bluuebunny/crossref_metadata_2025 huggingface dataset:
Ice crystal c-axis orientation and mean grain size measurements from the Dome Summit South ice core, Law Dome, East Antarctica
Authors: Treverrow Adam , Jun Li , Jacka Tim H.
Published: June 2016

Abstract. We present measurements of crystal c-axis orientations and mean grain area from the Dome Summit South (DSS) ice core drilled on Law Dome, East Antarctica. All measurements were made on location at the borehole site during drilling operations. The data are from 185 individual thin sections obtained between a depth of 117 m below the surface and the bottom of the DSS core at a depth of 1196 m. The median number of c-axis orientations recorded in each thin section was 100, with values ranging from 5 through to 111 orientations. The data from all 185 thin sections are provided in a single comma-separated value (csv) formatted file which contains the c-axis orientations in polar coordinates, depth information for each core section from which the data were obtained, the mean grain area calculated for each thin section and other data related to the drilling site. The data set is also available as a MATLAB™ structure array. Additionally, the c-axis orientation data from each of the 185 thin sections are summarized graphically in figures containing a Schmidt diagram, histogram of c-axis colatitudes and rose plot of c-axis azimuths. All these data are referenced by doi:10.4225/15/5669050CC1B3B and are available free of charge at https://data.antarctica.gov.au.
Randomly correcting model errors in the ARPEGE-Climate v6.1 component of CNRM-CM: applications for seasonal forecasts
Authors: Batté Lauriane , Déqué Michel
Published: June 2016

Abstract. Stochastic methods are increasingly used in global coupled model climate forecasting systems to account for model uncertainties. In this paper, we describe in more detail the stochastic dynamics technique introduced by Batté and Déqué (2012) in the ARPEGE-Climate atmospheric model. We present new results with an updated version of CNRM-CM using ARPEGE-Climate v6.1, and show that the technique can be used both as a means of analyzing model error statistics and accounting for model inadequacies in a seasonal forecasting framework.The perturbations are designed as corrections of model drift errors estimated from a preliminary weakly nudged re-forecast run over an extended reference period of 34 boreal winter seasons. A detailed statistical analysis of these corrections is provided, and shows that they are mainly made of intra-month variance, thereby justifying their use as in-run perturbations of the model in seasonal forecasts. However, the interannual and systematic error correction terms cannot be neglected. Time correlation of the errors is limited, but some consistency is found between the errors of up to 3 consecutive days.These findings encourage us to test several settings of the random draws of perturbations in seasonal forecast mode. Perturbations are drawn randomly but consistently for all three prognostic variables perturbed. We explore the impact of using monthly mean perturbations throughout a given forecast month in a first ensemble re-forecast (SMM, for stochastic monthly means), and test the use of 5-day sequences of perturbations in a second ensemble re-forecast (S5D, for stochastic 5-day sequences). Both experiments are compared in the light of a REF reference ensemble with initial perturbations only. Results in terms of forecast quality are contrasted depending on the region and variable of interest, but very few areas exhibit a clear degradation of forecasting skill with the introduction of stochastic dynamics. We highlight some positive impacts of the method, mainly on Northern Hemisphere extra-tropics. The 500 hPa geopotential height bias is reduced, and improvements project onto the representation of North Atlantic weather regimes. A modest impact on ensemble spread is found over most regions, which suggests that this method could be complemented by other stochastic perturbation techniques in seasonal forecasting mode.
A review of green- and blue-water resources and their trade-offs for future agricultural production in the Amazon Basin: what could irrigated agriculture mean for Amazonia?
Authors: Lathuillière Michael J. , Coe Michael T. , Johnson Mark S.
Published: June 2016

Abstract. The Amazon Basin is a region of global importance for the carbon and hydrological cycles, a biodiversity hotspot, and a potential centre for future economic development. The region is also a major source of water vapour recycled into continental precipitation through evapotranspiration processes. This review applies an ecohydrological approach to Amazonia's water cycle by looking at contributions of water resources in the context of future agricultural production. At present, agriculture in the region is primarily rain-fed and relies almost exclusively on green-water resources (soil moisture regenerated by precipitation). Future agricultural development, however, will likely follow pathways that include irrigation from blue-water sources (surface water and groundwater) as insurance from variability in precipitation. In this review, we first provide an updated summary of the green–blue ecohydrological framework before describing past trends in Amazonia's water resources within the context of land use and land cover change. We then describe green- and blue-water trade-offs in light of future agricultural production and potential irrigation to assess costs and benefits to terrestrial ecosystems, particularly land and biodiversity protection, and regional precipitation recycling. Management of green water is needed, particularly at the agricultural frontier located in the headwaters of major tributaries to the Amazon River, and home to key downstream blue-water users and ecosystem services, including domestic and industrial users, as well as aquatic ecosystems.
PRODUCT ACCURACY EFFECT OF OBLIQUE AND VERTICAL NON-METRIC DIGITAL CAMERA UTILIZATION IN UAV-PHOTOGRAMMETRY TO DETERMINE FAULT PLANE
Authors: Amrullah C. , Suwardhi D. , Meilano I.
Published: June 2016

