نشریه اطلاعات جغرافیایی (سپهر)
پیاپی 122 (تابستان 1401)

The concentration of particulate matters has recently increased in the metropolitan area of Tehran resulting in many severe hazards for both the environment and citizens. Particulate matters (PM) with a diameter less than 2.5 microns (PM2.5) are considered to be one of the most dangerous types of pollution. Estimating the concentration of these particles in Tehran is challenging due to the existence of various sources of pollution and the lack of sufficient ground stations. Aerosol optical depth (AOD) data retrieved from satellite imagery can be an alternative. However, AOD are not easily convertible into surface pollution and requires the development of appropriate models such as those based on data-driven approaches and machine learning techniques. Thus, the present study seeks to create a model to estimate the concentration of PM2.5 in Tehran employing deep generative models and in-situ measurements, meteorological data, and AOD data extracted from MODIS satellite imagery. Reviewed literature has proved the ability of deep learning techniques to solve regression and classification problems. Deep learning techniques are divided into various categories, one of which is based on the generative models seeking to reconstruct the input features. In this way, high-level and efficient features can be employed to explore the relationship between PM2.5 and AOD. Thus, the present study has investigated the potential of deep generative models for estimating PM2.5 concentration from high resolution AOD data retrieved from satellite imagery. 

Materials and Study Area:

 As a metropolitan area suffering from air pollution particularly in winters, the capital city of Iran, Tehran was selected as the study area. PM2.5, the main source of pollution in Tehran, is mainly emitted from vehicles and especially old urban public transportfleet.Aerosol data collected by Aqua and Terra sensors of MODIS and retrieved by Multi-angle Implementation of Atmospheric Correction (MAIAC) algorithm were used in the present study. Meteorological data were obtained from the global ECMWF climate model, and the concentration of PM2.5 was measured at air quality monitoring stations. Data were collected for a time interval of January 2013 to January 2020.

The present study has investigated the potential of deep generative models used to provide an estimate of PM2.5 concentration based on satellite AOD data. To reach such an aim, three types of deep generative neural networks, deep autoencoder (DAE), deep belief network (DBN) and conditional generative adversarial network (CGAN) were developed. Moreover, the performance of deep generative modes was compared with linear regression techniques as typical models used to explore the relation between PM2.5 and AOD data. Finally, the most accurate model for the generation of high resolution (1km) PM2.5 maps from AOD data was selected based on the performance of models.

 Results and Discussion:   

The accuracy of each developed model was evaluated using the test data and the obtained results were compared with results obtained from other basic linear regression models. Accuracy evaluation indicated that the developed deep autoencoder (DAE) combined with support vector regression led to the highest correlation (R2 = 0.69) and lowest RMSE (10.34) and MAE (7.95) and thus, can be potentially used for high resolution estimation of PM2.5 concentration. Next was the developed deep belief network which with a performance close to DAE demonstrated its potential capability to estimate PM2.5 concentration from satellite AOD data. The CGAN network acted less accurately in the estimation of PM2.5 concentration as compared to other deep generative models, but outperformed the linear regression algorithms on the test data. To sum up, findings indicated that deep generative models have outperformed classical linear regression techniques used for high resolution estimation of PM2.5 from satellite AOD data. Among the linear methods, the highest accuracy was achieved by the Lasso algorithm with an RSME of 12.14 and MAE of 9.46 on the test data which showed the significance of regularization for the improvement of performance in linear regression algorithms. Nevertheless, the accuracy of linear regression techniques was much lower than deep generative models.

Finally, DAE was selected as the best model for the estimation of PM2.5 concentration across the study area and high resolution maps of PM2.5 concentration were generated using the developed model. Investigating the daily PM2.5 maps generated for two days with different air quality conditions (clean and polluted) demonstrated the efficiency of the developed DAE for PM2.5 modeling.

Air pollution is now considered to be one of the most important challenges Iran faces and plays a major role in changes of its climate. Factors such as population growth and the consequent increase in the number of cars, as well as the presence of various (and often old) industries and the energy demand they satisfy have led to an increase in pollution in many Iranian metropolises. As one of the four Iranian industrial hubs, Arak has one of the worst air quality in this country. In addition to the presence of industries, having a relatively high population density (and consequently high traffic congestion level) and various climatic conditions affect the quality of air in Arak. It is essential to accurately measure air pollutants with a high spatial and temporal resolution, determine their distribution pattern and level of effectiveness, and provide provincial and national managers with applicable solutions. Unfortunately, air quality monitoring stations are not sufficiently and properly distributed in Iran.Many Iranian cities do not have even a single air monitoring station and many others have only one station. As the capital city of Markazi province and an industrial city, Arak has only four monitoring stations which are not simultaneously active in many cases.Failing to conduct proper site selection before the installation of ground-based monitoring stations results in local irregularities in the recorded concentration of pollutants. Furthermore, the stations are not usually calibrated on time and thus air quality monitoring observations are disrupted. In these cases, either this data is deleted from the final results or the station will be inactivated (for example, for a week or a month) by authorities. However, it seems that the observations made by these stations still include inaccurate data.

