bluuebunny/crossref_metadata_2025 huggingface dataset:
Reviews and syntheses: Soil N2O and NO emissions from land use and land-use change in the tropics and subtropics: a meta-analysis
Authors: van Lent J. , Hergoualc'h K. , Verchot L. V.
Published: December 2015

Abstract. Deforestation and forest degradation in the tropics may substantially alter soil N-oxide emissions. It is particularly relevant to accurately quantify those changes to properly account for them in a REDD+ climate change mitigation scheme that provides financial incentives to reduce the emissions. With this study we provide updated land use (LU)-based emission rates (104 studies, 392 N2O and 111 NO case studies), we determine the trend and magnitude of flux changes with land-use change (LUC) using a meta-analysis approach (44 studies, 135 N2O and 37 NO cases) and evaluate biophysical drivers of N2O and NO emissions and emission changes for the tropics. The average N2O and NO emissions in intact upland tropical forest amounted to 2.0 ± 0.2 (n = 90) and 1.7 ± 0.5 (n = 36) kg N ha−1 yr−1, respectively. In agricultural soils annual N2O emissions were exponentially related to N fertilization rates and average water-filled pore space (WFPS) whereas in non-agricultural sites a Gaussian response to WFPS fit better with the observed NO and N2O emissions. The sum of soil N2O and NO fluxes and the ratio of N2O to NO increased exponentially and significantly with increasing nitrogen availability (expressed as NO3− / [NO3−+NH4+]) and WFPS, respectively; following the conceptual Hole-In-the-Pipe model. Nitrous and nitric oxide fluxes did not increase significantly overall as a result of LUC (Hedges's d of 0.11 ± 0.11 and 0.16 ± 0.19, respectively), however individual LUC trajectories or practices did. Nitrous oxide fluxes increased significantly after intact upland forest conversion to croplands (Hedges's d = 0.78 ± 0.24) and NO increased significantly following the conversion of low forest cover (secondary forest younger than 30 years, woodlands, shrublands) (Hedges's d of 0.44 ± 0.13). Forest conversion to fertilized systems significantly and highly raised both N2O and NO emission rates (Hedges's d of 1.03 ± 0.23 and 0.52 ± 0.09, respectively). Changes in nitrogen availability and WFPS were the main factors explaining changes in N2O emissions following LUC, therefore it is important that experimental designs monitor their spatio-temporal variation. Gaps in the literature on N oxide fluxes included geographical gaps (Africa, Oceania) and LU gaps (degraded forest, wetland (notably peat) forest, oil palm plantation and soy cultivation).
Radiative forcing and feedback by forests in warm climates – a sensitivity study
Authors: Port U. , Claussen M. , Brovkin V.
Published: December 2015

Abstract. The biogeophysical effect of forests in a climate with permanent high-latitude ice cover has already been investigated. We extend this analysis to warm, basically ice-free climates, and we choose the early Eocene, some 54 to 52 million years ago, as paradigm for such type of climate. We use the Max Planck Institute for Meteorology Earth System Model to evaluate the radiative forcing of forests and the feedbacks triggered by forests in early Eocene and pre-industrial climate, respectively. To isolate first-order effects, we compare idealised simulations in which all continents are covered either by dense forests or by deserts with either bright or dark soil. In comparison with desert continents covered by bright soil, forested continents warm the planet in the early Eocene climate and in the pre-industrial climate. The warming can be attributed to different feedback processes, though. The lapse-rate – water-vapour feedback is stronger in early Eocene climate than in pre-industrial climate, but strong and negative cloud-related feedbacks nearly outweigh the positive lapse-rate – water-vapour feedback in the early Eocene climate. Subsequently, global mean warming by forests is weaker in the early Eocene climate than in the pre-industrial climate. Sea-ice related feedbacks are weak in the almost ice-free climate of the early Eocene, thereby leading to a weaker high-latitude warming by forests than in the pre-industrial climate. When the land is covered with dark soils, forests cool the early Eocene climate stronger than the pre-industrial climate because the lapse-rate and water-vapour feedbacks are stronger in the early Eocene climate. Cloud-related feedbacks are equally strong in both climates. We conclude that radiative forcing by forests varies little with the climate state, while most subsequent feedbacks depend on the climate state.
Estimating the volume of Alpine glacial lakes
Authors: Cook S. J. , Quincey D. J.
Published: December 2015

