to the Caspian Sea and the coastline, because of the construction of villas and commercial areas, there has been a loss of vegetation, such as in area (2) based on the changed map, a major part of the vegetation in that area has been destroyed because of the establishment of a settlement and construction of a road. As a result of comparing the evaluation of two anomaly approaches, it has also been concluded that both modes show almost the same trend of changes, but the graphs in "Anomaly compared to the overall average" mode compared to "Anomaly compared to the average of each set" display the change process better.
Geographic Information System (GIS)
Sakine Koohi; Asghar Azizian
Abstract
Extended AbstractIntroductionDue to the high costs of land surveying, remotely sensed digital elevation models (DEMs) are a common method used to demonstrate topographic variations of the land surface. Generally, these DEM datasets are freely accessible to engineers and researchers covering most parts ...
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Extended AbstractIntroductionDue to the high costs of land surveying, remotely sensed digital elevation models (DEMs) are a common method used to demonstrate topographic variations of the land surface. Generally, these DEM datasets are freely accessible to engineers and researchers covering most parts of the world in different spatial resolutions. DEMs can be classified into two categories of high (small pixel size) and low (large pixel size) resolution DEMs. Several studies have addressed the vertical accuracy of different digital elevation datasets especially in countries lacking access to high quality ground-based data. Despite the widespread application of these products, vertical accuracy of these datasets in different land uses has not been addressed in Iran and most engineering studies use 1:1000 and 1:2000 topographic maps which are very expensive and time-consuming to obtain. The present study seeks to assess vertical accuracy of different resolution DEM datasets used to estimate elevation in various land uses in two Iranian provinces of Qazvin (urban, agricultural lands, garden, and forest, mountainous areas, plains, and rivers) and Mazandaran (urban, agricultural, forest/mountain, plains, and rivers). Materials & MethodsASTER and SRTM DEMs with a resolution of 30-meter and SRTM DEM with a resolution of 90 m resolution were collected in the present study to investigate their vertical accuracy in various land uses of Qazvin and Mazandaran provinces. Several topographic maps and GPS based datasets of the study areas were also investigated for a better assessment of these DEM datasets. Finally, common statistical measures such as standard deviation (SD), mean absolute difference (MAD) and root mean square error (RMSE) were used to compare remotely sensed DEMs with ground-based observations. Results & DiscussionFindings indicated that 30m SRTM DEMs showed a better agreement with ground-based observations in both study areas. RMSE of this dataset in Qazvin and Mazandaran provinces equaled 3.8m and 5.8 m, respectively. Results also indicated that in 30m SRTM DEM, 87% of points in Qazvin and 79.7% of points in Mazandaran provinces showed a lower than 5m mean absolute difference (MAD), while in the case of 30m ASTER DEM 79% of points in Qazvin and 53% of points in Mazandaran showed a lower than 5m mean absolute difference (MAD). For 90m STRM DEM, around 29% of points in Qazvin and 74% of points in Mazandaran showed a lower than 5m mean absolute difference (MAD). Although 90m SRTM DEM did not work efficiently in Qazvin province, its result in Mazandaran province was almost as efficient as 30m SRTM dataset. Assessing the vertical accuracy of different elevation datasets in different land uses indicated that 30m SRTM showed an acceptable result in most land uses except for mountainous areas and forests. This was mainly due to forest canopies blocking the radio waves penetrating such areas and low density of points generated by STRM sensors. Moreover, 30m ASTER did not show an acceptable result in most land uses except for plains in Qazvin along with urban and agricultural land uses in Mazandaran. Despite having a lower resolution, 90m SRTM worked better than 30m ASTER. However, 90m SRTM showed considerable errors in mountainous, urban and forest land uses, and therefore it shall not be used in such areas. ConclusionResults indicated that 30m STRM DEM is a valuable resource which makes elevation estimation for areas lacking ground-based information possible. Moreover, the type of land cover has a significant effect on the vertical accuracy of elevation datasets and thus, increased vegetation results in decreased accuracy of DEM datasets. Therefore depending on the land cover type in the study area, ground control points can be used along with remotely sensed DEMs to decrease errors.
