Behnam Ghasemzade Qurmic; Alireza Safdarinejad
Abstract
Extended Abstract
Introduction
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- ...
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Extended Abstract
Introduction
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.
Materials and Methods
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.
Results and Discussion
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.
Narges Nonejad; Elham Nazemi; Hamid Saberi
Abstract
Extended Abstract Introduction The concept of place has long been considered an issue of importance for sociology, anthropology, and human geography. Geography begins with human beings and will not exist without human activities and their effects on the Earth’s surface. Humanistic geographers believe ...
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Extended Abstract Introduction The concept of place has long been considered an issue of importance for sociology, anthropology, and human geography. Geography begins with human beings and will not exist without human activities and their effects on the Earth’s surface. Humanistic geographers believe that place is a part of the geographic space occupied by someone or something in which perceived values are manifested. Studying the concept of place begins with the distinction between space and place. Sociology and human geography experts believe that space is made up of the material and human-made environment as well as the natural environment, and with the meaning added by individuals, groups or culturalprocesses, it changes into place. Since human geography examines the relations among human communities andbetween these communities and their environment, it can identify social patterns dominant in them. Definitely human communities cannot function properly in providing their memberswith a social identification without planning and providing rich and well-defined facilities tailored to their needs, up-to-date values and requirements. Therefore, considering the enormous and comprehensive transformations of the Information Age and the necessity of aligning with this global movement on one hand, and the importance of the meaning as one of the most important qualitative variables of urban spaces, exploring and recognizing the effects of cyberspace on the perception, sense of attachment and belonging to a place are of special importance. Therefore,studying factors influencing the meaning of place is necessary to improve the quality of urban space. In fact, meaning of place is aninternal emotion individual feels toward a place formed by the interaction of different factors. Many studies have been performed on the meaning of place some of which consider meaning as an inherent characteristic of the place, and others believe that meaning is induced by the individual in different circumstances. In fact, meaning is created by presence in the place and our perception of it. The continuity of space-based experiences formed by motor system, and the recognition and perception of space creates a sense of satisfaction for people in contact with the place. Therefore, the quality of urban spaces can be improved by creation ofmeaningful spaces based on appropriate space-based rules, measures and disciplines. To reach this aim, we need to investigate and realize factors influencing perceptions of place by its residents. Thus, we must inevitably understand changes in and influences ofthe values, attitudes and demands of society. Nowadays, we are witnessing rapid changes in cities which seems to reduce the effectiveness of old ideas and assumptions about urban development, planning, and management, and subsequently, question accepted concepts about the nature of space, place, time, distance and processes of urban life. The advent of the Information Age achievements has redefined space and provided us with a new experience of space. Cyberspace is considered as the main axis of development in the world, and its achievementshave different effects on various dimensions of human life. Thus,Cyberspace is replacing the real world in a way. Undoubtedly, these changes in different dimensions of human lifeare influencing the perception of space. The present study seeks to evaluate the effect of Cyberspace usage time in different users on the physical, personal, social and functional components of the meaning of space and their defining indices in urban spaces. In this study, we believe that users of this environmenthave a different understanding of their space, place, and face different dimensionsof space based on their usage time, and thus, perceive the meaning of urban space differently. Materials & Methods In order to answer the main question of the study, “How does the use of cyberspace affect the perception of meaning in traditional and modern urban spaces?”, Thus, the effect of cyberspace usage on defining components of perception including physical, individual, social and functional components was investigated. A traditional urban space (Imam Square) and a modern urban space (City Center) was selected as the study area in Isfahan and the statistical samplespresent in these places were studied. Correlationalresearch method was used. The statistical tests of Kolmogorov-Smirnov regression and Pearson correlation were used to determine the relationshipbetween independent and dependent variables, and its intensity and direction. Conclusion Results indicate that using cyberspace increasesthe users’ understanding of the meaning of place while being present in urban spaces.In this regard, the incremental effect on the four factors, the degree of correlation and the impact of cyberspace usage on the components of meaning has been extracted and analyzed in two traditional and modern urban spaces.
Ali Erfanzadeh; Mohammad Saadatseresht
Abstract
Extended AbstractIntroductionNowadays, UAV photogrammetry has become one of the most effective methods of collecting spatial data according to the factors time, cost, quality and variety of outputs among terrestrial and aerial mapping technologies. Because the quality of a UAV photogrammetry products ...
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Extended AbstractIntroductionNowadays, UAV photogrammetry has become one of the most effective methods of collecting spatial data according to the factors time, cost, quality and variety of outputs among terrestrial and aerial mapping technologies. Because the quality of a UAV photogrammetry products depends on the network design parameters setting according to the existing conditions and limitations, therefore, awareness of the behavior and impact of network design parameters on the quality of 3D reconstruction to achieve optimal quality of outputs is a very important issue. However, due to the time-consuming and the high cost of doing this study with huge real data, comprehensive research has not yet been conducted to measure the behavior of the effective parameters in network design and 3D reconstruction. There are various parameters include camera field of view, positioning error and imaging tilt in flight navigation, flight altitude and designed ground pixel dimensions, amount of sidelap and overlap images, image observation noise due to image quality, aerial triangulation error, in the process of preparing the map from aerial images, which is known as the most important parameters of UAV photogrammetric network design. In this paper, the simulation method is used to investigate the effect and behavior of the above parameters on the quality of three-dimensional reconstruction. Materials & MethodsIn the proposed method in MATLAB software environment, from a point with known 3D coordinates, using the collinearity equations and the value set for the network design parameters and their standard deviation according to the reality and experience of the expert, the imaging is done in a simulated manner. Then, by applying random and systematic errors on the visual observations and aerial triangulation parameters, the collinearity equations of the photographic observations form the desired point and using the least squares method of error in solving nonlinear equations, three-dimensional reconstruction, and quality are performed, then it has been evaluated by the Monte Carlo method. To achieve the results with high reliability, the quality of three-dimensional reconstruction is evaluated in five modes, respectively, ideal, excellent, good, average and bad, according to the expert opinion in setting the values of each parameter.Results & DiscussionThe results of this study show, most effective parameters in the quality of three-dimensional reconstruction in ideal conditions are camera instability, error of exterior orientation parameters and image quality, respectively, which gradually give way to parameters of flight altitude, imaging coverage and camera field of view in bad conditions. The results of the flight navigation error show, increased imaging platform instability has no significant effect on the average accuracy of 3D reconstruction, however, the accuracy changes in different places increase up to 20% due to the heterogeneity of the coverage and the visibility of different parts of the earth in the video network. The results also show that with increasing geometric instability of the non-metric camera, the accuracy of 3D reconstruction decreases linearly, in this regard, the imaging in bad conditions and the quality of the camera, the slower the reduction speed. It has also been shown that with increasing image observation error, which depends on image quality, the accuracy of 3D reconstruction decreases linearly. The results of the study of aerial triangulation parameters show that the three-dimensional reconstruction error increases linearly with increasing tie point matching error. In addition, as the focal length increases in the fixed flight altitude mode, the horizontal accuracy increases in proportion to the inverse magnification, and as the focal length decreases, the altitude accuracy decreases linearly, in the fixed ground sampling distance (GSD) mode, the horizontal error of 3D reconstruction is slowly reduced to 20%, while the height error increases with increasing height and decreasing the geometric resistance of the network by a factor of half magnification. The results also show that unlike traditional photogrammetry here, with increasing flight altitude, the horizontal and altitude errors of the 3D reconstruction increase linearly. The results of the study of the parameters of sidelap and overlap images show that the sidelap and overlap images can change the surface error up to 10 times and the height error and complete three-dimensional reconstruction up to 5 times. ConclusionThis study, while introducing the effective parameters in three-dimensional reconstruction by UAV photogrammetric method, has investigated the behavior and effect of these parameters on the quality of three-dimensional reconstruction in the simulation environment. This means how the quality of the reconstruction changes with minor changes to each of the parameters from half to twice the standard mode. Therefore, the closer this simulation is to reality, the more practical the results will be. Naturally, this complicates the simulation and increases the computational volume. Although this simulation is not entirely consistent with the actual situation, it can provide a kind of behavioral measurement of the parameters that serves as a complementary research to routine try and error investigations.
Roghayeh Adabi; Rahim Ali Abbaspour; Alireza Chehreghan
Abstract
Extended AbstractIntroductionIn recent years, data has become the life-giving force of developing innovations in smart cities all around the world. The up-to-date, availability, and freeness of this data are the deciding factors in their frequent use in smart city projects. Today, different sources of ...
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Extended AbstractIntroductionIn recent years, data has become the life-giving force of developing innovations in smart cities all around the world. The up-to-date, availability, and freeness of this data are the deciding factors in their frequent use in smart city projects. Today, different sources of information on city-related issues are available. They are crucial for driving towards “Smart Cities”. Among these sources is the Open Street Map (OSM) project, which is a free and open-source information repository used in many urban and non-urban-related applications. At present, OSM is used for a wide range of applications, for example, navigation, location-based services, construction of 3D city models, and traffic simulation. In the meantime, building blocks are among the OSM data that plays a key role in urban-related studies. These studies include constructing 3D building models, modeling urban energy systems, and land-use management in smart cities. Regarding the importance of completeness in the quality of spatial data, this study will assess the historical course of building blocks data completeness in OSM. Materials and methodsThe 20 districts of the Tehran metropolis have been selected as the study area. This city, with an area of 730 square kilometers and a population of around 8 million people covers the center of Tehran. The main purpose of this study is to present an analysis of the completeness of building block data in the OSM for the Tehran metropolis in 10 years (between 2011 and 2020). To reach this aim, an object-based approach based on object matching was used to assess the completeness parameter. Results and DiscussionThe findings of this study demonstrate that during the recent two years, OSM building block data in Tehran increased in terms of the number of features and the completeness of geometric information considerably. The number of data increased from 300 features in 2011 to 40.138 features in 2020, as well as the number of features edited and added to the OSM dataset increased from 38 and 194 in 2011 to 28680 and 10705 in the end of 2020, respectively. The completeness of OSM building block data in Tehran has increased from 0.18% in 2011 to 2.7% in 2020. Moreover, the evaluation of the completeness of OSM data in different regions of Tehran shows that the completeness of all regions of Tehran was less than 1% from 2011 to 2014, and in the last two years, for 12 of 20 regions of Tehran, the completeness is still less than 1%, but for the other eight regions (i.e., the regions no. 1, 2, 4, 5, 11, 15, and 20), which are mostly located in the northern part of Tehran, the completeness has increased. However, the data have many weaknesses in terms of the attribute information completeness. ConclusionThis study has provided a clear view of OSM building block status in Tehran. In addition, it has provided a better view of OSM data in different regions of Tehran. The insights gained from this study can lead toward creating the awareness required to use of these data in various fields of application. It can also assist local and national managers and related organizations to support active regions and encourage inactive regions. This paper represents a potential starting point for many possible future research directions in smart cities, especially in Tehran. Smart cities can conduct similar studies to understand the state of OSM data in their regions, make plans based on the findings, and manage their space more efficiently. To conduct future research, we evaluate the factors affecting the growth and development of OSM data and the efficiency of the OSM data in some smart city applications.
