bluuebunny/crossref_metadata_2025 huggingface dataset:
Reanalysis comparisons of upper tropospheric–lower stratospheric jets and multiple tropopauses
Authors: Manney Gloria L. , Hegglin Michaela I. , Lawrence Zachary D. , Wargan Krzysztof , Millán Luis F. , Schwartz Michael J. , Santee Michelle L. , Lambert Alyn , Pawson Steven , Knosp Brian W. , Fuller Ryan A. , Daffer William H.
Published: September 2017

Abstract. The representation of upper tropospheric–lower stratospheric (UTLS) jet and tropopause characteristics is compared in five modern high-resolution reanalyses for 1980 through 2014. Climatologies of upper tropospheric jet, subvortex jet (the lowermost part of the stratospheric vortex), and multiple tropopause frequency distributions in MERRA (Modern-Era Retrospective analysis for Research and Applications), ERA-I (ERA-Interim; the European Centre for Medium-Range Weather Forecasts, ECMWF, interim reanalysis), JRA-55 (the Japanese 55-year Reanalysis), and CFSR (the Climate Forecast System Reanalysis) are compared with those in MERRA-2. Differences between alternate products from individual reanalysis systems are assessed; in particular, a comparison of CFSR data on model and pressure levels highlights the importance of vertical grid spacing. Most of the differences in distributions of UTLS jets and multiple tropopauses are consistent with the differences in assimilation model grids and resolution – for example, ERA-I (with coarsest native horizontal resolution) typically shows a significant low bias in upper tropospheric jets with respect to MERRA-2, and JRA-55 (the Japanese 55-year Reanalysis) a more modest one, while CFSR (with finest native horizontal resolution) shows a high bias with respect to MERRA-2 in both upper tropospheric jets and multiple tropopauses. Vertical temperature structure and grid spacing are especially important for multiple tropopause characterizations. Substantial differences between MERRA and MERRA-2 are seen in mid- to high-latitude Southern Hemisphere (SH) winter upper tropospheric jets and multiple tropopauses as well as in the upper tropospheric jets associated with tropical circulations during the solstice seasons; some of the largest differences from the other reanalyses are seen in the same times and places. Very good qualitative agreement among the reanalyses is seen between the large-scale climatological features in UTLS jet and multiple tropopause distributions. Quantitative differences may, however, have important consequences for transport and variability studies. Our results highlight the importance of considering reanalyses differences in UTLS studies, especially in relation to resolution and model grids; this is particularly critical when using high-resolution reanalyses as an observational reference for evaluating global chemistry–climate models.
Harmonisation and trends of 20-years tropical tropospheric ozone data
Authors: Leventidou Elpida , Weber Mark , Eichmann Kai-Uwe , Burrows John P.
Published: September 2017

Abstract. Using the convective clouds differential (CCD) method on total ozone and cloud data from three European satellite instruments GOME/ERS-2 (1995–2003), SCIAMACHY/Envisat (2002–2012), and GOME-2/MetOp-A (2007–2015) it is possible to retrieve tropical tropospheric columns of ozone (TTCO) which are in good agreement with in-situ measurements. Small differences in TTCO between the individual instruments are evident and therefore the individual datasets retrieved are harmonised into one consistent time-series starting from 1996 until 2015. Correction offsets (bias) between the instruments using SCIAMACHY as intermediate reference have been calculated and six different harmonisation scenarios have been tested. Finally, the datasets have been harmonised applying no correction to GOME data while GOME-2 has been corrected using for each grid-box the mean bias with respect SCIAMACHY for the years of common operation (2007–2012). Depending on the choice of harmonisation, the magnitude, pattern, and uncertainty of the trend can strongly vary. The harmonisation represents an additional source of uncertainty in the merged dataset and derived trend estimates. For the preferred harmonised dataset, the trend ranges between −4 and 4 DU decade-1. The trend of the tropically averaged tropospheric ozone is equal to 0 ± 0.64 DU decade-1 (2σ). Regionally, tropospheric ozone has a statistically significant increase by ~ 3 DU decade-1 over southern Africa (~ 1.5 % year-1), the southern tropical Atlantic (~ 1.5 % year-1), southeastern tropical Pacific Ocean (~ 1 % year-1), and central Oceania (~ 2 % year-1). Additionally, over central Africa (2–2.5 % year-1) and south India (~ 1.5 % year-1), tropospheric ozone increases by ~ 2 DU decade-1. These regional positive tropospheric ozone trends maybe linked to anthropogenic activities such as emissions in mega cities or biomass burning in combination with changes in meteorology or/and long range transport of precursor emissions. On the other hand, tropospheric O3 decreases by ~ −3 DU decade-1 over the Caribbean sea and parts of the North Pacific Ocean (~ −2 % year-1), and by less than −2 DU decade-1 over some regions of the southern Pacific and Indian Ocean (~ −0.5–−1 % year-1). Possible reasons for this decrease are changes in dynamical processes, convection, STE, and precipitation. The comparison of the calculated trends from the current study with tropospheric ozone trends from Heue et al. (2016) and Ebojie et al. (2016) in ten selected mega-cities showed that they agree within 2σ of the trend uncertainty.
High-Dynamic-Range Imaging for Cloud Segmentation
Authors: Dev Soumyabrata , Savoy Florian M. , Lee Yee Hui , Winkler Stefan
Published: September 2017