Abstract. This study aims to see the effect of non-metric oblique and vertical camera combination along with the configuration of the ground control points to improve the precision and accuracy in UAV-Photogrammetry project. The field observation method is used for data acquisition with aerial photographs and ground control points. All data are processed by digital photogrammetric process with some scenarios in camera combination and ground control point configuration. The model indicates that the value of precision and accuracy increases with the combination of oblique and vertical camera at all control point configuration. The best products of the UAV-Photogrammetry model are produced in the form of Digital Elevation Model (DEM) compared to the LiDAR DEM. Furthermore, DEM from UAV-Photogrammetry and LiDAR are used to define the fault plane by using cross-section on the model and interpretation to determine the point at the extreme height of terrain changes. The result of the defined fault planes indicate that two models do not show any significant difference.
RESEARCH INTO THE COLLIMATION AND HORIZONTAL AXIS ERRORS INFLUENCE ON THE Z+F LASER SCANNER ACCURACY OF VERTICALITY MEASUREMENT
Authors: Sawicki J. , Kowalczyk M.
Published: June 2016

Abstract. Aim of this study was to appoint values of collimation and horizontal axis errors of the laser scanner ZF 5006h owned by Department of Geodesy and Cartography, Warsaw University of Technology, and then to determine the effect of those errors on the results of measurements. An experiment has been performed, involving measurement of the test field , founded in the Main Hall of the Main Building of the Warsaw University of Technology, during which values of instrumental errors of interest were determined. Then, an universal computer program that automates the proposed algorithm and capable of applying corrections to measured target coordinates or even entire point clouds from individual stations, has been developed.
BUILDING CHANGE DETECTION IN VERY HIGH RESOLUTION SATELLITE STEREO IMAGE TIME SERIES
Authors: Tian J. , Qin R. , Cerra D. , Reinartz P.
Published: June 2016

Abstract. There is an increasing demand for robust methods on urban sprawl monitoring. The steadily increasing number of high resolution and multi-view sensors allows producing datasets with high temporal and spatial resolution; however, less effort has been dedicated to employ very high resolution (VHR) satellite image time series (SITS) to monitor the changes in buildings with higher accuracy. In addition, these VHR data are often acquired from different sensors. The objective of this research is to propose a robust time-series data analysis method for VHR stereo imagery. Firstly, the spatial-temporal information of the stereo imagery and the Digital Surface Models (DSMs) generated from them are combined, and building probability maps (BPM) are calculated for all acquisition dates. In the second step, an object-based change analysis is performed based on the derivative features of the BPM sets. The change consistence between object-level and pixel-level are checked to remove any outlier pixels. Results are assessed on six pairs of VHR satellite images acquired within a time span of 7 years. The evaluation results have proved the efficiency of the proposed method.
MAPPING OF HIGH VALUE CROPS THROUGH AN OBJECT-BASED SVM MODEL USING LIDAR DATA AND ORTHOPHOTO IN AGUSAN DEL NORTE PHILIPPINES
Authors: Candare Rudolph Joshua , Japitana Michelle , Cubillas James Earl , Ramirez Cherry Bryan
Published: June 2016

Abstract. This research describes the methods involved in the mapping of different high value crops in Agusan del Norte Philippines using LiDAR. This project is part of the Phil-LiDAR 2 Program which aims to conduct a nationwide resource assessment using LiDAR. Because of the high resolution data involved, the methodology described here utilizes object-based image analysis and the use of optimal features from LiDAR data and Orthophoto. Object-based classification was primarily done by developing rule-sets in eCognition. Several features from the LiDAR data and Orthophotos were used in the development of rule-sets for classification. Generally, classes of objects can't be separated by simple thresholds from different features making it difficult to develop a rule-set. To resolve this problem, the image-objects were subjected to Support Vector Machine learning. SVMs have gained popularity because of their ability to generalize well given a limited number of training samples. However, SVMs also suffer from parameter assignment issues that can significantly affect the classification results. More specifically, the regularization parameter C in linear SVM has to be optimized through cross validation to increase the overall accuracy. After performing the segmentation in eCognition, the optimization procedure as well as the extraction of the equations of the hyper-planes was done in Matlab. The learned hyper-planes separating one class from another in the multi-dimensional feature-space can be thought of as super-features which were then used in developing the classifier rule set in eCognition. In this study, we report an overall classification accuracy of greater than 90% in different areas.
ANALYSIS OF SCATTERING COMPONENTS FROM FULLY POLARIMETRIC SAR IMAGES FOR IMPROVING ACCURACIES OF URBAN DENSITY ESTIMATION
Authors: Susaki J.
Published: June 2016