The present study has introduced a method based on composition and voting to validate the observations made by air quality monitoring stations using Sentinel-5 satellite images. Arak city was used as the study area. Level three images (L3) of the Sentinel-5 TROPOMI sensor received from the Google Earth Engine were used to monitor the concentration of pollutants in the present study. Sentinel-5 is a powerful atmospheric monitoring tool. Equipped with a spectrometer called TROPOMI, the satellite measures ultraviolet radiation reaching the Earth's surface in a high range. TROPOMI sensor is highly capable of imaging and monitoring a large number of pollutants. The present study has compared the concentration of NO2, SO2, CO and ozone pollutants monitored by ground-based stations in Arak city with Sentinel-5 images. Since the time resolution of ground-based observations is higher than satellite observations, a monthly average of pollutants' concentrations was calculated to increase the reliability of observations. In other words, the concentrations of pollutants were compared on a monthly basis. The proposed method has assumed that more accurate sets of ground observations show a higher linear correlation with satellite observations.
In order to select the appropriate set, the number of observations with an acceptable accuracy must be determined. To do so, a method based on a mixture of composition and voting has been used. As previously mentioned, each observation showed average pollutant concentration in a specific month of the study period. The process started with at least four monthly observations. As a result, assuming that all 19 monthly observations were available, 16 subsets were obtained with a maximum linear correlation between ground-based observations and their satellite correspondence which showed the accuracy of the observations. The second step was the proposed voting method which showed that the monthly ground-based observations (for example October 1398) were repeated several times. The high frequency of a monthly observation indicated its higher accuracy. The presence of this particular observation in different permutations has increased the linear correlation coefficient of the observations. Therefore, for an instance a frequency of 15 or 16 for the observation made by the ground-based station in October 2017 indicated high accuracy of the observation.

The present study has compared the concentration of NO2, SO2, CO and ozone pollutants Using the proposed method, some observations have been identified as outliers or errors. RMSE criterion was used to evaluate the accuracy of the proposed method. Some observations made by the ground-based station were not consistent with other ground-based and satellite observations, and removing them increased the correlation coefficient. Removing outliers from the observations, the RMSE (originally 2%) was improved and reached 47%.

Findings indicated that some observations made by ground-based monitoring stations were incorrect, or at least the stations had sometimes failed to exhibit the real general trend of environmental pollution correctly due to local irregularities caused by various reasons, such as improper location or lack of proper calibration.

Today, the increasing growth of urbanization and urban population and consequently, heavier traffic and larger number of motor vehicles in urban and suburban areas have created many problems for the transportation system. On the other hand, the unresolved problem of traffic congestion in cities and the related air pollution have had seriously damaged health and life quality of many citizens and resulted in the death of many patients diagnosed with lung and heart diseases. Therefore, the present study seeks to investigate the desirability of urban routes' design for cycling and its relationship with the indices of a bike-friendly city in Yazd. The present study addresses these questions: how desirable is the design of urban routes for cycling in Yazd? Do present rules and standards increase the satisfaction of cyclists? What is the relationship between the desirability of urban cycling route designs and developing a bike-friendly city?

The present descriptive study is considered to be a survey in terms of nature and methodology and applied-developmental in terms of purpose. Data collection was performed using library, documentary and survey methods. Citizens of Yazd were selected as the statistical population. Personal estimation method was used to ensure a homogeneous and standard sample is selected with an appropriate size. The sample included 20 experts and urban designers and 100 cyclists who have cycled on urban routes in Yazd. Purposeful methods of sampling such as snowball and theoretical sequence have been used in the present study.

Results obtained from the UTA technique and the Fuller hierarchical method used to weigh relevant indicators show that the security criterion has ranked first (with a weight of 0/344) while the continuity criterion has ranked last (with a weight of 0/181). Pearson correlation analysis did not find any significant relationship between income, gender, age and education with cyclists' satisfaction level, but a significant relationship was found between the observance of standards in urban routes and the level of satisfaction. Considering the linear regression diagram and r2 = 0/52, a desirable design for urban cycling routes can provide up to 52 percent of the conditions required for turning Yazd into a bike-friendly city.In general, findings of the present study are closely related with Bicalho et al. (2019), Yang et al. (2019) and Nazarpour and Saedi (2020) concluding that developing cycling infrastructure in accordance with appropriate rules and standards, holding workshops to create a positive attitude and a greater understanding in urban planners toward cycling, improving street connections and the desirability of the cycling routes' designs for cyclists will enhance the creation of a bike-friendly city. The present study indicates that compliance with national standards and regulations in urban routes is mandatory for cyclists. Findings are also closely related with Podgórniak-Krzykacza and Trippner-Hrabi (2021), Babiano et al. (2017), Shabanpour and Zareh (2019), Manafi Azar et al. (2018) and Soleimani et al. (2017) which indicate that cycling increases access to transportation network, prevents congestion and inefficiency of public transportation, reduces traffic jams, increases safety and security, prevents environmental pollution and results in sustainable urban transportation.  Thus, the present study has concluded that a desirable design for urban cycling routes can turn Yazd into a bike-friendly city.

Results of UTA technique indicated that rules, regulations, and bylaws assigned for cycling paths in Iran such as longitudinal slope, cross slope, open sight distance and stopping sight distance, minimum radius of curvature of the bike lanes, horizontal signs, and special traffic lights shall be reviewed and practically used to create a more comfortable space for cyclists. The analysis indicates that urban routes in Iran must be designed in accordance with the standards of cycling routes, and the respondents have also emphasized on this necessity. Moreover, it was indicated that there is a positive correlation between compliance with standards and the level of comfort in cyclists. In other words, compliance with standards in urban routes' designs increases the level of comfort in cyclists. Finally, it can be concluded that there is a positive and meaningful relationship between the desirability of urban routes' designs for cycling and the chance of turning Yazd into a bike-friendly city.

Soil is considered to be dynamic and complex both spatially and temporally and thus, many physical, chemical and biological properties should be determined before assessing its quality. To reach this purpose, a large sample must be collected for laboratory tests which is both time-consuming and costly and requires lots of attention and precision. Compared to other components of soil, sand is closely related with the quality of soil and crop growth. Therefore, environmental modeling and digital soil mapping projects should pay special attention to this part of soil texture. However, large-scale detection, mapping and monitoring of sand content using common traditional sampling and usual laboratory analytical procedures are both time-consuming and costly due to the vast spatial variability of sand. Compared to laboratory-based and field spectroscopy, spaceborne and airborne remote sensing have a lower level of accuracy due to atmospheric effects, compositional and structural effects, lower spatial and spectral resolution, geometric distortions and spectral mixing. Hence, an appropriate technology is required to overcome these imperfections and study spatially variable factors. Lab Diffuse reflectance Spectroscopy (LDRS) which utilizes fundamental vibration, overtones and a combination of functional groups has been introduced as a promising tool for soil investigation. The present research uses proximal soil sensing technology to study sand content.