Abstract. Sky/cloud images obtained from ground-based sky-cameras are usually captured using a fish-eye lens with a wide field of view. However, the sky exhibits a large dynamic range in terms of luminance, more than a conventional camera can capture. It is thus difficult to capture the details of an entire scene with a regular camera in a single shot. In most cases, the circumsolar region is over-exposed, and the regions near the horizon are under-exposed. This renders cloud segmentation for such images difficult. In this paper, we propose HDRSeg – an effective method for cloud segmentation using High-Dynamic-Range (HDR) imaging based on multi-exposure fusion. We describe the HDR generation process and release a new database to the community for benchmarking. Our proposed approach is the first using HDR images for cloud segmentation and achieves very good results.
Algal diversity of temperate biological soil crusts depends on land use intensity and affects phosphorus biogeochemical cycling
Authors: Glaser Karin , Baumannn Karen , Leinweber Peter , Mikhailyuk Tatiana , Karsten Ulf
Published: September 2017

Abstract. Biological soil crusts (BSCs) form the most productive microbial biomass in many drylands and disturbed areas with a diverse microalgal community as key component. In temperate regions, BSCs are also common, but generally less studied, and they conduct important ecological functions, like stabilization of soil and enrichment of nutrients. Changes in land use and its intensity strongly influence biodiversity per se and it's role for ecosystem processes, particularly in regions which are densely populated like Europe. But systematic studies on land use (i.e. management intensity) gradients in temperate forests on BSCs are missing up to now. To close this gap of knowledge and enhance the understanding of management effects on BSCs, Cyanobacteria and eukaryotic microalgae as key primary producers of these communities were identified from pine and beech forests under different management regimes. Algae were identified morphologically based on enrichment cultivation and categorized in either coccal taxa, which occur typically in high diversity, or filamentous taxa, which have the potential to initiate BSC formation. In total, 52 algal species were recorded, most from the phylum Chlorophyta, followed by Streptophyta and Heterokontophyta; Cyanobacteria were much less abundant in temperate forest BSCs. The most abundant crust-initiating filamentous algae were three species of Klebsormidium (Streptophyta) a ubiquitous genus often associated with BSCs worldwide and a high tolerance to low pH. Increasing management intensity resulted in a higher richness of algae, especially the proportion of coccal algae rose. Furthermore, the proportion of inorganic phosphorus was positively correlated with the algal richness, indicating that higher diversity of algae results in a more closed P cycle. Thus, management of forests has an impact on the diversity of phototrophic organisms in BSCs, which in turn affects P cycling in the BSC.
Estimating global cropland production from 1961 to 2010
Authors: Han Pengfei , Zeng Ning , Zhao Fang , Lin Xiaohui
Published: September 2017

Abstract. Global cropland net primary production (NPP) has tripled over the last 50 years, contributing 17–45 % to the increase in global atmospheric CO2 seasonal amplitude. Although many regional-scale comparisons have been made between statistical data and modeling results, long-term national comparisons across global croplands are scarce due to the lack of detailed spatiotemporal management data. Here, we conducted a simulation study of global cropland NPP from 1961 to 2010 using a process-based model called Vegetation–Global Atmosphere–Soil (VEGAS) and compared the results with Food and Agriculture Organization of the United Nations (FAO) statistical data on both continental and country scales. According to the FAO data, the global cropland NPP was 1.3, 1.8, 2.2, 2.6, 3.0, and 3.6 PgC yr−1 in the 1960s, 1970s, 1980s, 1990s, 2000s, and 2010s, respectively. The VEGAS model captured these major trends on global and continental scales. The NPP increased most notably in the US Midwest, western Europe, and the North China Plain and increased modestly in Africa and Oceania. However, significant biases remained in some regions such as Africa and Oceania, especially in temporal evolution. This finding is not surprising as VEGAS is the first global carbon cycle model with full parameterization representing the Green Revolution. To improve model performance for different major regions, we modified the default values of management intensity associated with the agricultural Green Revolution differences across various regions to better match the FAO statistical data at the continental level and for selected countries. Across all the selected countries, the updated results reduced the RMSE from 19.0 to 10.5 TgC yr−1 (∼  45 % decrease). The results suggest that these regional differences in model parameterization are due to differences in socioeconomic development. To better explain the past changes and predict the future trends, it is important to calibrate key parameters on regional scales and develop data sets for land management history.
An evaluation of the importance of spatial resolution in a global climate and hydrological model based on the Rhine and Mississippi basin
Authors: Benedict Imme , van Heerwaarden Chiel C. , Weerts Albrecht H. , Hazeleger Wilco
Published: September 2017