Abstract. In this paper, we analyze probability density functions (PDFs) of scatterings derived from fully polarimetric synthetic aperture radar (SAR) images for improving the accuracies of estimated urban density. We have reported a method for estimating urban density that uses an index Tv+c obtained by normalizing the sum of volume and helix scatterings Pv+c. Validation results showed that estimated urban densities have a high correlation with building-to-land ratios (Kajimoto and Susaki, 2013b; Susaki et al., 2014). While the method is found to be effective for estimating urban density, it is not clear why Tv+c is more effective than indices derived from other scatterings, such as surface or double-bounce scatterings, observed in urban areas. In this research, we focus on PDFs of scatterings derived from fully polarimetric SAR images in terms of scattering normalization. First, we introduce a theoretical PDF that assumes that image pixels have scatterers showing random backscattering. We then generate PDFs of scatterings derived from observations of concrete blocks with different orientation angles, and from a satellite-based fully polarimetric SAR image. The analysis of the PDFs and the derived statistics reveals that the curves of the PDFs of Pv+c are the most similar to the normal distribution among all the scatterings derived from fully polarimetric SAR images. It was found that Tv+c works most effectively because of its similarity to the normal distribution.
A PLUGIN TO INTERFACE OPENMODELLER FROM QGIS FOR SPECIES' POTENTIAL DISTRIBUTION MODELLING
Authors: Becker Daniel , Willmes Christian , Bareth Georg , Weniger Gerd-Christian
Published: June 2016

Abstract. This contribution describes the development of a plugin for the geographic information system QGIS to interface the openModeller software package. The aim is to use openModeller to generate species’ potential distribution models for various archaeological applications (site catchment analysis, for example). Since the usage of openModeller’s command-line interface and configuration files can be a bit inconvenient, an extension of the QGIS user interface to handle these tasks, in combination with the management of the geographic data, was required. The implementation was realized in Python using PyQGIS and PyQT. The plugin, in combination with QGIS, handles the tasks of managing geographical data, data conversion, generation of configuration files required by openModeller and compilation of a project folder. The plugin proved to be very helpful with the task of compiling project datasets and configuration files for multiple instances of species occurrence datasets and the overall handling of openModeller. In addition, the plugin is easily extensible to take potential new requirements into account in the future.
BENCHMARKING DEEP LEARNING FRAMEWORKS FOR THE CLASSIFICATION OF VERY HIGH RESOLUTION SATELLITE MULTISPECTRAL DATA
Authors: Papadomanolaki M. , Vakalopoulou M. , Zagoruyko S. , Karantzalos K.
Published: June 2016

Abstract. In this paper we evaluated deep-learning frameworks based on Convolutional Neural Networks for the accurate classification of multispectral remote sensing data. Certain state-of-the-art models have been tested on the publicly available SAT-4 and SAT-6 high resolution satellite multispectral datasets. In particular, the performed benchmark included the AlexNet, AlexNet-small and VGG models which had been trained and applied to both datasets exploiting all the available spectral information. Deep Belief Networks, Autoencoders and other semi-supervised frameworks have been, also, compared. The high level features that were calculated from the tested models managed to classify the different land cover classes with significantly high accuracy rates i.e., above 99.9%. The experimental results demonstrate the great potentials of advanced deep-learning frameworks for the supervised classification of high resolution multispectral remote sensing data.
MAPPING AND CHANGE ANALYSIS IN MANGROVE FOREST BY USING LANDSAT IMAGERY
Authors: Dan T. T. , Chen C. F. , Chiang S. H. , Ogawa S.
Published: June 2016

Abstract. Mangrove is located in the tropical and subtropical regions and brings good services for native people. Mangrove in the world has been lost with a rapid rate. Therefore, monitoring a spatiotemporal distribution of mangrove is thus critical for natural resource management. This research objectives were: (i) to map the current extent of mangrove in the West and Central Africa and in the Sundarbans delta, and (ii) to identify change of mangrove using Landsat data. The data were processed through four main steps: (1) data pre-processing including atmospheric correction and image normalization, (2) image classification using supervised classification approach, (3) accuracy assessment for the classification results, and (4) change detection analysis. Validation was made by comparing the classification results with the ground reference data, which yielded satisfactory agreement with overall accuracy 84.1% and Kappa coefficient of 0.74 in the West and Central Africa and 83.0% and 0.73 in the Sundarbans, respectively. The result shows that mangrove areas have changed significantly. In the West and Central Africa, mangrove loss from 1988 to 2014 was approximately 16.9%, and only 2.5% was recovered or newly planted at the same time, while the overall change of mangrove in the Sundarbans increased approximately by 900 km2 of total mangrove area. Mangrove declined due to deforestation, natural catastrophes deforestation and mangrove rehabilitation programs. The overall efforts in this study demonstrated the effectiveness of the proposed method used for investigating spatiotemporal changes of mangrove and the results could provide planners with invaluable quantitative information for sustainable management of mangrove ecosystems in these regions.
THE EFFECT OF WIND ON TREE STEM PARAMETER ESTIMATION USING TERRESTRIAL LASER SCANNING
Authors: Vaaja M. T. , Virtanen J.-P. , Kurkela M. , Lehtola V. , Hyyppä J. , Hyyppä H.
Published: June 2016

