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.
Remote Sensing (RS)
Nazanin Hassanzadeh; Reza Hassanzadeh; Mahdieh Hosseinjanizadeh; Mehdi Honarmand
Abstract
Extended AbstractIntroductionAir pollution is one of the most crucial environmental problem in the glob and its impact on human live and ecosystem is undeniable. The International Agency for Research on Cancer introduced air pollution as one of main causes of cancer. Therefore, by monitoring air pollution ...
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Extended AbstractIntroductionAir pollution is one of the most crucial environmental problem in the glob and its impact on human live and ecosystem is undeniable. The International Agency for Research on Cancer introduced air pollution as one of main causes of cancer. Therefore, by monitoring air pollution would be a necessity in industrialized cities. Air quality index include evaluation of the amount of NO2, SO2, O3, CO and Aerosol in the air. As, ground station has limited ability to assess the amount and distribution of these harmful gases in the urban and rural areas, therefore, remote sensing technology become a popular tool in assisting research to shed light on this subject. The current study evaluates air pollution caused by Khatoonabad Copper Smelting Factory using Sentinel P5 satellite images.Materials & MethodsThis research investigates the air pollution created by Khatoonabad Copper Smelting Factory and determines its impact radius, using Google Earth Engine system and Sentinel P5 satellite images. Khatoonabad Copper Smelting Factory is located in the northwest of Kerman province at the latitude of 29 Degree 59 Minute to 30 Degree 32 Minute and longitude of 54 Degree 52 Minute to 55 Degree 55 Minute. By performing the coding operation in the Google Earth Engine system, the images related to the average air pollution for So2 and No2 in the area of 50 km from the factory and in a period of 30 months from 07/04/2018 to 12/30/2021 were obtained. The amount and distribution of pollutants were examined based on one-day, seven-days, fourteen-days, one-month, two-months, three-months, six-months and twelve-months’ time periods from December 2020 to assess the concentration of pollution in the cold months of the year, also for the same time periods from June 2021 to assess the concentration of pollution in the warm months of the year.In order to map distribution of each pollutants, Natural Break Classification and Hot Spot Analysis methods were performed on the images obtained from Google Earth Engine in GIS. Natural Break Classification method is based on Jenk optimization and classify spatial data based on statistical properties of each input where variances between classes maximize. Hot Spot Analysis methods is a spatial and statistical method that consider spatial autocorrelation among the spatial data to classify the data according to statistical significance of each class. Points that surrounded by high values and they are statistically significant called hot spot and areas that are surrounded by low values and have high negative Z score and low P values ( P value < 0.05) are called cold spot.Results & DiscussionThe results based on an averaged image for the period of 30 months indicated that the amount of So2 from 0.0000987 to 0.000698 (mol/m2) and the amount of No2 from 0.00005854 to 0.00006932 (mol/m2) in the study area that by increasing the distance from the factory, the amount of So2 and No2 decreased. Furthermore, analyzing the average amount of So2 and No2 in different period of daily, weekly, two weeks, and monthly have showed dispersed spatial distributions in warm and cold season of the year. Therefore, Sentinel 5P data in short-term periods such as daily, weekly, two-week and even one-month cannot provide accurate information on the spatial distribution of No2 and So2 in the study area.In the data obtained from the two-month, three-month, six-month and one-year intervals, the amount of sulfur dioxide concentration has less dispersion than the short-term intervals, and as the time interval increases, the images show less dispersion of sulfur dioxide gas in polluted areas. Therefore, the obtained results indicate that Sentinel 5P images with longer time intervals of two months are able to provide more accurate and logical information about the concentration of sulfur dioxide gas in the area. However, in case of nitrogen dioxide, the imaged longer than two weeks can provide accurate information regarding the spatial distribution of this pollutant in the area.Hot spot analysis was also performed on the images obtained in one-day, seven-day, fourteen-day, one-month, two-month, and