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Nowadays, the development of urbanization and the increase of urban population have caused the air to heat up more than in the past and create urban heat islands. Urban heat islands are a phenomenon caused by the urbanization effects, due to which the temperature in the urban environment rises higher than in the suburbs. This phenomenon can cause irreparable damage due to the increasing atmospheric and environmental temperature, such as biological pollution, greenhouse gas emissions, diseases caused by heat, and impact on water quality brought to communities and the environment. This research proposes an effective and efficient approach with the help of remote sensing and optimization algorithms based on replacing the roof covering of an area with less heat-absorbing coverings to reduce the temperature and try to eliminate the heat island phenomenon. In this research, we are trying to reduce the urban heat island effect based on algorithms and statistical parameters affecting the ambient temperature, which has had few studies in past research. Also, using the intelligent optimization method in this field can cause innovation and create better and more accurate results. The new way that this study examines is to change the roof covering of an area with other functional coverings that reduce the air temperature in that area. The coverings that we considered to replace the covering of the roofs to moderate and cool the temperature of the studied area are two types of coverings: soil and vegetation.

The proposed approach of this research is to use two optimization algorithms of genetic and particle swarm, and the parameters that form the objective function of these two algorithms are the temperature standard deviation and the average financial cost of the coverage changing of each building parcel. The research dataset is Landsat 8 satellite images of Andisheh neighborhood in Tehran. This research uses satellite images for purposes such as preparing color images, mapping the vegetation and non-vegetation indices of the study area, and calculating the earth's surface temperature and urban heat islands.

The results indicate that both optimization algorithms have provided good performance and improved the problem parameters, but the genetic optimization algorithm obtained a better result in less time and iteration. In comparing the two algorithms, the genetic optimization algorithm reduced the standard deviation by 19%, bringing its value to 0.42. On the other hand, the particle swarm optimization algorithm for a longer time, reduced the standard deviation by 14%, bringing its value to 0.44.

The genetic algorithm in optimizing the building roofs obtained excellent results with a total cost of 4678 and a standard deviation of 0.4177. It converged quickly with the 12100 number of objective function evaluations and significantly reduced both the cost function parameters (The genetic algorithm has reached the best possible answer). The particle swarm optimization algorithm also failed to achieve an answer as good as the genetic algorithm with a total cost of 4965, a standard deviation of 0.4430, and a 20100 number of objective function evaluations. About the comparison between these two algorithms, the genetic, with less than 3000 objective function evaluations, was able to experience the most optimal solution that particle swarm algorithm reached with the 20100 number of function evaluations. The use of metaheuristic algorithms in practical problem optimizations, which we frequently encounter in various industries today, can be very efficient. The results of these algorithms are very suitable despite the differences in the outputs, and it will be impossible to reach such answers to different problems without using such algorithms. In future work, based on what we obtained in this research, we suggest using other optimization algorithms or even powerful modeling algorithms such as artificial neural networks. Also, it is possible to study the change in building roof covers and the use of newer coverings in moderating the temperature by adopting new parameters from the cost function in optimization and deep learning algorithms.