Abstract. The hydrological cycle of river basins can be simulated by combining global climate models (GCMs) and global hydrological models (GHMs). The spatial resolution of these models is restricted by computational resources and therefore limits the processes and level of detail that can be resolved. To further improve simulations of precipitation and river-runoff on a global scale, we assess and compare the benefits of an increased resolution for a GCM and a GHM. We focus on the Rhine and Mississippi basin. Increasing the resolution of a GCM (1.125° to 0.25°) results in more realistic large-scale circulation patterns over the Rhine and an improved precipitation budget. These improvements with increased resolution are not found for the Mississippi basin, most likely because precipitation is strongly dependent on the representation of still unresolved convective processes. Increasing the resolution of vegetation and orography in the high resolution GHM (from 0.5° to 0.05°) shows no significant differences in discharge for both basins, because the hydrological processes depend highly on other parameter values that are not readily available at high resolution. Therefore, increasing the resolution of the GCM provides the most straightforward route to better results. This route works best for basins driven by large-scale precipitation, such as the Rhine basin. For basins driven by convective processes, such as the Mississippi basin, improvements are expected with even higher resolution convection permitting models.
How to simulate radiative inputs in complex topographic areas, an analysis on 115 Swiss Alps weather stations
Authors: Riboust Philippe , Le Moine Nicolas , Thirel Guillaume , Ribstein Pierre
Published: September 2017

Abstract. In hydrology, solving the energy balance could be needed to estimate evapotranspiration or snowmelt. Shortwave (SW) and longwave (LW) incoming radiation fluxes are often derived from climate models or reanalyses. The resolution of these models is often too scarce to reproduce the variability of the energy fluxes in complex topography situations. For hydrological purposes, these radiations can also be simulated using empirical formulations with only maximum and minimum daily air temperature data as inputs. Many different formulations have been developed, mainly for specific areas. In this paper, we modify the existing Bristow atmospheric transmissivity equation so that it can be adapted to different areas and different elevation ranges. A calibration method for the transmissivity equation, coupled to the Sicart emissivity parameterization, has been determined. The new parameterization was developed and validated on a total of 115 meteorological stations from the MeteoSwiss network over the Swiss Alps. The results showed an increase in performances for simulating shortwave radiations in high-elevation areas, without decreasing the performance for lower elevations. However, the performance increase on transmissivity did not improve the longwave simulations as had been expected. The parameterization was also validated on reference surface temperature by solving a simplified energy balance. This makes it possible to evaluate the performance of the two parameterizations combined, considering a typical retro-action of the snow surface. This provided good results with a slight increase in performance compared to the original formulation. It also showed that the inaccuracy of SW radiations has a greater impact on the performance of the reference surface temperature than LW radiation errors. The drawback of this new formulation is that it converges to unexpected parameter values at the calibration process, which requires setting some parameters before calibration.
Hydrological connectivity from glaciers to rivers in the Qinghai–Tibet Plateau: roles of suprapermafrost and subpermafrost groundwater
Authors: Ma Rui , Sun Ziyong , Hu Yalu , Chang Qixin , Wang Shuo , Xing Wenle , Ge Mengyan
Published: September 2017

Abstract. The roles of groundwater flow in the hydrological cycle within the alpine area characterized by permafrost and/or seasonal frost are poorly known. This study explored the role of permafrost in controlling groundwater flow and the hydrological connections between glaciers in high mountains and rivers in the low piedmont plain with respect to hydraulic head, temperature, geochemical and isotopic data, at a representative catchment in the headwater region of the Heihe River, northeastern Qinghai–Tibet Plateau. The results show that the groundwater in the high mountains mainly occurred as suprapermafrost groundwater, while in the moraine and fluvioglacial deposits on the planation surfaces of higher hills, suprapermafrost, intrapermafrost and subpermafrost groundwater cooccurred. Glacier and snow meltwaters were transported from the high mountains to the plain through stream channels, slope surfaces, and supra- and subpermafrost aquifers. Groundwater in the Quaternary aquifer in the piedmont plain was recharged by the lateral inflow from permafrost areas and the stream infiltration and was discharged as baseflow to the stream in the north. Groundwater maintained streamflow over the cold season and significantly contributed to the streamflow during the warm season. Two mechanisms were proposed to contribute to the seasonal variation of aquifer water-conduction capacity: (1) surface drainage through the stream channel during the warm period and (2) subsurface drainage to an artesian aquifer confined by stream icing and seasonal frost during the cold season.
DISCOVERING OPTIMUM METHOD TO EXTRACT DEPTH INFORMATION FOR NEARSHORE COASTAL WATERS FROM SENTINEL-2A IMAGERY- CASE STUDY: NAYBAND BAY, IRAN
Authors: Kabiri K.
Published: September 2017