three-month intervals from June in order to investigate the concentration and dispersion of pollution in the hot days of the year. Then the maps obtained from the hot months were compared with the maps of the same period from the cold months of the year. This comparison showed that in the maps obtained from the short-term intervals related to the hot months of the year, the density of hot spots was more observed in areas prone to the presence of sulfur dioxide gas. For example, the one-day image from December showed a lot of dispersion, while the one-day image from June indicated less dispersion and more density of gases in polluted areas. In addition, in the one-week, two-week and one-month maps from December hot spots and cold spots show much greater dispersion compared to similar maps in the same periods from June. However, by comparing the two-months and three-months hot spot maps of the cold months to the same maps of the hot months of the year, almost similar results were obtained, even more density were observed in the hot spot map of longer periods (more than two months) in winter time. The same trend happened by analyzing nitrogen dioxide in the studied area. ConclusionThe results obtained from the classification of images related to sulfur dioxide gas showed that the concentration of sulfur dioxide gas in the area around the desired factory has the highest concentration value and as the distance from the factory increases, the concentration of sulfur dioxide gas decreases. Also, according to the minimum and maximum concentration of sulfur dioxide in the studied area, it is concluded that more sulfur dioxide is observed in the cold months of the year than in the warm months of the year. However, in the cold months the concentration of sulfur dioxide has a greater range of changes than the hot months of the year.According to the results, the dispersion of sulfur dioxide concentration in short time intervals such as daily, weekly, fortnightly and even one month was very high in these time intervals. As a result, Sentinel 5P images are not able to provide logical and accurate information about the distribution of atmospheric sulfur dioxide concentration in daily, weekly, two-week and one-month intervals. In order to obtain accurate and logical information, images with time intervals longer than one month should be used, and the longer the time interval is, the more reliable the results will be.The results of the hot spot analysis of the images related to sulfur dioxide concentration also indicated a high concentration of sulfur dioxide gas in the area around the factory. According to the obtained results, the activity of the studied factory can be a reason for the increase in the concentration of sulfur dioxide gas in this area, which has affected a radius of about 4 to 6 kilometers and an area of about 10,700 hectares around the factory.The results obtained from the classification of images related to nitrogen dioxide gas show that the concentration of nitrogen dioxide in the area around the factory has a higher limit. According to the minimum and maximum concentration of this gas in the study area, it can be concluded that in the hot months of the year, the concentration of nitrogen dioxide gas is higher than in the cold months of the year. Considering the rapid spread of nitrogen dioxide gas in the atmosphere by the wind due to the high dynamics of this gas (Vîrghileanu et al., 2020), it can be concluded that the images obtained from the time intervals of two weeks of more can provide more information about the concentration of nitrogen dioxide in the atmosphere.The results of the hot spot analysis of the images related to nitrogen dioxide gas showed that in the time intervals of two weeks to two months in the cold months of the year, there are hot spots that indicated the presence of nitrogen dioxide gas in the atmosphere located above the factory. However, in long-term intervals such as three months, six months, one year and thirty months, in the cold and hot months, hot spots are observed towards the northwest and at a distance from the factory.The result of this research can assist environmentalist and researchers in using and interpreting Sentinel 5P data by considering different periods in cold and warm seasons for making informed decisions.
Hadi Farhadi; Tayebe Managhebi; Hamid Ebadi
Abstract
Extended Abstract1- IntroductionRemote Sensing (RS), as one of the most efficient mapping technologies, is employed in wide areas due to its speed, cost-effectiveness, monitoring over wide areas and using time series data. So far, several data and methods are used for this purpose. In general, RS active ...