Biomass is a crucial component of the carbon cycle; thus, accurate evaluation is essential to manage the forest and understand its role in climate change. Biomass estimation also supports the international reduced emission from deforestation and forest degradation (REDD), including cases such as deforestation reduction, sustainable management of forests, protection and enhancement of forest carbon reserves). Today, using remote sensing techniques with the help of field data has revolutionized the estimation of forest biomass. Forest biomass estimation can be based on the processing of remote sensing data obtained from active sensors (for example, lidar and radar) and passive sensors (for example, optical sensors). In most previous studies, mainly vegetation indices (such as Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), and Soil-Adjusted Vegetation Index (SAVI)) have been used to estimate biomass. Using terrain data, the amount of biomass is estimated using allometric equations, and the required pre-processing is done on optical and radar images. The attributes obtained from the scattering matrix and the ratios of the components of the scattering matrix and the attributes obtained from H/α decomposition are extracted from the radar image and the attributes of vegetation, soil and water are extracted from the optical image. Results and discussion  In this study, in order to improve the accuracy of estimating the biomass of forest areas, the features extracted from the optical images of Sentinel-2 sensor and Sentinel-1 radar data as well as field data of Noor forest areas, Mazandaran province, whose forest cover type, Carpinus betulus and Quercus Castaneifolia and also includes rare species such as Populus Caspica Bornm trees. In this study, we used the genetic optimization method in four classes of mixed vegetation, natural forest, degraded forest, and forest reserves were studied. In this regard, multivariate linear regression and support vector regression have been used to model between ground data and radar and optical features. Genetic algorithm (GA) is one of the most common evolutionary algorithms. This method finds potential solutions to optimize problems at the right time, especially when the search space is very wide. Also, a genetic algorithm has been used during the modeling process using multivariate linear regression to select the optimal features extracted from radar and optical images. Evaluation of the results showed that the use of the multivariate regression method led to more accurate results than the support vector regression method in the study area. Also, evaluation of the results showed that using features selected by a genetic algorithm led to an accurate R2 of 0.78, 0.87, 0.68, and 0.79 for first to fourth vegetation classes, respectively. Therefore, the results showed that the efficiency of the genetic algorithm in feature selection for biomass estimation from satellite images using the multivariate regression method.

Land subsidence due to uncontrolled extraction of groundwater resources is one of the events that has been observed in abundance in Iran. Due to the improper extraction of groundwater resources in the country, unfortunately, . Kabudrahangplain of Hamedan is the richest plain of Hamedan province in terms of having groundwater resources, but unfortunately in the last 30 years due to excessive abstraction of groundwater to meet agricultural and industrial needs and lack of adequate monitoring of this issue, Groundwater level has decreased, which has caused subsidence and, of course, sinkholes in Kabudrahang city. The purpose of this study is to identify sinkholes to prevent or reduce the damage caused by it.In this research, the radar interference method with the help of SENTINEL-1 satellite radar images using SNAP software has been used to monitor subsidence and subsidence, and the subsidence rate and the number of subsidence have been reported.After reviewing the approximately quarterly periods of the study area using the image of the first period and the last period to a period of 361 days, i.e. the images of 09/15/2017 to 10/09/2018 in the study area in general was examined. The results of this study show that in the period of one year, the study area has subsided by 14.5 cm, which is more subsidence in Kordabad, Noabad, KaboudarAhang, Famenin and in general in Fameninplain,Kabudrahang, Qahvand and around Hamedan has happened. These results can indicate the danger of landslides in these areas.The final results show a total subsidence of about 14.5 cm in the range that most of these subsidence occurred around urban areas and close to residential areas that this factor can be a news of occurrence. To bring about imminent human catastrophes in Hamedan province, especially KaboudarAhang plain.

Migration to cities and urban development have led to the irregular growth of cities and the uncontrolled transformation of natural land cover into artificial and impenetrable cover. As a result, it has created numerous environmental consequences for cities, including the phenomenon of heat islands, as a result of which the temperature of urban areas has increased compared to the surrounding areas, causing changes in ambient temperature, air pollution and harmful effects such as greenhouse emissions. Therefore, measuring and controlling the effects of urban heat islands, based on scientific and justifiable principles, helps decision-makers to overcome the resulting problems. Today, one of the most effective ways to control the effects of heat islands in developed countries is to use less heat-absorbing coverings, such as green infrastructure, high-albedo materials and flagstone, to cover the roofs of buildings. Therefore, in this study, an optimal planning based on spatial analysis, using the remote sensing and computational intelligence in the form of a spatial decision support system that can determine the effects of changing the roof covering of buildings in the study area.