128 samples were collected from a soil depth of 20cm in accordance with stratified randomized sampling method and supplementary data (geology, pedology, land use, etc.). The samples were then divided into two subsets: calibration subset with 96 and validation subset with 32 samples. Afterward, definitive calibration model was developed and reviewed with two & four latent variables in accordance with R, R2, RMSE, RPD and RPIQ indices using multivariate regression analysis-PLSR method, LOOCV cross-validation technique and preprocessing algorithms such as spectral averaging (spectral reduction method), smoothing and 1st derivative (Savitzky-Golay algorithm).
 

Results & Discussion:

The estimating model indicated that out of the seven latent variables, the first two and four variables can provide the best estimate of the volume of sand in 96 calibration samples and the 32 validation subset. Since more than 60% of the variance of sand variable and 95% of the variance of spectral variables can be concentrated in these selected factors, the predicting model was calibrated based on the first four LVs and the full LOOCV procedure. The best model was calibrated with these features: Rc=0.76, R2C=0.57, RMSEc= 9.77 and SEc of about 9.82. The correlation coefficients (R) between sand contents and the effective spectral bands were calculated and equaled UV-390nm= 0.46, Vis-510 to 540nm about 0.53, 680 to 690 about 0.55, NIR- 950 to 970 about 0.67 and 1100nm= 0.70, SWIR- 1410 nm=0.76, 1860 to 1900 about 0.76, 2180 to 2220 about 0.77 indicating that the selected spectral bands (spectral ranges) with the maximum R contents were the most effective independent predictors in the present modeling process. Furthermore, the most influential spectral domains in the modeling process were determined as follows: UV-390 nm, Vis-440-540 nm, NIR- 740-990 nm, SWIR- 1430-1890, 1930, 2190-2240, 2330-2440 nm which was in agreement with previous studies. The quality of the calibrated sand predicting model was evaluated with Hotelling, Adjusted leverage and residual variances tests. The model was validated based on 32 independent samples. General characteristics of the validation process for LV=4 were Rp= 0.82, R2p= 0.67, RMSEp= 8.83, SEp= 8.92 and bias= -0.93 and Rp= 0.83, R2p= 0.68, RMSEp= 8.68, SEp= 8.72 and bias= -1.26 for LV=2.

Results indicate that the final model was capable of predicting sand contents and thus for two factors (LV=2): RPDc= 1.51, RPIQc= 2.44, RPDp= 1.78 and RPIQp= 2.45 were obtained while for four factors (LV=4): RPDc= 1.54, RPIQc= 2.48, RPDp= 1.75 and RPIQp= 2.41 were reached. A RPIQ of more than 2 shows that the model is capable of estimating soil sand content in Mazandaran province using data collected through diffuse reflectance spectroscopy. Since a new generation of hyperspectral remote sensors with high spectral resolution is now available, results of the present study can be the starting point for more accurate mapping of sand particles in soil texture using RS platforms. However, proximal spectroscopy must be more thoroughly investigated. Determining and detecting the key wavelengths in the modeling process can enhance the upscaling operation and the new airborne/satellite hyperspectral sensors and thus result in more precise mapping of the soil texture. Finally, the VNIR-DRS technology was proved to be potentially capable of estimating soil sand content in Mazandaran province. The present model and key spectral domains identified in the present study can make a basis for future studies investigating the sand content in very large-scale samples using airborne/satellite hyperspectral data. This shows the importance of LDRS and its role in identifying optical wavelengths which will be used in space-borne data (upscaling process).

Cultivation Pattern is a roadmap that shows which, how much, when, and where crops should be cultivated given the constraints and available resources. Cultivation pattern program determines appropriate crop types in accordance with the climatic condition of the province and thus ensures the sustainability of agricultural products, food security, and optimal utilization of resources, capabilities and potentials of each region. Review of the related literature indicates that AHP  and TOPSIS  methods are among the most widely used methods in decision making and prioritization. Moreover, previous studies have shown that AHP method is suitable for qualitative data and TOPSIS method is suitable for quantitative data, whereas both quantitative and qualitative factors are involved in determining the cultivation pattern. Therefore, the present study has utilized a larger number of criteria (nine criteria), and combined AHP and TOPSIS models in an attempt to make use of their strengths and avoid their weaknesses. Linear programming was also used with four scenarios. In one of the scenarios, two lowest ranking crops in TOPSIS method were eliminated. The present study has innovatively utilized these models and a larger number of criteria simultaneously to determine the cultivation pattern. It also has precisely identified the appropriate crop for each plot of land using SWOT  tables.

The case study was located in Qeyghaj plain in west Azerbaijan province. In accordance with the geographical location and climate, wheat, barley, alfalfa, sugar beet, rapeseed, potato, maize and fodder corn are mostly cultivated in the area which have been considered as alternatives for cultivation in each plot of the present study. The present study has begun with evaluating the slope and aspect of each cultivation plot. Then, crops are ranked and optimal crops are selected based on various criteria and using a combination of AHP and TOPSIS models and different decision matrices. Afterward, the maximum and minimum appropriate volume of crop production is determined using linear programming in accordance with the maximum profit. Finally, the most suitable crops for each land parcel are determined using SWOT tables.The present study has proposed a multicriteria decision model which includes the strong points of AHP and TOPSIS models and avoids their weaknesses. In order words, relative weights were obtained from AHP pairwise comparisons and also the compatibility index was evaluated using AHP model while crop alternatives were ranked using TOPSIS model. The hierarchical structure included the goal, nine criteria used to evaluate the strategies and eight strategies (options).Matrices used in pairwise comparisons were all obtained from experts' opinions. These include comparisons made to determine the weight of each criteria to be used in the TOPSIS model, as well as pairwise comparisons made between options which could not be quantitatively compared. Then, the general structure of the hierarchical model was developed in Superdecision software and the final weights, compatibility index of each matrix and quantity of each product were obtained based on each of the indicators. The values were then entered into the TOPSIS model and used to rank the crops, compare different options and select the best crop.