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Extended Abstract1- IntroductionRemote Sensing (RS), as one of the most efficient mapping technologies, is employed in wide areas due to its speed, cost-effectiveness, monitoring over wide areas and using time series data. So far, several data and methods are used for this purpose. In general, RS active and passive sensors provide useful information in various applications such as building extraction, natural resource management, agricultural monitoring, etc. The extraction of accurate information about the location, density and distribution of buildings in the urban areas is one of the major challenges in the urban study which is used in various applications. In this framework, the monitoring of the urban parameters, such as urban green space, public health, and environmental justice, urban density and so on has been accomplished by radar and optical image processing, in the last three decades. So far, various methods, including Artificial Intelligence (AI), Deep Learning (DL), object-based methods, etc. have been proposed to extract information in the urban areas. However, an important issue is access to the powerful computer hardware to process the time-series images. In such a situation, the use of the Google Earth Engine (GEE) as a web-based RS platform and its ability to perform spatial and temporal aggregations on a set of satellite images has been considered by many researchers. In this research, a semi-automatic method was developed building extraction in Tabriz, northwest of Iran, based on the satellite images using the GEE cloud computing platform. Since accessible data is one of the most important challenges in the use of space RS, in this study, the free Sentinel-1 and sentinel-2 data, which belongs to the European Space Agency (ESA), has been utilized. 2- Materials & Methods2-1- Study AreaThe study area is central part of the city of Tabriz East Azerbaijan Province, which is located in northwestern of Iran. 2-2- DataVarious data sources have been used in this study, including Sentinel-1, Sentinel-2, and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM). In addition, 400 training samples were created using High-Resolution Google Earth Imagery (GEI) in two classes: urban-residential (buildings) and non-residential areas (vegetation, soil, road, water and etc.).2-3- MethodologyThe goal of this research is to develop a method for identifying the buildings in an urban area. For this purpose, after importing images and pre-processing them in the GEE Platform, a map of the Primary Urban Areas (PUA) and High-Potential Buildings (HPB) was produced from Sentinel-1 images according to the sensitivity of the radar images to the target physical parameters. Then, in order to remove the annoying features and extract the Secondary Urban Areas (SUA), spectral indices such as Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Renormalized Difference Vegetation Index (RDVI), Normalized Difference Water Index (NDWI), Soil Extraction Index (SOEI), Normalized Difference Built-up Index (NDBI), and Build-up Extraction Index (BUEI) were extracted from Sentinel-2 images. Also, the high slope of the area and the mountainous areas was extracted from the SRTM DEM data and used as a mask in the final results. Afterwards, the unimodal histogram thresholding method was used in order to determine the threshold value for each index. Finally, by merging the map of HPB and the map of SUA, the final map was produced and evaluated by other methods. In this research, the proposed method used images from GEI with a very high spatial resolution to validate the generated map. As a result, sampling was carried out using a visual interpretation of GEI in two classes: residential areas (buildings) and non-residential areas. The samples were selected randomly and 400 points were collected for each residential and non-residential class. In the study area, a total of 800 test points were used to evaluate the results of the proposed method. To evaluate the accuracy of the results, the criteria of overall accuracy (OA), kappa coefficient (KC), user accuracy (UA) and producer accuracy (PA) were used. 3- Results & DiscussionAccording to the visual interpretation, all buildings in urban areas with a length and width greater than 10 meters (spatial resolution of the four major bands of Sentinel2) can be extracted using the proposed method in this study, and the results are acceptable in various features. According to the proposed method, annoying features such as vegetation and water body areas were removed from the building identification process with high accuracy, and the accuracy in the study area was improved. The results showed that the OA and KC were 90.11 % and 0.803, respectively. Based on the quantitative and qualitative comparisons, the proposed method had a very satisfying performance. 4- ConclusionDue to the spectral diversity and the presence of various features in urban environments, preparing a map related to it in a large area is extremely difficult. In this regard, the current study presented a very fast semi-automatic method for preparing the urban area map and extracting buildings in Tabriz using Sentinel-1 and Sentinel-2 satellite images as a time series in the GEE platform. One of the most significant benefits of the proposed method is that the data and processing system used in our study is free. Thus, in addition to not having to download large amounts of data, the method presented in the current study has the ability to eliminate many of the limitations of traditional methods, such as classification methods and their requirement for large training samples. The proposed method did not extract the map of buildings using heavy and complex algorithms, which was an important consideration in the discussion of computational cost. Therefore, it can be concluded that the simultaneous use of Radar and optical RS data in the GEE Web-Based platform has a very high potential in distinguishing features and building mapping.
