نشریه اطلاعات جغرافیایی (سپهر)
پیاپی 123 (پاییز 1401)

Introduction, Materials:

Improving energy efficiency in buildings has become a major topic of interest in recent studies. Modern technologies have improved energy performance in new buildings. However, there is a growing demand for inspecting old buildings and enhancing their energy efficiency. Areas of heat dissipation are the most significant faults in insulation occurring as a result of thermal bridge, excessive heat loss, air leakage, or defective thermal insulation in building components. Heat dissipation mainly occurs on the facade. Lack of sufficient information on the energy performance and associated costs of retrofitting buildings have made visualization and determination of the heat dissipation areas crucial for improving energy efficiency. The present study primarily seeks to determine areas of heat dissipation on building facades in order to optimize energy efficiency and energy storage in buildings. A vertical flight Unmanned Aerial Vehicle (UAVs) with low altitude flight, equipped with Post-Processing Kinematic (PPK) module and MC1-640s thermal infrared camera made by KeiiElectro Optics Technology at a rate of 30 frames per second have been utilized in the present study to gather the needed data. Also, thermal infrared images of the building facade were collected from PedarSalar palace in Aliabad village, Aradan-Garmsar city with a longitude of 52.3034 and a latitude of 35.1600 in order to assess the proposed method.

The present study seeks to propose a method for visualizing and determining the heat dissipation areas in facades with the aim of increasing energy efficiency. The proposed research method was divided into two parts. The first stage involved the generation of a dense point cloud and related orthophotomosaics utilizing thermal infrared images collected by UAVs, bundles adjustment, Structure from Motion (SfM) and Multi View Stereo (MVS) algorithms. The second stage involved converting the thermal infrared orthophotomosaic to HSV color space in order to choose the seed pixels for the Region-Growing-based segmentation algorithm. Since Hue-Saturation-Value (HSV) color space performs better when visualizing the concept of light, seed pixels were chosen from the HSV color space pixels with the highest degrees of grayscale to enter the segmentation algorithm. Then, introducing the seed pixels as input to the Region-Growing algorithm, areas of heat dissipation were automatically determined in the facade.A dense thermal infrared point cloud was produced with a density of 1779067 points per square meter, Reprojection error of 0.41 pixels and Ground Sample Distance (GSD) of 0.75 cm using 45 thermal infrared images captured by UAVs flying perpendicular to the facade of the building at a distance of 11 meters and a flight altitude of 1.70 meters. The Precision and Recall evaluation criteria have been employed to analyze detected areas of heat dissipation. Precision and recall evaluation criteria equaled 90 percent and 87 percent, respectively. Results indicated that the proposed method has improved precision and recall evaluation criteria and determined areas of heat dissipation with higher accuracy.

Given the critical importance of improving energy efficiency, and potential energy storage and reducing energy consumption in buildings and costs of production, obtaining related data to find optimization solutions is critical especially in older buildings. Since heat dissipation mainly occurs on the facade, the present study seeks to identify and determine areas of heat dissipation on the facade to visualize and improve energy efficiency applying the Region-Growing segmentation algorithm on the thermal infrared orthophotomosaic generated by photogrammetry UAVs. Since the HSV color space shows higher resolution in distribution of pixels used to extract areas of high temperature, seed pixels were introduced to the Region-Growing segmentation algorithm. Finally, precision and recall evaluation criteria were used to determine the accuracy of heat dissipation areas automatically detected on orthophotomosaics. Thus, the accuracy of the proposed method has been evaluated using the precision and recall criteria resulting in 90% and 87 %, respectively. Results indicated increased accuracy of the proposed heat dissipation detection method as compared to previous studies.

Analyzing the image blocks captured before and after geometrical changes is known as the conventional approach for detecting them in photogrammetric applications. Developed methods can be categorized into 1- comparison of 3D models generated via the image blocks and 2- direct comparison of single images. The occurrence of radiometric differences in the geometrically changed areas can increase their discrimination and facilitate their detection. However, the occurrence of geometric changes without sensible radiometric effects is a special type of change that its identification is faced with more challenges. Slight displacement of the objects in the scene, small landslides, subsidence or uplift, the effects of local pressure and tension on objects in the industrial procedures and etc. are some examples of geometric changes that do not have a noticeable radiometric appearance in the images.In the absence of incorrect observations, simultaneous triangulation of image blocks captured before and after geometric changes is a simple and effective way of reaching to detection of changes. In other words, by identifying the corresponding points in the fixed regions of the scene in the image blocks, the simultaneous triangulation of the image blocks captured in both epochs can align them in a unique object coordinate system. Thus, it can be possible to generate two independent and co-registered 3D models for identifying the occurred changes. However, maintaining the radiometric similarity of the changed areas leads to the identification of wrong-matched points when using automatic image matching methods.The inclusion of an unknown 3D position for each wrong-matched point in the changed areas leads to a defect in the design of the mathematical model for the bundle adjustment. These defects result in incorrect generation of the 3D models, large and systematic errors in the residuals of observations, and incorrect estimation of the extrinsic parameters of images. The remedy to this defect is to assign two distinct unknown 3D positions for each wrong-matched point before and after changes in the bundle adjustment. Lack of prior knowledge of the wrong-matched points located in the changed areas is the cause of this problem. In this article, an iterative solution is proposed to identify and correct the effects of the wrong-matched points in the process of simultaneous bundle adjustment.