Abstract. The capabilities of Sentinel-2A imagery to determine bathymetric information in shallow coastal waters were examined. In this regard, two Sentinel-2A images (acquired on February and March 2016 in calm weather and relatively low turbidity) were selected from Nayband Bay, located in the northern Persian Gulf. In addition, a precise and accurate bathymetric map for the study area were obtained and used for both calibrating the models and validating the results. Traditional linear and ratio transform techniques, as well as a novel integrated method, were employed to determine depth values. All possible combinations of the three bands (Band 2: blue (458-523 nm), Band 3: green (543-578 nm), and Band 4: red (650-680 nm), spatial resolution: 10 m) have been considered (11 options) using the traditional linear and ratio transform techniques, together with 10 model options for the integrated method. The accuracy of each model was assessed by comparing the determined bathymetric information with field measured values. The correlation coefficients (R2), and root mean square errors (RMSE) for validation points were calculated for all models and for two satellite images. When compared with the linear transform method, the method employing ratio transformation with a combination of all three bands yielded more accurate results (R2Mac = 0.795, R2Feb = 0.777, RMSEMac = 1.889 m, and RMSEFeb =2.039 m). Although most of the integrated transform methods (specifically the method including all bands and band ratios) have yielded the highest accuracy, these increments were not significant, hence the ratio transformation has selected as optimum method.
THERMAL AND VISIBLE SATELLITE IMAGE FUSION USING WAVELET IN REMOTE SENSING AND SATELLITE IMAGE PROCESSING
Authors: Ahrari A. H. , Kiavarz M. , Hasanlou M. , Marofi M.
Published: September 2017

Abstract. Multimodal remote sensing approach is based on merging different data in different portions of electromagnetic radiation that improves the accuracy in satellite image processing and interpretations. Remote Sensing Visible and thermal infrared bands independently contain valuable spatial and spectral information. Visible bands make enough information spatially and thermal makes more different radiometric and spectral information than visible. However low spatial resolution is the most important limitation in thermal infrared bands. Using satellite image fusion, it is possible to merge them as a single thermal image that contains high spectral and spatial information at the same time. The aim of this study is a performance assessment of thermal and visible image fusion quantitatively and qualitatively with wavelet transform and different filters. In this research, wavelet algorithm (Haar) and different decomposition filters (mean.linear,ma,min and rand) for thermal and panchromatic bands of Landast8 Satellite were applied as shortwave and longwave fusion method . Finally, quality assessment has been done with quantitative and qualitative approaches. Quantitative parameters such as Entropy, Standard Deviation, Cross Correlation, Q Factor and Mutual Information were used. For thermal and visible image fusion accuracy assessment, all parameters (quantitative and qualitative) must be analysed with respect to each other. Among all relevant statistical factors, correlation has the most meaningful result and similarity to the qualitative assessment. Results showed that mean and linear filters make better fused images against the other filters in Haar algorithm. Linear and mean filters have same performance and there is not any difference between their qualitative and quantitative results.
ESTIMATING SOIL MOISTURE USING POLSAR DATA:
A MACHINE LEARNING APPROACH
Authors: Khedri E. , Hasanlou M. , Tabatabaeenejad A.
Published: September 2017

Abstract. Soil moisture is an important parameter that affects several environmental processes. This parameter has many important functions in numerous sciences including agriculture, hydrology, aerology, flood prediction, and drought occurrence. However, field procedures for moisture calculations are not feasible in a vast agricultural region territory. This is due to the difficulty in calculating soil moisture in vast territories and high-cost nature as well as spatial and local variability of soil moisture. Polarimetric synthetic aperture radar (PolSAR) imaging is a powerful tool for estimating soil moisture. These images provide a wide field of view and high spatial resolution. For estimating soil moisture, in this study, a model of support vector regression (SVR) is proposed based on obtained data from AIRSAR in 2003 in C, L, and P channels. In this endeavor, sequential forward selection (SFS) and sequential backward selection (SBS) are evaluated to select suitable features of polarized image dataset for high efficient modeling. We compare the obtained data with in-situ data. Output results show that the SBS-SVR method results in higher modeling accuracy compared to SFS-SVR model. Statistical parameters obtained from this method show an R2 of 97% and an RMSE of lower than 0.00041 (m3/m3) for P, L, and C channels, which has provided better accuracy compared to other feature selection algorithms.
OBJECT-ORIENTED ANALYSIS OF SATELLITE IMAGES USING ARTIFICIAL NEURAL NETWORKS FOR POST-EARTHQUAKE BUILDINGS CHANGE DETECTION
Authors: Khodaverdi zahraee N. , Rastiveis H.
Published: September 2017

Abstract. Earthquake is one of the most divesting natural events that threaten human life during history. After the earthquake, having information about the damaged area, the amount and type of damage can be a great help in the relief and reconstruction for disaster managers. It is very important that these measures should be taken immediately after the earthquake because any negligence could be more criminal losses. The purpose of this paper is to propose and implement an automatic approach for mapping destructed buildings after an earthquake using pre- and post-event high resolution satellite images. In the proposed method after preprocessing, segmentation of both images is performed using multi-resolution segmentation technique. Then, the segmentation results are intersected with ArcGIS to obtain equal image objects on both images. After that, appropriate textural features, which make a better difference between changed or unchanged areas, are calculated for all the image objects. Finally, subtracting the extracted textural features from pre- and post-event images, obtained values are applied as an input feature vector in an artificial neural network for classifying the area into two classes of changed and unchanged areas. The proposed method was evaluated using WorldView2 satellite images, acquired before and after the 2010 Haiti earthquake. The reported overall accuracy of 93% proved the ability of the proposed method for post-earthquake buildings change detection.
SHIFTS OF START AND END OF SEASON IN RESPONSE TO AIR TEMPERATURE VARIATION BASED ON GIMMS DATASET IN HYRCANIAN FORESTS
Authors: Kiapasha K. H. , Darvishsefat A. A. , Zargham N. , Julien Y. , Sobrino J. A. , Nadi M.
Published: September 2017