Alireza Taheri Dehkordi; Seyyed Mohammad Milad Shahabi; Mohammad Javad Valadan Zouj; Mahmood Reza Sahebi; Alireza Safdarinejad
Abstract
Extended Abstract
Introduction
Over the past three decades, with the rapid development of spatial-based satellite imagery, remote sensing technology has found a special place in various applications of urban management. Production of status maps of urban structures, the study of energy loss status, ...
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Extended Abstract
Introduction
Over the past three decades, with the rapid development of spatial-based satellite imagery, remote sensing technology has found a special place in various applications of urban management. Production of status maps of urban structures, the study of energy loss status, identification of thermal islands, monitoring of urban vegetation, and assessment of air pollution are just a few examples of areas related to urban management that remote sensing technology is the basis for indirect measurement of the related quantities. Maps of urban structures such as building blocks are commonly used in crisis management, urban design, and urban development studies.
Materials
In this study, the production of urban building block maps using Sentinel 1 and 2 satellite images has been conducted. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Building Index ( NDBI ) for three consecutive months, the slope feature derived from the 30-meter Shuttle Radar Topographic Mission (SRTM)Digital Elevation Model of the study area, along with two Vertical – Vertical (VV) and Vertical - Horizontal ( VH ) polarization in both ascending and descending orbits, form the set of input features.
Methods
The proposed method of this paper relies on the use of a generalizable trained classifier. Initially, the classifier is trained in 2015 using training samples obtained from a new rigorous refining process using different remote sensing and spatial products. This rigorous refining process uses a reference urban map of 2015. In the first step, the corresponding areas related to the ways and roads are removed using the OpenStreetMap data layer. Areas suspected of vegetation with NDVI greater than 0.2 are then discarded. Also, due to the high backscattering of buildings in Synthetic Aperture Radar images, areas with a value less than the average backscattering coefficient of the remaining areas are eliminated. Finally, the residual map is refined using the Mahalanabis distance and the Otsu automatic thresholding method. The trained classifier is then used to generate a map of building blocks at similar time intervals for the three target years (2018, 2019, and 2020). Due to the diversity of texture and density of building blocks in the metropolis of Tehran, the proposed method has been evaluated in this area. Due to the concentration of political, welfare, and social facilities, Tehran has experienced more unplanned and irregular expansion and urbanization than other cities in Iran, which has lead to changes in buildings and constructions. Also, due to the availability of free satellite images and various online processing operations, the Google Earth Engine platform has been used in this study. The performance of three different classifiers including Random Forest (RF), Minimum Mahalanabis Distance (MD), and Support Vector Machines (SVM) are examined in this process. In order to evaluate the proposed method, reference samples obtained from visual interpretation of high-resolution satellite images (Google Earth) in all three target years have been used.
Results
The performance of the aforementioned classifiers has been investigated using 3 different criteria: overall accuracy, user accuracy, and F-score of building blocks. The RF method with an overall accuracy of over 93% in all three target years has shown the best performance. The SVM method ranks second with an accuracy of about 91% every three years. However, the MD method with an overall accuracy below 85% in all three target years has not performed well.
Discussion
The results show better performance of the RF method in all three target years with an overall accuracy of over 93%. It should be noted that the MD classifier with higher user accuracy than other methods, has shown better performance in detecting the class of building blocks. However, the RF method is the best classifier in terms of the user accuracy of the background class. The effect of using two VV and VH polarization and also the slope derived from the SRTM Model in the input feature set on the final accuracy of classification was also investigated. According to the results, the simultaneous use of these three features produces more accurate results in both target classes. However, the results show that the use of VV polarization increases the final classification accuracy compared to VH polarization. The presence of slope feature along with both polarizations has also increased the classification accuracy of each class, especially the background class. However, the exclusion of both VV and VH features from the input feature set has resulted in a more than 10% reduction in overall classification accuracy.