In the proposed method, at first, all the confident radiometrically matched points among all images taken before and after the geometric changes are detected via the well-known feature-based image matching methods. Their matched positions, then, are again accurately rectified and verified by the least squares image matching method. The matched points identified after refinement are classified into two categories. 1- The matched points that have been detected only in the images of one image block and 2- The matched points that have been detected at least in two images in each image block. Among the points of the second category, there probably are matched points that are geometrically changed between two epochs, but their radiometric similarities have made to incorrectly identified as the matched points between two image blocks. In this paper, these were called the wrong-matched points which are iteratively identified and their corresponding mathematical models are corrected in the triangulation process.To do so, three different bundle adjustments are performed as the first step. Independent triangulation of the image blocks captured before and after the geometric changes and the simultaneous bundle adjustment of both blocks via the initially detected matched points of the first and second categories are the first three triangulations. Due to the existence of wrong-matched points, the initial simultaneous triangulation has a defect in the design of the mathematical model, which is gradually and in an iterative process, the wrong-matched points located in the changed areas would be identified.Identification of the wrong-matched points is done using the relative comparisons on their residual vectors. The comparisons are designed in two consecutive statistical tests. The main idea of this method has been inspired by the well-known Baarda test in the detection of gross errors in the observations of geodetic networks. By gradual identification of the wrong-matched points, their corresponding mathematical model will be modified in the bundle adjustment.To do so, the unknown values of the 3D coordinates of these points are separated for the time before and after the change epochs.This action by modification of the mathematical model in the bundle adjustments brings back the relative equilibrium in the estimation of the residual vector of observations.

Implementation and comparison of the proposed method with a conventional geometric approach in identifying the incorrectly matched points (using robust estimation of the epipolar geometry) have shown the adequacy and superiority of the proposed method. The proposed method, on average in more than 11 different experiments, was able to achieve an average accuracy of 85.8% in identifying the changed points. Meanwhile, the proposed method shows a 34.5% improvement compared to the conventional geometric approach based on epipolar geometry.       

Conclusions and suggestions:

The proposed method is an effective solution for identifying the geometrically changed points in the simultaneous triangulation of image blocks before and after geometric changes when the changed areas have a stable radiometric similarity. This method is more sensitive to the occurred changes than the conventional method of identifying incorrect correspondences based on epipolar geometry. Iterative adjustment of the observations’weight matrix through the Variance Components Estimation (VCE) techniques in order to detect and eliminate the effects of wrong-matched points can be considered a future research topic in this field.

Nowadays, updating information collected from urban areas is of great importance, since it provides the basis for many fields of study such as land cover changes and environmental studies. Remote sensing provides an opportunity to obtain information from urban areas at different levels of accuracy while widely used in various change detection applications. Detecting changes in buildings as one of the most important features in urban areas is of particular importance. Powerful and expensive processing systems are the only way to process large volume of remote sensing and photogrammetry data generated by the ever increasing number of sources to which laymen do not have access. The present study has applied deep learning methods and high computational volume of data processing in free clouds to make this possible for the public.

Two case studies have been selected in the present study. The first includes DSM and Orthophoto images captured by drones from Mashhad in 2011 and 2016. DSM and Orthophoto images in the second case study has been collected by drones from Aqda in Yazd province in 2015 and 2018. In accordance with the type of data used and high computational volume used for processing, the present study has applied fuzzy clustering method to detect buildings with a high computational speed and deep learning method to detect their changes. Object-based method and fuzzy logic theory have been used in the first step to classify features and detect buildings. In the second step, deep learning method and DSM differentiation method were also used to detect changes in buildings and evaluate results obtained from deep learning method. In the first step, buildings were detected using descriptors extracted from terrestrial and non-terrestrial features, and related decisions were made using fuzzy logic. In the second step, DSM differentiation method has applied the masks extracted from buildings in both epochs on the related DSMs to find their difference and detects changes using an elevation threshold. In deep learning method, a convolutional neural network model was trained to detect changes in buildings during both epochs. Using the DSM of buildings in both epochs and a part of their interface, the network input layers were generated for training. Changes detected in the buildings by the differentiation method were also introduced as the output layer. Following the training and introducing the entire interface in both epochs as the input layer, the trained neural network has detect changes in the buildings. The same process was performed once more using the difference between two DSMs. In other words, a single input layer was used in the network and the rest of the process was the same as before. Finally, changes detected by the neural network was compared with changes detected in the DSM differentiation