Abstract. Supraglacial, moraine-dammed and ice-dammed lakes represent a potential glacial lake outburst flood (GLOF) threat to downstream communities in many mountain regions. This has motivated the development of empirical relationships to predict lake volume given a measurement of lake surface area obtained from satellite imagery. Such relationships are based on the notion that lake depth, area and volume scale predictably. We critically evaluate the performance of these existing empirical relationships by examining a global database of glacial lake depths, areas and volumes. Results show that lake area and depth are not always well correlated (r2 = 0.38) and that although lake volume and area are well correlated (r2 = 0.91), and indeed are auto-correlated, there are distinct outliers in the data set. These outliers represent situations where it may not be appropriate to apply existing empirical relationships to predict lake volume and include growing supraglacial lakes, glaciers that recede into basins with complex overdeepened morphologies or that have been deepened by intense erosion and lakes formed where glaciers advance across and block a main trunk valley. We use the compiled data set to develop a conceptual model of how the volumes of supraglacial ponds and lakes, moraine-dammed lakes and ice-dammed lakes should be expected to evolve with increasing area. Although a large amount of bathymetric data exist for moraine-dammed and ice-dammed lakes, we suggest that further measurements of growing supraglacial ponds and lakes are needed to better understand their development.
Development studies towards an 11-year global gridded aerosol optical thickness reanalysis for climate and applied applications
Authors: Lynch P. , Reid J. S. , Westphal D. L. , Zhang J. , Hogan T. F. , Hyer E. J. , Curtis C. A. , Hegg D. A. , Shi Y. , Campbell J. R. , Rubin J. I. , Sessions W. R. , Turk F. J. , Walker A. L.
Published: December 2015

Abstract. While standalone satellite and model aerosol products see wide utilization, there is a significant need in numerous climate and applied applications for a fused product on a regular grid. Aerosol data assimilation is an operational reality at numerous centers, and like meteorological reanalyses, aerosol reanalyses will see significant use in the near future. Here we present a standardized 2003–2013 global 1° × 1° and 6 hourly modal aerosol optical thickness (AOT) reanalysis product. This dataset can be applied to basic and applied earth system science studies of significant aerosol events, aerosol impacts on numerical weather prediction, and electro-optical propagation and sensor performance, among other uses. This paper describes the science of how to develop and score an aerosol reanalysis product. This reanalysis utilizes a modified Navy Aerosol Analysis and Prediction System (NAAPS) at its core and assimilates quality controlled retrievals of AOT from the Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua and the Multi-angle Imaging SpectroRadiometer (MISR) on Terra. The aerosol source functions, including dust and smoke, were regionally tuned to obtain the best match between the model fine and coarse mode AOTs and the Aerosol Robotic Network (AERONET) AOTs. Other model processes, including deposition, were tuned to minimize the AOT difference between the model and satellite AOT. Aerosol wet deposition in the tropics is driven with satellite retrieved precipitation, rather than the model field. The final reanalyzed fine and coarse mode AOT at 550 nm is shown to have good agreement with AERONET observations, with global mean root mean square error around 0.1 for both fine and coarse mode AOTs. This paper includes a discussion of issues particular to aerosol reanalyses that make them distinct from standard meteorological reanalyses, considerations for extending such a reanalysis outside of the NASA A-Train era, and examples of how the aerosol reanalysis can be applied or fused with other model or remote sensing products. Finally, the reanalysis is evaluated in comparison with other available studies of aerosol trends, and the implications of this comparison are discussed.
ACCURACY IMPROVEMENT BY THE LEAST SQUARES IMAGE MATCHING EVALUATED ON THE CARTOSAT-1
Authors: Afsharnia H. , Azizi A. , Arefi H.
Published: December 2015

Abstract. Generating accurate elevation data from satellite images is a prerequisite step for applications that involve disaster forecasting and management using GIS platforms. In this respect, the high resolution satellite optical sensors may be regarded as one of the prime and valuable sources for generating accurate and updated elevation information. However, one of the main drawbacks of conventional approaches for automatic elevation generation from these satellite optical data using image matching techniques is the lack of flexibility in the image matching functional models to take dynamically into account the geometric and radiometric dissimilarities between the homologue stereo image points. The classical least squares image matching (LSM) method, on the other hand, is quite flexible in incorporating the geometric and radiometric variations of image pairs into its functional model. The main objective of this paper is to evaluate and compare the potential of the LSM technique for generating disparity maps from high resolution satellite images to achieve sub pixel precision. To evaluate the rate of success of the LSM, the size of the y-disparities between the homologous points is taken as the precision criteria. The evaluation is performed on the Cartosat-1 stereo along track images over a highly mountainous terrain. The precision improvement is judged based on the standard deviation and the scatter pattern of the y-disparity data. The analysis of the results indicate that, the LSM has achieved the matching precision of about 0.18 pixels which is clearly superior to the manual pointing that yielded the precision of 0.37 pixels.
APPLICABILITY OF MULTI-SEASONAL X-BAND SAR IMAGERY FOR MULTIRESOLUTION SEGMENTATION: A CASE STUDY IN A RIPARIAN MIXED FOREST
Authors: Dabiri Z. , Hölbling D. , Lang S. , Bartsch A.
Published: December 2015