Conclusion
Based on calculated overall accuracies which are above 80% in the majority of investigated cases, two different results can be concluded. First, the trained classifier has shown good temporal generalization and has achieved acceptable accuracy in the target years. Second, due to the different collection processes of training and evaluation data, the proposed rigorous refining method for the preparation of training data has shown good performance. The effect of using two VV and VH polarization and also the slope derived from the SRTM Digital Elevation Model in the input feature set on the final accuracy of classification was also investigated. According to the results, the simultaneous use of these three features produces more accurate results in both target classes. However, the results show that the use of VV polarization increases the final classification accuracy compared to VH polarization. The presence of slope feature along with both polarizations has also increased the classification accuracy of each class, especially the background class. However, the exclusion of both VV and VH features from the input feature set has resulted in a tangible decreasein overall classification accuracy.
Geographic Data
Shahin Jafari; Saeid Hamzeh; Hadi Abdolazimi; Sara Attarchi
Abstract
Extended AbstractIntroductionHuman activities as well as environmental and climate changes affect the trends of wetlands. Detecting and monitoring aquifers are considered to be very important for evaluation of past, present, and future influential factors, and the findings of such studies are essential ...
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Extended AbstractIntroductionHuman activities as well as environmental and climate changes affect the trends of wetlands. Detecting and monitoring aquifers are considered to be very important for evaluation of past, present, and future influential factors, and the findings of such studies are essential for taking measures and making decisions based on the goals of sustainable water and soil resources management. Over the past decade, many researchers around the world have been attracted to remote sensing and especially satellite remote sensing and used this technology to detect such changes over time. The present study has used Landsat (monitoring the area of water body), TRMM (monitoring rainfall), MODIS (monitoring vegetation and evapotranspiration), Grace (monitoring groundwater) satellite images available in Google Earth Engine to study last two decades changes (from 2000 to 2019) in Maharloo wetland, Goshnegan catchment and their surroundings. Materials & MethodsMaharloo wetland is located in Fars province and Goshnegan catchment (426 square kilometers). The present study has used Landsat 7 and 8 images to extract the area of water body, TRMM images to obtain precipitation values, MODIS products to calculate NDVI and evapotranspiration, and data received from Grace to extract changes in groundwater level. These satellite images were available in Google Earth Engine. Mann-Kendall test was also used to assess the overall trend of the aforementioned factors. Results & DiscussionThe automated water extraction index was used in the present study to identify and estimate the area covered by water bodies in the study area. The largest area belonged to 2006 (216.76 square kilometers) and the smallest belonged to 2018 (66 square kilometers). In 2000 (the beginning of the reference period), an area of 216.52 square kilometers was covered by this wetland which is close to what was observed in 2006. In 2018, this has reduced to 66 square kilometers. Thus, there is about 150.72 square kilometers (69.54 percent) difference between these two years. In 2009, the total area has reduced to 66.67 square kilometers. A numerical comparison between 2000 and 2019 also indicates a reduction of 91.17 square kilometers (42% decrease) in the total area covered by this wetland. Also, a 53.72 square kilometers (29.60%) difference was observed between the average area covered by the water body in the first and second ten years. Since calculated p-value value (< 0.00001) is less than the alpha level (0.05), so a significant trend was observed in the average annual data of the area covered by this wetland. Kendall's tau also indicated declining trend of the collected data. Groundwater level was calculated using data received from Grace Satellite to investigate the role of groundwater level in reducing the area covered by the water body. Results indicated that since 2008, groundwater level have always showed a negative value (a decreasing trend). For an instance, a groundwater level of -10.86 cm in 2019 indicates a decrease in the water level in the study area. As the calculated