Yasser Ebrahimian Ghajari
Abstract
Introduction Natural hazards have always been a part of our surrounding environment and human life would be unimaginable without considering these hazards. With the development of social life, and particularly with urbanization and increasing expansion of cities, the dimensions of such incidents have ...
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Introduction Natural hazards have always been a part of our surrounding environment and human life would be unimaginable without considering these hazards. With the development of social life, and particularly with urbanization and increasing expansion of cities, the dimensions of such incidents have become more complicated. Earthquake is one of the most important natural hazards that takes the lives of many people every year. Although definite prediction of earthquake is not still possible, high-risk areas can be identified by zoning earthquake hazard using new technologies such as GIS, and measures can be taken to deal with the critical situation of identified regions during an earthquake. Planning of temporary accommodation with the aim of crisis management and reduction of secondary damages caused by the earthquake have always been amongmajor concerns of urban planners and managers. In the past, the policy of creating temporary accommodation centers and disaster relief sites lacked a specific program, so that locating a vacant land, with no owner was the most important principle for the creation of these centers in urban areas. It is now proved that these methods lack efficiency. However, recent advances in modern technologies such as GIS have improved planning process. This kind of planning procedure takes effective parameters and criteriainto account, many of which have spatial nature. Urban resiliency is one of the most important branches of urban crisis management, thus risk assessment and risk reduction planning, including site selection for temporary accommodation (as a principle of urban resiliency),are highly essential. Materials and methods The study area of the present research is Babol, one of the major and central cities of Mazandaran Province. Babol is located in BabolCounty, 14 km from the Caspian Sea and 10 km from the Alborz Mountains. With a total area of approximately 32 km2 and a population of250,217 (at the2016 census), it is the second most populous city in Mazandaran province.The 600 km long Caspian faults and 680 km long Alborz faults are among the effective faults of the study area. In the present study, effective measures for selectionof temporary accommodation siteswere extracted and weighted using expert opinions specialized in structural engineering, earthquake, urban planning, crisis management, passive defense, traffic and transportation. Identified criteria included distance from the river, distance from the fault, land use, distance from installations network, access to the transit network, distance from fire stations, population density, distance from tall buildings, distance from police stations and distance from health centers. Then, using GIS analytic functions, standard maps were produced and combined to identify the best areas for temporary accommodation (after a possible earthquake) in Babol. Criteria were weighted using fuzzy analytic hierarchy process and weighted overlay method was also used to combine them. Results and discussion Analyzing the results indicated that only 7% of the total study area (Babol City) is appropriate for temporary accommodation. Identified areas were examined according to other temporary accommodation standards. Finally, six sites and a total of 107 hectares (less than 4% of the study area) were identified as suitable sitesfor temporary accommodation. With a very large area (37 hectares) and full access to water, electricity and gas facilities,the first site is locatednear eastern beltway of Baboland Lotus PondRecreational Complex. The second proposed site is a 11-hectarevacant arealocated in the northeastern part of Babol City, between Ramenet and Pari Kola Villages. With a total area of 22 hectares,the third proposed site is located in the south-east of Babol City and near Babol-Qa’emShahr Road. Unlike the previous three sites, the fourth proposed site is located almost inside the city. It is a vacant 5-hectarearea in the northern side of the Motamedi Martyrs’ Cemetery. The next site, also located inside the city, is Aminian Dormitory (Noushirovani University of Technology) with a total area of 4 hectares. Although the last proposed site was ranked lower than the other five sites in the final analysis, it has the highest score among available sites inwestern side of Babol river. With a total area of 28 hectares, this site is located within a short distance of Imam Khamenei Highway. Conclusion According to the international standards, per capita area for temporary accommodation is approximately 4 m2. Therefore,with a population of about 250,217,Babol needs an average space of 100 hectares for temporary accommodation. Although, the proposed space for temporary accommodation (107 hectares) in Babol almost equals the required space (100 hectares), with the present rate of population growth inBabol, increasedconstructions, and consequently, reduction of appropriate space for temporary accommodation, Babol will definitely face a shortage of suitable space for temporary accommodation of earthquake victimsin near future. Moreover, the spatial distribution of suitable sites for temporary accommodation is not reasonable, as most of the suitable sites are located in the eastern part and within the boundaries of the city. While, these sites are expected to be scattered throughout the city with an equal access for all residents.Finally, it can be concluded that temporary accommodation of earthquake victimswas not considered in urban planning of Babol, and as a result, the city does not have a suitable status regarding temporary accommodation of earthquake victims.
Kobra Bozorgniya; Hani Rezayan; Javad Sadidi
Abstract
Introduction The accuracy of positioning depends on the quality of the technology used. Various technologies and techniques are used for positioning which are classified as absolute and dead-reckoning groups. Classified as absolute positioning technologies,GPS receiversface a variety of different errors ...
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Introduction The accuracy of positioning depends on the quality of the technology used. Various technologies and techniques are used for positioning which are classified as absolute and dead-reckoning groups. Classified as absolute positioning technologies,GPS receiversface a variety of different errors in the real-time positioning of a moving object, which reduces the accuracy and precision of the position received from these receivers. On the other hand, dead-reckoning sensors such as gyroscopes and magnetometers which measure real-time state of a moving object also have cumulative errors.Therefore, observations made by all of these sensors are not free from the noise generated during the measurement process.The amount of this noise may vary depending on various factors, including the precision of the sensor and features of the measuring environment. Thus,due to thecorrelation between observations made by these two categories of sensors and the difference between their precision and the nature of their errors,ifnoise is reduced inobservations made by them, their complementary features can be used to reduce errors made by each of them.High-quality positioning technologies are expensive and require high expertise.As a result,lower quality and cheaper global navigation satellite systems (like GPS) widelyavailable in smartphones are more commonly used. One of the most important features of these inexpensive technologies is that they are highly susceptible to factors producing noise. Methodology The present studyinvestigates the effect of gradual reduction of noise from data collected by sensors, accelerometers, magnetometers, gyroscopes, and GPS technology in smartphones on improvement of vehicle positioning. The proposed method is based on using acceleration, azimuth, latitude, longitude and roll angle parameters as an input for the Kalman algorithm and investigates the effect of reducing noise produced by these parameters using the least-squares method onimprovement of the resulting position calculated by the Kalman algorithm. To reach this aim, the roll angle parameter is extracted from the angular Velocity() in y-direction and the azimuth parameter is extracted from the magnetic field() in both x and y directions. These parameters along with the acceleration(a) parameter in x and y directions and the geographic coordinates are selected for the Kalman filtering algorithm. In the proposed method, data received from sensors share common sources of noise produceddue to drift, random movements and bias errors.To reduce this noise independently and systematically, method of averaging with the least-squares is usedfor data produced by each sensor. Thus, noise in the received data is considered as a random parameter and noise reduction is performed based on the percentage of changes in the corrected and observed data in the range of 1 to 10%. Kalman algorithm is implemented for 10 levels of noise reduction and the results areinvestigated and compared.The filter calculates and improves an estimate of position vector x, denoted by with minimum mean square error using a recursive model. The main objective is to derive an accurate estimate of for the state of the observed system at time of k. Implementing Kalman filter consists of a prediction step and an updating step. The result is compared todata received from a more accurate reference using RMSE. Results and Discussions The study area consists of lane no. 2 of the South-North (East-West) Azadegan Highway, Tehran, Iran with a total area of about 26km. Results show that compared to the reference data, using Kalman filter has decreased errorsin positioning the car from 0.8274 m to 0.6763 m with a 2%noise reduction. With a 10% noise reduction, the accuracy of this method has increases to 0.6771 m. This improved accuracy is due to noise reduction and consequently an increase in the correlation between the parameters. Accordingly, the threshold limit for noise reduction and improved positioning using Kalman filter is low and can be recognized by an investigation of a few lowlimits. According to the findings, although reducing the effect of noise can improve positioning with Kalman filter and smart phone sensors, irregular changes in the accuracy of noise reduction methods require determining an optimal percentage for noise reduction.
Amir Reza Moradi; Mohammad Amin Ghannadi
Abstract
Extended Abstract
Introduction
Digital Elevation Model (DEM) is a physical representation of the earth and a way of determining its topography through a 3D digital model. DEMs with high spatial resolution and appropriate precision and accuracy of elevation are widely used in various applications, such ...
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Extended Abstract
Introduction
Digital Elevation Model (DEM) is a physical representation of the earth and a way of determining its topography through a 3D digital model. DEMs with high spatial resolution and appropriate precision and accuracy of elevation are widely used in various applications, such as natural resource management, engineering, and infrastructure projects, crisis management and risk analysis, archaeology, security, aviation industry, forestry, energy management, surveying and topography, landslide monitoring, subsidence analysis, and spatial information system (Makineci&Karabörk, 2016).
Satellite images are one of the main sources used to produce DEM. In satellite remote sensing, optical and radar imagery are often used to generate DEM. Compared to optical satellite images, the main advantage of using radar satellite images for DEM production is that they are available in different weather conditions and even at nights. Two strategies used to produce DEM from radar satellite images include radar interferometry and radargrammetry(Saadatseresht&Ghannadi, 2018).
Phase information of the images is used in radar interferometry, whereas domain information of the images is used in radargrammetry (Ghannadi, Saadatseresht, &Eftekhary, 2014). Moreover, short baseline image pairs are used in radar interferometry, while long baseline image pairs are useful in radargrammetry. These technologies both have their own advantages and disadvantages,which were investigated in previous studies (Capaldo et al., 2015).
With radar interferometry, it is possible to produce DEM forlarge areas. Sentinel is one of the recent projects in satellite remote sensing. Sentinel constellation collects multi-spectral imagery, radar imagery and thermal imagery from the earth. Sentinel-1 is the radar satellite of the constellation.
Recent studies have investigated the precision of radar interferometry using Sentinel-1 imagery (Yagüe-Martínez et al., 2016) and the precision of DEM produced using these images(Letsios, Faraslis, &Stathakis; Nikolakopoulos &Kyriou, 2015). Generally, DEMs generated through radar interferometry needs to be improved, mainly due tothe phase errors which in many cases turn into outlier points (Zhang, Wang, Huang, Zhou, & Wu, 2012). Various methods have been used to improve DEM generated from SAR imagery, one of which use the information obtained from SRTM DEM. For instance, a previous study used SRTM DEM to improve DEM generated from ESRI/2.Using the information obtained from SRTM, the interferometric phase of areas with lower coherency were improved (Zhang et al., 2012).
The present study proposed a method to improve the accuracy of DEMs generated by Sentinel-1 imagery. In this method, using ascending and descending Sentinel-1 image pairs from the study area, DEM is generated using radar interferometry process. Then, precision is improved using SRTM DEM and a method based on 2D wavelet transform.
Wavelet transform and 2D wavelet transform methods
As a spectral analysis tool, wavelet transform is based on expanding any function like f(t)
(1)
inwhichaiis the expansion coefficient and 𝜓iis the expansion function.