Abstract. The increasing availability of synthetic aperture radar (SAR) data from a range of different sensors necessitates efficient methods for semi-automated information extraction at multiple spatial scales for different fields of application. The focus of the presented study is two-fold: 1) to evaluate the applicability of multi-temporal TerraSAR-X imagery for multiresolution segmentation, and 2) to identify suitable Scale Parameters through different weighing of different homogeneity criteria, mainly colour variance. Multiresolution segmentation was used for segmentation of multi-temporal TerraSAR-X imagery, and the ESP (Estimation of Scale Parameter) tool was used to identify suitable Scale Parameters for image segmentation. The validation of the segmentation results was performed using very high resolution WorldView-2 imagery and a reference map, which was created by an ecological expert. The results of multiresolution segmentation revealed that in the context of object-based image analysis the TerraSAR-X images are applicable for generating optimal image objects. Furthermore, ESP tool can be used as an indicator for estimation of Scale Parameter for multiresolution segmentation of TerraSAR-X imagery. Additionally, for more reliable results, this study suggests that the homogeneity criterion of colour, in a variance based segmentation algorithm, needs to be set to high values. Setting the shape/colour criteria to 0.005/0.995 or 0.00/1 led to the best results and to the creation of adequate image objects.
USING MULTIVARIATE ADAPTIVE REGRESSION SPLINE AND ARTIFICIAL NEURAL NETWORK TO SIMULATE URBANIZATION IN MUMBAI, INDIA
Authors: Ahmadlou M. , Delavar M. R. , Tayyebi A. , Shafizadeh-Moghadam H.
Published: December 2015

Abstract. Land use change (LUC) models used for modelling urban growth are different in structure and performance. Local models divide the data into separate subsets and fit distinct models on each of the subsets. Non-parametric models are data driven and usually do not have a fixed model structure or model structure is unknown before the modelling process. On the other hand, global models perform modelling using all the available data. In addition, parametric models have a fixed structure before the modelling process and they are model driven. Since few studies have compared local non-parametric models with global parametric models, this study compares a local non-parametric model called multivariate adaptive regression spline (MARS), and a global parametric model called artificial neural network (ANN) to simulate urbanization in Mumbai, India. Both models determine the relationship between a dependent variable and multiple independent variables. We used receiver operating characteristic (ROC) to compare the power of the both models for simulating urbanization. Landsat images of 1991 (TM) and 2010 (ETM+) were used for modelling the urbanization process. The drivers considered for urbanization in this area were distance to urban areas, urban density, distance to roads, distance to water, distance to forest, distance to railway, distance to central business district, number of agricultural cells in a 7 by 7 neighbourhoods, and slope in 1991. The results showed that the area under the ROC curve for MARS and ANN was 94.77% and 95.36%, respectively. Thus, ANN performed slightly better than MARS to simulate urban areas in Mumbai, India.
USE SATELLITE IMAGES AND IMPROVE THE ACCURACY OF HYPERSPECTRAL IMAGE WITH THE CLASSIFICATION
Authors: Javadi P.
Published: December 2015

Abstract. The best technique to extract information from remotely sensed image is classification. The problem of traditional classification methods is that each pixel is assigned to a single class by presuming all pixels within the image. Mixed pixel classification or spectral unmixing, is a process that extracts the proportions of the pure components of each mixed pixel. This approach is called spectral unmixing. Hyper spectral images have higher spectral resolution than multispectral images. In this paper, pixel-based classification methods such as the spectral angle mapper, maximum likelihood classification and subpixel classification method (linear spectral unmixing) were implemented on the AVIRIS hyper spectral images. Then, pixel-based and subpixel based classification algorithms were compared. Also, the capabilities and advantages of spectral linear unmixing method were investigated. The spectral unmixing method that implemented here is an effective technique for classifying a hyperspectral image giving the classification accuracy about 89%. The results of classification when applying on the original images are not good because some of the hyperspectral image bands are subject to absorption and they contain only little signal. So it is necessary to prepare the data at the beginning of the process. The bands can be stored according to their variance. In bands with a high variance, we can distinguish the features from each other in a better mode in order to increase the accuracy of classification. Also, applying the MNF transformation on the hyperspectral images increase the individual classes accuracy of pixel based classification methods as well as unmixing method about 20 percent and 9 percent respectively.
USING SMOS PASSIVE MICROWAVE DATA TO DEVELOP SMAP FREEZE/THAW ALGORITHMS ADAPTED FOR THE CANADIAN SUBARCTIC
Authors: Kalantari P. , Bernier M. , McDonal K. C. , Poulin J.
Published: December 2015

Abstract. Seasonal terrestrial Freeze/Thaw cycle in Northern Quebec Tundra (Nunavik) was determined and evaluated with passive microwave observations. SMOS time series data were analyzed to examine seasonal variations of soil freezing, and to assess the impact of land cover on the Freeze/Thaw cycle. Furthermore, the soil freezing maps derived from SMOS observations were compared to field survey data in the region near Umiujaq. The objective is to develop algorithms to follow the seasonal cycle of freezing and thawing of the soil adapted to Canadian subarctic, a territory with a high complexity of land cover (vegetation, soil, and water bodies). Field data shows that soil freezing and thawing dates vary much spatially at the local scale in the Boreal Forest and the Tundra. The results showed a satisfactory pixel by pixel mapping for the daily soil state monitoring with a > 80% success rate with in situ data for the HH and VV polarizations, and for different land cover. The average accuracies are 80% and 84% for the soil freeze period, and soil thaw period respectively. The comparison is limited because of the small number of validation pixels.
UNSUPERVISED CHANGE DETECTION IN SAR IMAGES USING GAUSSIAN MIXTURE MODELS
Authors: Kiana E. , Homayouni S. , Sharifi M. A. , Farid-Rohani M.
Published: December 2015