p-value (< 0.0001) is less than the alpha level (0.05), so a significant decreasing trend was observed in the groundwater level. Results of Mann-Kendall test (-0.6) also indicated that changes in water bodies, vegetation, rainfall and groundwater level had a decreasing, increasing, increasing and decreasing trend, respectively. No significant trend was observed in evapotranspiration. It seems that the expansion of agricultural lands and subsequent water extraction from aquifers have intensified the decreasing trend of water bodies in this wetland. ConclusionWetlands provide many ecological services including water treatment, natural hazard prevention, soil and water protection, and coastline management (Amani et al., 2019). Therefore, understanding the importance of wetlands and their management need to be seriously considered by relevant organizations in different countries of the world, and Iran is no exception. Satellite data and remote sensing methods and techniques are considered to be one of the most important and cost-effective methods of monitoring wetlands. The present study used satellite data collected by Landsat, MODIS, Grace, and TRMM to monitor water bodies, vegetation, groundwater level, and rainfall in Goshnegan catchment in which Maharloo wetland is located. The results of Mann-Kendall test showed a decreasing annual trend for changes in the average area of this wetland. This decreasing trend is considered to be a serious threat to human settlements around the wetland which can intensify over time. It will also affect the thermal islands of Shiraz and Sarvestan in near future. Obviously, management of agricultural and forest land uses with the aim of stopping their increasing trend can improve water balance in catchment areas. A 132.2 ha (approximately 36.16%) difference was observed between the average vegetation cover in this catchment area over the first and second ten years (233.4 vs. 365.6 ha). It seems that the expansion of agricultural lands and subsequent water extraction from aquifers have intensified the decreasing trend of water bodies in this wetland. Due to the proximity of this wetland to the city of Shiraz and its importance as an ecological and tourist attraction, it is suggested that related authorities (Department of Environment and Water Organization) demarcate lake bed and riparian zone with the help of remote sensing researchers to improve the management of this wetland and prevent it from drying up. Also, it is suggested that the Organization of Agriculture Jihad review and improve water consumption methods and cultivation patterns in the areas surrounding this wetland.
Geographic Information System (GIS)
Abolfazl Ghanbari; Vahid Isazadeh
Abstract
Extended AbstractIntroductionAir pollution is a major problemin large industrial cities and affects the life of urban citizens.Due to population growth,significant increase in the number of motor vehicles as well as the concentration and accumulation of industries, Tehran is in the grip of an air pollution ...
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Extended AbstractIntroductionAir pollution is a major problemin large industrial cities and affects the life of urban citizens.Due to population growth,significant increase in the number of motor vehicles as well as the concentration and accumulation of industries, Tehran is in the grip of an air pollution crisis. Previous studies have indicated that once every three days, Tehran faces increased levels of pollutants and air pollution.Ozone is produced through photochemical reactions between hydrocarbons in carexhaust and nitrogen oxides in the atmosphere. Producedthrough reactions between atmospheric pollutants,this pollutant is not primarily released into the environment by a specific sourceand thus, it is called a secondary pollutant.Concentration of ground-level ozone has doubled over the last century.Exposure to this pollutant is very harmful for human health, especially those who exercise outdoors because it severely damages their lungs.Therefore, increased concentration of pollutants has become a major challenge for the management of metropolises such as Tehran. Having information about the spatial distribution of pollutants allows urban managers to take appropriate measures and reduce pollution related risksfor areas and people in danger.Due to excessive concentration of industries and factories inside the geographical boundaries of Tehran, along with its specific geographical condition, topography and climate, Tehran has become one of the seven most polluted cities of the world.The present study seeks to model the spatial and temporal changes of ozone and nitrogen oxidesin Tehran metropolis. Methods and MaterialsIn this cross-sectional