In the first step, a slope map was produced for the study area using digital elevation model based on which an aspect map was also produced. In accordance with these maps, the physiological suitability of the study area for the cultivation of eight crop types was evaluated. Results indicate that the study area is physiologically very suitable for cultivation of alfalfa, suitable for wheat, barley and canola and fairly suitable for the other four remaining crops.Then, pairs were compared in hierarchical analysis using expert opinions and the weights of criteria and crops were obtained. Then, weights were assigned to each alternative (crops) and decision criterion (nine selected criteria) using the TOPSIS model and more appropriate products were selected. A decision matrix was first created in TOPSIS. Some criteria such as economic index were initialized directly in accordance with the available quantitative values whilst the values of some other criteria (such as temperature whose quantitative values cannot be obtained) were initialized using the results of AHP.In the next steps, a weighted normalized matrix was developed and positive and negative ideals were found. The distance between positive and negative ideals was calculated and then the ideal solutions were obtained. Finally, the score obtained by each alternative or similarity index was calculated. The closer similarity index is to one, the superior that alternative will be. Linear programming is a method in mathematics that finds the minimum or maximum value of a linear function on a polygon. The present study seeks to reach maximum profit under various restrictions such as water restriction, restrictions on area under cultivation and maximum and minimum amount of cultivated crop. Water restriction included all surface and subsurface resources for crop cultivation. Crop coefficients were defined as the need for crop irrigation. Water constraints included the constraints assigned to allocated water in spring, summer, autumn and total amount of allocated water.Three scenarios were developed with or without the previously mentioned constraints. Then the goal function was changed in accordance with the MOTAD method and another scenario was developed. The scenarios are explained as follows:Scenario 1 (Without any restrictions on the minimum and maximum crop yield): In this case, the goal was reaching the maximum profit and the restriction included the lowest amount of water consumption, regardless of the requirements in the study area. In this scenario, variables x1 (wheat), x5 (Canola) and x8 (fodder corn) were included in the cultivation pattern. Consequently, farmers' income was maximized and the amount of water consumption was reduced. However, obtained results were not acceptable in accordance with the regional and national policies since cultivation of most crop types will thus be stopped.Scenario 2 (locally acceptable size and local farming customs and the restrictions assigned by the agriculture office): the present scenario seeks to maximize profit, satisfy requirements of the area and achieve the goals of the agriculture office. All crops are included in the cultivation pattern. Therefore, minimum and maximum cultivation restrictions have been used in addition to water and land restrictions.Scenario 3 (not cultivating some water-intensive crops): As previously mentioned, Poldasht agriculture office has introduced reduced cultivation of some low yielding crops or even stopping the cultivation of such crop types as one of its main goals. Corn and potato are highly water -intensive with a low yield in the study area and thus gain one of the lowest ranks. Therefore, potato and corn were removed to determine the cultivation pattern of the region in their absence.Scenario 4 (MOTAD approach): MOTAD is a linear programming approach aiming to maximize the profit whose objective function equals the sum of deviations between total gross income and the expected income based on the average gross income of the sample. Linear programming with MOTAD requires having access to income gained from each crop type in previous years. Restrictions such as fund and manpower restriction must also be considered. The statistical period used in MOTAD approach starts in 2011 and lasts till 2016.Income values in MOTAD approach lead to a constraint relation. Just as the previous scenarios, water and land constraints are considered in this approach and fertilizers and pesticides restrictions have not been taken into account.

Based on the collected information, available parameters, SWOT analytical model and tables developed for each field, a suitable crop was selected for each farm (parcel). Accordingly, 112.3 hectares was identified as suitable for the cultivation of wheat, 59.9 hectares for barley, 32.1 hectares for alfalfa, 37.6 hectares for sugar beet, 85.7 hectares for Canola, 15.5 hectares for potato, 13.2 hectares for Maize and 63.7 hectares for fodder corn. In this case, the resulting profit equaled 23, 503,410,000 Rials and the water consumption equaled 2,542,293.8 cubic meters which shows 2,052,120,000 Rials increase in profit and 90,770.6 cubic meters decrease in water consumption as compared to the present cultivation pattern.Comparing the profit and water consumption in each of the five models and the current cultivation pattern, it can be concluded that the pattern obtained from the SWOT analytical model is more feasible since it includes various parameters and particularly farmers' opinions.

Due to the complexity of frame processing used for positioning and mapping in visual odometry (VO) and visual simultaneous localization and mapping (VSLAM) algorithms, key-frame selection methods have been introduced to improve the performance and decrease the number of frames required for processing while maintaining accuracy and robustness of the algorithms. Selected key-frames in these methods make a very good representation of all available frames. The current key-frame selection methods rely on heuristic thresholds in their selection procedure. Researchers have used several datasets to find optimum values for these thresholds through trial and error. In fact, proposed methods may not work as expected with a new dataset due to changes occurring in the sensor, environment and the platform.

The present study has proposed an improved geometric and photogrammetric key-frame selection method built upon ORB-SLAM3, as the state of the art visual SLAM algorithm. The proposed Photogrammetric Key-frame Selection (PKS) algorithm has replaced inflexible heuristic thresholds with photogrammetric principles and thus guaranteed the robustness of the algorithm and the quality of the point cloud obtained from the key-frames. First, an adaptive threshold decides the allowable number of points whose line of sight zone has changed on a four-zone cone built upon each point. Increased number of points whose line of sight zone has changed means increased changes and displacements of the frame and thus, increased need for a new key-frame. Then, a 3*3 grid was formed in each frame and the number of points with a more than 30-degree change in line of sight angle (effective points) in each cell were counted. Later, the Equilibrium of Center Of Gravity (ECOG) criterion decides whether the distribution of points is appropriate using the center of gravity of the points inside the frame. Appropriate distribution of effective points within the frame shows a high geometric strength and thus will improve the strength of key-frames network. IMU sensor  is not dependent on the position of the frames and the camera sensor. Thus, it independently obtains the key-frame in case significant changes occur in acceleration. The threshold value of acceleration has been experimentally considered equal to 1 meter per square second, which entirely depends on the type of robot. For ground robots with slower moving speeds, this threshold must be reset.