Abstract. In this paper, we propose a method for unsupervised change detection in Remote Sensing Synthetic Aperture Radar (SAR) images. This method is based on the mixture modelling of the histogram of difference image. In this process, the difference image is classified into three classes; negative change class, positive change class and no change class. However the SAR images suffer from speckle noise, the proposed method is able to map the changes without speckle filtering. To evaluate the performance of this method, two dates of SAR data acquired by Uninhabited Aerial Vehicle Synthetic from an agriculture area are used. Change detection results show better efficiency when compared to the state-of-the-art methods.
AN IMAGE-BASED TECHNIQUE FOR 3D BUILDING RECONSTRUCTION USING MULTI-VIEW UAV IMAGES
Authors: Alidoost F. , Arefi H.
Published: December 2015

Abstract. Nowadays, with the development of the urban areas, the automatic reconstruction of the buildings, as an important objects of the city complex structures, became a challenging topic in computer vision and photogrammetric researches. In this paper, the capability of multi-view Unmanned Aerial Vehicles (UAVs) images is examined to provide a 3D model of complex building façades using an efficient image-based modelling workflow. The main steps of this work include: pose estimation, point cloud generation, and 3D modelling. After improving the initial values of interior and exterior parameters at first step, an efficient image matching technique such as Semi Global Matching (SGM) is applied on UAV images and a dense point cloud is generated. Then, a mesh model of points is calculated using Delaunay 2.5D triangulation and refined to obtain an accurate model of building. Finally, a texture is assigned to mesh in order to create a realistic 3D model. The resulting model has provided enough details of building based on visual assessment.
DECISION MAKING ON REGIONAL LANDFILL SITE SELECTION IN HORMOZGAN PROVINCE USING SMCE
Authors: Majedi A. S. , Kamali B. M. , Maghsoudi R.
Published: December 2015

Abstract. Landfill site selection and suitable conditions to bury hazardous wastes are among the most critical issues in modern societies. Taking several factors and limitations into account along with true decision making requires application of different decision techniques. To this end, current paper aims to make decisions about regional landfill site selection in Hormozgan province and utilizes SMCE technique combined with qualitative and quantitative criteria to select the final alternatives. To this respect, we first will describe the existing environmental situation in our study area and set the goals of our study in the framework of SMCE and will analyze the effective factors in regional landfill site selection. Then, methodological procedure of research was conducted using Delphi approach and questionnaires (in order to determine research validity, Chronbach Alpha (0.94) method was used). Spatial multi-criteria analysis model was designed in the form of criteria tree in SMCE using IL WIS software. Prioritization of respective spatial alternatives included: Bandar Abbas city with total 4 spatial alternatives (one zone with 1st priority, one zone with 3rd priority and two zones with 4thpriority) was considered the first priority, Bastak city with total 3 spatial alternatives (one zone with 2nd priority, one zone with 3rdpriorit and one zone with 4th priority) was the second priority and Bandar Abbas, Minab, Jask and Haji Abad cities were considered as the third priority.
AUTOMATIC EXTRACTION OF IndoorGML CORE MODEL FROM OpenStreetMap
Authors: Mirvahabi S. S. , Abbaspour R. A.
Published: December 2015

Abstract. Navigation has become an essential component of human life and a necessary component in many fields. Because of the increasing size and complexity of buildings, a unified data model for navigation analysis and exchange of information. IndoorGML describes an appropriate data model and XML schema of indoor spatial information that focuses on modelling indoor spaces. Collecting spatial data by professional and commercial providers often need to spend high cost and time, which is the major reason that VGI emerged. One of the most popular VGI projects is OpenStreetMap (OSM). In this paper, a new approach is proposed for the automatic generation of IndoorGML data core file from OSM data file. The output of this approach is the file of core data model that can be used alongside the navigation data model for navigation application of indoor space.
A GIS-BASED MODEL FOR POST-EARTHQUAKE PERSONALIZED ROUTE PLANNING USING THE INTEGRATION OF EVOLUTIONARY ALGORITHM AND OWA
Authors: Moradi M. , Delavar M. R. , Moradi A.
Published: December 2015

Abstract. Being one of the natural disasters, earthquake can seriously damage buildings, urban facilities and cause road blockage. Post-earthquake route planning is problem that has been addressed in frequent researches. The main aim of this research is to present a route planning model for after earthquake. It is assumed in this research that no damage data is available. The presented model tries to find the optimum route based on a number of contributing factors which mainly indicate the length, width and safety of the road. The safety of the road is represented by a number of criteria such as distance to faults, percentage of non-standard buildings and percentage of high buildings around the route. An integration of genetic algorithm and ordered weighted averaging operator is employed in the model. The former searches the problem space among all alternatives, while the latter aggregates the scores of road segments to compute an overall score for each alternative. Ordered weighted averaging operator enables the users of the system to evaluate the alternative routes based on their decision strategy. Based on the proposed model, an optimistic user tries to find the shortest path between the two points, whereas a pessimistic user tends to pay more attention to safety parameters even if it enforces a longer route. The results depicts that decision strategy can considerably alter the optimum route. Moreover, post-earthquake route planning is a function of not only the length of the route but also the probability of the road blockage.
ASSESSMENT OF RAINFALL-RUNOFF SIMULATION MODEL BASED ON SATELLITE ALGORITHM
Authors: Nemati A. R. , Zakeri Niri M. , Moazami S.
Published: December 2015