One of the interesting characteristics of discrete wavelet transform is that it can be used as a multi-resolution analysis tool. To do so, a series of scaling functions or are used along with the wavelets to determine coarse data of the signals. Signal detailsare also covered by different wavelets with different scales.
Separatingcoarsedataand details of the signal isthe actual basis of discrete wavelet transform algorithm which wasintroduced by Mallat (Mallat, 1989) and improved by Beylkin et al. (Beylkin, Coifman, &Rokhlin, 1991). As a fast and simple method for discrete wavelet transform,the process is performed based on the followingrecursive relationships betweenahighpass and a lowpass filters with the impulse responses h(n) and g(n), respectively (Primer et al., 1998):
(2)
and
(3)
Where the expansion coefficients h and g are scaling filter and wavelet respectively.
(4)
and
(5)
These formulas can be expanded to calculate 2D discrete wavelet transform.
Proposed Method
This section proposes a method of enhancing DEM generated from Sentinel-1 imagery using SRTM DEM and 2D wavelet transform. Considering the capability of wavelet transform as a multi-resolution analysis tool which can separate coarse data from details, figure 2 shows the proposed process of improving DEM. First, using discrete 2DWT, coarse information and details of each DEMs are separated using the ascending and descending conditions of Sentinel-1 images. Then, two stages are considered based on the nature of these models. First, filtering coefficients usingthresholding and considering the average as the detail or high frequency part of the enhanced model. Second, coarse information derived from wavelet transform method have a resolution of40m and thus data derived from SRTM (30m) has a higher quality. Therefore, inverse 2DWT will improve the results and reach a resolution of 20 m.
Experiments and Results
The study area is located in Northern areas of Tehran (Iran) at the UTM coordinates of (542450 ,3964590) northeast to (539010, 3962350) southwest. Two Sentinel-1 satellite image pairs (one ascending and one descending) are used in this study.
In addition, a SRTM DEM with a spatial resolution of30m is used to improve DEM generated from Sentinel-1 images. Sentinel-1 derived DEM is evaluated using the 1m resolutionreference DEM. RMSE values shows the effectiveness of the proposed method in enhancing the Sentinel-1 derived DEM, which means that using information obtained from the SRTM and 2D wavelet transform was reasonable. RMSE values are reduced from 24.2097m to 11.1749m which shows 54% improvement. The proposed method can enhance results to 30 - 82 percentapproximately.
Conclusion
The present study investigates methods used for generating DEM from satellite images especially Sentinel-1 radar imagery. DEM derived from Sentinel-1 data has a high spatial resolution.Yet, it has some outliers or errors in elevation points whichneeds to be modified. Therefore, the present study proposes a method based on 2D wavelet transformfor deriving elevation model witha spatial resolution (20m) equal to that of Sentinel-1 DEM and improved precision and accuracy. In this method, filtering the details of the model using discrete 2D wavelet transform and modifying coarse information using SRTM DEM results in an enhanced DEM with higher spatial resolution.
Masoud Taefi Feijani; Saeed Azadnejad
Abstract
Extended Abstract
1- Introduction
The present study primarily sought to present a new FCD model to eliminate two limitations of the initial FCD model.These limitations included the fact thatimplementing the initial FCD model for sensors without a thermal bandwas not possible, sincethe model ...
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Extended Abstract
1- Introduction
The present study primarily sought to present a new FCD model to eliminate two limitations of the initial FCD model.These limitations included the fact thatimplementing the initial FCD model for sensors without a thermal bandwas not possible, sincethe model took advantage of a combination of shadow index and thermal index to detect black soil and calculate advanced shadow index.To overcome this limitation, we replaced thermal index with NLI and GNDVI indices, and combined shadow, BI, NLI and GNDVI indices to detect black soil and calculate advanced shadow index.Defining a global threshold for thermalindex used fordetectingblack soil was the next limitation of initial FCD model.Due to variations in regional climate and temperature, selecting a global threshold for the whole scene does not seem logical.Thus, a local thresholding process was used to define a threshold level for BI, NLI and GNDVI indices.In this regard, the study sitewas divided into 14 sections and an appropriate threshold wasselected for each section.A digital elevation model was also used to define a specific threshold level for forests in flat areasand elevated areas.
2- Materials & Methods
2.1 Study area and dataset description
The present study was performed within the basin of the Caspian Sea.Drainage basin is considered to be a standard unit ofstudy in environmental studies and thus due to the applied nature of the present study, the Caspian Basin was selected as our study site. In this study, a new FCD model was implemented for data collected from Landsat 5(1366) and Landsat 8 (1396).
2.2 Proposed approach
In the present study, an improved FCD model was obtained by adding two steps to the initial FCD model. In the following paragraph, these two steps will be explained.
2.2.1 Removing thermal index
The first limitation of the initial FCD model lies in the fact that implementing this model for data collected bysensors without thermal band is impossible, because advanced shadow index in the initial FCD model is calculated by combining shadow and thermal indices. Thermal index is only used to separate the shadow of vegetation cover from black soil.In order to overcome this limitationin the improved FCD model, thermalindex is replaced with NLI and GNDVI indices. In this way, black soil and vegetation shadows are separatedusingacombinationofshadow, BI, NLI and GNDVI indices.
The NLI index can be calculated using(1):
(1)
The GNDVI index is also calculatedusing(2):
(2)
2.2.2 Local thresholding
In the initial FCD model,black soil identification and shadow index improvement (advanced shadow index calculation) wereperformedusingthresholdingand based on the combination of shadow and thermal indices.In this model, a number is selected as the threshold of the heat index, and shadow index pixels with values less than this threshold are considered as black soil.Obviously, it is practically impossible to define a threshold and calculate advanced shadow index for large scale areas.
Localthresholding is a much more accurate method of thresholding, which is also used in the improved FCD model.In this method, image received from the study site was divided into 14 sections and a suitable threshold value was selected for BI, NLI and GNDVI indices in each section to calculate advanced shadow index.
Moreover, different thresholds were selected forforests in flat areasand elevated areas.In this regard, digital elevation model of the region was used to separate low-altitude and high-altitude areas.
3. Discussion& Conclusion
Results indicated that the proposed improved FCD model has provided a more accurate estimate of forest canopy density as compared to the initial FCD model.
According to the results, the overall accuracy and kappa coefficient of the initial FCD model were 86.24% and 68.43%, respectively.However, the improved FCD model had an overall accuracy of 96.98% and a kappa coefficient of 92.31% which confirms improved performance of the model.
Moreover, the statistical analysis of changes in the canopy densityindicated that the total area of Hyrcanian forests increased by about 161,963 hectares from 1366 to 1396. This includes an increase ofabout 79, 50 and 33 hectares in Mazandaran, Gilan and Golestan provinces, respectively.
Saeed Farzaneh; Mohammad Ali Sharifi; Amir Abdolmaleki; Masood Dehvari
Abstract
Extended AbstractIntroductionSatellites in geodesy receive and transport important information. Among those, satellites with Low Earth Orbit (LEO), which are at altitudes less than 1000 km, have a significant role in the advancement of geophysical sciences such as earth’s potential field. ...
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Extended AbstractIntroductionSatellites in geodesy receive and transport important information. Among those, satellites with Low Earth Orbit (LEO), which are at altitudes less than 1000 km, have a significant role in the advancement of geophysical sciences such as earth’s potential field. Many parameters have an impact on the precision and accuracy of their information. Atmospheric friction is one of the most principal forces on satellites, which may cause deviation and falling of satellite on a short period. From the beginning of aerospace missions, many efforts have been done to determine atmospheric friction by geodesists, e.g., empirical models of atmosphere neutral density. Because of the complex nature of atmosphere behavior and also data limitations, these models may have low accuracy. So, there is a need for methods to improve the accuracy of empirical models by means of combining observations of atmospheric density to predict its future state. Materials & MethodsAlong with the extension of computer science, new reliable algorithms have been introduced which are able to predict a time series; Artificial Intelligent (AI) and Neural Networks (NN) are the best of these methods. These simple algorithms are inspirations of the human brain and its ability to learn and have been used in many different scientific fields. In these techniques without any requirement for constructing complex modeling, the relation between input and output will be provided only using weight and bias vectors during the training procedure. Simple Neural Networks are memoryless meaning that the value of time-series in previous can’t be used for predicting the future value of time series and therefore some important dependency of signal values with time will be lost. A Recurrent Neural Network (RNN) has been implemented to overcome this issue. RNN’s can store some important information of the values of the time series in the previous steps in a chain-like structure and using this information for predicting the next value of time series that will improve the accuracy of prediction. In this study, the Long Short-Term Memory (LSTM) Neural Network which is a kind of Recurrent Neural Network’s has been implemented to predict the scale for correcting atmospheric density of numerical models. The data of Grace Accelerometer observation in the 6 first month of the year 2014 have been used for training the LSTM for univariate training. Also, the LSTM has been trained in multi-variants mode once with using the coefficient of atmospheric correction expansion up to degree 2 and once with using sun geomagnetic information along with information of k_p index. Results & DiscussionAfter training the LSTM network, by using the estimated parameters of the model, the zero degrees coefficient of harmonic expansion for a scale factor of correcting atmospheric density has been predicted in periods of 7, 14, 30, 60, and 90 days. The results of the univariate model show that the lower RMSE (Root Mean Square Error) is obtained about 0.054 in the period of prediction of about 14 days. Also, the results show that the multi-variants model with input data of sun geomagnetic information and k_p index has lower RMSE values in considered prediction periods compared to the other modes and the lowest RMSE is about 0.03 and belongs to the prediction of about 7 days. For evaluation of LSTM parameters in the obtained results, the predictions have been implemented with various Window sizes. The results show that by increasing windows size, the RMSE of the prediction will be reduced and the lowest RMSE was for prediction of 7 days with a window size of about 90 days. For the purpose of more evaluation, with the predicted atmospheric densities correction coefficient, the orbit of GRACE satellites has been propagated and the calculated position and velocity of satellites have been compared with the real orbit data. The results show that the lower RMSE will be provided with the prediction of 7 days with an RMSE for position and velocity of about 50 meters and 0.15 m/s respectively. ConclusionIn this study, due to the complex nature of the atmosphere, the LSTM Neural Network has been used for modeling and predict the zero-order scale for correcting atmospheric densities harmonic expansion. For training the network, the data of Grace Satellites Accelerometer in the 180 days of the year 2014 have been used. The LSTM has been in univariate and multi-variant models. In the multi-variants model, once with using the coefficient of atmospheric correction expansion up to degree two and once with using sun geomagnetic information along with information of k_p index the network have been trained. The period of prediction was considered of about 7, 14, 30, 60, and 90 days.The results show that the LSTM is capable to predict the correction coefficient in considered periods with a mean RMSE of about 0.05 for zero-order degree. Also, the results show that the lowest RMSE was for the 7 and 14 days of prediction and by increasing the window size of LSTM the RMSE will be decreased. The results of calculating the position of GRACE satellites position and velocity using predicted correction coefficients with real data show that the lowest RMSE was for prediction of 7 days for implemented method.