Geographic Data
Hossein Asakereh; Ava Gholami
Abstract
Extended AbstractIntroductionAs global warming and changes in global temperature are considered to be the most important instances of climate change in the present century, temperature can be introduced as an indicator reflecting the response and feedback of climate system to these changes. In this regard, ...
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Extended AbstractIntroductionAs global warming and changes in global temperature are considered to be the most important instances of climate change in the present century, temperature can be introduced as an indicator reflecting the response and feedback of climate system to these changes. In this regard, climate forecasting is performed using "simulation" approach. Using atmospheric general circulation models such as RCPs and climate scenarios developed as their output is an accepted method of simulating climate variables, especially temperature. In each of these scenarios, radiative forcing changes at a certain rate until 2100. Downscaling is the main technique used in RCPs. Different methods are used for downscaling among which artificial neural network is more widely accepted due to its more accurate evaluations. Materials & MethodsData collected for the purpose of the present study include: 1) Daily maximum temperature recorded in Qazvin synoptic station during 1961-2005. These records were derived from Iran Meteorological Organization and used as an output for calibration, fitting, and finally selecting the best fit model for the observations, 2) Atmospheric observations including daily records of 26 atmospheric variables. These data were recorded by the United States National Centers for Environmental Predictions (NCEP) and the United States National Center for Atmospheric Research (NCAR) during 1961-2005 reference period and used as input or explanatory (predictor or independent) variables in the present study 3) Representative Concentration Pathway (RCP) extracted from atmospheric general circulation model (including the output of HadCM3 model) which is used to simulate 2006-2100 reference period.Artificial neural network model was used to downscale atmospheric data and simulate maximum temperature recorded in Qazvin synoptic station. Using Pearson correlation coefficient, the correlation between maximum temperature recorded in Qazvin synoptic station and each of the 26 atmospheric variables was estimated. Then, forward selection and backward deletion, percentage decrease index, and stepwise methods were used to preprocess the variables, select the most appropriate predictor variables (input variable in the network) and perform statistical downscaling. Following the selection of suitable explanatory variables in each of the above mentioned methods, selected variables were separately given as input to the network to reach a proper design for the neural network architecture and perform the final simulation. In other words, the artificial neural network model was fitted four times with different input variables. Then, number of neurons and network layers were determined, a suitable weight was assigned to each variable and the neural network was trained to reach the most appropriate architecture for the neural network. Finally, emission scenarios (RCP2.6, RCP4.5, and RCP8.5) were given as input to the selected architecture, and maximum temperature was simulated for 2006-2100 reference period. Results & DiscussionAppropriate explanatory variables were selected in the present study using four different preprocessing methods. Forward selection method with the lowest minimum mean square error (MMSE) of 6.7 and the highest correlation coefficient of 0.97 was selected as the most appropriate method. Therefore, variables obtained from this method, including average temperature near the surface, average pressure at sea level, and altitude at 500 and 850 hPa level, were selected as predictor variables entering the artificial neural network to simulate future temperature of the station. Finally, a neural network with 8 inputs, a hidden layer with 10 neurons and sigmoid transfer function, and an output layer with 1 neuron and Linear transfer function were confirmed using Levenberg-Marquardt algorithm. There were then used to simulate the future temperature of Qazvin synoptic station. The highest and the lowest temperature values were estimated for December with 9.9°C and January with 6.6°C, respectively. The lowest error rate also belonged to December (-1.5°C). Simulation results indicated that the highest increase in maximum temperature of Qazvin synoptic station in 2006-2100 reference period was observed in RCP8.5, RCP4.5 and RCP2.6 scenarios, respectively. The increasing trend in RCP8.5 scenario was estimated much higher than the base temperature. Moreover, the highest temperature increase (6.7°C) in RCP8.5 scenario belongs to June and the highest temperature decrease (3°C) in the optimistic scenario (RCP2.6) belongs to October. ConclusionSelecting appropriate explanatory variables is an important step in the process of simulating future temperature. Various methods of variables selection, statistical downscaling and artificial neural network model were used to estimate and simulate temperature parameter. Then, RCP 2.6, RCP4.5, and RCP8.5 scenarios were simulated for the 2006-2100 reference period. Maximum temperature of Qazvin synoptic station in the simulated RCP scenarios (belonging to the reference period) was compared with maximum temperature in 1961-2005 period. Results indicate that the highest temperature increase in Qazvin station occurs in the pessimistic scenario (RCP8.5). The increasing trend of temperature begins with RCP2.6 scenario and reaches its highest level in RCP8.5 scenario. In these three scenarios, summer temperature of the next 94 years may increase at a higher rate as compared to other seasons in Qazvin. This means that not only Iran is located in an arid region, but also its temperature will be increasing in the future. Since temperature and precipitation in different parts of the world are considered to be among the most important indicators of climate change and global warming, various models designed to forecast and simulate these phenomena and the future climate suggest an increase in temperature during the coming decades.