Abstract. Simulation of rainfall-runoff process is one of the most important research fields in hydrology and water resources. Generally, the models used in this section are divided into two conceptual and data-driven categories. In this study, a conceptual model and two data-driven models have been used to simulate rainfall-runoff process in Tamer sub-catchment located in Gorganroud watershed in Iran. The conceptual model used is HEC-HMS, and data-driven models are neural network model of multi-layer Perceptron (MLP) and support vector regression (SVR). In addition to simulation of rainfall-runoff process using the recorded land precipitation, the performance of four satellite algorithms of precipitation, that is, CMORPH, PERSIANN, TRMM 3B42 and TRMM 3B42RT were studied. In simulation of rainfall-runoff process, calibration and accuracy of the models were done based on satellite data. The results of the research based on three criteria of correlation coefficient (R), root mean square error (RMSE) and mean absolute error (MAE) showed that in this part the two models of SVR and MLP could perform the simulation of runoff in a relatively appropriate way, but in simulation of the maximum values of the flow, the error of models increased.
SNOW DEPTH ESTIMATION USING TIME SERIES PASSIVE MICROWAVE IMAGERY VIA GENETICALLY SUPPORT VECTOR REGRESSION (CASE STUDY URMIA LAKE BASIN)
Authors: Zahir N. , Mahdi H.
Published: December 2015

Abstract. Lake Urmia is one of the most important ecosystems of the country which is on the verge of elimination. Many factors contribute to this crisis among them is the precipitation, paly important roll. Precipitation has many forms one of them is in the form of snow. The snow on Sahand Mountain is one of the main and important sources of the Lake Urmia’s water. Snow Depth (SD) is vital parameters for estimating water balance for future year. In this regards, this study is focused on SD parameter using Special Sensor Microwave/Imager (SSM/I) instruments on board the Defence Meteorological Satellite Program (DMSP) F16. The usual statistical methods for retrieving SD include linear and non-linear ones. These methods used least square procedure to estimate SD model. Recently, kernel base methods widely used for modelling statistical problem. From these methods, the support vector regression (SVR) is achieved the high performance for modelling the statistical problem. Examination of the obtained data shows the existence of outlier in them. For omitting these outliers, wavelet denoising method is applied. After the omission of the outliers it is needed to select the optimum bands and parameters for SVR. To overcome these issues, feature selection methods have shown a direct effect on improving the regression performance. We used genetic algorithm (GA) for selecting suitable features of the SSMI bands in order to estimate SD model. The results for the training and testing data in Sahand mountain is [R²_TEST=0.9049 and RMSE= 6.9654] that show the high SVR performance.
AUTOMATIC ROAD EXTRACTION BASED ON INTEGRATION OF HIGH RESOLUTION LIDAR AND AERIAL IMAGERY
Authors: Rahimi S. , Arefi H. , Bahmanyar R.
Published: December 2015

Abstract. In recent years, the rapid increase in the demand for road information together with the availability of large volumes of high resolution Earth Observation (EO) images, have drawn remarkable interest to the use of EO images for road extraction. Among the proposed methods, the unsupervised fully-automatic ones are more efficient since they do not require human effort. Considering the proposed methods, the focus is usually to improve the road network detection, while the roads’ precise delineation has been less attended to. In this paper, we propose a new unsupervised fully-automatic road extraction method, based on the integration of the high resolution LiDAR and aerial images of a scene using Principal Component Analysis (PCA). This method discriminates the existing roads in a scene; and then precisely delineates them. Hough transform is then applied to the integrated information to extract straight lines; which are further used to segment the scene and discriminate the existing roads. The roads’ edges are then precisely localized using a projection-based technique, and the round corners are further refined. Experimental results demonstrate that our proposed method extracts and delineates the roads with a high accuracy.
DECISION LEVEL FUSION OF LIDAR DATA AND AERIAL COLOR IMAGERY BASED ON BAYESIAN THEORY FOR URBAN AREA CLASSIFICATION
Authors: Rastiveis H.
Published: December 2015

Abstract. Airborne Light Detection and Ranging (LiDAR) generates high-density 3D point clouds to provide a comprehensive information from object surfaces. Combining this data with aerial/satellite imagery is quite promising for improving land cover classification. In this study, fusion of LiDAR data and aerial imagery based on Bayesian theory in a three-level fusion algorithm is presented. In the first level, pixel-level fusion, the proper descriptors for both LiDAR and image data are extracted. In the next level of fusion, feature-level, using extracted features the area are classified into six classes of “Buildings”, “Trees”, “Asphalt Roads”, “Concrete roads”, “Grass” and “Cars” using Naïve Bayes classification algorithm. This classification is performed in three different strategies: (1) using merely LiDAR data, (2) using merely image data, and (3) using all extracted features from LiDAR and image. The results of three classifiers are integrated in the last phase, decision level fusion, based on Naïve Bayes algorithm. To evaluate the proposed algorithm, a high resolution color orthophoto and LiDAR data over the urban areas of Zeebruges, Belgium were applied. Obtained results from the decision level fusion phase revealed an improvement in overall accuracy and kappa coefficient.
AUTOMATIC BLOCKED ROADS ASSESSMENT AFTER EARTHQUAKE USING HIGH RESOLUTION SATELLITE IMAGERY
Authors: Rastiveis H. , Hosseini-Zirdoo E. , Eslamizade F.
Published: December 2015