In the first step, buildings were detected and images were classified in accordance with the fuzzy logic. The overall accuracy of the first epoch classification in Mashhad equaled 94.6% indicating higher acuracy of object-based methods as compared to pixel-based methods. The overall accuracy of first epoch in Aqda equaled 95.5%. Neural network method detected changes in buildings with an overall accuracy of 90%. In accordance with the ground truth used in network training (both using DSMs as the input layer and the difference between the epochs as the input layer), results indicated that deep learning method is highly accurate in one-dimensional convolution mode. Moreover, the second step has applied the difference between DSMs in the two epochs and thus, many areas lacking a change in height were removed in both epochs and the network was trained more appropriately and accurately.

Necessity of extracting features, especially urban features such as buildings and identifying their changes over time have been investigated in the present study. Due to the high computational volume of modern remote sensing and photogrammetry data and highly expensive systems required for their processing, a new method was presented in the present study to solve this problem. Considering the type of data used and the complexity of features, object-based methods were selected instead of pixel-based methods to identify features and buildings. Deep learning method was used to detect changes in buildings. The method was also compared with DSM differentiation method. A one-dimensional convolutional neural network was used in the deep learning method. Two different modes were used in the network to train and predict changes. In the first, DSMs extracted from the buildings in each epoch were used as the input layer, while in the second one, the difference between DSMs were introduced as a single input layer to the network and the network was trained in accordance with the ground truth collected from areas with and without change obtained from the DSM differentiation method. Following the training process, changes were predicted using the trained network. Much better results were obtained from the second mode in which the difference between DSMs were used.

Unmanned Aerial System (UAS) photogrammetry now provides a low-cost, fast, and effective approach to real-time acquisition of high resolution and digital geospatial information, as well as automatic 3D modeling of objects, for a variety of applications including topographical mapping, 3D city modelling, orthophoto generation, and cultural heritage preservation. UASs are known by a variety of names and acronyms, including aerial robots or simply drones, with UAV and drone being the most commonly used terminology. Because of the versatility of their on-board Global Navigation Satellite System (GNSS) navigation systems and inertial measurement unit (IMU) sensors, UASs open up new options for photogrammetric projects. In this research, the ability of four different state-of-the-art and professional drone-based software packages, including AgisoftMetashape, InphoUASmaster, Photomodeler UAS, and Pix4D Mapper, to generate a high density point cloud as well as a Digital Surface Model (DSM) and true orthoimage over barren, residential, green space, and uniform textured areas in urban and exurban areas is investigated.

The following are the major processes in this study: image acquisition, point cloud, DSM, DEM generation, and accuracy assessment. Data planning and acquisition are the initial steps in commencing any project. The overlapping images are initially obtained using four data sets with distinct surface feature attributes and camera kinds with different shooting situations. The data sets that must be acquired include pictures taken with FC6310 (8.8 mm), NEX-5R (5.2 mm), and Canon IXUS 220HS (4.3 mm) cameras at varied flight heights and spatial resolutions ranging from 52 to 246 m. The four data sets, two of which are connected to Iran and two of which are related to other nations, were chosen from barren, residential, green space, and uniform texture areas. GPS coordinates for these photos must also be recorded using a GPS device. This is done to geo-reference the images for improved model accuracy. The calibration of the camera must also be addressed, and its characteristics and readings must be determined at the start of the project. The images will be calibrated first in order to determine camera pose estimate. The following stage is to compare survey measurements to model measurements in order to assess the overall correctness of the 3D model. The correctness of the point cloud, DSM, and 3D textured model is next evaluated. The accuracy evaluation evaluates the orientation correctness, and measurement uncertainties in the various modeling procedures. Finally, the various products of the mentioned software packages were statistically and qualitatively evaluated.

The outcomes of this study demonstrate the ability of commercial photogrammetric software packages to do automatic 3D reconstruction of numerous attributes across urban and exurban regions using high quality aerial imagery. This assessment employs a variety of visual and geometric measurements to assess the quality of produced point clouds as well as the performance of the four software packages. According to the visual quality findings, AgisMesh software performs better in 3D modeling of all varieties of surfaces in all locations, but badly in the reconstruction of building edges in urban regions. Pix4D software, on the other hand, performs poorly in areas with uniform texture but excels at recognizing height changes and reconstructing building site boundaries. In terms of visual outcomes, the other software falls somewhere in the middle. In quantitative tests, they were tested first with checkpoints and then with randomly selected points in three distinct classes of urban and exurban regions. Check point findings revealed that the root mean square error (RMSE) in AgisMesh, UASmas, Pix4D, and PhUAS software packages was 2.82, 2.63, 5.84, and 3.03 cm, respectively. Furthermore, quantitative findings obtained by choosing random locations revealed that UASmas had an accuracy of 1.83, 1.20, and 2.74 cm, respectively, in three residential, barren, and green space zones. In addition to the 6.90, 2.96, and 7.24 cm accuracy of the PhUAS, the Pix4D was 4.72, 3.46, and 3.59 cm more accurate than AgisMesh software in the three stated classes. Table 1 displays the assessment findings based on the RMSE criterion.