Geographic Data
Yaser Moarrab; Esmaiel Salehi; Mohammad Javad Amiri; Hassan Hoveidi
Abstract
Extended AbstractIntroductionThe global rise in urbanization and settlement of the majority of the world’s population in urban areas create opportunities and challenges for improving the quality and sustainability of life. Potential of cities for meeting the basic needs of people has become an ...
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Extended AbstractIntroductionThe global rise in urbanization and settlement of the majority of the world’s population in urban areas create opportunities and challenges for improving the quality and sustainability of life. Potential of cities for meeting the basic needs of people has become an important part of recent scientific and political debates. Covering only a small area of land, cities are responsible for many global environmental problems such as carbon emissions, energy and resource consumption, biodiversity degradation, and ecosystem degradation. They also convert natural forests to human settlements, farms, roads, gardens, and other human-made land uses, leaving many direct and indirect effects on natural conditions and ecological functions of upstream and downstream in forests (such as changes in quantity and quality of water, changes in water flow in rivers, changes in climatic condition and habitat quality). These structural and functional changes undermine environmental services provided by ecological infrastructure and threaten the environmental security of cities and their sustainable development. Therefore, urban managers and experts have always sought a suitable way for urban planning to regulate the structure of cities, support the stability of ecosystem and its performance, and maintain the ecological security of cities. Case studyLavasanat is a district in Shemiranat County in Tehran province of Iran, which is located in the northeast of Tehran. MethodsThe present study analyzes temporal-spatial changes of land use / land cover and then, uses InVEST 3.7.0 model to evaluate temporal-spatial changes of land uses. Results & DiscussionChanges occurring in the reference period were depicted in maps prepared for various land cover / land use classes. Validation of image classification shows a total accuracy of 95.72%, 96.26% and 95.32% and a Kappa coefficient of 0.948, 0.943 and 0.936 for classifications in 2000, 2010 and 2020, respectively, which is acceptable and indicates the compatibility of classified land uses and reality. Classification of images using maximum likelihood algorithm showed the presence of five classes of residential areas (urban area, villages, industries and roads), barren lands, pastures, water bodies and green space in the region.Land use maps and information derived from satellite images indicate that residential areas have experienced a growing trend due to increasing population, demand for land and consequent growth of urbanism, while green space had a decreasing trend during the reference period. Development of residential areas and reduction in green space are quite evident between 2010 and 2020. According to the present trend of land use change, there will be a sharp decline in green space in the coming years. Pastures experienced a decreasing trend from 2000 to 2010. However, it faces an increasing trend from 2010 to 2020 since more green areas were converted into pastures. Barren lands experienced a decreasing trend from 2000 to 2020. ConclusionThe present paper offers the results of modeling water production services in Lavasanat Basin in different decades. Results indicate that the water production in the entire Lavasanat basin equals 2641734.816 cubic meters in 2000, 3318950.915 cubic meters in 2010 and 7737201.215 cubic meters in 2020. Of these volumes, 1677926.367 cubic meters in 2000, 2287145.055 cubic meters in 2010, and 4908786.651 cubic meters in 2020 belonged to residential areas. This class contained an area of 4820578.505 square meters in 2000, 6885513.787 square meters in 2010 and 10407948.705 square meters in 2020 in the whole basin.The results obtained from InVEST scenario building model and water production model showed that the increasing trend of human-made land uses in the study area has a significant impact on increasing water production and, consequently, increases runoff. In fact, water production has experienced a growth rate of 1.25 or 125% from 2000 to 2010, and a growth rate of 2.33 or 233% from 2010 to 2020. Thus in 20 years, water production has increased by 2.92 (292%). The volume of water production in residential areas has increased by 1.36 times (136 %) from 2000 to 2010, 2.14 times (214 %) from 2010 to 2020 and 2.92 times (292%) in 20 years. Also, the total area covered by residential land use has grown 1.42 times from 2000 to 2010 (142 %), and 1.51 times (151%) from 2010 to 2020. Therefore, an increase of 2.15 or 215% was observed in residential areas over this 20 year period.