Abstract. In 2010, an earthquake in the city of Port-au-Prince, Haiti, happened quite by chance an accident and killed over 300000 people. According to historical data such an earthquake has not occurred in the area. Unpredictability of earthquakes has necessitated the need for comprehensive mitigation efforts to minimize deaths and injuries. Blocked roads, caused by debris of destroyed buildings, may increase the difficulty of rescue activities. In this case, a damage map, which specifies blocked and unblocked roads, can be definitely helpful for a rescue team. In this paper, a novel method for providing destruction map based on pre-event vector map and high resolution world view II satellite images after earthquake, is presented. For this purpose, firstly in pre-processing step, image quality improvement and co-coordination of image and map are performed. Then, after extraction of texture descriptor from the image after quake and SVM classification, different terrains are detected in the image. Finally, considering the classification results, specifically objects belong to “debris” class, damage analysis are performed to estimate the damage percentage. In this case, in addition to the area objects in the “debris” class their shape should also be counted. The aforementioned process are performed on all the roads in the road layer.In this research, pre-event digital vector map and post-event high resolution satellite image, acquired by Worldview-2, of the city of Port-au-Prince, Haiti's capital, were used to evaluate the proposed method. The algorithm was executed on 1200×800 m2 of the data set, including 60 roads, and all the roads were labelled correctly. The visual examination have authenticated the abilities of this method for damage assessment of urban roads network after an earthquake.
PROPOSING A MULTI-CRITERIA PATH OPTIMIZATION METHOD IN ORDER TO PROVIDE A UBIQUITOUS PEDESTRIAN WAYFINDING SERVICE
Authors: Sahelgozin M. , Sadeghi-Niaraki A. , Dareshiri S.
Published: December 2015

Abstract. A myriad of novel applications have emerged nowadays for different types of navigation systems. One of their most frequent applications is Wayfinding. Since there are significant differences between the nature of the pedestrian wayfinding problems and of those of the vehicles, navigation services which are designed for vehicles are not appropriate for pedestrian wayfinding purposes. In addition, diversity in environmental conditions of the users and in their preferences affects the process of pedestrian wayfinding with mobile devices. Therefore, a method is necessary that performs an intelligent pedestrian routing with regard to this diversity. This intelligence can be achieved by the help of a Ubiquitous service that is adapted to the Contexts. Such a service possesses both the Context-Awareness and the User-Awareness capabilities. These capabilities are the main features of the ubiquitous services that make them flexible in response to any user in any situation. In this paper, it is attempted to propose a multi-criteria path optimization method that provides a Ubiquitous Pedestrian Way Finding Service (UPWFS). The proposed method considers four criteria that are summarized in Length, Safety, Difficulty and Attraction of the path. A conceptual framework is proposed to show the influencing factors that have effects on the criteria. Then, a mathematical model is developed on which the proposed path optimization method is based. Finally, data of a local district in Tehran is chosen as the case study in order to evaluate performance of the proposed method in real situations. Results of the study shows that the proposed method was successful to understand effects of the contexts in the wayfinding procedure. This demonstrates efficiency of the proposed method in providing a ubiquitous pedestrian wayfinding service.
MODELLING TEMPORAL SCHEDULE OF URBAN TRAINS USING AGENT-BASED SIMULATION AND NSGA2-BASED MULTIOBJECTIVE OPTIMIZATION APPROACHES
Authors: Sahelgozin M. , Alimohammadi A.
Published: December 2015

Abstract. Increasing distances between locations of residence and services leads to a large number of daily commutes in urban areas. Developing subway systems has been taken into consideration of transportation managers as a response to this huge amount of travel demands. In developments of subway infrastructures, representing a temporal schedule for trains is an important task; because an appropriately designed timetable decreases Total passenger travel times, Total Operation Costs and Energy Consumption of trains. Since these variables are not positively correlated, subway scheduling is considered as a multi-criteria optimization problem. Therefore, proposing a proper solution for subway scheduling has been always a controversial issue. On the other hand, research on a phenomenon requires a summarized representation of the real world that is known as Model. In this study, it is attempted to model temporal schedule of urban trains that can be applied in Multi-Criteria Subway Schedule Optimization (MCSSO) problems. At first, a conceptual framework is represented for MCSSO. Then, an agent-based simulation environment is implemented to perform Sensitivity Analysis (SA) that is used to extract the interrelations between the framework components. These interrelations is then taken into account in order to construct the proposed model. In order to evaluate performance of the model in MCSSO problems, Tehran subway line no. 1 is considered as the case study. Results of the study show that the model was able to generate an acceptable distribution of Pareto-optimal solutions which are applicable in the real situations while solving a MCSSO is the goal. Also, the accuracy of the model in representing the operation of subway systems was significant.
DETERMINATION OF OPTIMUM CLASSIFICATION SYSTEM FOR HYPERSPECTRAL IMAGERY AND LIDAR DATA BASED ON BEES ALGORITHM
Authors: Samadzadega F. , Hasani H.
Published: December 2015

