bluuebunny/crossref_metadata_2025 huggingface dataset:
The interaction between nitrogen and phosphorous is a strong predictor of intra-plant variation in nitrogen isotope composition in a desert species
Authors: Zhang Jinxin , Gu Lianhong , Zhang Jingbo , Wu Rina , Wang Feng , Lin Guanghui , Wu Bo , Lu Qi , Meng Ping
Published: June 2016

Abstract. Understanding intra-plant variations in δ15N is essential for fully utilizing the potential of δ15N as an integrator of terrestrial nitrogen (N) cycle and as an indicator of relative limitation of N and phosphorous (P) on plant growth. Studying such variations can also yield insights into N metabolism by plant as a whole or by specific organs. However, few researchers have systematically evaluated intra-plant variations in δ15N and their relationships with organ nutrient contents. We excavated whole plant architectures of Nitraria tangutorum Bobrov, a C3 species of vital regional ecological importance, in two deserts in northwestern China. We systematically and simultaneously measured N isotope ratios and N and P contents of different parts of the excavated plants. We found that intra-plant variations in δ15N of N. tangutorum were positively correlated with corresponding organ N and P contents. However, it was the N × P interaction, not N and P individually or their linear combination, that was the strongest predictor of intra-plant δ15N. Additionally, we showed that root δ15N increased with depth into soil, a pattern similar to profiles of soil δ15N reported by previous studies in different ecosystems. We hypothesized that the strong positive intra-plant δ15N – N and P relationships are caused by three processes acting in conjunction: 1) N and P content-driven fractionating exchanges of ammonia between leaves and the atmosphere (volatilization) during photorespiration, 2) resorption and remobilization of N and P from senescing leaves, and 3) mixture of the re-translocated foliar N and P with existing pools in stems and roots. To test our hypothesis, future studies should investigate plant N volatilization and associated isotope fractionation and intra-plant variations in δ15N in different species across ecosystems and climates.
The integrated water balance and soil data set of the Rollesbroich hydrological observatory
Authors: Qu Wei , Bogena Heye R. , Huisman Johan A. , Schmidt Marius , Kunkel Ralf , Weuthen Ansgar , Schilling Bernd , Sorg Jürgen , Vereecken Harry
Published: June 2016

Abstract. The Rollesbroich headwater catchment located in Western Germany is a densely instrumented hydrological observatory and part of the TERENO (Terrestrial Environmental Observatories) initiative. The measurements acquired in this observatory present a comprehensive dataset that contains key hydrological fluxes in addition to important hydrological states and properties. Meteorological data (i.e. precipitation, air temperature, air humidity, radiation components, and wind speed) are continuously recorded and actual evapotranspiration is measured using the eddy covariance technique. Runoff is measured at the catchment outlet with a gauging station. In addition, spatio-temporal variations in soil water content and temperature are measured at high resolution with a wireless sensor network (SoilNet). Soil physical properties were determined using standard laboratory procedures from samples taken at a large number of locations in the catchment. This comprehensive data set can be used to validate remote sensing retrievals and hydrological models; to improve the understanding of spatial temporal dynamics of soil water content; to optimize data assimilation and inverse techniques for hydrological models; and to develop upscaling and downscaling procedures of soil water content information. The complete data set is freely available online (http://www.tereno.net).
CONSISTENT TONAL CORRECTION FOR MULTI-VIEW REMOTE SENSING IMAGE MOSAICKING
Authors: Xia Menghan , Yao Jian , Li Li , Xie Renping , Liu Yahui
Published: June 2016

Abstract. In this paper, we propose an effective approach for consistent tonal correction of multi-view images during mosaicking. Our method is specifically designed for mosaicking multi-view remote sensing images acquired under different conditions and/or presenting inconsistent tone. To avoid the correlation of three channels in original RGB images, we convert them to an orthogonal color space lαβ in advance. First of all, the tones of sequential images are transferred from an example image reasonably via our improved color transfer algorithm. Secondly, the more refined adjustments take place in the luminance channel l and color channels α and β, independently. In the luminance channel, the global gain compensation is applied to minimize the luminance difference between pairs of images by the least square estimator. In the color channels, the specifically designed stepwise histogram adjustments make all the images consistent tone as a whole, including the initial correction transferring the color characteristics of the automatically selected reference subset to other images in an optimal order and the consistent correction readjusting each image by referring all their neighbors based on the overlaps. Thirdly, we creatively transfer the original structures to the previously corrected images by a local linear model, which can preserve the local structures of the original images. Finally, several groups of convincing experiments on both challenged synthetic and real data demonstrate the validity of our proposed approach.
METRIC CALIBRATION OF A FOCUSED PLENOPTIC CAMERA BASED ON A 3D CALIBRATION TARGET
Authors: Zeller N. , Noury C. A. , Quint F. , Teulière C. , Stilla U. , Dhome M.
Published: June 2016