The findings of this study indicate the capacity of specialist drone-based photogrammetric software packages to automatically reconstruct 3D features from high quality aerial images over desolate, residential, green space, and uniform texture environments. In this study, all conditions and parameters in all software were regarded the same, and owing to the similarity of statistical parameters, number of points, and so on in various products, only the discrepancies and their differences were discussed in depth. Various visual and geometric parameters are utilized in this evaluation to analyze the quality of generated 3D point clouds, DSM, and true orthophoto. AgisMesh offers a simple and easy user interface in general and visual assessment, and it is possible to describe and execute data from any camera, even unknown models, without utilizing coordinate images by utilizing powerful processing methods. In contrast, the UASmas program has a highly complex user interface, and the user must be familiar with all of the concepts of photogrammetry as well as the camera parameters file, which is not readily set. It is possible to manually alter restricted processing results in Pix4D. As a result, faulty results are not obtained in regions with the same texture, while production points in other areas are of poor quality. When compared to the other three applications, PhUAS fared poorly aesthetically and geometrically. The user must enter many parameters or thresholds in the processing phases. Therefore, the user must be sufficiently informed of the specifics of photogrammetric and machine vision algorithms to understand that the quality of software output is largely reliant on these factors. Furthermore, check point findings revealed that theRMSE in AgisMesh, UASmas, Pix4D, and PhUAS software packages was 2.82, 2.63, 5.84, and 3.03 cm, respectively. Furthermore, quantitative findings obtained by picking random points revealed that UASmas has an accuracy of 3.51 cm, PhUAS has 10.45 cm, and Pix4D was 6.87 cm more accurate than AgisMesh in three residential, barren, and green space regions. Taking into account all of the benefits and evaluations of visual and geometric correctness, the performance and accuracy of AgisMesh, UASmas, Pix4D, and PhUAS may be ranked from one to four, accordingly.

On true orthophotos, there are some distortions on the structural edges of buildings, which is due to defects in these areas in the point cloud used in the digital surface model. This problem is greater for orthophotos that have been made from UAV images in urban areas because of their lower altitude. Before interpolation of the point cloud and preparation of the digital surface model and then preparation of orthophotos of it, it is necessary to complete the point cloud in areas with defects. Some studies have shown that adding edge points has the effect of decreasing the distortion of true orthophotos. In this study, a new method for completing point clouds using a trained deep learning network is proposed, which includes steps: 1) Preparation and normalization of point cloud data, 2) completion of the point cloud by learned networks; 3) reversion of the completed point cloud to real-world coordinates and, 4) integration with the existing original point cloud and preparation of the digital surface model and generation of true orthophotos.

In this study, the imaging of the Yazd region was done with a Phantom 4 drone equipped with a DJI camera. The SfM algorithm has been used to calibrate the camera, estimate the internal and external camera parameters, and produce images without distortion and low-density point clouds, and SGM has been used to produce dense point clouds. In the proposed method, the trained SnowflakeNet network is used to complete the incomplete roof points of the building. Assuming that the points on the roof of each building are predetermined, without noise, and have incomplete edges, these point clouds were introduced as inputs to the network to complete. Points related to edge points were extracted for each roof and added to the existing point cloud after increasing the density and returning to the actual coordinates. The final point cloud was used in the preparation of digital models to produce irregular and then regular surfaces and in the preparation of true orthophotos using camera parameters and undistorted images. One of the images with buildings marked as numbers 1 to 4 was selected to perform tests and prepare orthophotos.

The lack of structural edge points on any roof, which is the distance between severe height differences between levels, causes the greatest amount of distortion on the edge of the roof and around it. Adding these points with edge line recognition and reconstruction algorithms to the point cloud improves the resulting digital surface model. Since the quality and accuracy of the digital elevation model directly affects the resulting orthophoto, using a more accurate digital elevation model improves these images. In the proposed method, these point clouds are complemented by the deep learning method, and quantitative and qualitative comparisons show better results in reducing distortion in most of the buildings tested. The reasons for the superiority of the proposed method over previous methods include determining and calculating a more complete and integrated form of the roof of each building instead of multiple line segments and considering the outermost edges of the buildings.