Abstract. Climate change is one of the most important environmental challenges in the world and forest as a dynamic phenomenon is influenced by environmental changes. The Hyrcanian forests is a unique natural heritage of global importance and we need monitoring this region. The objective of this study was to detect start and end of season trends in Hyrcanian forests of Iran based on biweekly GIMMS (Global Inventory Modeling and Mapping Studies) NDVI3g in the period 1981-2012. In order to find response of vegetation activity to local temperature variations, we used air temperature provided from I.R. Iran Meteorological Organization (IRIMO). At the first step in order to remove the existing gap from the original time series, the iterative Interpolation for Data Reconstruction (IDR) model was applied to GIMMS and temperature dataset. Then we applied significant Mann Kendall test to determine significant trend for each pixel of GIMMS and temperature datasets over the Hyrcanian forests. The results demonstrated that start and end of season (SOS & EOS respectively) derived from GIMMS3g NDVI time series increased by -0.16 and +0.41 days per year respectively. The trends derived from temperature time series indicated increasing trend in the whole of this region. Results of this study showed that global warming and its effect on growth and photosynthetic activity can increased the vegetation activity in our study area. Otherwise extension of the growing season, including an earlier start of the growing season, later autumn and higher rate of production increased NDVI value during the study period.
IMPROVING THE ACCURACY OF EXTRACTING SURFACE WATER QUALITY LEVELS (SWQLs) USING REMOTE SENSING AND ARTIFICIAL NEURAL NETWORK: A CASE STUDY IN THE SAINT JOHN RIVER, CANADA
Authors: Sharaf El Din E. , Zhang Y.
Published: September 2017

Abstract. Delineating accurate surface water quality levels (SWQLs) always presents a great challenge to researchers. Existing methods of assessing surface water quality only provide individual concentrations of monitoring stations without providing the overall SWQLs. Therefore, the results of existing methods are usually difficult to be understood by decision-makers. Conversely, the water quality index (WQI) can simplify surface water quality assessment process to be accessible to decision-makers. However, in most cases, the WQI reflects inaccurate SWQLs due to the lack of representative water samples. It is very challenging to provide representative water samples because this process is costly and time consuming. To solve this problem, we introduce a cost-effective method which combines the Landsat-8 imagery and artificial intelligence to develop models to derive representative water samples by correlating concentrations of ground truth water samples to satellite spectral information. Our method was validated and the correlation between concentrations of ground truth water samples and predicted concentrations from the developed models reached a high level of coefficient of determination (R2) > 0.80, which is trustworthy. Afterwards, the predicted concentrations over each pixel of the study area were used as an input to the WQI developed by the Canadian Council of Ministers of the Environment to extract accurate SWQLs, for drinking purposes, in the Saint John River. The results indicated that SWQL was observed as 67 (Fair) and 59 (Marginal) for the lower and middle basins of the river, respectively. These findings demonstrate the potential of using our approach in surface water quality management.
DETECTION OF STREET LIGHT POLES IN ROAD SCENES FROM MOBILE LIDAR MAPPING DATA FOR ITS APPLICATIONS
Authors: Talebi Nahr S. , Saadatseresht M. , Talebi J.
Published: September 2017

Abstract. Identification of street light poles is very significant and crucial for intelligent transportation systems. Automatic detection and extraction of street light poles are a challenging task in road scenes. This is mainly because of complex road scenes. Nowadays mobile laser scanners have been used to acquire three-dimensional geospatial data of roadways over a large area at a normal driving speed. With respect to the high density of such data, new and beneficial algorithms are needed to extract objects from these data. In this article, our proposed algorithm for extraction of street light poles consists of five main steps: 1. Preprocessing, 2. Ground removal, 3. 3D connected components analysis, 4. Local geometric feature generation, 5. Extraction of street light poles using Bhattacharya distance metric. The proposed algorithm is tested on two rural roadways, called Area1 and Area2. Evaluation results for Area1 report 0.80, 0.72 and 0.62 for completeness, correctness and quality, respectively.
HYBRID OPTIMIZATION OF OBJECT-BASED CLASSIFICATION IN HIGH-RESOLUTION IMAGES USING CONTINOUS ANT COLONY ALGORITHM WITH EMPHASIS ON BUILDING DETECTION
Authors: Tamimi E. , Ebadi H. , Kiani A.
Published: September 2017