descriptive study, spatial analysis of pollutants (ozone(O3) and nitrogen oxides)is performed based on data measured by Tehran air quality monitoring stations for the 2008, 2009, and 2018reference periods. For 2008 reference period, data were collected on a monthly basisfrom the website ofTehranair quality control company,while for 2008 and 2018, data were collected annually. Arc GIS 10.5 released by ESRI was usedfor spatial analysis, and Microsoft Excel 2013 was usedto drawdiagrams and perform other analysis.Inverse distance weighting (IDW) model was used for spatial analysis of ozone and nitrogen oxidesin Tehran metropolitan area inthe three reference periods. Finally, the reference periods were compared and the most polluted one was zoned using the IDW model. In the second method, Google Earth Engine was used to model the spatial distribution of ozone and nitrogen oxides. In this method, Sentinel-5p NRTI O3: Near Real Time Ozone product was used to model ozone and nitrogen oxideson an annual basis (11/01/2018 and 28/03/2020).This is the date in which sentinel has started monitoring ozone and nitrogen pollutants. As the most important product available for measuring the average rate of change,column of ozone and nitrogen oxides’ changes in the atmosphere (O3_Column_number_density) was used in this study. Annual average concentration of ozone and nitrogen pollutants in Tehran was compared with the Sentinel-5 product in Google Earth Engine. Results & DiscussionIn 2018, average annual concentration of ozone and nitrogen oxides in studied stations equaled 12.7 ppb. The accuracy of modeling was also calculated using the coefficient of determination(R2) or coefficient of detection (CD). The average annual concentration of ozone and nitrogen oxides in 2008 was also measured for all air quality control stations to determine their correlation.All independent variables used in this model had an acceptable level of significance (P.> 0.001).In other words, all parameters improved the performance of the model in estimating the concentration of ozone and nitrogen oxidespollutants. The model was developed and R2 rate for 2008 monthly average equaled 0.9188%.The coefficient of determination (R2)for ozone and nitrogen oxides’ concentration in 2009 equaled 0.9134%, but the annual average of 2018showed a much lower R2which equaled 0.476%.It should be noted that not all stations have been evaluated in this study, because the concentrations of ozone and nitrogen oxidesin some air quality monitoring stations equaled zero. Thus, only stations showing a greater than zero value have been used in this study. ConclusionAs previously mentioned, various models have been proposed for modeling the concentration of ozone and nitrogen oxides, each showing a different result. In the present study, the inverse distance weighting (IDW) model was used for three reference periods (2008, 2009 and 2018), and the concentrations of ozone and nitrogen oxides in the atmosphere were also modeled using the variables related to air quality monitoring stations.Ozone concentration modeled by inverse distance weighting method was compared with the average annual change of ozone concentration derived from Sentinel-5 product in Google Earth Engine. Results obtained from the concentration of ozone and nitrogen oxides in the three reference periods were investigated using thecoefficient of detection.The resulting coefficient of determination for ozone concentration in 2008 and 2009 equaled 0.9188% and 0.9134%, respectively. The lowest coefficient ofdetermination for ozone and nitrogen oxidesconcentration was obtained for 2018 which equaled 0.476%. Regarding the spatial distribution of ozone and nitrogen oxides in 2008, the highest concentrations were observed inMasoudiyeh, Punak, Rose Park and Aqdasiyeh stations, and the highest concentration of nitrogen oxides was observed in District4, Crisis Management Headquarterand Sadr Expressway(District 3). In 2009,the station in Rose Park (District 22) showed the highest concentration of ozone and nitrogen oxides.In 2018, IDW modelling and spatial distribution of ozone and nitrogen oxidesshowed a different result. In this reference period, the station in district 4 received the highest annual concentration of ozone and nitrogen oxides, and north eastern areas ofTehran was regarded as the most polluted areas based on the concentration of these pollutants. But stations in16th, 19th and 20th districts and Masoudieh station (15th district) had the lowest annual concentration of ozone and nitrogen oxides. In general, it can be said that spatial modeling with Sentinel-5 product has been able to model the concentration of ozone and nitrogen oxides inall stationsof Tehran on a pixel by pixel basis.