In this study, a new method was introduced to improve the quality of true orthophoto edges by using a deep learning network to complete the point cloud, which was tested on several building images and compared with the results of previous methods. In this study, in addition to the fact that, for the first time, a deep learning network was used to improve point clouds to produce orthophotos, Compared to the previous method, the amount of distortion on the selected edge of four buildings has been significantly reduced and the success of the results with the latest proposed method of true orthophoto enhancement indicates an improvement of about 62% and 55% in the distortion decreasing of the structural edges and maintaining their coordinate accuracy. Despite the reduction of distortion on the selected structural edge using the proposed method, this value is increasing in curved areas as well as the corners of the roofs due to the type of network training and network output error. However, this can be reduced by improving the structure of the deep learning network and increasing the training data to a variety of roof modes with curved walls.

Due to its particular natural, economic and social situation, Kashan as a second-rate city confronts a variety of problems such as horizontal and scattered expansion, unbalanced development of the city in different directions, the spread of marginalization and unauthorized constructions. These problems have become the basis for major changes in land use and land use incompatibility.  Land use evaluation of Kashan city shows that this city has been encountering uneven growth in recent years and many of the surrounding areas that used to be agricultural lands, have gone to urban infrastructure and structures, especially the residential and industrial ones. Since one of the purposes of urban land use plan is the appropriate location of land uses in conjunction with the separation of incompatible uses from each other, accordingly, the current research aims to present applicable solutions for the optimal location detection and distribution of uses throughout the city and to separate incompatible uses from each other in order to achieve the major goal of urban planning, i.e., ensuring people's welfare by creating a better, healthier, more effective and pleasant environment by applying the spatial analysis tools of the geographic information system, while answering the following questions;What is the per-capita status of existing uses in Kashan with standard per capita in urban plans?What is the status of existing uses in Kashan regarding the compatibility index?

Regarding purpose, the type of research is applied and in terms of its method it is analytical-comparative; explicitly, the literature and sources have been initially examined and the theoretical framework of the subject has been compiled. Then the base map of urban land uses was prepared and reorganized in GIS environment. Subsequently, through surveys of the amounts of surfaces, the per capita for all types of uses was determined and annexed to the information of the base map in the form of separate layers in order to provide the basis for its use in the GIS environment. Afterwards, the evaluation of urban uses was proceeded regarding per capita standards and compatibility index by applying quantitative and qualitative method. Accordingly, the amount of land required by each use in the current situation was initially calculated and determined by specifying the ratio of shortages through the per capita standard and eventually, the location of each use in relation to neighboring users was analyzed in terms of compatibility index via GIS software using the overlap model (IO) and spatial analysis.

The Geographical Area of the Research:

Kashan city is the geographical area of the research that is currently the second most populous and industrial city after Isfahan city in the province.

Since examining the compatibility of different uses of the city is the most significant element in evaluating urban land use, therefore this research has proceeded to explain the degree of compatibility and non-disruption of one use to carry out the activities of other uses using the compatibility index. Accordingly, the degree of compatibility of city uses in relation to neighboring uses was primarily drawn using the convenient table of mutual compatibility matrix, then the quality of city uses was investigated through field studies in terms of compatibility according to the amenable fields in explaining compatibility such as land size, slope, accessibility, urban facilities and equipment, air quality, sound, light and smell.

In this research, the uses were examined and evaluated at both quantitative and qualitative levels. In the quantitative section, the current situation of the levels and per capita uses throughout the city was discussed with the standard per capita in urban plans. Consequently, statistical calculations showed that except for tourism and religious uses, other uses are encountering a deficiency of level. In the qualitative part, the degree of compatibility of land uses was investigated using the compatibility index through acquiescent components in the explanation of compatibility such as land size, slope, accessibility, urban facilities and equipment, air quality, sound, light and smell, through field studies in the form of mutual compatibility matrix. Finally, the location of each user in relation to neighboring users was analyzed in terms of compatibility index via GIS software applying the overlap model (IO) and spatial analysis. The results of the compatibility percentage of uses with neighboring use in Kashan show that residential use of more than 40 percent, educational use with more than 37 percent, administrative use with 36 percent, medical use with 27 percent and sports use with 19 percent in stand in relatively incompatible to completely incompatible conditions with their neighboring uses, and on the other hand, tourism use with more than 90%, religious and cultural use with more than 85%, and commercial use with more than 80% are in entirely compatible and relatively compatible conditions with their neighboring uses and take the least incompatibility with other uses.

Industrialization of communities and increased greenhouse gasses in the previous decades have resulted in increased global temperature and changes in climate parameters which are generally called climate change in scientific texts. Climate change has resulted in changes of temporal and local precipitation patterns all around the world. Consequently, hydrological cycle has changed affecting intensity, duration and frequency of rainfall events. Intensity- duration- frequency curves are used to provide an economic and safe design for drainage facilities, check dams, urban water management structures such as culverts, surface water and sewage systems. They are also used in landslides studies. The present study seeks to compare rainfall intensities in Babolsar Synoptic Station before and after 1993 to understand the effect of climate changes on rainfall intensities during the mentioned 52-year statistical period.