To survey the research, a neighborhood from a central region of Tehran, the 7th region, was chosen to develop a software package for green roof planning. It is expected that the UHI effect has a significant role in this neighborhood since the region that the neighborhood belongs to, is one of the central regions in Tehran. Moreover, for developing the software package, map of parcels with attributes related to the area and land use and Images of Landsat 8 over the neighborhood are employed. Two main groups have a pivotal role in calculating UHI indices including vegetation and urban groups. When the indices are developed, the relationship between UHI and indices is investigated using the linear regression method (LRM) to obtain indices’ coefficients. Afterward, the software package tries to find some parcels, which constitute a certain and predefined percentage of area, that have a significant impact on UHI’s standard deviation by changing their roofs’ covers into three types cover including green, high albedo materials, and flagstones as the novelty of the research. Since there are a lot of feasible solutions, it is necessary to use a metaheuristic algorithm for finding the optimal solution. Therefore, in the second step of the proposed method, the optimal solution is conveyed by the Genetic algorithm (GA), as the most common algorithm in metaheuristic algorithms. After finding the optimum parcels for changing roofs’ cover, the UHI effect is computed once again to show the improvements.

As mentioned, the Genetic Algorithm is used to select the optimum subset of parcels for changing their roofs’ infrastructure with three covering classes including vegetation, high-albedo materials, and flagstones. This subset is assumed as 10 percent of all parcels in the area. However, some parameters should be set before using the algorithm such as the number of population and generation, the ratio of selection, crossover, and mutation. Besides, minimizing the standard deviation of SHI values was assumed as a fitness function for GA. As a result of the algorithm, the selected parcels and their appropriate roofs’ infrastructure for minimizing the standard deviation of SHI in the area are presented. This optimal solution was obtained through 252 generations that its convergence trend is presented. Additionally, based on the changes of selected parcels for roofs’ cover, the SHI values for the study area are computed again. These new values for the SHI and UHI effects are presented. However, the obtained standard deviation of SHI values for the changed roofs’ cover is 10.781°C while this value before changes is 13.222°C.
By examining the selected parcels obtained from the GA results with green spaces in and around the study area, it is found that the GA selects parcels for changing the roof covering with vegetation that is not contiguous with the green spaces in their surrounding area. whereas, according to results, the GA did not choose any parcel in these areas to change their roofs’ infrastructure to vegetation cover. However, highly efficient covering in SHI values such as vegetation and high albedo materials circumscribed the study area. This fact shows that in order to control the variation of UHI in the center of the area, it is necessary to curb the SHI values in the border of the study area. However, the less efficient cover compared to vegetation and high-albedo materials, which is flagstones, are located dominantly in the center of the study area since their influence is more limited and local than the other types.
It is also can be perceived that all changes in roofs’ infrastructure are not in line with changing to the vegetation cover, although this type of covering has the best effect to reduce SHI value. This consequence is because of the fitness function of GA, which is based on the standard deviation and not the mean value. The type of vegetation for covering decreases the SHI value, and thereby leads to decreasing mean value, while the objective of the software is to minimize the variation of SHI values. Therefore, vegetation cover is used in a location where the study area confronts with hotspot SHI value at that location. To verify this claim, the vegetation cover is utilized for all parcels selected by the GA to compute the SHI value for this scenario

With the widespread growth of cities and the increase in population, natural covers have been changed to artificial and impenetrable land cover, which lead to several environmental problems for cities, including UHI effects. Due to these changes, which are caused by the UHI effects, the temperature of urban areas becomes higher than the surrounding areas. One of the most practical and efficient methods for controlling the effects of urban heat islands is utilizing the green infrastructure and high albedo materials for roofs’ infrastructures; however, previous studies did not model this subject in quantitative practice. Based on this shortcoming, the present study proposed a software package to investigate quantitatively the changes of UHI effects based on the substitution of present roofs’ infrastructures to three selected types of covering class including vegetation, high-albedo materials, and flagstones. Additionally, the software used GA as a sub-model of the software to select the best set of parcels in the study area for changing their roofs’ infrastructure according to a specified fitness function. The fitness function assumed for this research is the standard deviation of the SHI values in the study area. This fitness function controls the variation of the SHI values and prevents the creation of UHI hotspots in the study area. . Examining the selected parcels obtained from the results of the GA with the surrounding green space and the study area, it was found that the genetic algorithm selects parcels to change the roof covering with vegetation that is not adjacent to the green space. This fact shows that in order to control the change of UHI in the center of the region, it is necessary to limit these values at the border of the region. However, less efficient cover compared to vegetation and high albedo materials, which is flagstone, is predominantly studied in the center of the area. It can be also seen that not all changes in roof infrastructure are consistent with changes in vegetation, although this type of cover has the best effect on reducing the amount of urban heat islands. This result is due to the fitness function of the genetic algorithm, which is based on minimizing the standard deviation of SHI in the area. Therefore, the vegetation is used in a place where the study area is exposed to a high amount of urban heat islands. Additionally, this cover type is more effective in the range of 100 and 150 meters of green areas.