Abstract. Automatic building detection from High Spatial Resolution (HSR) images is one of the most important issues in Remote Sensing (RS). Due to the limited number of spectral bands in HSR images, using other features will lead to improve accuracy. By adding these features, the presence probability of dependent features will be increased, which leads to accuracy reduction. In addition, some parameters should be determined in Support Vector Machine (SVM) classification. Therefore, it is necessary to simultaneously determine classification parameters and select independent features according to image type. Optimization algorithm is an efficient method to solve this problem. On the other hand, pixel-based classification faces several challenges such as producing salt-paper results and high computational time in high dimensional data. Hence, in this paper, a novel method is proposed to optimize object-based SVM classification by applying continuous Ant Colony Optimization (ACO) algorithm. The advantages of the proposed method are relatively high automation level, independency of image scene and type, post processing reduction for building edge reconstruction and accuracy improvement. The proposed method was evaluated by pixel-based SVM and Random Forest (RF) classification in terms of accuracy. In comparison with optimized pixel-based SVM classification, the results showed that the proposed method improved quality factor and overall accuracy by 17% and 10%, respectively. Also, in the proposed method, Kappa coefficient was improved by 6% rather than RF classification. Time processing of the proposed method was relatively low because of unit of image analysis (image object). These showed the superiority of the proposed method in terms of time and accuracy.
STATISTICAL ANALYSIS OF SPORT MOVEMENT OBSERVATIONS: THE
CASE OF ORIENTEERING
Authors: Amouzandeh K. , Karimipour F.
Published: September 2017

Abstract. Study of movement observations is becoming more popular in several applications. Particularly, analyzing sport movement time series has been considered as a demanding area. However, most of the attempts made on analyzing movement sport data have focused on spatial aspects of movement to extract some movement characteristics, such as spatial patterns and similarities. This paper proposes statistical analysis of sport movement observations, which refers to analyzing changes in the spatial movement attributes (e.g. distance, altitude and slope) and non-spatial movement attributes (e.g. speed and heart rate) of athletes. As the case study, an example dataset of movement observations acquired during the “orienteering” sport is presented and statistically analyzed.
SPATIO-TEMPORAL PATTERN MINING ON TRAJECTORY DATA USING ARM
Authors: Khoshahval S. , Farnaghi M. , Taleai M.
Published: September 2017

Abstract. Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user’s visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users’ behaviour in a system and can be utilized in various location-based applications.
A NEW MODEL FOR FUZZY PERSONALIZED ROUTE PLANNING USING FUZZY LINGUISTIC PREFERENCE RELATION
Authors: Nadi S. , Houshyaripour A. H.
Published: September 2017

Abstract. This paper proposes a new model for personalized route planning under uncertain condition. Personalized routing, involves different sources of uncertainty. These uncertainties can be raised from user’s ambiguity about their preferences, imprecise criteria values and modelling process. The proposed model uses Fuzzy Linguistic Preference Relation Analytical Hierarchical Process (FLPRAHP) to analyse user’s preferences under uncertainty. Routing is a multi-criteria task especially in transportation networks, where the users wish to optimize their routes based on different criteria. However, due to the lake of knowledge about the preferences of different users and uncertainties available in the criteria values, we propose a new personalized fuzzy routing method based on the fuzzy ranking using center of gravity. The model employed FLPRAHP method to aggregate uncertain criteria values regarding uncertain user’s preferences while improve consistency with least possible comparisons. An illustrative example presents the effectiveness and capability of the proposed model to calculate best personalize route under fuzziness and uncertainty.
WOODLAND MAPPING AT SINGLE-TREE LEVELS USING OBJECT-ORIENTED CLASSIFICATION OF UNMANNED AERIAL VEHICLE (UAV) IMAGES
Authors: Chenari A. , Erfanifard Y. , Dehghani M. , Pourghasemi H. R.
Published: September 2017

Abstract. Remotely sensed datasets offer a reliable means to precisely estimate biophysical characteristics of individual species sparsely distributed in open woodlands. Moreover, object-oriented classification has exhibited significant advantages over different classification methods for delineation of tree crowns and recognition of species in various types of ecosystems. However, it still is unclear if this widely-used classification method can have its advantages on unmanned aerial vehicle (UAV) digital images for mapping vegetation cover at single-tree levels. In this study, UAV orthoimagery was classified using object-oriented classification method for mapping a part of wild pistachio nature reserve in Zagros open woodlands, Fars Province, Iran. This research focused on recognizing two main species of the study area (i.e., wild pistachio and wild almond) and estimating their mean crown area. The orthoimage of study area was consisted of 1,076 images with spatial resolution of 3.47 cm which was georeferenced using 12 ground control points (RMSE=8 cm) gathered by real-time kinematic (RTK) method. The results showed that the UAV orthoimagery classified by object-oriented method efficiently estimated mean crown area of wild pistachios (52.09±24.67 m2) and wild almonds (3.97±1.69 m2) with no significant difference with their observed values (α=0.05). In addition, the results showed that wild pistachios (accuracy of 0.90 and precision of 0.92) and wild almonds (accuracy of 0.90 and precision of 0.89) were well recognized by image segmentation. In general, we concluded that UAV orthoimagery can efficiently produce precise biophysical data of vegetation stands at single-tree levels, which therefore is suitable for assessment and monitoring open woodlands.
AN INDOOR POSITIONING TECHNIQUE BASED ON A FEED-FORWARD ARTIFICIAL NEURAL NETWORK USING LEVENBERG-MARQUARDT LEARNING METHOD
Authors: Pahlavani P. , Gholami A. , Azimi S.
Published: September 2017