The first synoptic station of Mazandaran province was set up in Babolsar city in 1952. With an elevation of -21 m from sea level and 7 m from the Caspian Sea level, it is located at the east longitude of 52o, 39̍, 30̎ and the north latitude of 36o, 43. The station has a mean annual rainfall of 928 mm and an average of 99 rainy days.To understand the effect of climate changes on rainfall intensities in different durations and return periods in Babolsar Synoptic Station, statistical period was divided into two 26-year subperiods (before: from 1968 to 1993 and after: from 1994 to 2019). Rainfall intensities were calculated separately for each of the 14 duration series (10, 20, 30, 40, 50, 60, 90 minutes and 2, 4, 6, 9, 12, 18 and 24 hours) with return periods of 2, 5, 10, 25, 50 and 100 years and compared together. Then, a paired t-test was conducted to prove the difference between two series of rainfall intensity to be significant. Moreover, 5 annual air temperature parameters including minimum absolute temperature, maximum absolute temperature, average minimum temperature, average maximum temperature and average temperature were investigated in both subperiods and analyzed using a paired t-test in SPSS software. Results were used to investigate temperature and precipitation changes during the statistical period and prove the difference between before and after time series data to be significant.  Mann-Kendall test was also carried out on 5 air temperature parameters collected during the 52-year time series data to find ascending or descending trends.

Compared to the first subperiod, the average rainfall intensities have increased in 10, 20, 30 minute and 12, 18 and 24-hour durations of the second statistical subperiod, while the opposite has occurred in 40, 50, 60, 90-minute, and 2, 4, 6 and 9-hour durations. However, statistical analysis has proved increased rainfall intensities in 10 and 20-minute, and 18 and 24-hour durations and decreased rainfall intensities in 50, 60, 90-minute, and 2, 4, 6 and 9-hour durations of the second statistical period to be significant. A paired t-test was conducted to compare rainfall intensity in the statistical subperiods and find out its effects on climate change. Results indicated that except for data collected in 30 and 40-minute and 12-hour durations, the difference between other paired series was significant at a less than 5% level.Moreover, except for maximum absolute air temperature, other air temperature parameters showed a significant difference at less than 0.5% level. Furthermore, all 5 parameters showed an increase in the second study period indicating a warmer climate in Babolsar.However, paired t-test results indicated that despite the reduction of mean annual rainfall in the second statistical period, difference between the two series was not significant at any acceptable level of significance. Moreover, results of the Mann-Kendall test indicated that average air temperature, average maximum air temperature, average minimum air temperature and minimum absolute air temperature have shown an ascending trend at a 1% significant level, while maximum absolute temperature lacked a specific trend and showed leap changes. Annual rainfall also showed random changes and lacked a specific trend during the 52 year statistical period.

Results of the Man-Kendall and paired t-test have shown that a significant increase have occurred in air temperature during the 52-year statistical period (1968-2019) resulting in climate changes.
It can be concluded that climate change has increased the intensity of short-term (shorter than 40 minutes) and long-term (longer than 12 hours) precipitations and reduced the intensity of medium-term precipitations in Babolsar Synoptic Station. Moreover, climate change has increased the intensity of precipitations with short and long return periods while reducing the intensity of precipitations with medium-term return periods in the aforementioned Synoptic Station.

awareness of the type and percentage of land use and cover types is a fundamental requirement for understanding and management of a region. The growth of urbanization and increasing use of resources due to the development of the required facilities in urban life has changed the pattern of demand for resources and lands, changing the nature and quality of agricultural land, Historical and natural landscapes and surrounding urban areas through the transformation of these lands into residential areas. In recent decades, the suburban lands of cities have changed their use due to the urbanization process and the need of citizens for new residential areas and the surrounding lands, which are often high quality agricultural lands and gardens. This, along with things like industrialization and changing rainfall patterns, has destroyed the cover and natural environment of cities, and thus has posed many social and environmental challenges and endangered sustainable urban development, and as a result of this process, a lot of ecological pressure has been imposed on the natural ecosystem of the region. These changes are considered as one of the important and effective factors of social and environmental challenges. Today, remote sensing technology and GIS due to capabilities such as high monitoring power and resolution, frequent images, cost reduction, etc., To effectively identify and quantify land use changes and their effects on the environment and monitoring And rapid management of the growth and development of cities are used. In the present study, the aim is to evaluate the urban development of Ardabil in the last 30 years using remote sensing technology and satellite images.