The present study has employed data collected by the European Robotics Challenge (EuRoC) flying robot containing the information collected by the synchronized camera and IMU information, as well as the ground truth data such as the robot trajectory and point cloud formed by the laser scanner. To evaluate the proposed method, extensive experiments have been implemented on the EuRoC dataset in mono-inertial and stereo-inertial modes. Then, trajectory of each algorithm was compared with the reference trajectory and point clouds formed by the key-frames were also compared. Apart from these qualitative evaluations, absolute trajectory error (ATE) obtained from running the PKS and ORB-SLAM3 algorithm 10 times were also compared quantitatively and finally, the error histogram was used to evaluate the point clouds. The processing time of each algorithm was also evaluated for each EuRoC dataset sequence. Results indicated that the proposed algorithm has improved ORB-SLAM3 accuracy in stereo-inertial by 18.1% and in the mono-inertial mode by 20.4% producing a more complete and accurate point cloud and thus, extracting more details from the environment. Furthermore, despite higher density of the point cloud, the error histogram has not changed significantly and fewer errors were observed in the ORB-SLAM3 algorithm.

Findings indicated that the PKS method has succeeded in extracting key-frames using photogrammetric and geometric principles. Apart from improving the positioning accuracy of the robot, the method has produced a much more complete and dense point cloud as compared to the ORB-SLAM3 algorithm. Also, dependency of the PKS method on the environment conditions and the type of system used (stereo camera or mono camera) was greatly reduced. Future studies can expand our key-frame selection method to include fisheye cameras or visual-only systems. More geometric conditions (near and far point condition and the vertex angle in the triangle formed by the points in the current frame, the camera and the corresponding points in the last key-frame) can also be added to the key-frame selection method.

Vegetation is considered to be one of the most important elements in all major ecosystems on the Earth. Thus, a proper understanding of vegetation and its growth trends and other environmental factors has always been of particular importance for environmental research. Estimating vegetation phenology parameters (VPPs) requires continuous NDVI data collection over a specific period of time. However, soil moisture, cloud cover, and particulate matter may affect the energy reflected from the vegetation cover and result in noisy images or erroneous data. Vegetation phenology parameters cannot be extracted from raw data due to the presence of random errors. These errors do not follow the phenological process and thus, overestimate or underestimate NDVI and fail to produce accurate results. Smoothing functions and especially the TIMESAT model are used to resolve this issue and eliminate errors in the NDVI time series. There is still no general consensus on which function acts more efficient and accurate in the TIMESAT model especially regarding the highlands. Naturally, each method yields different results in different regions, and thus it is necessary to compare and evaluate different functions used in the TIMESAT model and determine their accuracy in producing a continuous time series. The present study aimed to evaluate the performance of various functions such as asymmetric Gaussian (AG), double-logistic (DL), and Savitzky–Golay (SG) used to extract VPPs especially in mountainous regions.

TIMESAT model is a time-series analysis model based on remote sensing (RS) vegetation indices. It includes three functions: Savitzky–Golay, asymmetric Gaussian, and double-logistic, which are used to smooth collected data and identify outliers. Savitzky–Golay is an adaptive-degree polynomial filter (ADPF). The other two functions fit the information using nonlinear functions. These functions use unmodified NDVI data as input to produce modified and smoothed NDVI output. Four wheat farms in cold and warm regions of Khorramabad were used in the present study to investigate plant phenological behaviors and extract VPPs. The northern and eastern parts of Khorramabad have a cold climate, while the southern and western parts have a warm climate. One-year time series (2020) data of MODIS sensor was used in the present study. Using the infrared and near-infrared spectral reflectance values, NDVI was calculated in the Google Earth Engine environment. Errors of the NDVI time series were first corrected and a phenology curve was produced for wheat in both warm and cold farms. Asymmetric Gaussian, double-logistic, and Savitzky–Golay filter functions were also used to adapt the NDVI data. Following the reconstruction of growth curves in the time series of vegetation indices and smoothing the curve, various VPPs such as start of the season (SOS), end of the season (EOS), middle of the season (MOS), length of the growing season (LOS), base limit and value, maximum NDVI, vegetation growth season range, large seasonal integral, and small seasonal integral were extracted.

 Results and Discussion:

The model indicated that on average, beginning of the wheat growing season (SOS) in the warm regions of Khoramabad coincided with the 31.5th day of the year in the Gregorian calendar, whereas it happened on the 90th day of the year in the cold regions, thus indicating a 1.5-2 month difference between the beginning of the wheat growing season in cold and warm regions. The wheat growing season ended (EOS) on the 163rd day of year in the warm regions and on the 193rd day in the cold regions. In addition, in order to analyze the effect of climate on VPPs such as SOS and EOS, a comparison was made between the parameters obtained from farms in warm and cold regions. On average, the peak of vegetation growth has occurred in late March (Mar. 28, 2020) in farms of warm regions while cold regions experienced the peak of growth on May 20, 2020. In other words, warm regions have experienced peak growth approximately two months earlier than cold regions. Finally, the models were assessed and obtained values were compared with ground-based data collected in field surveys. Validation results showed that with an average RMSE of 2, Savitzky–Golay smoothing model reconstruct data more accurately as compared to asymmetric Gaussian, and double logistic function with an RMSE of 4 and 11, respectively. In other words, Savitzky–Golay estimates SOS and EOS with a higher accuracy and lower errors.