Abstract. This paper presents an indoor positioning technique based on a multi-layer feed-forward (MLFF) artificial neural networks (ANN). Most of the indoor received signal strength (RSS)-based WLAN positioning systems use the fingerprinting technique that can be divided into two phases: the offline (calibration) phase and the online (estimation) phase. In this paper, RSSs were collected for all references points in four directions and two periods of time (Morning and Evening). Hence, RSS readings were sampled at a regular time interval and specific orientation at each reference point. The proposed ANN based model used Levenberg–Marquardt algorithm for learning and fitting the network to the training data. This RSS readings in all references points and the known position of these references points was prepared for training phase of the proposed MLFF neural network. Eventually, the average positioning error for this network using 30% check and validation data was computed approximately 2.20 meter.
TOWARDS A CLOUD BASED SMART TRAFFIC MANAGEMENT FRAMEWORK
Authors: Rahimi M. M. , Hakimpour F.
Published: September 2017

Abstract. Traffic big data has brought many opportunities for traffic management applications. However several challenges like heterogeneity, storage, management, processing and analysis of traffic big data may hinder their efficient and real-time applications. All these challenges call for well-adapted distributed framework for smart traffic management that can efficiently handle big traffic data integration, indexing, query processing, mining and analysis. In this paper, we present a novel, distributed, scalable and efficient framework for traffic management applications. The proposed cloud computing based framework can answer technical challenges for efficient and real-time storage, management, process and analyse of traffic big data. For evaluation of the framework, we have used OpenStreetMap (OSM) real trajectories and road network on a distributed environment. Our evaluation results indicate that speed of data importing to this framework exceeds 8000 records per second when the size of datasets is near to 5 million. We also evaluate performance of data retrieval in our proposed framework. The data retrieval speed exceeds 15000 records per second when the size of datasets is near to 5 million. We have also evaluated scalability and performance of our proposed framework using parallelisation of a critical pre-analysis in transportation applications. The results show that proposed framework achieves considerable performance and efficiency in traffic management applications.
ON THE COMPUTATION OF A PRECISE GEOID – TO – QUASIGEOID SEPARATION
Authors: Hejrati S. , Najafi-Alamdari M.
Published: September 2017

Abstract. In geodesy, orthometric and normal heights are considered as basic height systems on the earth. The reference surfaces for these heights are the geoid and quasigeoid respectively. Taking advantage of GNSS measurements, one can achieve a precise solution for the geoid and for the quasigeoid. Two methods, called direct and indirect, are worked out in this research for the computation of separation between geoid and quasigeoid in a mountainous region in the USA. The area selected for this purpose is mountainous and rough enough in order to be able to show the effect of roughness of topography in the sought quantity. The results of the two methods and testing them against GNSS-Levelling on 445 known points indicates an accuracy of 1.3 cm in RMS scale with the direct method, where there is 7 cm as an average difference between the observed geoid and quasigeoid separation and the same quantity derived from the direct method. Using Chi-squared goodness of fit test showed that the distribution of the residual quantities are normally distributed in the test area.
INVESTIGATING AND MODELLING EFFECTS OF CLIMATICALLY AND HYDROLOGICALLY INDICATORS ON THE URMIA LAKE COASTLINE CHANGES USING TIME SERIES ANALYSIS
Authors: Ahmadijamal M. , Hasanlou M.
Published: September 2017

Abstract. Study of hydrological parameters of lakes and examine the variation of water level to operate management on water resources are important. The purpose of this study is to investigate and model the Urmia Lake water level changes due to changes in climatically and hydrological indicators that affects in the process of level variation and area of this lake. For this purpose, Landsat satellite images, hydrological data, the daily precipitation, the daily surface evaporation and the daily discharge in total of the lake basin during the period of 2010-2016 have been used. Based on time-series analysis that is conducted on individual data independently with same procedure, to model variation of Urmia Lake level, we used polynomial regression technique and combined polynomial with periodic behavior. In the first scenario, we fit a multivariate linear polynomial to our datasets and determining RMSE, NRSME and R² value. We found that fourth degree polynomial can better fit to our datasets with lowest RMSE value about 9 cm. In the second scenario, we combine polynomial with periodic behavior for modeling. The second scenario has superiority comparing to the first one, by RMSE value about 3 cm.
TIME SERIES ANALYSIS OF INSAR DATA TO STUDY LAND SUBSIDENCE INDUCED BY GROUNDWATER LEVEL DECLINE IN SIRJAN PLAIN
Authors: Choopani A. , Dehghani M. , Nikoo M. R. , Zeinali S.
Published: September 2017