Landsat satellite imagery was used to prepare land use maps for 1987, 2000 and 2017. In order to ensure the quality of data and bands, the images used in this research were first corrected for radiometric errors in ENVI 5.3 software environment. Then RVI, SAVI, NDVI, BI and IPVI indices were extracted. In the next step, maps related to filter texture, vegetation delineation and tasseled cap were prepared. At the end of this step, all the extracted layers were merged with the corrected image bands. Then satellite imagery using support vector machine algorithms, maximum similarity and artificial neural network with acceptable accuracy in six user classes (residential areas, covered agricultural lands, fallow, barren lands, urban forest and water) floor were classified. Then, to evaluate the classification accuracy, the overall accuracy and kappa coefficient were calculated for each of the maps.

According to the values of overall accuracy and kappa coefficient, which in 1987 for the support vector machine algorithm were 90% and 0.86, respectively, the maximum likelihood was 84.5% and 0.78, and the neural net was 90.5% and 0.87, respectively, in 2000. Overall accuracy and kappa coefficient for support vector machine algorithm 92% and 0.90, maximum likelihood 92.5% and 0.90 and neural net 92.6% and 0.90, and in 2017 overall accuracy and kappa coefficient for backup vector machine algorithm 90.6% and 0.88, maximum likelihood of 82.8% and 0.78 and for neural net were 88% and 0.85, it was found that the support vector machine algorithm has the highest accuracy compared to the other two algorithms. According to the results obtained from the study of satellite images classified by the support vector machine algorithm, the area of land built in Ardabil has increased from 20.023 square kilometers in 1987 to 41.554 square kilometers in 2017.

In general, it can be concluded that to evaluate the trend of urban sprawl and awareness of land use change patterns for optimal management and planning of cities, the use of satellite images, especially Landsat images is a suitable and low cost option. The results also showed that the rate of land use change to land uses is increasing and since land is the main element in urban development, so control how to use it and also calculate the real need of the city for land, to In order to provide different uses is effective. As a result, according to the findings of this study, in the absence of proper planning for this city due to favorable lands for urban development around the city, in the not too distant future, witness the destruction of agricultural lands around the city of Ardabil and conversion they will be residential areas.

Determining the landfill site for municipal waste is an important issue in the urban planning process due to its great impact on the economy, ecology and environment of each region. In the process of site locating, it is tried to consider areas with the least risks to the environment and human health and conservation. During the recent three decades, the production of municipal solid wastes has increased considerably, beside that their specifications has been changed meaningfully due to the change in people’s life style, progress of industrialization and world economies. Still, one of the best methods of waste disposal is waste dumping or burying. Optimized selection of landfill sites may minimize any negative environmental or financial effect. Searching various places to locate landfills requires choosing an appropriate method. Therefore, applying mathematical methods and determining the influence of different criteria the selection of a suitable place can be very useful. This subject was examined here for the city of Karaj, which is one of the Iran’s megacities with a fast and uncontrolled population growth and increase in waste production.

In this research, the indicators and effective criteria in locating the landfill of Karaj city were identified, evaluated and prioritized with a sustainable development approach using GIS and Fuzzy Analytical Hierarchy Process (AHP). The research data were collected through literature review, internet searching, and technical survey. Using fuzzy logic and decision making techniques based on expert opinions, it was tried to limit the gap in the research field. The current research is descriptive-survey, and functional. To carry out the research, at first, the major influencing criteria were identified. The criteria were categorized into five major groups of geotechnical, environmental, municipal developing, socio economic, and hydrological items. Afterwards, an initial survey was utilized to control the items, and then, a pairwise comparison questionnaire was designed to collect the expert opinions. The research population was 30 experts, adopted from academia, industry, and environmental engineering sector, that 27 of them were selected randomly to answer the questions. It was adequate according to the Cochran’s formula. To ensure the data collected were acceptable, the validity and reliability of the questions were examined sufficiently. Due to its simplicity and accuracy, the triangular fuzzy number was adopted to assess the descriptive variables. In continue, and based on the GIS analysis method, extra specifications of the potential landfill sites proposed were further examined. It was accomplished through categorizing the information layers, then by weighting the potential landfill sites according to the total scores obtained. The information layers included: geotechnical effect, ground slope, land use, permeability, being subjected to flood, water quality, water level, distance from the city, and distance from power transmission lines. Based on the influence level of these layers upon the landfill sites, they were categorized into four classes of highly suitable, suitable, relatively suitable, and unsuitable. For overall ranking, the score of each landfill site in each information layer was calculated by multiplying each layer score by its weight. After completion of this computation phase, all available information layers obtained their own scores, demonstrating their suitability level to be a landfill site. Using the ArcView software, the simple additive weighting method was utilised for site locating.