Qhasem Keikhosravi; Shahriar Khaledi; Ameneh Yahyavi
Abstract
Introduction Foehn is thedecending of hot and dry air that occurs under certain conditions in the lee of a mountain range.In an adiabatic process, the humid air rises toward mountain peaks on the windward hillside. With sufficient humidity, it is saturated and thus, forms clouds or precipitation. ...
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Introduction Foehn is thedecending of hot and dry air that occurs under certain conditions in the lee of a mountain range.In an adiabatic process, the humid air rises toward mountain peaks on the windward hillside. With sufficient humidity, it is saturated and thus, forms clouds or precipitation. In this way, it loses moisture, and passing over the lee of maintain, descends and heat upin an adiabatic process. Thus, the air in the lee side gets warmer and drier than the air in the windward hillside. Moving upward toward the mountain peak, the air loses temperature. At the mountain peak, the saturated air hasreached dew point temperature, and begins to rain to discharge its moisture. This dry air descends, and cross the leeward hillside with increasing velocity, and at the base of the mountain, its temperature is higher than the initial air temperature (Haji Mohammadi, 1396). Data& Methods In order to extract the frequency of days with foehn windsin the present study, daily temperature, relative and hourly humidity and wind speed were prepared for a 10-year statistical period (2015-2006) and then heat wave index was used to extract the number of days with foehn winds. To investigate the effect of foehn on thermal stress of plants using Landsat 8 OLI images, factors affecting thermal stress inplants,such as albedo, short wavelength radiations reaching the Earth surface, long wavelengthradiations emitted from the Earth surface, long wavelength radiations entering the earth surface, net radiation flux and soil heat flux were analyzed. ENVI 5.3 and Arc GIS 10.1 wereused to perform calculations and produce the aforementioned maps. Results&Discussion The present study was conducted to investigate thefoehn phenomenon in the west Alborz Mountains and its effect on the amount of thermal stress in the vegetation cover.First, the frequency of foehn wind occurrence in the statistical period of 2006 to 2015, in stations under study was extracted using wind direction, baldiindex (heat wave index) and increasing temperature and decreasing relative humidity compared to the previous day. In other words, days with temperature higher than 0 degree Celsius were considered as a heat wave. Based on wind direction, temperature increase and relative humidity decrease compared to the previous day (which in some cases is twice or even more), days are associated with foehn wind. In order to investigate the effect of foehn on thermal stressin plants, a sample of images with better atmospheric conditions (lacking clouds) collected by Landsat 8 OLI sensor on September 24, 2015 –in which foehn phenomenon had taken place-was received from the website of US Geological Survey (Earth Explorer)in the present study.The study area (West Alborz Mountains) was selected and cut out ofthese images and radiometric corrections were performed on the resulting images using ENVI 5.3 software. Afterwards, parameters like atmospheric thickness (atmospheric conductivity), Top of AtmosphereAlbedo, Earth’s surface albedo, Earthdistancefrom the Sun, solar altitude, Normalized difference vegetation index (NDVI), leaf area index (LAI), Fracture value, brightness temperature, ground surface temperature were determined and net radiation flux reaching vegetation cover and soil heat fluxwere calculated using these parameters. The output maps were produced in ARCGIS 10.1 environment. Conclusion According to the study sample (September 4, 2015), results indicated that areas with dense forest cover (eastern hillsides of the Alborz Range) receives the highest values of net radiation.The effect of foehn infiltration on these hillsides has increased the amount of radiation received up to 600 or 700 W / m 2. In contrast, the net radiation received on the downstream of thewindwardhillsides (western hillsides) is about 75 and at higher altitudes 150 W / m 2less than areas under the influence offoehn.Due to lower vegetation densityand lower heat transfer,soil heat flux in the western hillsides is much higher than the eastern hillsides.Most of windward hillsides has a heat flux of between 80 and 120 W / m2, while in leeward hillsides,sunlight is absorbed by the canopy and the soil heat flux is between 20 and 40 W / m2.Thus, most of solar radiation is used to raise the temperature around the vegetation crown, provide the necessary conditions for higher evaporation from the vegetation and create thermal stressin the vegetation organs. Therefore, descending of air mass on trees and plants causes severe evapotranspiration.This will lead to rapid drying of the leaves, which will cause thermal stress in the plant’s organs and intensify the likelihood of forest fires.
Remote Sensing (RS)
Fateme Amjadipour; Hamid Dehghani; Mojtaba Behzad Fallahpour
Abstract
Extended AbstractIntroductionThe complexity of interpreting SAR radar images makes target recognition difficult despite many studies performed in this regard. Various factors including material and dimensions of the target, radar frequency, polarization, target shape, and vision geometry affect the response ...
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Extended AbstractIntroductionThe complexity of interpreting SAR radar images makes target recognition difficult despite many studies performed in this regard. Various factors including material and dimensions of the target, radar frequency, polarization, target shape, and vision geometry affect the response received from SAR radar. Investigating these characteristics facilitate target recognition.Synthetic Aperture Radar sensors are widely used in both airborne and space-borne systems. Space-borne systems equipped with Synthetic Aperture Radar sensors are side-looking and because of their nature as a radar, many parameters such as vision geometry will affect their ability (or disability) in seeing the target and change the resulting images. Therefore, it is very important to study the effect of this parameter to identify the target and interpret these images. The visibility geometry includes incidence angle, look angle, and the direction of the imaging. Materials & MethodsThe present study investigates visibility geometry in revision images and ascending and descending scenes. To reach this aim, a single scene captured by Sentinel-1 from a residential area is examined in different images with different directions, incidence angles, and imaging time. Results indicate that incidence angle changed slightly (4 degrees) and thus, left a negligible effect on the image. Moreover, there was a 5-day time interval between the captured images and therefore, this factor had the least effect on Synthetic Aperture Radar images. Unlike optical images, the direction of imaging had the greatest effect on SAR images. For an instance, a single ramp behaves differently in two images captured from different directions. Therefore, direction of imaging and its effects on seeing (or not seeing) the target are analyzed in ascending and descending images. Results & DiscussionThe effect of vision geometry on radar images has been rarely investigated in similar studies, and the present paper has taken a step forward in this regard. Fallahpour et al., (2016) have simulated the effect of incidence angle, which is a parameter of visibility geometry and the shape of the targets in SAR images. Shapes such as cones, cylinders, and cubes were used in this simulation representing real buildings, niches, tree trunks, etc. which are very common in SAR images. Moreover, behavioral pattern of the aforementioned geometric shapes were simulated at different landing angles (30, 40, 45, 50, and 60 degrees) from the viewpoint of SAR imaging systems to reach a more comprehensive result.Then, various studies investigating the effects of incidence angle and direction on radar images have been reviewed. Some of these studies have dealt with the effect of these parameters on the classification of radar images. Dumitru et al. have examined the effects of resolution, pixel spacing, patch size, path direction, and incidence angle on the classification of TerraSAR-X images. To reach this aim, they have selected an optimal TerraSAR-X product and then specified the number of classes. They have finally investigated the effects of incidence angle and path direction on the classification results. Results indicated that images captured in ascending direction were 80% better than the descending images. Moreover, images captured from an incidence angle near the upper wing showed better results. ConclusionThe present study has investigated the effect of usually neglected parameter of visibility geometry on SAR images. Images were captured by Sentinel-1 in both ascending and descending directions. Following speckle noise reduction and geometric correction, incidence angle and its effects on the detected changes were investigated. The slight 4-degree changes of this parameter have not caused the resulting changes. Moreover, there was a 5 day time interval between these two images and thus, time could not be an effective parameter too. Results indicate that detected changes in the residential area were due to a change in the direction of imaging. Changes of this parameter can result in seeing (or not seeing) the target, and therefore, it is very important to investigate the effects of this parameter and correct it.
Hadi Farhadi; Tayebe Managhebi; Hamid Ebadi
Abstract
Extended Abstract1- IntroductionRemote Sensing (RS), as one of the most efficient mapping technologies, is employed in wide areas due to its speed, cost-effectiveness, monitoring over wide areas and using time series data. So far, several data and methods are used for this purpose. In general, RS active ...
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Extended Abstract1- IntroductionRemote Sensing (RS), as one of the most efficient mapping technologies, is employed in wide areas due to its speed, cost-effectiveness, monitoring over wide areas and using time series data. So far, several data and methods are used for this purpose. In general, RS active and passive sensors provide useful information in various applications such as building extraction, natural resource management, agricultural monitoring, etc. The extraction of accurate information about the location, density and distribution of buildings in the urban areas is one of the major challenges in the urban study which is used in various applications. In this framework, the monitoring of the urban parameters, such as urban green space, public health, and environmental justice, urban density and so on has been accomplished by radar and optical image processing, in the last three decades. So far, various methods, including Artificial Intelligence (AI), Deep Learning (DL), object-based methods, etc. have been proposed to extract information in the urban areas. However, an important issue is access to the powerful computer hardware to process the time-series images. In such a situation, the use of the Google Earth Engine (GEE) as a web-based RS platform and its ability to perform spatial and temporal aggregations on a set of satellite images has been considered by many researchers. In this research, a semi-automatic method was developed building extraction in Tabriz, northwest of Iran, based on the satellite images using the GEE cloud computing platform. Since accessible data is one of the most important challenges in the use of space RS, in this study, the free Sentinel-1 and sentinel-2 data, which belongs to the European Space Agency (ESA), has been utilized. 2- Materials & Methods2-1- Study AreaThe study area is central part of the city of Tabriz East Azerbaijan Province, which is located in northwestern of Iran. 2-2- DataVarious data sources have been used in this study, including Sentinel-1, Sentinel-2, and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM). In addition, 400 training samples were created using High-Resolution Google Earth Imagery (GEI) in two classes: urban-residential (buildings) and non-residential areas (vegetation, soil, road, water and etc.).2-3- MethodologyThe goal of this research is to develop a method for identifying the buildings in an urban area. For this purpose, after importing images and pre-processing them in the GEE Platform, a map of the Primary Urban Areas (PUA) and High-Potential Buildings (HPB) was produced from Sentinel-1 images according to the sensitivity of the radar images to the target physical parameters. Then, in order to remove the annoying features and extract the Secondary Urban Areas (SUA), spectral indices such as Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Renormalized Difference Vegetation Index (RDVI), Normalized Difference Water Index (NDWI), Soil Extraction Index (SOEI), Normalized Difference Built-up Index (NDBI), and Build-up Extraction Index (BUEI) were extracted from Sentinel-2 images. Also, the high slope of the area and the mountainous areas was extracted from the SRTM DEM data and used as a mask in the final results. Afterwards, the unimodal histogram thresholding method was used in order to determine the threshold value for each index. Finally, by merging the map of HPB and the map of SUA, the final map was produced and evaluated by other methods. In this research, the proposed method used images from GEI with a very high spatial resolution to validate the generated map. As a result, sampling was carried out using a visual interpretation of GEI in two classes: residential areas (buildings) and non-residential areas. The samples were selected randomly and 400 points were collected for each residential and non-residential class. In the study area, a total of 800 test points were used to evaluate the results of the proposed method. To evaluate the accuracy of the results, the criteria of overall accuracy (OA), kappa coefficient (KC), user accuracy (UA) and producer accuracy (PA) were used. 3- Results & DiscussionAccording to the visual interpretation, all buildings in urban areas with a length and width greater than 10 meters (spatial resolution of the four major bands of Sentinel2) can be extracted using the proposed method in this study, and the results are acceptable in various features. According to the proposed method, annoying features such as vegetation and water body areas were removed from the building identification process with high accuracy, and the accuracy in the study area was improved. The results showed that the OA and KC were 90.11 % and 0.803, respectively. Based on the quantitative and qualitative comparisons, the proposed method had a very satisfying performance. 4- ConclusionDue to the spectral diversity and the presence of various features in urban environments, preparing a map related to it in a large area is extremely difficult. In this regard, the current study presented a very fast semi-automatic method for preparing the urban area map and extracting buildings in Tabriz using Sentinel-1 and Sentinel-2 satellite images as a time series in the GEE platform. One of the most significant benefits of the proposed method is that the data and processing system used in our study is free. Thus, in addition to not having to download large amounts of data, the method presented in the current study has the ability to eliminate many of the limitations of traditional methods, such as classification methods and their requirement for large training samples. The proposed method did not extract the map of buildings using heavy and complex algorithms, which was an important consideration in the discussion of computational cost. Therefore, it can be concluded that the simultaneous use of Radar and optical RS data in the GEE Web-Based platform has a very high potential in distinguishing features and building mapping.