Abstract. The 3D measurement technique of terrestrial laser scanning (TLS) in forest inventories has shown great potential for improving the accuracy and efficiency of both individual tree and plot level data collection. However, the effect of wind has been poorly estimated in the error analysis of TLS tree measurements although it causes varying deformations to the trees. In this paper, we evaluated the effect of wind on tree stem parameter estimation at different heights using TLS. The data consists of one measured Scots pine captured from three different scanning directions with two different scanning resolutions, 6.3 mm and 3.1 mm at 10 m. The measurements were conducted under two different wind speeds, approximately 3 m/s and 9 m/s, as recorded by a nearby weather station of the Finnish Meteorological Institute. Our results show that the wind may cause both the underestimation and overestimation of tree diameter when using TLS. The duration of the scanning is found to have an impact for the measured shape of the tree stem under 9 m/s wind conditions. The results also indicate that a 9 m/s wind does not have a significant effect on the stem parameters of the lower part of a tree (<28% of the tree height). However, as the results imply, the wind conditions should be taken into account more comprehensively in analysis of TLS tree measurements, especially if multiple scans from different positions are registered together. In addition, TLS could potentially be applied to indirectly measure wind speed by observing the tree stem movement.
ESTIMATING CORN YIELD IN THE UNITED STATES WITH MODIS EVI AND MACHINE LEARNING METHODS
Authors: Kuwata K. , Shibasaki R.
Published: June 2016

Abstract. Satellite remote sensing is commonly used to monitor crop yield in wide areas. Because many parameters are necessary for crop yield estimation, modelling the relationships between parameters and crop yield is generally complicated. Several methodologies using machine learning have been proposed to solve this issue, but the accuracy of county-level estimation remains to be improved. In addition, estimating county-level crop yield across an entire country has not yet been achieved. In this study, we applied a deep neural network (DNN) to estimate corn yield. We evaluated the estimation accuracy of the DNN model by comparing it with other models trained by different machine learning algorithms. We also prepared two time-series datasets differing in duration and confirmed the feature extraction performance of models by inputting each dataset. As a result, the DNN estimated county-level corn yield for the entire area of the United States with a determination coefficient (R2) of 0.780 and a root mean square error (RMSE) of 18.2 bushels/acre. In addition, our results showed that estimation models that were trained by a neural network extracted features from the input data better than an existing machine learning algorithm.
COASTLINE EXTRACTION FROM AERIAL IMAGES BASED ON EDGE DETECTION
Authors: Paravolidakis V. , Moirogiorgou K. , Ragia L. , Zervakis M. , Synolakis C.
Published: June 2016

Abstract. Nowadays coastline extraction and tracking of its changes become of high importance because of the climate change, global warming and rapid growth of human population. Coastal areas play a significant role for the economy of the entire region. In this paper we propose a new methodology for automatic extraction of the coastline using aerial images. A combination of a four step algorithm is used to extract the coastline in a robust and generalizable way. First, noise distortion is reduced in order to ameliorate the input data for the next processing steps. Then, the image is segmented into two regions, land and sea, through the application of a local threshold to create the binary image. The result is further processed by morphological operators with the aim that small objects are being eliminated and only the objects of interest are preserved. Finally, we perform edge detection and active contours fitting in order to extract and model the coastline. These algorithmic steps are illustrated through examples, which demonstrate the efficacy of the proposed methodology.
COMPARISON OF HIGH AND LOW DENSITY AIRBORNE LIDAR DATA FOR FOREST ROAD QUALITY ASSESSMENT
Authors: Kiss K. , Malinen J. , Tokola T.
Published: June 2016

Abstract. Good quality forest roads are important for forest management. Airborne laser scanning data can help create automatized road quality detection, thus avoiding field visits. Two different pulse density datasets have been used to assess road quality: high-density airborne laser scanning data from Kiihtelysvaara and low-density data from Tuusniemi, Finland. The field inventory mainly focused on the surface wear condition, structural condition, flatness, road side vegetation and drying of the road. Observations were divided into poor, satisfactory and good categories based on the current Finnish quality standards used for forest roads. Digital Elevation Models were derived from the laser point cloud, and indices were calculated to determine road quality. The calculated indices assessed the topographic differences on the road surface and road sides. The topographic position index works well in flat terrain only, while the standardized elevation index described the road surface better if the differences are bigger. Both indices require at least a 1 metre resolution. High-density data is necessary for analysis of the road surface, and the indices relate mostly to the surface wear and flatness. The classification was more precise (31–92%) than on low-density data (25–40%). However, ditch detection and classification can be carried out using the sparse dataset as well (with a success rate of 69%). The use of airborne laser scanning data can provide quality information on forest roads.
BEYOND MAXIMUM INDEPENDENT SET: AN EXTENDED MODEL FOR POINT-FEATURE LABEL PLACEMENT
Authors: Haunert Jan-Henrik , Wolff Alexander
Published: June 2016