The results showed that the urban development criteria with a weight of 0.270 was the most important criterion in locating municipal waste landfill, followed by the environmental criterion with a weight of 0.226. Accordingly, the socio-economic criterion with a weight of 0.152 was placed in the last rank. Moreover, in the geological group, the fault index weighted 0.261 and the climatic conditions index weighted 0.236. In the environmental group, the surface water distance index weighted 0.201, and the landfill odor index weighted 0.172. In addition, in the urban development group, the land use index with a weight of 0.283 and the access to equipment and facilities with a weight of 0.258 were the most influencing items. The Inconsistency Ratio of pairwise comparison matrix (I.R) for all matrices was less than 0.1, which confirmed the compatibility of the components.

In the complementary analysis, using the Fuzzy TOPSIS technique and the Geographic Information System (GIS) and utilizing the simple incremental weighting method (SAW), it was determined that Nazarabad site and Halqe Dare new-site are the most suitable options for constructing a new landfill site.

Among the climatic elements, the effect of temperature in an area and its changes is the perception of land reclamation and can be maintained and land use of a place. Mean while, surface temperature is an important factor in global warming studies and as a representative for climate change and radiation balance estimation in energy balance studies. Due to the special heat that each cover has on the ground. Vegetation land uses, barren lands, water resources, residential areas, absorb some of the sun's radiant energy and increase the temperature of the earth's surface. Finally, this heat is emitted from the surface of various coatings to the environment in the form of long wavelength radiation. If the surface temperature is calculated in different periods, the process of increasing or decreasing the surface temperature of different types of surface coverings can be modeled.

In this study, to study the changes in land cover, MODIS images related to land cover from 2001 to 2019 were received. Surface cover product (MCD12Q1) Surface temperature product (MOD11) was prepared on a daily scale for both Terra and Aqua satellites to provide a variety of surface temperature indicators in the Google Earth engine system. In environmental studies, we often deal with observations that are not independent of each other and their interdependence with each other is due to the location and location of the observations in the study space. For this purpose, to reveal the effect of land cover on surface temperature components, global Moran correlation analysis tool was used and to analyze clusters and non-clusters, local Moran insulin index was used. In the last step, to evaluate the relationship between circadian surface temperature, daily temperature and night temperature After converting NDVI and LST raster maps to vector maps, Pearson correlation coefficient, regression relationship and significant value between variables in R programming environment were calculated.

Based on the land cover product of Modis 5 sensor, the predominant cover including shrubs, grasslands, agricultural lands, scattered vegetation and residential areas were identified between 2001 and 2019. The largest area of the region is scattered vegetation (50%) and secondarily grasslands (20%). During these 19 years, the cover of shrublands and the cover layer of scattered plants has an increasing trend and the cover of grasslands and arable lands has a decreasing trend. The surface temperature of this region has a spatial structure and is distributed in the form of clusters, so it has a spatial relationship with the natural features of the region. Spatial patterns of spatial data on surface temperature are divided into three categories: hot spots, cold spots, and clusters. Low-lying areas of the south and part of the east and west of the area, hot spots, high-altitude areas that include parts of the central areas in the south and north of the area, cold spots and cold spots margin, clusters (foothills) they give. On the 24-hour surface temperature scale, the land use layer of settlements and agricultural lands shows the most significant relationship between the types of land surface cover. In the daily temperature scale, the land use layers, grasslands and scattered vegetation have a decreasing trend and the use layer of shrubs and settlements has an increasing temperature. At night surface temperature scale, the trend of significant surface coatings in relation to the microclimatic element of surface temperature intensifies so that field cover, scattered vegetation and habitat layer have the highest correlation with increasing night surface temperature Show them selves. Therefore, in the study of spatial pattern of surface temperature, latitude and altitude are the most influential factors and in the study of the effects of land cover, the layer of settlements in three surface temperature parameters (minimum, maximum, average) of the highest temperature increase compared to others. Uses have been enjoyed.

Land use type and land use changes and vegetation have a significant effect on land surface temperature changes. In the northeastern region of the country, shrub cover, grasslands, arable lands, scattered vegetation cover and residential areas are the dominant cover of the region. During 19 years, the increase in the area of scattered vegetation and barren shrubs indicates negative changes in the ecosystem of the region. In such a way that the area of other classes such as arable lands and grasslands has been reduced and the area of these classes has been increased. The surface temperature of this region has a spatial structure and is distributed in the form of clusters in 3 clusters. Hot clusters, low-lying areas, cold clusters, high-altitude areas and inconveniences covered the foothills. Elevation factor, latitude are influential in the distribution of clusters. In studying the effects of land cover on the surface temperature of the land, during 19 years, the circadian temperature of the settlement layer has increased by about 1.12 degrees and the arable land layer by 0.41 degrees Celsius. On the daily temperature scale, the settlement layer has a temperature increase of about 1 degree. At night surface temperature scale, arable land cover, scattered vegetation cover and habitat layer recorded 6.2, 0.8 and 0.6 ° C temperature increase, respectively.