Mohamad Hosain Saraei; Shahabadin Hajforoush
Abstract
Extended Abstract
Introduction
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 ...
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Extended Abstract
Introduction
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?
Materials & Methods
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 & Discussion
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.
Conclusion
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.
Mohsen Abedi; Mohammad SaadatSeresht; Reza Shahhoseini
Abstract
Extended Abstract
Introduction
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 ...
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Extended Abstract
Introduction
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.
Materials & Methods
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 method
Results & Discussion
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.
Conclusion
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.
Sara Haghbayan; Behnam Tashayo
Abstract
Extended Abstract Introduction Air pollution has become a life-threatening hazard with severe consequences. Previous studies have indicated that long-term exposure to air pollution can pose a significant threat to human health or even cause death. Usually, air quality is monitored by ground-based ...
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Extended Abstract Introduction Air pollution has become a life-threatening hazard with severe consequences. Previous studies have indicated that long-term exposure to air pollution can pose a significant threat to human health or even cause death. Usually, air quality is monitored by ground-based stations that can collect data regarding temperature, humidity, pressure, and several pollutants such as Ozone (O3), Carbon Monoxide (CO), Carbon Dioxide (CO2), Sulfur dioxide (SO2), Nitrogen dioxide (NO2), and nanoparticles (e.g. PM1, PM2.5, and PM10). However, ground-based stations are costly, scattered, and often cannot cover large areas. These stations collect the concentration ofparticulate matter with a diameter of less than 2.5 µm (PM2.5) over a year.Collected data may be lost due to an unexpected shutdown of the device. Datacollected in ground-based stations are not sufficient by their own and as a result they are modeled. The resulting models also have flaws, so new resources are needed to solve this problem. One of these resources is the use of mobile sensors to produce high-resolution temporal and spatial air quality data. As opposed to traditional air quality monitoring stations, the use of dynamic and mobile sensors is quickly developing. These mobile sensors measure the concentration of the same air pollutants as those measured by ground stations. Land-use regression (LUR) models are increasingly used to estimate the level of PM2.5exposure in urban areas. Land-use regression models often use data received fromground-based stations. Therefore, modeling the concentrations of particulate matter in a city leads to a significant increase in modeling error. Data from mobile sensors can increase the accuracy of this contaminant modeling process. The present study aims to improve modeling accuracy by integrating ground-based stations with mobile sensors. Therefore, using the proposed framework, we can accurately estimate air quality at any time and place and provide higher resolution estimations for heterogeneous urban environments. Materials & Methods The study area covers Isfahan city. With a population of more than two million and an area of 200 square kilometers, Isfahan is located in central Iran. 13% of the total pollutants entering Isfahan belong to urban industries, 11% to domestic sources, and 76% of all pollutants belong to traffic related sources in Isfahan. Therefore, most of the PM2.5concentrations are generated by the transportation system in Isfahan. The effective solution to the air pollution problem needs to have a comprehensive understanding of the air pollution process. Such an understanding primarily depends on reliable records that can depict the temporal and spatial variations in air pollution which is not possible due to the limited number of ground-based stations. The proposed method of the present study is to combine ground-based stations with mobile sensors to increase the accuracy of PM2.5concentration estimation and modeling. One of the existing methods used to estimate PM2.5levels is land use regression. Previous studies used only ground-based stations to create this model, which was not sufficiently accurate. The present study sought to increase the accuracy of PM2.5concentration modelling in contamination values of near or beyond the threshold. Using the LUR model, a prediction map was generated usinga combination of ground-based stations and mobile sensor which helps us to reach a more accurateestimation and prediction of PM2.5concentrations in a heterogeneous region such as this city. Results & Discussion Reliable and accurate estimate of temporal/spatial distribution of air pollutant concentration cannot be achieved using a limited number of ground-based stations. The present study took advantage of 14 mobile sensors along with 7 ground-based stations. Results indicated that the root mean square error of the seven ground-based stationsequaled 1.80 while the RMSE of the combination of these stations equaled 0.59. The skewness index shows asymmetry of data as compared to the standard normal distribution.This index is used to determine whether the data distribution is normal or not. Skewnessvalue of standard normal curvesequals zero. In the histogram obtained from a combination of all stations, this value is 0.11, while in the histogram obtained from the ground-based stations, skewness value equals 0.8803. In general, the results indicated that integrating ground-based stations with mobile sensors results in a PM2.5concentration distribution which looks more like a normal distribution. The normality of data distribution implies that the histogram of data frequency is approximately a normal curve, and thus T-test is used to examine whether or not the results were significant. Conclusion In this study, a new framework was proposed to integrateground-basedstations and mobile sensors with the aim of improving the accuracy of PM2.5 pollutant concentration estimation. The results of the t-test show that with only ground-based stations, the actual pattern and its distribution over the city will fail. In fact, data received from mobilesensors provide additional data necessary for air pollution profiling.
Mostafa Mahdavifard; Khalil Valizadeh Kamran; Ehsan Atazadeh; Nasrin Moradi
Abstract
Extended Abstract
Introduction
The oceans cover about 70% of the earth's surface and contain the most water on Earth, as well as important marine ecosystems.Ingenerally,global waters are classified into two types of water.In waters of the first type, such as the waters of the open ocean, phytoplankton ...
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Extended Abstract
Introduction
The oceans cover about 70% of the earth's surface and contain the most water on Earth, as well as important marine ecosystems.Ingenerally,global waters are classified into two types of water.In waters of the first type, such as the waters of the open ocean, phytoplankton dominate the inherent optical properties of water. However second type waters, like coastal waters, are complex waters that are affected by a variety of active light compounds such as phytoplankton, colored dissolved organic matter andtotalsuspended matter.Coastal wetlands are considered as the Case-2 water. These types of areas are dynamic environments that are threatened by the entry of pollutants and because the wetlands have a calm environment and away from open sea waves, they are exposed to the accumulation of natural and human pollution. As a result, the identification and monitoring of coastal and marine pollution is essential to minimize their destructive effects on human health and the environment and economic damage to coastal communities.Phytoplankton are floating or scattered single-celled algae that travel primarily through water waves.Chlorophyll-A considered as an indicator of the abundance of phytoplankton and biomass in oceanic, coastal and lake waters. Field and laboratory methods are difficult and time consuming and weak for spatial and temporal observations. In contrast to the weakness of field methods, remote sensing methods can provide the spatial perspective needed to gather information on ocean and coastal water surface on a regional and global scale.The purpose of this study was to compare and evaluate atmospheric correction methods (high atmospheric radiation and high atmospheric reflectance) on the algorithm for estimating the concentration of chlorophyll-A based on blue and green bands (OC2) in Landsat-8 and Sentinel-2 data, evaluating the results using Field data and finally the time series mapping of chlorophyll-A concentration.
Materials & Methods
In this study, Landsat-8, Sentinel-2 satellite time series data and field data collected from the study area,were used.First, the satellite images used in ENVI 5.3.1 softwarewereconverted to Surface Reflectance and Top of Atmosphere Reflectance.Then, MATLAB 2018a software was used for image processing and coding.To estimate the chlorophyll-A concentration, the bio-optical algorithm OC2 was used, which in fact uses a nonlinear relationship to link between field data and satellite data. In order to evaluate the results,two statistical parameters R2 and RMSE were used.
Results & Discussion
Based on the analysis of field data, the concentration of chlorophyll-A in all sampled stations was less than 1 mg/m3. Water in the Surface Reflectance and Top of Atmosphere ReflectanceSentinel-2 and Landsat-8 data had a relatively similar spectral signature at wavelengths, due to the similarity in the spectral signature of water on the satellites used, covering the same spectral range in the Landsat-8 and Sentinel-2 satellites systems. The OC2 algorithm had amounts R2 (0.91 and 0.64) and RMSE (0.13 and 0.33) in Landsat-8 and Sentinel-2 Surface Reflectance data, respectively, while Landsat-8 and Sentinel-2 Top of Atmosphere Reflectance data had amounts R2 (0.12 and 0.53) and RMSE (0.45 and 0.51), respectively. The time series of chlorophyll-A concentration estimated using surface reflectance data (Landsat-8) corresponds to the natural conditions of the region, However, the time series of chlorophyll-A concentrations using the surface reflectance data (Sentinel-2) during the seasons estimated the chlorophyll-A concentration to be uniformly and downward.The reason for this poor performance in the Sentinel-2 is the lack of sufficient field data for calibration.
Conclusion
In this study, we tried to evaluate and compare the reflectancealgorithms (Landsat-8 and Sentinel-2) in the OC2 algorithm.Preliminary results indicate that the type of satellite data used (Surface ReflectanceandTop Atmospherereflectance) is of great importance for entering the OC2 bio-optical algorithm because the satellite image to enter the OC2 algorithm must be surface reflectance data and atmospheric correction that In fact, these algorithms are sensitive to high-atmosphere reflectance data.In general, the results showed that 10 field data is enough to calibrate with Landsat-8 data, but for Sentinel-2 data, more than 10 numbers field data must be calibrated to obtain a good result.
Sepide Imeni; Hassan Sadough; Shahram Bahrami; Ahmad Reza Mehrabian; Kazem Nosrati
Abstract
Extended Abstract
Introduction
Geomorphologists have always considered geomorphological processes such as weathering, erosion, and sedimentation, and tectonic processes as the main factor creatingdifferent landforms in the ecosystem. Moreover, a large part of the earth’s surface is affected ...