Findings indicate that Savitzky–Golay filter outperformed asymmetric Gaussian and double logistic functions in extracting VPPs in mountainous areas. Accordingly, it is suggested to use Savitzky–Golay in future studies aiming to investigate the phenological behavior of different vegetation covers in other Iranian highlands. The study has also showed that different climatic conditions within the study area affect plant phenological behaviors, which can lead to differences in SOS, peak of growing season, and EOS in different cold and warm regions of the province. Growing season of plants in cold regions of the province has occurred with an approximately two-month delay compared to the warm regions of the province.

As an influential element of climate, precipitation affects human activities and societies. It is thus considered to be the essence of any study conducted as a part of environmental and economic planning. Precipitation in Iran, especially in its west and southwest is affected by thermal, dynamic, and thermodynamic low-pressure centers such as the Red Sea trough. The trough is an extension of Sudanese low-pressure with a central pressure of about 1006 hPa. The Red Sea is stretched in a southeast to northwest direction and thus connects tropical and subtropical regions. Considering the importance of the Red Sea low-pressure system for precipitation events in west and southwest Iran, any change in this system will affect precipitation patterns in the region. Analyzing the activity of this system and resulting precipitation in west and southwest Iran will thus provide more accurate understanding of the climate of this region.

Environmental and precipitation data retrieved from Asfezari national database and atmospheric data (geopotential height) extracted from the European Center for Medium-Range Weather Forecasts (ECMWF) were utilized in the present study. A numerical algorithm was also used to identify the cyclones. The algorithm identified 459 cyclones in the statistical period.

Time distribution of days in which the Red Sea trough is active showed increased activity in summer (198 days) especially August (99 days) and spring (178 days) especially April. However, the Red Sea trough showed decreased activity in autumn and winter. Activities of the Red Sea trough have shown a slightly decreasing but significant annual trend during the statistical period. A sharply and significantly decreasing slope can be observed in summer which results in a decreasing annual trend. Average daily precipitation of the study area in the statistical period ranged from 0 to 2.5 mm. The minimum average precipitation (less than 1 mm) was observed in 29.58% of the study area while maximum average precipitation (more than 2 mm) was observed in 3.64% of the study area. The largest part of the study area (66.87%) experienced an average daily precipitation of 1 to 2 mm. Moreover, 24.28% of the region with minimum precipitation (less than 1 mm) was located in the south and southwest of the study area. This indicates a relatively less severe impact of the Red Sea trough in this area. Around 70.88% of the study area has experienced a precipitation between 1 and 2 mm. Subtracting average daily precipitation recorded throughout the statistical period from the average daily precipitation occurring simultaneously with the activities of the Red Sea trough showed a positive anomaly (more than 0.4 mm) in the north and northeast of the study area. Therefore, it can be inferred that most of the precipitation in this area is originated over the Red Sea. It seems that the presence of the Zagros Mountains has also had a significant effect on precipitation in the study area. Areas with a negative anomaly (less than -0.4 mm) in which precipitation is not affected by the Red Sea trough include spatially scattered regions in Khuzestan, and Kohkiluyeh and Boyer-Ahmad provinces (0.74% of the study area). In other words, precipitation associated with the activity of the Red Sea trough was less than the total precipitation, and thus, most of the precipitation in these regions has other sources.

Results indicated that during the statistical period, minimum average daily precipitation has occurred in south, southwest, and northeast of the study area. Moreover, south and southwest of the study area experienced precipitation simultaneously with the activity of the Red Sea trough. The maximum precipitation in either cases (during the statistical period and also during the activity of the Red Sea trough) has been concentrated in parts of the northwest, west, and east of the study area (along the Zagros mountain range). Significant latitude difference between the north and south of the study area, and existence of the Zagros Mountains and consequently the heterogeneous topography have created two different zones in the study area experiencing minimum and maximum precipitation. In the presence of the Red Sea trough, a higher percentage of the study area experienced maximum precipitation. The frequency of days with more than one millimeter precipitation and their spatial distribution showed that under general conditions, the maximum precipitation has occurred in the north, northwest, west, and east covering 61.11% of the study area. Kurdistan province has recorded a maximum precipitation in more than 3500 days under the influence of different air masses. More than 73% of the factors associated with precipitation in Iran, especially in its northwest, west, and southwest are various synoptic systems (cyclones and short waves) entering the country from the Mediterranean with westerly winds. The minimum number of rainy days during the whole statistical period and also during the low-pressure activity of the Red Sea were also recorded in the southern and southwestern parts of the study area.

The rapid expansion of urbanization along with irregular changes in land use and Landscape System of Tehran has disrupted the composition and distribution pattern of urban green infrastructure. The present study seeks to analyse the spatial-temporal changes of urban green infrastructure in Tehran Landscape System affected by the spatial processes of land use changes in the statistical period (4 decades of 1990 to 2030). To reach this aim, the present study has identified (1) the effect of spatial processes on the changing landscape pattern and (2) the relationship between the spatial pattern and ecological processes of landscape and its influence on the capacities and constraints of green urban infrastructure.

The present study has focused on the landscape system of Tehran and its 22 districts as the study area. The descriptive-analytical study consists of following stages: 1) Classifying urban land uses in1990-2000, 2000-2010 and 2010-2020 statistical periods using Landsat satellite images: (in Envi 5.3, Google Earth and Arc GIS 10.2 software), 2) Modelling and forecasting land use changes in 2030 using integrated model of Markov chain, automated cells (CA-MARKOV) and TerrSetsoftware), 3) Determining spatial processes of landscape changes via decision tree algorithm. 4) Quantifying landscape metrics of composition and configuration of landscape pattern (green, open & built patches) at both class and landscape levels in the mentioned periods (in Fragstate 4.2 software).
 