Abstract. Map labeling is a classical problem of cartography that has frequently been approached by combinatorial optimization. Given a set of features in the map and for each feature a set of label candidates, a common problem is to select an independent set of labels (that is, a labeling without label–label overlaps) that contains as many labels as possible and at most one label for each feature. To obtain solutions of high cartographic quality, the labels can be weighted and one can maximize the total weight (rather than the number) of the selected labels. We argue, however, that when maximizing the weight of the labeling, interdependences between labels are insufficiently addressed. Furthermore, in a maximum-weight labeling, the labels tend to be densely packed and thus the map background can be occluded too much. We propose extensions of an existing model to overcome these limitations. Since even without our extensions the problem is NP-hard, we cannot hope for an efficient exact algorithm for the problem. Therefore, we present a formalization of our model as an integer linear program (ILP). This allows us to compute optimal solutions in reasonable time, which we demonstrate for randomly generated instances.
COMPARATIVE ANALYSIS OF DATA STRUCTURES FOR STORING MASSIVE TINS IN A DBMS
Authors: Kumar K. , Ledoux H. , Stoter J.
Published: June 2016

Abstract. Point cloud data are an important source for 3D geoinformation. Modern day 3D data acquisition and processing techniques such as airborne laser scanning and multi-beam echosounding generate billions of 3D points for simply an area of few square kilometers. With the size of the point clouds exceeding the billion mark for even a small area, there is a need for their efficient storage and management. These point clouds are sometimes associated with attributes and constraints as well. Storing billions of 3D points is currently possible which is confirmed by the initial implementations in Oracle Spatial SDO PC and the PostgreSQL Point Cloud extension. But to be able to analyse and extract useful information from point clouds, we need more than just points i.e. we require the surface defined by these points in space. There are different ways to represent surfaces in GIS including grids, TINs, boundary representations, etc. In this study, we investigate the database solutions for the storage and management of massive TINs. The classical (face and edge based) and compact (star based) data structures are discussed at length with reference to their structure, advantages and limitations in handling massive triangulations and are compared with the current solution of PostGIS Simple Feature. The main test dataset is the TIN generated from third national elevation model of the Netherlands (AHN3) with a point density of over 10 points/m2. PostgreSQL/PostGIS DBMS is used for storing the generated TIN. The data structures are tested with the generated TIN models to account for their geometry, topology, storage, indexing, and loading time in a database. Our study is useful in identifying what are the limitations of the existing data structures for storing massive TINs and what is required to optimise these structures for managing massive triangulations in a database.
A PROBABILITY-BASED STATISTICAL METHOD TO EXTRACT WATER BODY OF TM IMAGES WITH MISSING INFORMATION
Authors: Lian Shizhong , Chen Jiangping , Luo Minghai
Published: June 2016

Abstract. Water information cannot be accurately extracted using TM images because true information is lost in some images because of blocking clouds and missing data stripes, thereby water information cannot be accurately extracted. Water is continuously distributed in natural conditions; thus, this paper proposed a new method of water body extraction based on probability statistics to improve the accuracy of water information extraction of TM images with missing information. Different disturbing information of clouds and missing data stripes are simulated. Water information is extracted using global histogram matching, local histogram matching, and the probability-based statistical method in the simulated images. Experiments show that smaller Areal Error and higher Boundary Recall can be obtained using this method compared with the conventional methods.
A NOVEL SIMILARITY ASSESSMENT FOR REMOTE SENSING IMAGES VIA FAST ASSOCIATION RULE MINING
Authors: Liu Jun , Chen Kai , Liu Ping , Qian Jing , Chen Huijuan
Published: June 2016

Abstract. Similarity assessment is the fundamentally important to various remote sensing applications such as image classification, image retrieval and so on. The objective of similarity assessment is to automatically distinguish differences between images and identify the contents of an image. Unlike the existing feature-based or object-based methods, we concern more about the deep level pattern of image content. The association rule mining is capable to find out the potential patterns of image, hence in this paper, a fast association rule mining algorithm is proposed and the similarity is represented by rules. More specifically, the proposed approach consist of the following steps: firstly, the gray level of image is compressed using linear segmentation to avoid interference of details and reduce the computation amount; then the compressed gray values between pixels are collected to generate the transaction sets which are transformed into the proposed multi-dimension data cube structure; the association rules are then fast mined based on multi-dimension data cube; finally the mined rules are represented as a vector and similarity assessment is achieved by vector comparison using first order approximation of Kullback-Leibler divergence. Experimental results indicate that the proposed fast association rule mining algorithm is more effective than the widely used Apriori method. The remote sensing image retrieval experiments using various images for example, QuickBird, WorldView-2, based on the existing and proposed similarity assessment show that the proposed method can provide higher retrieval precision.
FUTURE ESTIMATION OF CONVENIENCE LIVING FACILITIES WITHDRAWAL DUE TO POPULATION DECLINE ALL OVER JAPAN FROM 2010 TO 2040 - FOCUS ON SUPERMARKETS, CONVENIENCE STORES AND DRUGSTORES
Authors: Nishimoto Yuka , Akiyama Yuki , Shibasaki Ryosuke
Published: June 2016