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Extended Abstract
Introduction
Geomorphologists have always considered geomorphological processes such as weathering, erosion, and sedimentation, and tectonic processes as the main factor creatingdifferent landforms in the ecosystem. Moreover, a large part of the earth’s surface is affected by the presence and existence of organisms, thus these biological species play a major role in environmental changes and consequently in the creation of landforms. In fact, geomorphology is one of the important factors affecting vegetation heterogeneity in the scope of landscape. Alluvial fans are among the important and majorgeomorphologicalforms in which two natural parameters of landform and vegetation coexist. Various methods are used to study vegetation density. Vegetation variables are commonly estimated using land surveying, but satellite images have made more accurate methods of rangeland management and alsoestimationof plant quantities in inaccessible areas possible. However, usingdata obtained from satellite imageries for partial measurements has some limitations due to unavailabilityof high spatial resolution images such as QuickBird satellite images or high expenses of retrieving such imagery.In the present study, plant variables were investigated using large-scale aerial imagery and field sampling. Plant density and percent canopy cover were also determined in the study area using the same methods.
Materials & Methods
Study area
The area under study is located in the northeastern regions of Semnan province, northernShahroud city. The study area includes three alluvial fans including Saran, Moghatelan and Hot-Sokhteh.
Methods
Based on field observations, Google Earth images, and drainage pattern, alluvial fans were divided into active (young surfaces) and inactive (old surfaces) parts. Six sites (P1 to P6) including upstream, downstream, active and inactive parts of the alluvial fans under study were selected in order to determine the density and percent of canopy cover in channels, interfluves (in old surfaces), bars and swales (in young surfaces). The aerial image was acquired using a Dji Phantom 4 Pro Drone with a relative flying height of 100 m, and a 20 megapixel, FC6210 digital camerain December 2018 (Table 1 and Fig. 3). The canopy covers in alluvial fan landforms (including channels, interfluves, bars and swales) were measured using large-scale images (1: 500) acquiredby drone. In the next stage, 50 rectangle and squareshaped plots were selected to determine the density and percent canopy cover of the aforementioned landforms in the upstream and downstream of the three alluvial fans; 5 squareshaped plots with a dimension of 10*10 m were selected from the interfluves, 45 rectangularshaped plots with a dimension of 3*10 m were selected from the channels, swales and bars. Then, percent canopy cover was calculated in each plot and the average percent canopy cover was finally calculated for the 50 plots of each site.
Experimental studies
In order to investigate physical and chemical characteristics of soil and its effects on the density and vegetation type across alluvial fans, 48 soil samples were collected from a depth of 0-20 cm in the three alluvial fanseach including active and inactive parts, bars, swales, channels, and interfluves. PH, EC, phosphorus (P), absorbable potassium (K) and sodium (Na), calcium carbonate (CaCO3), Saturation percentage (Sp), water retention capacity of soil (WHC), soil texture, and total organic carbon (OCT) were also measured in the samples.
Sampling vegetation and identifying plant species
In order to identify plant species, field work was carried out in June 2019. Plant species of the study area were identified and a sample was collected, dried and pressed. Systematic random sampling was used in the specified types. In fact, a 200-meter transect was selected in each site, and 8 plots with a dimension of 8 * 8 m were identified along each transect including channels, interfluves, swales, and bars of the upstream and downstream alluvial fans. Therefore, 43 vegetation sampling plots were selected along the 200-meter transect.
Results & Discussion
In the active surfaces of both upstream and downstream alluvial fans, density and percent canopy cover of bars arehigher than those of swales, because of the higher amount of silt and clay in bars. Larger plant species such as shrubs and sub-shrubs requiringfine-textured soil grow in these bars. On the other hand, swales have a higher amount of organic materials and calcium carbonate. EC and PH are lower in the bars as compared to the swales. Water-holding capacity (WHC) and Saturation percentage (Sp) of the soil are higher in the swales as compared to the bars. There are more absorbable potassium and phosphorus in the bars. However, vegetation density and percent canopy cover in swales are lower than those of bars despite their high soil fertility and moisture. This is probably due to the lower stability of the swales whichresults in their higher exposure to unstable currents during occasional storms and floods.
Overall, plant species adapted to the specific environmental conditions are settled in each landform. PerovskiaAbrotanoides is the dominant plant species in active surfaces ofbars. The vegetation type is more limited in the swales of active surfaces including species likePoabulbosa and Bromusdanthoniae.
In inactive surfaces of alluvial fans, elementsrequired for soil fertility (organic materials, calcium carbonate, absorbable potassium and sodium, phosphorus, pH, saturated moisture of the soil, and soil retention) are higher in the interfluves as compared to channels. The relative higher fertility of interfluves can be attributed to their gentle slopes, higher stability and hence higher possibility of soil formation. Long-term exposure of sediments or alluviums to weathering elements on relatively flat surfaces of interfluves has resulted in the formation of more clay and silt, and thereby denser vegetation in interfluves compared to channels. Herbaceous and shrub species, which require fine-textured soils, settle in interfluves. On the other hand, vegetation density of channels with higher amounts of sand and pebbles is lower likely due to their steep slopes as well as their higher level of erosion. However, percent of canopy cover is higher in channels as compared to interfluves. Channels have a relatively higher level of moisturesince they are in the shade and in vicinity of groundwater. Hence, shrubsare settled in these landforms. These species havea denser canopy cover, and deeper roots and require coarser soil texture.
Artemisia sieberi is the dominant plant species in inactive surfacesofinterfluves.This species is a sun-loving plant requiring lots ofsunshine to grow.Apart from Artemisia sieberi, other plants such as Astragalus sp., Acanthophyllum sp., Peganumharmala, AmygdalusScoparia and convolvulus acanthocladus have also settled in the interfluves.
Conclusion
Analyzing vegetation density and percent canopy cover of alluvial fans and their related landforms indicated that bushes are more frequent in the interfluves of old surfaces as compared to other parts of these fans. Despitelower vegetation densityin bars of young fans and channels of old fans, they have a larger type of vegetation (mainly shrubs) and thus, a higherpercent canopy cover. Generally, this study has revealed that bushes are more frequent in the old alluvial fans, especially upstream parts of the fans as compared to other areas. Overall, the results indicate that geomorphological processes such as aggradation and degradation affect the texture and fertility of soil as well as type and density of vegetation.
Mohammad Kazemi Garaje; Khalil Valizadeh Kamran
Abstract
1- Introduction Direct measurement of physical parameters of water, such as sea surface temperature and water depth through traditional methods is very time-consuming and costly. Thus, new cost-effective methods, such as remote sensing technology, have always been of interest to experts, managers and ...
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1- Introduction Direct measurement of physical parameters of water, such as sea surface temperature and water depth through traditional methods is very time-consuming and costly. Thus, new cost-effective methods, such as remote sensing technology, have always been of interest to experts, managers and decision-makers. Satellite imagery is used to estimate sea surface temperature and water depth. Therefore, the present study seeks to calculate sea surface temperature and water depth and investigate their relation using satellite imagery. 2- Materials and Methods In the present study, Landsat 8 satellite images of Urmia and Van Lake were retrieved from USGS website for August 16th, and August 23rd, 2018. Information about water temperature and water depth of 3 meteorological stations in the study area were also obtained from the Artemia Research Center and the Meteorological Organization of West Azerbaijan Province for a period of three months. In the next step, geometric and atmospheric corrections were performed on the images using ENVI5.3 software. In thermal remote sensing, thermal bandwidth of satellite imagery cannot reflect black-body radiation. Moreover, electromagnetic spectrum of radiation used in the Boltzmann relationship covers a range of 3 to 300 micrometers. This is while the thermal spectrum range of thermal sensors is generally between 10.5 to 12.5 micrometers.Thus, the split-window algorithm was used to calculate the land surface temperature. Water emission coefficient equals 0.98. Multiplying the amount of water emission by the amount of land surface temperature (LST) and subtracting the results from zero Kelvins (-273), we can obtain sea surface temperature in Celsius degrees. 2-1- Calculating relative depth of water As one of the dynamic characteristics of water, water depth has an important role in the management and optimal use of marine resources. Water depth measurement refers to the underwater study of oceans, lakes and rivers. Therefore, Stump Method was used to calculate water depthin the present study. 2-2- Accuracy assessment In order to estimate the accuracy, information about water surface temperature and relative water depth in three stations in Lake Urmia, namely Qalqachi, MalekAshtar and Ashk stations, were collected from the Artemia Research Center and the Meteorological Organization of West Azerbaijan Province. 3- Results Results indicate high accuracy of remote sensing methods in sea surface temperature and water depth measurements. The lowest RMSE of sea surface temperature measurement is related to MalekAshtar station (1/1). This station also has the lowest amount of RMSE (1/5) obtained in water depthmeasurement. According to the results, a negative correlation coefficient is observed between the values of sea surface temperature and water depthvariables. The correlation between sea surface temperature and water depth in Lake Van equals -0.52, while this correlation equals -0.24in Lake Urmia. 4- Discussion Despite their relatively high accuracy, usinginformation collected from meteorological stations to calculate physical parameters of water,such as water surface temperature and water depth, has some limitations. However, new technologies such as remote sensing can overcome the limitations of traditional methods. Remote sensing technology has made estimating the physical parameters of water on a regional to a global scale possible. Results of the present study indicate high accuracy of remote sensing technology in measuring physical parameters of water such as surface temperature and depth. In this regard, shallow water bodies have the highest surface temperature and deeper water show lower temperatures. The results also indicate that fluctuations in the water surface temperature and water depth can increase or decrease the correlation coefficient between these two variables. Thus, higher correlation coefficient between water surface temperature and water depth in Lake Van compared to Lake Urmia is due to its greater depth of water. 5- Conclusion Results indicate that the upstream of Lake Urmia is deeper than itsdownstream and also has a higher level of salinity which reduce evapotranspiration in the upstream of the lake. Thus, theupstreamof Lake Urmia has not been as severely affected by the drought. The correlation coefficient between water surface temperature and water depth of Lake Van also shows that this lake has a relatively lower water surface temperature compared to Lake Urmia due to its greater depth. Therefore, the rate of evapotranspiration in this lake is less than Lake Urmia and the drying process is negligible. Due to the fact that Lake Urmia and Van are in the same climate, the high temperature of the water level of Lake Urmia due to its shallower depth can be one of the causes of Lake Urmiadrying. The amount of water in the lake can be increased by increasing the volume of water entering the lake.This can be achieved by destroying a number of dams built on the rivers flowing into the lake or by water transfer from adjacent water bodies. Therefore, increasingwater depth and reducingwater surface temperature can be considered as one of the main solutions to prevent the drying of Lake Urmia.
Parisa Golshani; Yasser Maghsoudi; Hormoz Sohrabi
Abstract
Extended Abstract Introduction Estimation of forest Carbon stocks plays an important role in assessing the quantity of carbon exchange between the forest ecosystem and the atmosphere. Direct methods of measuring carbon stock are not economically efficient. Optical remote sensing methodsalso have limited ...