Abstract. In this paper we present a new calibration approach for focused plenoptic cameras. We derive a new mathematical projection model of a focused plenoptic camera which considers lateral as well as depth distortion. Therefore, we derive a new depth distortion model directly from the theory of depth estimation in a focused plenoptic camera. In total the model consists of five intrinsic parameters, the parameters for radial and tangential distortion in the image plane and two new depth distortion parameters. In the proposed calibration we perform a complete bundle adjustment based on a 3D calibration target. The residual of our optimization approach is three dimensional, where the depth residual is defined by a scaled version of the inverse virtual depth difference and thus conforms well to the measured data. Our method is evaluated based on different camera setups and shows good accuracy. For a better characterization of our approach we evaluate the accuracy of virtual image points projected back to 3D space.
A COMPARISON OF UAV AND TLS DATA FOR SOIL ROUGHNESS ASSESSMENT
Authors: Milenković M. , Karel W. , Ressl C. , Pfeifer N.
Published: June 2016

Abstract. Soil roughness represents fine-scale surface geometry which figures in many geophysical models. While static photogrammetric techniques (terrestrial images and laser scanning) have been recently proposed as a new source for deriving roughness heights, there is still need to overcome acquisition scale and viewing geometry issues. By contrast to the static techniques, images taken from unmanned aerial vehicles (UAV) can maintain near-nadir looking geometry over scales of several agricultural fields. This paper presents a pilot study on high-resolution, soil roughness reconstruction and assessment from UAV images over an agricultural plot. As a reference method, terrestrial laser scanning (TLS) was applied on a 10 m x 1.5 m subplot. The UAV images were self-calibrated and oriented within a bundle adjustment, and processed further up to a dense-matched digital surface model (DSM). The analysis of the UAV- and TLS-DSMs were performed in the spatial domain based on the surface autocorrelation function and the correlation length, and in the frequency domain based on the roughness spectrum and the surface fractal dimension (spectral slope). The TLS- and UAV-DSM differences were found to be under ±1 cm, while the UAV DSM showed a systematic pattern below this scale, which was explained by weakly tied sub-blocks of the bundle block. The results also confirmed that the existing TLS methods leads to roughness assessment up to 5 mm resolution. However, for our UAV data, this was not possible to achieve, though it was shown that for spatial scales of 12 cm and larger, both methods appear to be usable. Additionally, this paper suggests a method to propagate measurement errors to the correlation length.
FROM POINT CLOUD TO DIGITAL FABRICATION: A TANGIBLE RECONSTRUCTION OF CA’ VENIER DEI LEONI, THE GUGGENHEIM MUSEUM IN VENICE
Authors: Balletti C. , D'Agnano F. , Guerra F. , Vernier P.
Published: June 2016

Abstract. The paper describes how new digital methodologies can be used within the field of Cultural Heritage, not only with the aim of documenting the actual state of an architecture but to review the past transformations it has undergone, conserving and representing these histories as well. The premise to conservation and enhancement of our Heritage is a deep study in terms of position, shape, colour, and also of the historical and artistic features. Survey methods have acquired data acquisition techniques in line with technological progress: today’s electronic and IT technologies, that are the tools of modern Geomatics, allow the effective survey and representation of 3D objects, from architectural structures to sculptures or archaeological finds. Over the last few years, the methodologies of acquisition and integrated representation for 3D patrimony documentation have developed and consolidated considerably: the possibilities of the digital realm can augment the understanding and the valorisation of a monument. The specific case offered in the present paper, Ca' Venier dei Leoni, the palace where is the Guggenheim Museum in Venice, is a significant example. It suggests not only the theme of the “no longer existing”, or better never built, but also the opportunity to formulate hypotheses regarding its implementation and the impact that the palace would have had in the Venetian contest.
BIM FROM LASER SCANS… NOT JUST FOR BUILDINGS: NURBS-BASED PARAMETRIC MODELING OF A MEDIEVAL BRIDGE
Authors: Barazzetti L. , Banfi F. , Brumana R. , Previtali M. , Roncoroni F.
Published: June 2016

Abstract. Building Information Modelling is not limited to buildings. BIM technology includes civil infrastructures such as roads, dams, bridges, communications networks, water and wastewater networks and tunnels. This paper describes a novel methodology for the generation of a detailed BIM of a complex medieval bridge. The use of laser scans and images coupled with the development of algorithms able to handle irregular shapes allowed the creation of advanced parametric objects, which were assembled to obtain an accurate BIM. The lack of existing object libraries required the development of specific families for the different structural elements of the bridge. Finally, some applications aimed at assessing the stability and safety of the bridge are illustrated and discussed. The BIM of the bridge can incorporate this information towards a new “BIMonitoring” concept to preserve the geometric complexity provided by point clouds, obtaining a detailed BIM with object relationships and attributes.
STANDALONE TERRESTRIAL LASER SCANNING FOR EFFICIENTLY CAPTURING AEC BUILDINGS FOR AS-BUILT BIM
Authors: Bassier M. , Vergauwen M. , Van Genechten B.
Published: June 2016