Abstract. Population explosion is considered to be one of the most crucial problems in the world. However, in Japan, the opposite problem: population decline has become serious now. Japanese population is estimated to decrease by twenty millions in 2040. This negative situation will cause to increase areas where many residents cannot make a daily living all over Japan because many convenience living facilities such as supermarkets, convenience stores and drugstores will be difficult to maintain their market area population due to future population decline. In our research, we used point data of convenience living facilities developed by address geocoding of digital telephone directory and point data of future population projection developed by distribution of Japanese official population projection data proportionally among the building volume of digital residential map, which can monitor building volumes all over Japan. In conclusion, we estimated that various convenience living facilities in Japan will shrink and close by population decline in near future. In particular, it is cleared that approximately 14.7% of supermarkets will be possible to withdraw all over Japan by 2040. In addition, it is cleared that over 40% of supermarkets in some countryside prefectures will be possible to withdraw by 2040. Thus, we estimated future distributions of convenience living facilities that cannot maintain their market area population due to future population decline. Moreover, we estimated the number of people that they will become inconvenience in buying fresh foods.
APPLICATION OF DSM IN OBSTACLE CLEARANCE SURVEYING OF AERODROME
Authors: Qiao X. , Lv S. H. , Li L. L. , Zhou X. J. , Wang H. Y. , Li D. , Liu J. Y.
Published: June 2016

Abstract. Compared to the wide use of digital elevation model (DEM), digital surface model (DSM) receives less attention because that it is composed by not only terrain surface, but also vegetations and man-made objects which are usually regarded as useless information. Nevertheless, these objects are useful for the identification of obstacles around an aerodrome. The primary objective of the study was to determine the applicability of DSM in obstacle clearance surveying of aerodrome. According to the requirements of obstacle clearance surveying at QT airport, aerial and satellite imagery were used to generate DSM, by means of photogrammetry, which was spatially analyzed with the hypothetical 3D obstacle limitation surfaces (OLS) to identify the potential obstacles. Field surveying was then carried out to retrieve the accurate horizontal position and height of the obstacles. The results proved that the application of DSM could make considerable improvement in the efficiency of obstacle clearance surveying of aerodrome.
ACCURACY AND SPATIAL VARIABILITY IN GPS SURVEYING FOR LANDSLIDE MAPPING ON ROAD INVENTORIES AT A SEMI-DETAILED SCALE: THE CASE IN COLOMBIA
Authors: Murillo Feo C. A. , Martnez Martinez L. J. , Correa Muñoz N. A.
Published: June 2016

Abstract. The accuracy of locating attributes on topographic surfaces when, using GPS in mountainous areas, is affected by obstacles to wave propagation. As part of this research on the semi-automatic detection of landslides, we evaluate the accuracy and spatial distribution of the horizontal error in GPS positioning in the tertiary road network of six municipalities located in mountainous areas in the department of Cauca, Colombia, using geo-referencing with GPS mapping equipment and static-fast and pseudo-kinematic methods. We obtained quality parameters for the GPS surveys with differential correction, using a post-processing method. The consolidated database underwent exploratory analyses to determine the statistical distribution, a multivariate analysis to establish relationships and partnerships between the variables, and an analysis of the spatial variability and calculus of accuracy, considering the effect of non-Gaussian distribution errors. The evaluation of the internal validity of the data provide metrics with a confidence level of 95% between 1.24 and 2.45 m in the static-fast mode and between 0.86 and 4.2 m in the pseudo-kinematic mode. The external validity had an absolute error of 4.69 m, indicating that this descriptor is more critical than precision. Based on the ASPRS standard, the scale obtained with the evaluated equipment was in the order of 1:20000, a level of detail expected in the landslide-mapping project. Modelling the spatial variability of the horizontal errors from the empirical semi-variogram analysis showed predictions errors close to the external validity of the devices.
VALIDATION AND UPSCALING OF SOIL MOISTURE SATELLITE PRODUCTS IN ROMANIA
Authors: Sandric I. , Diamandi A. , Oana N. , Saizu D. , Vasile C. , Lucaschi B.
Published: June 2016