Abstract. Sirjan located in Southwest of Kerman City, Iran, is a city with average annual rainfall of 132mm. Over-exploitation of groundwater used for irrigation of pistachios gardens has caused serious land subsidence in Sirjan Plain. In this research we have used the Interferometric Synthetic Aperture Radar technique to estimate the subsidence rate in Sirjan plain. 18 Interferograms extracted from 12 ENVISAT ASAR images spanning between April 2004 and September 2010, have been studied. The SBAS algorithm was then applied in order to estimate the mean subsidence velocity map. Maximum subsidence rate is estimated as 28cm/yr. Furthermore, groundwater level fluctuations in the study area has been investigated in the piezometer wells located in the study area. Comparing between the results obtained from the interferometry and groundwater level fluctuation maps, shows a strong correlation between head decline in groundwater and land subsidence.
WIND WAVE NUMERICAL MODELING IN THE CASPIAN SEA
Authors: Mahmoodi R. , Ardalan A. A. , Hasanlou M.
Published: September 2017

Abstract. Numerical simulation results of wind wave in the Caspian Sea by using wind forcing data are presented. The numerical modeling which is applied in this study is based on numerical spectral wave model which is based on Navier-Stokes equations. It solves these equations through each of mesh elements. Moreover, in this model high-resolution unstructured grid for the Caspian Sea has been used in order to reach finer accuracy. The wind forcing data are given from European Centre for Medium-Range Weather Forecasts (ECMWF). The measurement data, which are gained from Ports and Marine Organisation (PMO) of Iran, are used in order to estimate the accuracy of the model. The results have shown better accuracy in comparison with PMO simulation. Mean of the coefficient of determination (R-squared) for significant wave height in this simulation is 0.8059, though, in PMO simulation this coefficient is reported 0.7056. Better accuracy requires more measurement data and also finer resolution of bathymetry data.
CO-SEISMIC GRAVITY GRADIENT CHANGES OF THE 2006–2007 GREAT EARTHQUAKES IN THE CENTRAL KURIL ISLANDS FROM GRACE OBSERVATIONS
Authors: Rahimi A. , Shahrisvand M.
Published: September 2017

Abstract. GRACE satellites (the Gravity Recovery And climate Experiment) are very useful sensors to extract gravity anomalies after earthquakes. In this study, we reveal co-seismic signals of the two combined earthquakes, the 2006 Mw8.3 thrust and 2007 Mw8.1 normal fault earthquakes of the central Kuril Islands from GRACE observations. We compute monthly full gravitational gradient tensor in the local north-east-down frame for Kuril Islands earthquakes without spatial averaging and de-striping filters. Some of gravitational gradient components (e.g. ΔVxx, ΔVxz) enhance high frequency components of the earth gravity field and reveal more details in spatial and temporal domain. Therefore, co-seismic activity can be better illustrated. For the first time, we show that the positive-negative-positive co-seismic ΔVxx due to the Kuril Islands earthquakes ranges from − 0.13 to + 0.11 milli Eötvös, and ΔVxz shows a positive-negative-positive pattern ranges from − 0.16 to + 0.13 milli Eötvös, agree well with seismic model predictions.
HANDY MICROSCOPIC CLOSE-RANGE VIDEOGRAMMETRY
Authors: Esmaeili F. , Ebadi H.
Published: September 2017

Abstract. The modeling of small-scale objects is used in different applications such as medicine, industry, and cultural heritage. The capability of modeling small-scale objects using imaging with the help of hand USB digital microscopes and use of videogrammetry techniques has been implemented and evaluated in this paper. Use of this equipment and convergent imaging of the environment for modeling, provides an appropriate set of images for generation of three-dimensional models. The results of the measurements made with the help of a microscope micrometer calibration ruler have demonstrated that self-calibration of a hand camera-microscope set can help obtain a three-dimensional detail extraction precision of about 0.1 millimeters on small-scale environments.
Time difference of arrival estimation of microseismic signals based on alpha-stable distribution
Authors: Jia Rui-Sheng , Gong Yue , Peng Yan-Jun , Sun Hong-Mei , Zhang Xing-Li , Lu Xing-Min
Published: September 2017

Abstract. Microseismic signals are generally considered to follow the Gauss distribution. A comparison of the dynamic characteristics of sample variance and the symmetry of microseismic signals with the signals which follows α-stable distribution, reveals that the pulse characteristics of the microseismic signal is outstanding and that the probability density curve of the microseismic signal is approximately symmetric. Thus, the hypothesis that microseismic signals follow the symmetric α-stable distribution is proposed. On the premise of this hypothesis, the characteristic exponent α of the seismic signals is obtained by utilizing the fractional low-order statistics, and then a new method of time difference of arrival (TDOA) estimation of microseismic signals based on fractional low-order covariance (FLOC) is proposed. Upon applying this method to the TDOA estimation of Ricker wavelet simulation signals and real microseismic signals, experimental results show that the FLOC method, which is based on the assumption of the symmetric α-stable distribution, leads to enhanced spatial resolution of the TDOA estimation relative to the generalized cross correlation (GCC) method, which is based on the assumption of the Gaussian distribution.