Abstract. With the increasing popularity of as-built building models for the architectural, engineering and construction (AEC) industry, the demand for highly accurate and dense point cloud data is rising. The current data acquisition methods are labour intensive and time consuming. In order to compete with indoor mobile mapping systems (IMMS), surveyors are now opting to use terrestrial laser scanning as a standalone solution. However, there is uncertainty about the accuracy of this approach. The emphasis of this paper is to determine the scope for which terrestrial laser scanners can be used without additional control. Multiple real life test cases are evaluated in order to identify the boundaries of this technique. Furthermore, this research presents a mathematical prediction model that provides an indication of the data accuracy given the project dimensions. This will enable surveyors to make informed discussions about the employability of terrestrial laser scanning without additional control in mid to large-scale projects.
USING LABEL NOISE ROBUST LOGISTIC REGRESSION FOR AUTOMATED UPDATING OF TOPOGRAPHIC GEOSPATIAL DATABASES
Authors: Maas A. , Rottensteiner F. , Heipke C.
Published: June 2016

Abstract. Supervised classification of remotely sensed images is a classical method to update topographic geospatial databases. The task requires training data in the form of image data with known class labels, whose generation is time-consuming. To avoid this problem one can use the labels from the outdated database for training. As some of these labels may be wrong due to changes in land cover, one has to use training techniques that can cope with wrong class labels in the training data. In this paper we adapt a label noise tolerant training technique to the problem of database updating. No labelled data other than the existing database are necessary. The resulting label image and transition matrix between the labels can help to update the database and to detect changes between the two time epochs. Our experiments are based on different test areas, using real images with simulated existing databases. Our results show that this method can indeed detect changes that would remain undetected if label noise were not considered in training.
VIRTUAL DIMENSIONALITY ESTIMATION IN HYPERSPECTRAL IMAGERY BASED ON UNSUPERVISED FEATURE SELECTION
Authors: Ghamary Asl M. , Mojaradi B.
Published: June 2016

Abstract. Virtual Dimensionality (VD) is a concept developed to estimate the number of distinct spectral signatures in hyperspectral imagery. Intuitively, detecting the number of spectrally distinct signatures depends on determining the number of distinct bands of the data. Considering this idea, the current paper aims at estimating the VD based on finding independent bands in the image partition space. Eventually, the number of independent selected bands is accepted as the VD estimate. The proposed method is automatic and distribution-free. In addition, no tuning parameters and noise estimation processes are needed. This method is compared with three well-known VD estimation methods using synthetic and real datasets. Experimental results show high speed and reliability in the performance of the proposed method.
FOREST AREA DERIVATION FROM SENTINEL-1 DATA
Authors: Dostálová Alena , Hollaus Markus , Milenković Milutin , Wagner Wolfgang
Published: June 2016

Abstract. The recently launched Sentinel-1A provides the high resolution Synthetic Aperture Radar (SAR) data with very high temporal coverage over large parts of European continent. Short revisit time and dual polarization availability supports its usability for forestry applications. The following study presents an analysis of the potential of the multi-temporal dual-polarization Sentinel-1A data for the forest area derivation using the standard methods based on Otsu thresholding and K-means clustering. Sentinel-1 data collected in winter season 2014-2015 over a test area in eastern Austria were used to derive forest area mask with spatial resolution of 10m and minimum mapping unit of 500 m2. The validation with reference forest mask derived from airborne full-waveform laser scanning data revealed overall accuracy of 92 % and kappa statistics of 0.81. Even better results can be achieved when using external mask for urban areas, which might be misclassified as forests when using the introduced approach based on SAR data only. The Sentinel-1 data and the described methods are well suited for forest change detection between consecutive years.
INFLUENCE OF THE VIEWING GEOMETRY WITHIN HYPERSPECTRAL IMAGES RETRIEVED FROM UAV SNAPSHOT CAMERAS
Authors: Aasen Helge
Published: June 2016