Abstract. The study presents the validation of SMOS soil moisture satellite products for Romania. The validation was performed with in-situ measurements spatially distributed over the country and with in-situ measurements concentrated in on small area. For country level a number of 20 stations from the national meteorological observations network in Romania were selected. These stations have in-situ measurements for soil moisture in the first 5 cm of the soil surface. The stations are more or less distributed in one pixel of SMOS, but it has the advantage that covers almost all the country with a wide range of environmental conditions. Additionally 10 mobile soil moisture measurements stations were acquired and installed. These are spatially concentrated in one SMOS pixel in order to have a more detailed validation against the soil type, soil texture, land surface temperature and vegetation type inside one pixel. The results were compared and analyzed for each day, week, season, soil type, and soil texture and vegetation type. Minimum, maximum, mean and standard deviation were extracted and analyzed for each validation criteria and a hierarchy of those were performed. An upscaling method based on the relations between soil moisture, land surface temperature and vegetation indices was tested and implemented. The study was financed by the Romanian Space Agency within the framework of ASSIMO project http://assimo.meteoromania.ro.
NON LINEAR OPTIMIZATION APPLIED TO ANGLE-OF-ARRIVAL SATELLITE BASED GEO-LOCALIZATION FOR BIASED AND TIME-DRIFTING SENSORS
Authors: Levy Daniel , Roos Jason , Robinson Jace , Carpenter William , Martin Richard , Taylor Clark , Sugrue Joseph , Terzuoli Andrew
Published: June 2016

Abstract. Multiple sensors are used in a variety of geolocation systems. Many use Time Difference of Arrival (TDOA) or Received Signal Strength (RSS) measurements to estimate the most likely location of a signal. When an object does not emit an RF signal, Angle of Arrival (AOA) measurements using optical or infrared frequencies become more feasible than TDOA or RSS measurements. AOA measurements can be created from any sensor platform with any sort of optical sensor, location and attitude knowledge to track passive objects. Previous work has created a non-linear optimization (NLO) method for calculating the most likely estimate from AOA measurements. Two new modifications to the NLO algorithm are created and shown to correct AOA measurement errors by estimating the inherent bias and time-drift in the Inertial Measurement Unit (IMU) of the AOA sensing platform. One method corrects the sensor bias in post processing while treating the NLO method as a module. The other method directly corrects the sensor bias within the NLO algorithm by incorporating the bias parameters as a state vector in the estimation process. These two methods are analyzed using various Monte-Carlo simulations to check the general performance of the two modifications in comparison to the original NLO algorithm.
A VERSION-SIMILARITY BASED TRUST DEGREE COMPUTATION MODEL FOR CROWDSOURCING GEOGRAPHIC DATA
Authors: Zhou Xiaoguang , Zhao Yijiang
Published: June 2016

Abstract. Quality evaluation and control has become the main concern of VGI. In this paper, trust is used as a proxy of VGI quality, a version-similarity based trust degree computation model for crowdsourcing geographic data is presented. This model is based on the assumption that the quality of VGI objects mainly determined by the professional skill and integrity (called reputation in this paper), and the reputation of the contributor is movable. The contributor’s reputation is calculated using the similarity degree among the multi-versions for the same entity state. The trust degree of VGI object is determined by the trust degree of its previous version, the reputation of the last contributor and the modification proportion. In order to verify this presented model, a prototype system for computing the trust degree of VGI objects is developed by programming with Visual C# 2010. The historical data of Berlin of OpenStreetMap (OSM) are employed for experiments. The experimental results demonstrate that the quality of crowdsourcing geographic data is highly positive correlation with its trustworthiness. As the evaluation is based on version-similarity, not based on the direct subjective evaluation among users, the evaluation result is objective. Furthermore, as the movability property of the contributors’ reputation is used in this presented method, our method has a higher assessment coverage than the existing methods.
SIDELOADING – INGESTION OF LARGE POINT CLOUDS INTO THE APACHE SPARK BIG DATA ENGINE
Authors: Boehm J. , Liu K. , Alis C.
Published: June 2016

Abstract. In the geospatial domain we have now reached the point where data volumes we handle have clearly grown beyond the capacity of most desktop computers. This is particularly true in the area of point cloud processing. It is therefore naturally lucrative to explore established big data frameworks for big geospatial data. The very first hurdle is the import of geospatial data into big data frameworks, commonly referred to as data ingestion. Geospatial data is typically encoded in specialised binary file formats, which are not naturally supported by the existing big data frameworks. Instead such file formats are supported by software libraries that are restricted to single CPU execution. We present an approach that allows the use of existing point cloud file format libraries on the Apache Spark big data framework. We demonstrate the ingestion of large volumes of point cloud data into a compute cluster. The approach uses a map function to distribute the data ingestion across the nodes of a cluster. We test the capabilities of the proposed method to load billions of points into a commodity hardware compute cluster and we discuss the implications on scalability and performance. The performance is benchmarked against an existing native Apache Spark data import implementation.
HISTORICAL GIS DATA AND CHANGES IN URBAN MORPHOLOGICAL PARAMETERS FOR THE ANALYSIS OF URBAN HEAT ISLANDS IN HONG KONG
Authors: Peng F. , Wong M. S. , Nichol J. E. , Chan P. W.
Published: June 2016