Abstract. Hyperspectral imagery is a rich source of spectral information and plays very important role in discrimination of similar land-cover classes. In the past, several efforts have been investigated for improvement of hyperspectral imagery classification. Recently the interest in the joint use of LiDAR data and hyperspectral imagery has been remarkably increased. Because LiDAR can provide structural information of scene while hyperspectral imagery provide spectral and spatial information. The complementary information of LiDAR and hyperspectral data may greatly improve the classification performance especially in the complex urban area. In this paper feature level fusion of hyperspectral and LiDAR data is proposed where spectral and structural features are extract from both dataset, then hybrid feature space is generated by feature stacking. Support Vector Machine (SVM) classifier is applied on hybrid feature space to classify the urban area. In order to optimize the classification performance, two issues should be considered: SVM parameters values determination and feature subset selection. Bees Algorithm (BA) is powerful meta-heuristic optimization algorithm which is applied to determine the optimum SVM parameters and select the optimum feature subset simultaneously. The obtained results show the proposed method can improve the classification accuracy in addition to reducing significantly the dimension of feature space.
ANALYSING THE EFFECTS OF DIFFERENT LAND COVER TYPES ON LAND SURFACE TEMPERATURE USING SATELLITE DATA
Authors: Şekertekin A. , Kutoglu Ş. H. , Kaya S. , Marangoz A. M.
Published: December 2015

Abstract. Monitoring Land Surface Temperature (LST) via remote sensing images is one of the most important contributions to climatology. LST is an important parameter governing the energy balance on the Earth and it also helps us to understand the behavior of urban heat islands. There are lots of algorithms to obtain LST by remote sensing techniques. The most commonly used algorithms are split-window algorithm, temperature/emissivity separation method, mono-window algorithm and single channel method. In this research, mono window algorithm was implemented to Landsat 5 TM image acquired on 28.08.2011. Besides, meteorological data such as humidity and temperature are used in the algorithm. Moreover, high resolution Geoeye-1 and Worldview-2 images acquired on 29.08.2011 and 12.07.2013 respectively were used to investigate the relationships between LST and land cover type. As a result of the analyses, area with vegetation cover has approximately 5 ºC lower temperatures than the city center and arid land., LST values change about 10 ºC in the city center because of different surface properties such as reinforced concrete construction, green zones and sandbank. The temperature around some places in thermal power plant region (ÇATES and ZETES) Çatalağzı, is about 5 ºC higher than city center. Sandbank and agricultural areas have highest temperature due to the land cover structure.
RIVER SEDIMENT MONITORING USING REMOTE SENSING AND GIS (CASE STUDY KARAJ WATERSHED)
Authors: Shafaie M. , Ghodosi H. , Mostofi K. H.
Published: December 2015

Abstract. Whereas the tank volume and dehydrating digits from kinds of tanks are depended on repository sludge, so calculating the sediments is so important in tank planning and hydraulic structures. We are worry a lot about soil erosion in the basin area leading to deposit in rivers and lakes. It holds two reasons: firstly, because the surface soil of drainage would lose its fertility and secondly, the capacity of the tank decreases also it causes the decrease of water quality in downstream. Several studies have shown that we can estimate the rate of suspension sediments through remote sensing techniques. Whereas using remote sensing methods in contrast to the traditional and current techniques is faster and more accurate then they can be used as the effective techniques. The intent of this study has already been to estimate the rate of sediments in Karaj watershed through remote sensing and satellite images then comparing the gained results to the sediments data to use them in gauge-hydraulic station. We mean to recognize the remote sensing methods in calculating sediment and use them to determine the rate of river sediments so that identifying their accuracies. According to the results gained of the shown relations at this article, the amount of annual suspended sedimentary in KARAJ watershed have been 320490 Tones and in hydrologic method is about 350764 Tones .
ENVIRONMENTAL CONDITION AND ITS IMPACT ON LANDSCAPE DESCRIPTION BY SALIENT ELEMENT
Authors: Soleimani S. , Malek M. R. , Soleimani Z. , Arabsheibani R.
Published: December 2015

Abstract. Describing a landscape means making link between concepts of visible features and people’s perception. Most landscape description methods underline salient entities which are a key trigger for wayfinding problems and tourism management. Searching for a better understanding of landscape descriptions implies to explore and identify the main visual properties that differentiate between landscapes depending on both human cognition and environmental condition. Furthermore, this environmental condition affects the credibility of data produced by people, particularly when using Volunteered Geographical Information systems which brings forward a huge amount of information. Then this paper proposes an approach to emerge patterns by which describing landscape in general and choosing salient objects in particular have been influenced.
KAHROOD MONITORING USING SMALL BASELINE SUBSET SYNTHETIC APRETURE RADAR (SAR) INTERFEROMETRY
Authors: Tavakkoli A. , Dehghani M.
Published: December 2015