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Extended Abstract Introduction Estimation of forest Carbon stocks plays an important role in assessing the quantity of carbon exchange between the forest ecosystem and the atmosphere. Direct methods of measuring carbon stock are not economically efficient. Optical remote sensing methodsalso have limited capability in predicting forest biomass, because the spectral response of optical images is related to the interaction between solar radiation and canopy, especially in mature forests. These obstacles limit the efficiency of optical sensors for forest biomass estimation. Recently, airborne data has received a great deal of scientific and operational attention for estimation of forest features. LiDAR data also faces challengessuch as limited efficiency in large areas, high costs and large data volumes. In contrast to the optical and LiDAR systems, SAR systems have some advantages, such as the possibility of data collection in any weather condition, penetration through clouds and canopy, and easy access. The potential of SAR images with quad polarization for the estimation of Iranian Hyrcanian forests biomass will be investigated. The main purpose of this study was to investigate the efficiency of ALOS-2 /PALSAR-2 backscattering coefficients andpolarimetric features in leaf-on and Leaf-off crown conditions, evaluate the linear regression model and select the most appropriate variables for biomass estimation. Material and methods The study area is located in a part of forests of Mazandaran province. The region forms a part of the deciduous broadleaf temperate plain forests. The forestsunder study was classified into 4 major types: (1) Forest reserve, (2) Natural forest, (3) Degraded forest and (4) Mixed species forest plantations. 115 circular sample plots (each including 0.1 hectares)were collected from the 4 different sites with various forest structures and biomasses. In each sample, tree species and diameter at breast height (DBH) of all trees with DBH > 7.5 cm were recorded. Allometric equations were used to convert tree diameter to biomass. The present study is based on polarimetric L-band PALSAR-2 data collected in spring and winter. Backscattering matrix was generated using the PALSAR data which consists of amplitude and phase information. Speckle noise filtering was performed using the Refined Lee adaptive filter. Following the filtering, all polarimetric features were extracted. After converting the SAR products to NRCS, geometric correction and georeferencingwere performed and the average backscattering coefficient (sigma naught value)was extracted for each sample plot by overlaying the AOI layers on corresponding SAR images. Finally, the relationship between forest biomass and backscattering intensity was investigated. Results and discussion The resultsvary regarding to the forest type, the range of biomass and forest canopy cover percentage.Forest type and biomass range as well as canopy cover percentage affect the scattering mechanism and correlations between biomass and SAR backscattering coefficient. Canopy cover percentageofthe 1stand 4thsites were over 90% and consequently, the sensitivity of HV backscatter value to biomass was higher than HH and VV. In the 2nd and especially 3rd sites, the correlation between HH backscattervalueand AGB was better than its correlation with HV backscatter. This is mainly because of the canopy structure in these sites which is not complete and the fact that the sensitivity of HH backscatter value to biomass is higher than HV. Results indicate in the 1st and 4th sites, the correlation between volume scatter component of decomposition methods with AGB was better than its correlation with double-bounce scatter component. In contrast, the double-bounce decomposition componentsexhibited the best results in the 2nd and 3rd sites. These findings are in agreement with the results obtained from the T3 matrix components. The least correlation value was observed between Freeman decomposition components and AGB. The volume scatter component of Cloude and also double-bounce component of Freeman did not provide suitable results. Results also indicate higher efficiency of images collected in spring as compared to those collected in winter.Linear regression results show that in the best possible situation, RMSE of the first forest habitat was 34.68 t/ha, and 30.09, 27.07 and 23.69 t/ha were estimated for the 2nd, 3rd, and 4th forest habitats, respectively. Therefore, it seems that classification of forests is necessary before biomass estimation. Conclusion The potential of PALSAR-2 data for Hyrcanian forest biomass estimation was assessed in this study. We demonstrated that L-band data are sensitive to the above-ground biomass (AGB) of Hyrcanianforestsand can be used to provide accurate estimates of biomass. Findings confirmed that decomposition methods are more efficient than backscattering coefficients for biomass mapping.
Sara Khanbani; Reza Shahhoseini
Abstract
Extended AbstractIntroductionChange detection (CD) from remote sensing image considered an important topic among scientist because of its application in monitoring urban and non-urban area, environmental issue, damage assessment, etc. Presenting an efficient CD method from a high-resolution image can ...
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Extended AbstractIntroductionChange detection (CD) from remote sensing image considered an important topic among scientist because of its application in monitoring urban and non-urban area, environmental issue, damage assessment, etc. Presenting an efficient CD method from a high-resolution image can face a different challenge; most of the CD method from a high-resolution image requires training procedure to overcome this challenge. In this paper, an unsupervised (without needing training process) CD algorithm proposed from the high-resolution image. In this method spatial and spectral features extracted from bi-temporal images of the studied area. Difference images generated from high information content features. Then generated different images mapped into spherical space. The Primary change map created using implemented multi-thresholding method on created spherical space and the second change map created using hierarchical clustering regularized by Markov random field method. The final change map created by integrating the result of primary and secondary change maps. The final change map shows an overall accuracy of 92.56% in the studied area. Data and methodsThe data used in this paper is a subset of the main data with dimensions of 2000 * 2000 from an urban area in the city of Mashhad. These images corresponded to the two periods of 1390 and 1395 and were taken with UAV. The orthoimage is related to the first time with a spatial resolution of 6 cm and the second image is taken with a pixel size of 10 cm. In this paper, in order to detect of change of high-resolution images, first, the input images are registered in terms of spectral and spatial, and then feature images are extracted from each input image separately. In the next step, the differences images corresponding to high information content feature images are calculated. . The optimal difference images are mapped to the spherical space using selected statistical methods and in order to better analysis of the results. Otsu multi-thresholding method implemented on r component of sphere space. In the next step, the optimal difference image mapped to a spherical space is divided into non-overlapping blocks with the same dimensions; a cumulative hierarchical clustering method is applied for each block separately. In this case, the computational volume and space proposed in the hierarchical clustering method are reduced. The results of the cumulative clustering of the blocks are merged together and then the Markov random field method is used in order to regularize the results of the cluster in order to reduce noise.In final clustering, the class values below the lowest Otsu threshold are known as unchanged pixels with high reliability and the values above the maximum threshold are determined as changed pixels. The class of middle interval is unknown. For determining, the class of middle interval the corresponded output of hierarchy clustering regularized with a random Markov field is used. In the last step, a vegetation and shadow mask is used for final post-processing. Results and discussionIn order to an accurate assessment of the proposed method on the mentioned study area, a ground truth image with 11073 pixels has been used as a ground test image. The proposed method has shown an overall accuracy of 92.56 in the study area. The accuracy of detecting changed pixels shows 81.61% and the accuracy of detection unchanged pixels shows 92.77%. The false alarm percentage is 0.21 percent and the missed alarm accuracy is 0.0723 percent. For comparative evaluation, the proposed method is compared with the change vector analysis algorithm. In this section, the selected features in the feature extraction section are entered in the change analysis algorithm, and then the multi thresholding algorithm and shadow analysis used to create the final change map. This method has shown increasing the alarm in comparison with the proposed method. The accuracy of changed and un-changed pixels in the change vector analysis method is equal to 52.98 and 89.24%, respectively. Comparing these results with the results of the proposed method shows the efficiency of the proposed method. ConclusionIn this paper, the new unsupervised change detection method presented based on the combination of multi thresholding and the hierarchical clustering algorithm. Compared to supervised methods that require training data, this method does not require training data. In this method, textural and spatial-spectral features are extracted from images with high spatial resolution, which covers the discussion of the importance of neighborhoods in images with high spatial resolution. In the next step, the extracted features that have a high information content are selected, which helps to reduce the redundancy of the information. The contrast images of the features with high information content are created to differentiate the location of the changes. Spherical computing space is considered as the basic computing space. In order to create a binary change map, two analyzes have been performed on the spherical computational space. First, the Otsu multi-thresholding method has been applied. The values of the smaller and larger thresholds have definite classes. But the value of the middle interval needs to be further analyzed using the hierarchical clustering method. In this section, the middle pixel class is examined, and then a final adjustment is performed using Markov field and shadow and vegetation analysis in order to post-process and prevent false changes. In this paper, the parameters of changed accuracy – unchanged accuracy - overall accuracy - false and missed alarms have been used to evaluate the accuracy of the proposed method with a ground accuracy map. In order to make a comparative study, the proposed method is compared with the change vector analysis method of the created feature space. The results show the efficiency of the proposed method.
Nikrouz Mostofi; Hossein Aghamohammadi Zanjiirabad; Alireza Vafaeinezhad; Mahdi Ramezani; Amir Houman Hemmasi
Abstract
Introduction 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 ...
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Introduction 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. Materials and Methods 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. Result and Discussion 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).
Moslem Darvishi; Abouzar Ramezani
Abstract
Extended Abstract Introduction Due todecreased rainfall and increased groundwater harvesting, our country faces drought. With drastic decline of water levelin lakes and hydroelectric reservoirs, water scarcity is deeply felt. Thus, managers and officials shall find new ways of decreasing waterconsumption ...
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Extended Abstract Introduction Due todecreased rainfall and increased groundwater harvesting, our country faces drought. With drastic decline of water levelin lakes and hydroelectric reservoirs, water scarcity is deeply felt. Thus, managers and officials shall find new ways of decreasing waterconsumption and overcome this crisis. Due to the rising global temperatures and reportsof the World Wildlife Fund, water scarcitycrisis will dominate most countries of the world, especially in Europe and Asia in the next ten years (Sengupta, 2018). Therefore, advanced water management principles shall be applied to decrease water consumption in the agricultural sector and maintain water security. Iran is among the top five countries of the world in terms of having vast irrigated land (Bruinsma, 2017), which shows that in many parts of the country agricultural lands are irrigated. Thus, the country’s water resources reach a critical stage, and because of limited resources, no more water can be provided for agriculture. The present study primarily seeks to optimize crop cultivation using two approaches: first, reduce water consumption and increase farmers’ income and second, reduce water consumption and meet domestic demand. In order to achieve this goal, first, the type of crops and area under cultivation were determined using remote sensing and satellite imagery. Then,spatial information system was used for data analysisand optimization of crop cultivation. Materials & Methods Remotely sensed images were used to collect data about the area under cultivationin agricultural patches and crop type. Those images were then analyzed using remote sensing techniques.According to pixel-based classification ofmultitemporal satellite images using training data, a croplabel is assigned to each pixelin this method. Moreover, borders of each agricultural land are extracted from pan-chromatic images of the region with higher spatial resolution. Finally, fitting the results of pixel-based classification with the extracted bordersof each agricultural land,a final croplabel is determinedfor the total area of the agricultural landbased on the majority labels. In order to optimize the problem, two objective functions (relationships 1 and 2) are defined in which income maximization and water consumption minimization are considered. Typically, location and allocation problems include objective and constraints functionswhich are maximized or minimized based on the goal of the problem. Linear programming is used to solve the problem. Linear programming is a classical optimization method whichdevelop a deterministic algorithm tosolve the problem. This method can only be used when the relationships between variables are linear. In other words, the relationship between variables shall be perfectly proportional and directin this method. (1) (1) (2) Result &Discussion The study area consists of 198 hectares of agricultural land in vicinity of GolangTapeh village of Asadabad city. The city covers an area of 1195 km2 and constitutes 6.1% of Hamadan province. It is located between 34° 37› to34°50 ‹northern latitude and 47°9› to 47°51›eastern latitude. Its average height is 1607 meters above sea level. The city is bounded in northwest with the province of Kordestan,in west with the province of Kermanshah, in southeast with Tuyserkancity and in the northeast withBaharcity. Assad Abad consists of three plains and a mountainside, but since it mostly consists of fertile plains, it can be considered as a flat area (Fig. 1). Fig1: Case study area Figure 2 shows the results of pixel-basedclassificationusing neural network method. In this method, network is trained using ground data. After training the network on the basis of ground truth estimator data, the estimation accuracy is about 88%. Fig. 2: The results ofclassification using neural network Following the calculation of the area under cultivation in agricultural lands and the type of crops, optimization is investigated using two scenarios (Figure 3). In the first scenario, reduction of water consumption and increased farmers’ income and in the second scenario,meeting domestic demandsto prevent capital outflow is considered. Fig3: Crop type and boundaries of agricultural lands In the first scenario, our priority is to reduce water consumption and increase farmers’ income. In this scenario, the goal is to select the type of crops according to the modeling constraints so that the crop type and water consumption are optimized. Figure 4 shows the proposed crop type. Fig4: The results of thefirst scenario Conclusion The present study used a combination of remote sensing and spatial information system to find a solution for optimization ofcultivation pattern through two different scenarios. First, land boundaries and types of crops were determinedusing pan-chromatic images and artificial intelligence. Then, two objective functions were developed to minimize water consumption and maximize income. Also, constraints such as crop type, periodicity constraints and domestic demand were modeled. Considering two objective functions, an algorithm was presented to optimize the cultivation pattern and the results were implemented in this algorithm. Results indicated that the difference between the first scenario which seeks to minimize water consumption and maximize farmers’ income and the second scenario which seeks tominimize water consumption and maximizedomestically demanded crops is relatively small. In both scenarios, the water use rate inAsadabad plain have decreased by about 1000 m3. In other words, in both scenarios there was a 50% reduction in water consumption. Moreover, if priority is given to meeting domestic demand, water consumption increase by about 3% and income decrease by about 3%. In future studies, owners of each agricultural land can be determined and each farmer’s incomecan be considered to further optimize crop cultivation.