Abstract. Rapid urban development between the 1960 and 2010 decades have changed the urban landscape and pattern in the Kowloon Peninsula of Hong Kong. This paper aims to study the changes of urban morphological parameters between the 1985 and 2010 and explore their influences on the urban heat island (UHI) effect. This study applied a mono-window algorithm to retrieve the land surface temperature (LST) using Landsat Thematic Mapper (TM) images from 1987 to 2009. In order to estimate the effects of local urban morphological parameters to LST, the global surface temperature anomaly was analysed. Historical 3D building model was developed based on aerial photogrammetry technique using aerial photographs from 1964 to 2010, in which the urban digital surface models (DSMs) including elevations of infrastructures and buildings have been generated. Then, urban morphological parameters (i.e. frontal area index (FAI), sky view factor (SVF)), vegetation fractional cover (VFC), global solar radiation (GSR), Normalized Difference Built-Up Index (NDBI), wind speed were derived. Finally, a linear regression method in Waikato Environment for Knowledge Analysis (WEKA) was used to build prediction model for revealing LST spatial patterns. Results show that the final apparent surface temperature have uncertainties less than 1 degree Celsius. The comparison between the simulated and actual spatial pattern of LST in 2009 showed that the correlation coefficient is 0.65, mean absolute error (MAE) is 1.24 degree Celsius, and root mean square error (RMSE) is 1.51 degree Celsius of 22,429 pixels.
PARALLEL PROCESSING OF BIG POINT CLOUDS USING Z-ORDER-BASED PARTITIONING
Authors: Alis C. , Boehm J. , Liu K.
Published: June 2016

Abstract. As laser scanning technology improves and costs are coming down, the amount of point cloud data being generated can be prohibitively difficult and expensive to process on a single machine. This data explosion is not only limited to point cloud data. Voluminous amounts of high-dimensionality and quickly accumulating data, collectively known as Big Data, such as those generated by social media, Internet of Things devices and commercial transactions, are becoming more prevalent as well. New computing paradigms and frameworks are being developed to efficiently handle the processing of Big Data, many of which utilize a compute cluster composed of several commodity grade machines to process chunks of data in parallel. A central concept in many of these frameworks is data locality. By its nature, Big Data is large enough that the entire dataset would not fit on the memory and hard drives of a single node hence replicating the entire dataset to each worker node is impractical. The data must then be partitioned across worker nodes in a manner that minimises data transfer across the network. This is a challenge for point cloud data because there exist different ways to partition data and they may require data transfer. We propose a partitioning based on Z-order which is a form of locality-sensitive hashing. The Z-order or Morton code is computed by dividing each dimension to form a grid then interleaving the binary representation of each dimension. For example, the Z-order code for the grid square with coordinates (x = 1 = 012, y = 3 = 112) is 10112 = 11. The number of points in each partition is controlled by the number of bits per dimension: the more bits, the fewer the points. The number of bits per dimension also controls the level of detail with more bits yielding finer partitioning. We present this partitioning method by implementing it on Apache Spark and investigating how different parameters affect the accuracy and running time of the k nearest neighbour algorithm for a hemispherical and a triangular wave point cloud.
Determination of the material properties of polymers using laser-generated broadband ultrasound
Authors: Claes Leander , Meyer Thorsten , Bause Fabian , Rautenberg Jens , Henning Bernd
Published: June 2016

Abstract. In the non-destructive determination of material properties, the utilization of ultrasound has proven to be a viable tool. In the presented paper, a laser is used to create broadband acoustic waves in plate-shaped specimens by applying the photoacoustic effect. The waves are detected using a purpose-built ultrasonic transducer that is based on piezoceramics instead of the commonly used piezoelectric polymer films. This new transducer concept allows for detection of ultrasonic waves up to 10 MHz with high sensitivity, thereby allowing the characterization of highly damping materials such as polymers. The recorded data are analysed using different methods to obtain information on the propagation modes transmitted along the specimen. In an inverse procedure, the gained results are compared to simulations, yielding approximations for the specimen's material properties.
Tsunami arrival time detection system applicable to discontinuous time-series data with outliers
Authors: Lee Jun-Whan , Park Sun-Cheon , Lee Duk Kee , Lee Jong Ho
Published: June 2016

Abstract. Timely detection of tsunamis with water-level records is a critical but logistically challenging task because of outliers and gaps. We propose a tsunami arrival time detection system (TADS) that can be applied to discontinuous time-series data with outliers. TADS consists of three major algorithms that are designed to update at every new data acquisition: outlier detection, gap-filling, and tsunami detection. To detect a tsunami from a record containing outliers and gaps, we propose the concept of the event period. In this study, we applied this concept in our test of the TADS at the Ulleung-do surge gauge located in the East Sea. We calibrated the thresholds to identify tsunami arrivals based on the 2011 Tohoku tsunami, and the results show that the overall performance of TADS is effective at detecting a small tsunami signal superimposed on both an outlier and gap.