Abstract. The area of Kahrood is a small village located in the north-east of Damavand in the center of the Alborz range, north of Iran. Kahrood is located in Haraz valley exactly below the land slide area. To monitor the temporal evolution of the landslide, the conventional small baseline subset (SBAS), a radar differential Synthetic Aperture Radar interferometry (DInSAR) algorithm is used for time-series analysis. 19 Interferograms characterized by small spatial and temporal baselines are generated using 14 images. In order to remove the topographic effects, a digital elevation model from the Shuttle Radar Topography Mission (SRTM), with a spatial resolution of 90 m, is used. In the time-series analysis the first image was selected as the temporal reference. In the least squares solution, in order to increase the number of observational equation as well as decrease the temporal fluctuations due to atmospheric and unwrapping errors, a smoothing constraint is incorporated into the inversion problem. We divide the deformation time-series into two main parts. The maximum deformation rate estimated from the first part of the time-series is estimated as 3.3 cm within the landslide area. According to the time series results the land surface is moving away from the satellite. The second part of the deformation time-series showed a small landslide rate up to 0.7 cm. According to the time series results the land surface is moving toward the satellite. The deformation is estimated along the Mean line of sight (LOS). Considering the whole time series, the maximum LOS deformation rate is estimated as 14 cm.
APPLICATIONS OF MEDIUM C-BAND AND HIGH RESOLUTION X-BAND MULTITEMPORAL INTERFEROMETRY IN LANDSLIDE INVESTIGATIONS
Authors: Wasowski J. , Bovenga F. , Nutricato R. , Nitti D. O. , Chiaradia M. T.
Published: December 2015

Abstract. With the increasing quantity and quality of the imagery available from a growing number of SAR satellites and the improved processing algorithms, multi-temporal interferometry (MTI) is expected to be commonly applied in landslide studies. MTI can now provide long-term (years), regular (weekly-monthly), precise (mm) measurements of ground displacements over large areas (thousands of km2), at medium (~20 m) to high (up to 1-3 m) spatial resolutions, combined with the possibility of multi-scale (regional to local) investigations, using the same series of radar images. We focus on the benefits as well as challenges of multisensor and multi-scale investigations by discussing MTI results regarding two landslide prone regions with distinctly different topographic, climatic and vegetation conditions (mountains in Central Albania and Southern Gansu, China), for which C-band (ERS or ENVISAT) and X-band COSMO-SkyMed (CSK) imagery was available (all in Stripmap descending mode). In both cases X-band MTI outperformed C-band MTI by providing more valuable information for the regional to local scale detection of slope deformations and landslide hazard assessment. This is related to the better spatial-temporal resolutions and more suitable incidence angles (40°-30° versus 23°) of CSK data While the use of medium resolution imagery may be appropriate and more cost-effective in reconnaissance or regional scale investigations, high resolution data could be preferentially exploited when focusing on urbanized landslides or potentially unstable slopes in urban/peri-urban areas, and slopes traversed by lifelines and other engineering structures.
THE EFFECT OF LAND USE CHANGE ON LAND SURFACE TEMPERATURE IN THE NETHERLANDS
Authors: Youneszadeh S. , Amiri N. , Pilesjo P.
Published: December 2015

Abstract. The Netherlands is a small country with a relatively large population which experienced a rapid rate of land use changes from 2000 to 2008 years due to the industrialization and population increase. Land use change is especially related to the urban expansion and open agriculture reduction due to the enhanced economic growth. This research reports an investigation into the application of remote sensing and geographical information system (GIS) in combination with statistical methods to provide a quantitative information on the effect of land use change on the land surface temperature. In this study, remote sensing techniques were used to retrieve the land surface temperature (LST) by using the MODIS Terra (MOD11A2) Satellite imagery product. As land use change alters the thermal environment, the land surface temperature (LST) could be a proper change indicator to show the thermal changes in relation with land use changes. The Geographical information system was further applied to extract the mean yearly land surface temperature (LST) for each land use type and each province in the 2003, 2006 and 2008 years, by using the zonal statistic techniques. The results show that, the inland water and offshore area has the highest night land surface temperature (LST). Furthermore, the Zued (South)-Holland province has the highest night LST value in the 2003, 2006 and 2008 years. The result of this research will be helpful tool for urban planners and environmental scientists by providing the critical information about the land surface temperature.
A PHOTOGRAMMETRIC APPRAOCH FOR AUTOMATIC TRAFFIC ASSESSMENT USING CONVENTIONAL CCTV CAMERA
Authors: Zarrinpanjeh N. , Dadrassjavan F. , Fattahi H.
Published: December 2015

Abstract. One of the most practical tools for urban traffic monitoring is CCTV imaging which is widely used for traffic map generation and updating through human surveillance. But due to the expansion of urban road network and the use of huge number of CCTV cameras, visual inspection and updating of traffic sometimes seems to be ineffective and time consuming and therefore not providing real-time robust update. In this paper a method for vehicle detection accounting and speed estimation is proposed to give a more automated solution for traffic assessment. Through removing violating objects and detection of vehicles via morphological filtering and also classification of moving objects at the scene vehicles are counted and traffic speed is estimated. The proposed method is developed and tested using two datasets and evaluation values are computed. The results show that the successfulness of the algorithm decreases by about 12 % due to decrease in illumination quality of imagery.