Many environmental decisions presume that some types or composition of land use are preferred to others. It is assumed that the spatial arrangement of elements in a land-space mosaic controls its ecological processes. This proposition is known as the pattern/ process paradigm, and forms the central hypothesis of landscape ecology (a branch of science developed to study ecological processes in their spatial context). Ten spatial landscape processes are considered to reflect changes in various patterns of landscapes (aggregation, attrition, creation, deformation, dissection, enlargement, fragmentation, perforation, shift, and shrinkage). These processes actually change the spatial structure of urban landscape and affect the quality of ecological processes in Tehran Landscape System. To identify the spatial processes responsible for landscape pattern changes during a defined period of time, a decision tree algorithm was developed. Decision tree required the following input: area or size (a), the perimeter or edge length (p), and number of patches (n) in each land-cover class. The decision tree algorithm applied on Tehran Landscape System has indicated that spatial processes of 'attrition' and 'fragmentation' have led to a decrease in the integration of green and open patches in this landscape system. Measuring LSI and IJI metrics in 1990-2030 statistical period at the class level has also proved the previously mentioned finding. Increased ENN-MN and decreased PLAND of open and green patches during two periods of 1990-2000 and 2010-2020 due to the spatial process of 'attrition' have also showed this decreased integrity over time. These conditions have reduced the resilience of Tehran atmosphere and its capability to absorb air pollution and also have resulted in the recent development of thermal islands in different urban areas. Moreover, the COHESION metric has reduced in green and open patches due to the spatial processes of 'attrition' and 'fragmentation' at the class level. At the landscape level, the value of SIDI metric has also decreased from 1990 to 2020 and the same trend will continue according to 2030 forecast. Spatial process of 'aggregation' in constructed patches has resulted in a decrease of NP and PD at landscape level during 1990-2000 and 2010-2020. Findings indicate the effect of spatial process of aggregation on constructed lands (high-rise buildings) in the northern (such as District 1) and western parts of the city (such as District 22) which has interrupted wind movement and air purification in Tehran. The values of LSI and ED has also decreased at the landscape level due to the 'attrition' of open and green patches leading to a reduction in the heterogeneity order of urban landscape system. On the other hand, increased IJI value in 2020 and 2030 indicates increased turbulence in distribution and also increased fineness index of open and green patches in the landscape system of Tehran.

Findings indicate that spatial processes of 'attrition' and 'fragmentation' have resulted in a reduction in the number and area of green and open patches in the composition pattern and also decreased coherence at class level from 1990 to 2020. This has resulted in an unbalanced distribution of the patches in the configuration pattern of green urban infrastructure in Tehran. The spatial process of 'aggregation' has been repeated during the statistical period in the constructed patches. Data forecasted for 2030 shows the impact of 'attrition' on changes occurring in both green and open land use. The landscape is also getting more simplified due to the dominance of constructed land uses. Findings can be applied to determine a roadmap and plan the spatial pattern of urban green infrastructure.

Urban managers and designers are looking for ways to solve problems caused by population growth and increasing migration to large cities. Designing new cities on the outskirts of metropolitan areas to accommodate their overflowing population is one of these solutions. However, new cities will face demographic and ecological crises caused by rapid growth and sudden influx of population without purposeful and organized control and proper infrastructure. Developing new cities was first approved in 1985 as a policy to curb the uncontrolled population growth and migration to large cities in Iran. New cities are mainly developed to ensure proportional distribution of population in the desired urban area, decentralization of the metropolis, improvement of living and service standards, and prevention of unreasonable increase in land and housing prices. In accordance with the policies assigned for the development of new cities and serious restrictions on further development of Tehran metropolitan area especially on agricultural lands, and due to the high slope of land in the northern parts of the city, high groundwater level and inadequate soil penetration resistance in the southern parts of the city, the city being earthquake-prone, existing restrictions on the development of infrastructure, facilities, transportation network and water supply, inversion phenomenon and limitations the ecosystem will face with more population, development of 5 new cities in the suburban area of Tehran was approved by authorities.

Questionnaire and interview-based survey methods have been used in the present descriptive and analytical study. Data collection was performed using documentary and field study methods. Qualitative research techniques and content analysis tools have been used to select commonly used important research indicators from related literature. Following data collection from relevant organizations and institutions, a binary comparison questionnaire was prepared for each group of criteria. Using the Delphi method, urban planning experts were asked to comment on these tables. To evaluate and weight the obtained criteria, AHP method and Expert Choice 11 were used and the average was calculated in EXCEL. Pardis was selected as the case study to evaluate the opinions of new cities' residents. In survey and field study, tools such as questionnaires, interviews and observations were used to investigate the social and economic status in the new city of Pardis. A questionnaire was prepared based on Likert five-point scoring scale to determine the level of satisfaction in residents of Pardis city and to see whether in practice residents benefit more from the criteria assigned a higher weight by experts. The questionnaire was randomly distributed among 450 people and the results were evaluated in SPSS.

Findings indicate that proximity to the capital, acceptable roads, fair weather condition, fewer traffic jams, and lower house prices are among the reasons for satisfaction of Pardis residents. However, previous residents of Tehran expect a living standard similar to living standards in this city so there is still a long road ahead for Pardis city to fulfil its basic plans of offering settlements and employment for at least a population of 200 thousands and obviating the need for daily commutes. Findings indicate that 40% of the employed population commute daily which results in dissatisfaction and an unnecessary increase in household monthly expenses while turning the city into an unproductive dormitory town. Based on what was analyzed theoretically in the present study, as well as our knowledge of the prosperity, dynamism, population, and civilization level of new cities, it seems that a technocratic view based on instrumental rationality in a rent-seeking economy has ultimately led into the present situation in which large-scale urban development projects have been reduced into a series of housing projects. Therefore, various social issues and the problem of identity and dynamism have become a major issue in the urban system of new cities. However, paying attention to vitality and sustainable social development, as well as reviewing and redefining patterns and procedures have made an important turning point and created the required capacity for urban development management and foresight which shall be expanded to reach a useful executive plan and develop its theoretical and practical basis.