In recent years, use of non-metric imaging sensors is becoming increasingly common in unmanned vehicles. This can be considered as an alternative to terrestrial mapping. Due to the limitations such as dimension, system weight, and the nature of UAV platform, it is not possible to use high volume and heavy metric sensors. Consequently, most sensors used in UAV platforms are light weight non-metric cameras. Determining the values of the internal parameters of the sensor is important in monitoring the variations of these sensors and improving the triangulation results of these images. Therefore, laboratory geometric calibration for these sensors under similar operating conditions produces better results in later stages. Our station is capable of performing calibration observations and also the calculations of a wide range of non-metric cameras and is currently serving in related organizations with ability to evaluate the accuracy of calibration results.

Surface temperature is considered to be a substantial factor in urban climatology. Italso affects internal air temperature of buildings, energy exchange, and consequently the comfort of city life. An Urban heat island (UHI) is an urban area with a significantly higher air temperature than its surrounding rural areas due to urbanization. Annual average air temperature of an urban area with a populationof almost one million can be one to three degreeshigher than its surrounding rural areas. This phenomenon can affect societies by increasing costs of air conditioning, air pollution, heat-related illnesses, greenhouse gas emissions and decreasing water quality. Today, more than fifty percent of the world’s population live in cities, and thus, urbanization has become a key factor in global warming. Tehran, the capital of Iran and one of the world’smegacities, is selected as the case study area of the present research. A megacity is usually defined as a residential area with a total population of more than ten million. We encountered significant surface heat island (SHI) effect in this area due to rapid urbanization progress and the fact that twenty percent of population in Iran are currently living in Tehran.SHI has been usually monitored and measured by in situ observations acquired from thermometer networks. Recently, observing and monitoring SHIs using thermal remote sensing technology and satellite datahave become possible. Satellite thermal imageries, especially those witha higher resolution, have the advantage of providing a repeatable dense grid of temperature data over an urban area, and even distinctive temperature data for individual buildings.Previous studies of land surface temperatures (LST) and thermal remote sensing of urban and rural areas have been primarily conducted using AVHRR or MODIS imageries.