Geographic Data
Yaser Moarrab; Esmaiel Salehi; Mohammad Javad Amiri; Hassan Hoveidi
Abstract
Extended AbstractIntroductionThe global rise in urbanization and settlement of the majority of the world’s population in urban areas create opportunities and challenges for improving the quality and sustainability of life. Potential of cities for meeting the basic needs of people has become an ...
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Extended AbstractIntroductionThe global rise in urbanization and settlement of the majority of the world’s population in urban areas create opportunities and challenges for improving the quality and sustainability of life. Potential of cities for meeting the basic needs of people has become an important part of recent scientific and political debates. Covering only a small area of land, cities are responsible for many global environmental problems such as carbon emissions, energy and resource consumption, biodiversity degradation, and ecosystem degradation. They also convert natural forests to human settlements, farms, roads, gardens, and other human-made land uses, leaving many direct and indirect effects on natural conditions and ecological functions of upstream and downstream in forests (such as changes in quantity and quality of water, changes in water flow in rivers, changes in climatic condition and habitat quality). These structural and functional changes undermine environmental services provided by ecological infrastructure and threaten the environmental security of cities and their sustainable development. Therefore, urban managers and experts have always sought a suitable way for urban planning to regulate the structure of cities, support the stability of ecosystem and its performance, and maintain the ecological security of cities. Case studyLavasanat is a district in Shemiranat County in Tehran province of Iran, which is located in the northeast of Tehran. MethodsThe present study analyzes temporal-spatial changes of land use / land cover and then, uses InVEST 3.7.0 model to evaluate temporal-spatial changes of land uses. Results & DiscussionChanges occurring in the reference period were depicted in maps prepared for various land cover / land use classes. Validation of image classification shows a total accuracy of 95.72%, 96.26% and 95.32% and a Kappa coefficient of 0.948, 0.943 and 0.936 for classifications in 2000, 2010 and 2020, respectively, which is acceptable and indicates the compatibility of classified land uses and reality. Classification of images using maximum likelihood algorithm showed the presence of five classes of residential areas (urban area, villages, industries and roads), barren lands, pastures, water bodies and green space in the region.Land use maps and information derived from satellite images indicate that residential areas have experienced a growing trend due to increasing population, demand for land and consequent growth of urbanism, while green space had a decreasing trend during the reference period. Development of residential areas and reduction in green space are quite evident between 2010 and 2020. According to the present trend of land use change, there will be a sharp decline in green space in the coming years. Pastures experienced a decreasing trend from 2000 to 2010. However, it faces an increasing trend from 2010 to 2020 since more green areas were converted into pastures. Barren lands experienced a decreasing trend from 2000 to 2020. ConclusionThe present paper offers the results of modeling water production services in Lavasanat Basin in different decades. Results indicate that the water production in the entire Lavasanat basin equals 2641734.816 cubic meters in 2000, 3318950.915 cubic meters in 2010 and 7737201.215 cubic meters in 2020. Of these volumes, 1677926.367 cubic meters in 2000, 2287145.055 cubic meters in 2010, and 4908786.651 cubic meters in 2020 belonged to residential areas. This class contained an area of 4820578.505 square meters in 2000, 6885513.787 square meters in 2010 and 10407948.705 square meters in 2020 in the whole basin.The results obtained from InVEST scenario building model and water production model showed that the increasing trend of human-made land uses in the study area has a significant impact on increasing water production and, consequently, increases runoff. In fact, water production has experienced a growth rate of 1.25 or 125% from 2000 to 2010, and a growth rate of 2.33 or 233% from 2010 to 2020. Thus in 20 years, water production has increased by 2.92 (292%). The volume of water production in residential areas has increased by 1.36 times (136 %) from 2000 to 2010, 2.14 times (214 %) from 2010 to 2020 and 2.92 times (292%) in 20 years. Also, the total area covered by residential land use has grown 1.42 times from 2000 to 2010 (142 %), and 1.51 times (151%) from 2010 to 2020. Therefore, an increase of 2.15 or 215% was observed in residential areas over this 20 year period.
Arastou Zarei; Reza Shahhoseini; Ronak Ghanbari
Abstract
Extended Abstract
Introduction
As a key parameter describing physics of land surface processes on local and global scales, land Surface Temperature (LST) is the result of all interactions and energy flows between land surface and the atmosphere. Temperature changes rapidly on temporal ...
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Extended Abstract
Introduction
As a key parameter describing physics of land surface processes on local and global scales, land Surface Temperature (LST) is the result of all interactions and energy flows between land surface and the atmosphere. Temperature changes rapidly on temporal and spatial scales, and thus a complete description of LST require measurements involving spatial and temporal frequencies. Hence, climatological, meteorological, and hydrogeological studies require having access to wide scale information about spatial changes of air temperature. Since the LST product of SLSTR uses linear split-window algorithm, the present study has used nonlinear split-window algorithm to estimate LST in Sentinel-3 images. Linearity of the radiation transfer equation in linear algorithm and some approximations used in split-window algorithms (such as transfer approximation as a linear function of vapor value) result in considerable errors because of which nonlinear algorithm is used in the present study. Using linear split-window algorithm to estimate LST in tropical climates also leads to a high level of error. The present study seeks to estimate LST using a nonlinear split-window algorithm and data retrieved from Sentinel-3 in different seasons of 2018 and 2019. The results are also evaluated using temperature product of MODIS and SLSTR.
Materials & Method
A time series of sentinel-3 images retrieved from 2018 to 2019 was used as research data. Data were collected by Sentinel-3 SLSTR sensors operated by the European Space Agency (ESA). Obviously, images shall be radio-metrically corrected before calculating physical land surface parameters such as temperature, emissivity, reflectance and radiance, albedo, and etc. To reach this goal, it is necessary to omit or minimize the effect of atmosphere, epipolar geometry of sensor, sunlight, topography, and surface characteristics while estimating surface parameters in these images. The current study seeks to estimate LST applying a nonlinear split-window algorithm on Sentinel-3 data collected during different seasons of 2018 and 2019 and to evaluate the results using temperature product of MODIS, SLSTR, and in-situ data. Pearson Correlation Coefficient and Root Mean Square Error (RMSE) were also used as relative and quantitative criteria to evaluate the accuracy of the proposed method and determine the deference between temperature calculated by the proposed method and temperature product of MODIS and SLSTR sensor. Hence, four frames of LST product collected by MODIS, and SLSTR in April, June, and October, 2018 and January, 2019 were used to evaluate the proposed method.
Results & Discussion
The proposed method was also indirectly evaluated using temperature products of MODIS and SLSTR sensor. Applying parameters of mean and root mean square error, the evaluation has shown that the results obtained from the proposed method in the one-year reference period were more similar to the results obtained from MODIS sensor. Comparing nonlinear Split-Window algorithm and MODIS products, RMSE ranged from 1.21 to 2.46 and the highest and lowest accuracy belonged to winter and summer, respectively. Comparing this algorithm with the SLSTR product, RMSE ranged from 0.76 to 2.24 and the highest and lowest accuracy belonged to winter and summer, respectively. Proper performance of the algorithm in winter is due to the relative balance of atmospheric water vapour in this season. Comparing nonlinear modelling of atmospheric water vapour in the non-linear algorithm of a Split-window and the linear algorithm in SLSTR and MODIS products, the small difference between temperature calculated by the algorithm and the products can be justified. However, due to temperature fluctuations in summer, results obtained by the proposed method were not reliable enough compared to both temperature products. Generally, results obtained from the proposed method showed a higher correlation with the temperature product of SLSTR sensor, which is due to the similar spectral bands used in calculating the surface temperature. Relative comparison of the Split-Window and the MODIS product’s nonlinear algorithm showed a coefficient of determination ranging from 0.76 to 0.96, while comparing this algorithm with the SLSTR product showed a determination coefficient of 0.80 to 0.98. Comparing temperature obtained from the nonlinear Split-Window algorithm with SLSTR and MODIS temperature products, the proposed algorithm was relatively stable no matter which season was taken into account.
Conclusion
The present study seeks to estimate Land Surface Temperature using a nonlinear Split-Window algorithm and Sentinel-3 data collected in different seasons. Values obtained from the algorithm were validated using in-situ dataset retrieved from the meteorological station. They were also evaluated using temperature product of MODIS and SLSTR. To increase the accuracy level, temperature product of MODIS and SLSTR were also evaluated and compared with the in-situ dataset and provided good results. Generally, there is a significant difference between temperature values estimated by the NSW algorithm for different seasons especially summer. However, a similar trend was observed in temperature changes reported by SLSTR and MODIS, and the proposed algorithm in different seasons of the study area. Although, the nonlinear Split-Window algorithm showed a higher accuracy in spring and winter, overall results indicated that the proposed method was relatively stable no matter which season was taken into account. It can be concluded that LST estimation with nonlinear Split-window method and Sentinel-3 satellite data has an acceptable level of accuracy and thus, can be used in large scale environmental crises such as climate changes.
Majid Danesh; HosseinAli Bahrami; Roshanak Darvishzadeh; Ali Akbar Noroozi
Abstract
Extended Abstract
Introduction
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 ...
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Extended Abstract
Introduction
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.
Materials and Methods
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.
Conclusion
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).