Results indicate that the new city of Pardis has achieved objectives of the detailed plan to some extent, but poor infrastructure, lack of sufficient number of employments for the households and lack of economic dynamism have created a city dependent on external employment and thus failing to achieve the utmost goals of the plan.

Earthquake is a natural disaster which sometimes causes injury and loss of life to many people and generates tsunamis. As one of the most frequently occurring natural phenomena, it is considered by many as the most frightening and dangerous natural hazard. With recent developments of remote sensing, radar interferometry is accepted as an efficient and relatively accurate method of measuring ground surface displacement. The present study has investigated June 25, 2020 earthquake in Qotur (the city of Khoy).

The present study have utilized InSAR and PSI techniques to estimate the amount of displacement caused by the earthquake. Remotely sensed images collected in the upstream passages were processed in Sarproz software. Radar interferometry and other advanced methods such as PSI have made detection of vertical surface displacements possible even in a few millimeter range. The present pairs of images have been selected with a good correlation from Sentinel-1 data.

The present study seeks to estimate the extent of ground heave and subsidence caused by earthquakes. Images selected from statistical periods before and after the earthquake were processed, and outputs were presented as figures and diagrams. Graphs showed the accuracy of the work and annual cumulative displacement. Results indicated the presence of a surface displacement between -16 and +16. The most intense subsidence and ground heave have happened in the northeastern regions (Gogerd village) and the southern regions (Kotanabad, Mir Omar, Grenavik villages), respectively.

Maximum displacements (heave and subsidence) and other data collected from the earthquake show that the Bashkala left-Lateral Strike-Slip fault has caused this earthquake.

Recent scientific and technological development have provided comfort and well-being for communities while also resulting in new challenges such as environmental pollution. As a source of environmental pollution, fossil fuels emit toxic gases into the air while burning and thus trap heat in atmosphere, increase air temperature and result in wide-ranging climate changes. As the most significant source of energy, the sun can provide us with a proper alternative to fossil fuels. Related information can be collected through direct measurement of solar energy using devices such as a pyrometer. So far, various approaches such as remote sensing have been adopted to universalize solar irradiance maps. Due to their high accuracy and speed in estimating net radiation, remote sensing techniques can be an appropriate alternative to old experimental methods. Having access to precise information on the amount and intensity of solar radiation at low latitudes, including Iran is essential for the development of solar sites. The present study assesses solar energy and the feasibility of generating solar power or a photovoltaic (PV) system in rural areas of Joveyn County.

Elevation and related maps, sunshine hours, direct and indirect radiation, and total radiation were first collected and calculated. GIS-based solar radiation analysis was conducted in the present study and a zoning map was generated showing total solar radiation in 113 villages of Joveyn County. Atmospheric transmittance and diffuse radiation were extracted from the total radiation and extraterrestrial radiation of the studied stations. Then the annual radiation received in 2017 was estimated using the radiation analysis method and 30-meter resolution Digital Elevation Model (DEM) of the study area. Finally, the feasibility was assessed based on the consumption requirements of the villages and solar energy production capacity in the study area.

GIS radiation analysis sub-program was used to zone the total solar radiation in Joveyn County. Atmospheric transmittance and diffuse radiation were then estimated separately using the radiation recorded in each station and entered the model to determine the radiation. Elevation and related maps, sunshine hours, direct and indirect radiation, and total radiation were first collected and calculated in the present study. The highest altitude was recorded in the southern parts of the study area including Jalambadan, Ramshin, and Bid rural areas.Sunshine duration was the most important climatic parameter in the present study. Except for the southern elevations, the study area generally experienced long sunshine hours. The longest sunshine duration was observed in spring with an average of 1177.81 W/m², while the shortest was in winter with an average of 904.269 W/m². Tarsak village and Ghaem town have experienced the longest sunshine hours. The highest direct solar radiation was observed in the southern elevations of Joveyn County. Results indicate that the highest amount of direct solar radiation is observed in spring in rural areas of Karimabad, Rahmatabad, and Beyhagh, while the lowest is received in autumn.The highest amount of total radiation was observed in Jalambadan and Rahravi Bidkhor villages in spring, while the lowest was observed in autumn. Observed differences in radiation and altitude show that both parameters were affected by topographic conditions such as degree and aspect of slope and obstacles blocking direct radiation. Results indicated that Rahravi Bidkhor, Kalateh Fazel, and Bidkhor have received the highest total radiation throughout the study area.Finally, the total radiation potential was calculated. Accordingly, the highest solar radiation energy potential was recorded in Helamabad and Qale-e-Now villages. Results indicated that solar energy can be utilized in scattered and sparsely populated rural areas. Potential measurement map showed that 89.07% of the study area had an excellent potential, 8.58% had very good potential, and 2.33% had good potential. Finally, wind speed and direction were also evaluated. The highest wind speed was observed in the western and northwestern regions of the study area which results in a high potential for wind energy harvesting. Moving from east toward the study area, the potential decreases.

The present study has measured solar radiation reaching the Earth’s surface using the solar energy analyzer function of remote sensing and GIS with the aim of assessing the feasibility of using photovoltaic systems in the study area. Results indicated that solar radiation of the study area is between 27605 and 383675. Since a 1000 watts per square solar radiation is needed for photovoltaic cells, solar radiation in the study area has the necessary potential for solar photovoltaic systems. The wind speed potential in the study area decreases from west to east. Therefore, construction of wind power plants in the western parts of the study area is possible and economical. Moreover, environmental conditions show that solar panels can be installed and solar energy can be utilized in the mentioned region.Consistent with the present study, Sherbafian (2008) has assessed the feasibility of using solar energy in four provinces of Khorasan, Gilan, Qazvin, and East Azerbaijan, and concluded that Iran enjoys a high potential for solar energy generation. Findings are also consistent with Safaei et al. (2015) who have studied the potential of clean energy production in Esfarayen city.