Recently, most researchers use high resolution satellite imagery to monitor thermal anomalies in urban areas. The present study takes advantage of themost recentsatellite in the Landsat series (Landsat 8) to monitor SHI, and retrieve brightness temperatures and land use/cover types.Landsat 8 carries two kind of sensors: The Operational Land Imager (OLI) sensor has all former Landsat bands in addition of three new bands: a deep blue band for aerosol/coastal investigations (band 1), a shortwave infrared band for cirrus detection (band 9), and a Quality Assessment (AQ) band. The Thermal Infrared Sensor (TIRS) provides two high spatial resolution thirty-meter thermal bands (band 10 and 11). These sensors use corrected signal-to-noise ratio (SNR) radiometric performance quantized over a 12-bit dynamic range. Improved SNR performance results in a better determination of land cover type. Furthermore, Landsat 8 imageries incorporate two valuable thermal imagery bands with 10.9 µm and 12.0 µm wavelength. These two thermal bands improve estimation of SHI by incorporating split-window algorithms, and increase the probability of detectingSHI and urban climatemodification. Therefore, it is necessary to design and use new procedures to simultaneously (a) handle the two new high resolution thermal bands of Landsat 8 imageries and (b) incorporate satellite in situ measurement into precise estimation of SHI.Lately, quantitative algorithms written for urban thermal environment and their dependent factors have been studied. These include the relationship between UHI and land cover types, along with its corresponding regression model. The relation between various vegetation indices and the surface temperature was also modelled in similar works. The present paper employ a quantitative approach to detect the relationship between SHI and common land cover indices. It also seeks to select properland coverindices from indices like Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Build-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), Bare soil Index (BI), Urban Index (UI), Index based Built up Index (IBI) and Enhanced Built up and Bareness Index (EBBI). Tasseled cap transformation (TCT) which is a method used for Landsat 8 imageries, compacts spectral data into a few bands related to thecharacteristics of physical scene with minimal information loss. The three TCT components, Brightness, Greenness and Wetness, are computed and incorporated to predict SHI effect.The main objectives of this research include developing a non-linear and kernel base analysis model for urban thermal environment area using support vector regression (SVR) method, and also comparing the proposed method with linear regression model (LRM) using a linear combination of incorporated land cover indices (features). The primary aim of this paper is to establish a framework for an optimal SHI using proper land cover indices form Landsat 8 imageries. In this regard, three scenarios were developed:  a) incorporating LRM with full feature set without any feature selection; b) incorporating SVR with full feature set without any feature selection; and c) incorporating genetically selected suitable features in SVR method (GA-SVR). Findings of the present study can improve the performance of SHI estimation methods in urban areas using Landsat 8 imageries with (a) an optimal land cover indices/feature space and (b) customized genetically selected SVR parameters.

The present study selects Tehran city as its case study area. It employs a quantitative approach to explore the relationship between land surface temperature and the most common land cover indices. It also seeks to select proper (urban and vegetation) indices by incorporating supervised feature selection procedures and Landsat 8 imageries. In this regards, a genetic algorithm is applied to choose the best indices by employing kernel, support vector regression and linear regression methods. The proposed method revealed that there is a high degree of consistency between affected information and SHI dataset (RMSE=0.9324, NRMSE=0.2695 and R2=0.9315).

Due to the water shortage and drought that have occurred in recent years in our country, the planning for the optimal management of water use in different applications is necessary. The agricultural sector is the largest consumer of water and yet most of the water loss is also related to it. As it is said, about 60% of drinking water is wasted in agriculture.
Since irrigation is primarily a matter of spatial case, and is related to the location information such as the location and position of land, location of transmission canals and etc., so capabilities of spatial information systems (GIS) could be used for better and more successful planning.
In this paper, using optimization capabilities in GIS, program of irrigation of reservoir dams have been set, so that the farmer’s water need to be covered and the amount of waste water to be at least. For this purpose, the path of the water motion in the canals is minimized and the irrigation of farms that are under one canal be done simultaneously. If not enough water in the reservoir, Method of determining the optimum cropping pattern using the AHP method are provided. The method presented in this paper has been implemented and evaluated in a case study in Golestan dam irrigation network.

Building extraction becomes one of important fields of research in photogrammetry and machine vision. Involving airborne laser scanner (LiDAR) in geomatics, as an active sensor of 3D data acquisition, great evolution happened in data preparing to 3D outdoor object modeling. In consequence a great trend in developers and geomatics science researcher’s communities leaded them to develop techniques in extracting interesting objects from point cloud such as buildings. The LiDAR data don’t explicitly contain any geometric information and feature of objects. The feature such as planes, lines and corners can be only indirectly extracted by segmentation algorithms. In this research, a method for segmentation of LiDAR data for extracting buildings is proposed. In the proposed method, input data is an irregular LiDAR data and an adaptive clustering method for roof plane extraction from LiDAR data is implemented. So, to extract the roof structure, an assumption of planarity has been made. It is assumed that the roof can be modeled by a set of planar segments. In the proposed method the clustering is done without having any prior information about the number of the clusters. The clustering is done using FCM algorithm with considering maximum cluster number and then the extracted cluster will be modified using an iteratively split and merge technique. In this technique, small parallel planes are merged to create a new plane and a big plane created from different planes is divided to generate correct planes. This approach was evaluated using some buildings of used data set and the results prove high efficiency and reliability of this method for extraction of buildings.