Geographic Data
Keyvan Mohammadzdeh; Sayyed Ahmad Hosseini; Mehdi Samadi; Ilia Laaliniyat; Masoud Rahimi
Abstract
Extended Abstract
Introduction
Landforms represent influential processes affecting features on the earth’s surface both in the past and in the present while providing important information about the characteristics and potentials of the earth. The shape of the terrain and features such as landforms ...
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Extended Abstract
Introduction
Landforms represent influential processes affecting features on the earth’s surface both in the past and in the present while providing important information about the characteristics and potentials of the earth. The shape of the terrain and features such as landforms affect the flow in water bodies, sediment transport, soil production, and climate at a local and regional scale. Identification and classification of landforms are among the most important purposes of geomorphological maps and also a fundamental step in the process of producing such maps. Geomorphologists have always been interested in achieving a proper and accurate classification of landforms in which their morphometric properties and construction processes are clearly indicated. The present study has attempted to develop a new method and identify the relationship between morphometry of landforms and surface processes using a multi-scale and object-based analysis. Extraction and classification of landforms are especially important in mountainous areas, which are considered to be dynamic due to their special physical and climatic conditions. These areas are often remote and sometimes unknown. Mountainous topography has also made them difficult to access. However, they are of great importance due to their impact on the macro-regional system. Because of this significant importance, Maku County was selected as the study area.
Materials and methods
Maku County is located in northwestern Iran (West Azerbaijan Province) which borders Qarasu River and Turkey in the north, Aras River and the Republic of Azerbaijan in the east, Turkey in the west, and Shut County in the south. This County is located between 44° 17' and 44° 52' east longitude and 39° 8' and 39° 46' north latitude. The present study takes advantage of satellite images (sentinel-2A) with a spatial resolution of 10 m, derivatives of DEM layer (slope, maximum curvature, and minimum curvature, profile and plan curvature) and object-based methods to identify and extract landforms of the study area precisely.
Discussion and results
The present study applies various functions and capabilities of OBIA techniques to extract landforms precisely. These functions include texture features (GLCM), average bands in the image, geometric information (shape, compression, density, and asymmetry), brightness index, terrain roughness index (TRI), maximum and minimum curvature, texture, and etc. The image segmentation scale was first optimized in the present study using ESP tools and objects of the image were created on three levels (9, 17, and 27 scales). In the next step, sample landforms were introduced, membership weights were calculated and defined for the classes in accordance with the fuzzy functions, and finally, 14 types of landforms were extracted using object-oriented analysis.
Conclusion
Fuzzy method includes boundary conditions, defines membership function, and constantly considers landform changes in class definition. Thus, it seems to be ideal for the purpose of the present study. The present study used two types of data (data derived from satellite imagery and DEM layer) along with OBIA approach to extract landforms. Classification of landforms based on fuzzy theory makes it possible to collect more comprehensive information from the earth's surface. Results indicate that fuzzy object-based method has classified landforms with an accuracy of 87% and a kappa index of 85%. Considering the resolution of the images applied in the present study, all features were extracted with an acceptable accuracy except for debris. This can be attributed to the fact that debris is usually accumulated in a small area on steep mountainsides, and thus remains hidden from satellites in nadir images. OBIA approach shows a high efficiency because it can combine spectral characteristics of various types of data (i.e. images and DEM data) and their derivatives while analyzing the shape of the segment, and size, texture and spatial distribution of segments based on their class and other neighboring segments.