Abstract. Hyperspectral data has great potential for vegetation parameter retrieval. However, due to angular effects resulting from different sun-surface-sensor geometries, objects might appear differently depending on the position of an object within the field of view of a sensor. Recently, lightweight snapshot cameras have been introduced, which capture hyperspectral information in two spatial and one spectral dimension and can be mounted on unmanned aerial vehicles. This study investigates the influence of the different viewing geometries within an image on the apparent hyperspectral reflection retrieved by these sensors. Additionally, it is evaluated how hyperspectral vegetation indices like the NDVI are effected by the angular effects within a single image and if the viewing geometry influences the apparent heterogeneity with an area of interest. The study is carried out for a barley canopy at booting stage. The results show significant influences of the position of the area of interest within the image. The red region of the spectrum is more influenced by the position than the near infrared. The ability of the NDVI to compensate these effects was limited to the capturing positions close to nadir. The apparent heterogeneity of the area of interest is the highest close to a nadir.
BIDIRECTIONAL REFLECTANCE MODELING OF THE GEOSTATIONARY SENSOR HIMAWARI-8/AHI USING A KERNEL-DRIVEN BRDF MODEL
Authors: Matsuoka M. , Takagi M. , Akatsuka S. , Honda R. , Nonomura A. , Moriya H. , Yoshioka H.
Published: June 2016

Abstract. Himawari-8/AHI is a new geostationary sensor that can observe the land surface with high temporal frequency. Bidirectional reflectance derived by the Advanced Himawari Imager (AHI) includes information regarding land surface properties such as albedo, vegetation condition, and forest structure. This information can be extracted by modeling bidirectional reflectance using a bidirectional reflectance distribution function (BRDF). In this study, a kernel-driven BRDF model was applied to the red and near infrared reflectance observed over 8 hours during daytime to express intraday changes in reflectance. We compared the goodness of fit for six combinations of model kernels. The Ross-Thin and Ross-Thick kernels were selected as the best volume kernels for the red and near infrared bands, respectively. For the geometric kernel, the Li-sparse-Reciprocal and Li-Dense kernels displayed similar goodness of fit. The coefficient of determination and regression residuals showed a strong dependency on the azimuth angle of land surface slopes and the time of day that observations were made. Atmospheric correction and model adjustment of the terrain were the main issues encountered. These results will help to improve the BRDF model and to extract surface properties from bidirectional reflectance.
OBJECT BASED AGRICULTURAL LAND COVER CLASSIFICATION MAP OF SHADOWED AREAS FROM AERIAL IMAGE AND LIDAR DATA USING SUPPORT VECTOR MACHINE
Authors: Alberto R. T. , Serrano S. C. , Damian G. B. , Camaso E. E. , Celestino A. B. , Hernando P. J. C. , Isip M. F. , Orge K. M. , Quinto M. J. C. , Tagaca R. C.
Published: June 2016

Abstract. Aerial image and LiDAR data offers a great possibility for agricultural land cover mapping. Unfortunately, these images leads to shadowy pixels. Management of shadowed areas for classification without image enhancement were investigated. Image segmentation approach using three different segmentation scales were used and tested to segment the image for ground features since only the ground features are affected by shadow caused by tall features. The RGB band and intensity were the layers used for the segmentation having an equal weights. A segmentation scale of 25 was found to be the optimal scale that will best fit for the shadowed and non-shadowed area classification. The SVM using Radial Basis Function kernel was then applied to extract classes based on properties extracted from the Lidar data and orthophoto. Training points for different classes including shadowed areas were selected homogeneously from the orthophoto. Separate training points for shadowed areas were made to create additional classes to reduced misclassification. Texture classification and object-oriented classifiers have been examined to reduced heterogeneity problem. The accuracy of the land cover classification using 25 scale segmentation after accounting for the shadow detection and classification was significantly higher compared to higher scale of segmentation.
DETECTION OF DISEASE SYMPTOMS ON HYPERSPECTRAL 3D PLANT MODELS
Authors: Roscher Ribana , Behmann Jan , Mahlein Anne-Katrin , Dupuis Jan , Kuhlmann Heiner , Plümer Lutz
Published: June 2016

Abstract. We analyze the benefit of combining hyperspectral images information with 3D geometry information for the detection of Cercospora leaf spot disease symptoms on sugar beet plants. Besides commonly used one-class Support Vector Machines, we utilize an unsupervised sparse representation-based approach with group sparsity prior. Geometry information is incorporated by representing each sample of interest with an inclination-sorted dictionary, which can be seen as an 1D topographic dictionary. We compare this approach with a sparse representation based approach without geometry information and One-Class Support Vector Machines. One-Class Support Vector Machines are applied to hyperspectral data without geometry information as well as to hyperspectral images with additional pixelwise inclination information. Our results show a gain in accuracy when using geometry information beside spectral information regardless of the used approach. However, both methods have different demands on the data when applied to new test data sets. One-Class Support Vector Machines require full inclination information on test and training data whereas the topographic dictionary approach only need spectral information for reconstruction of test data once the dictionary is build by spectra with inclination.
TOWARDS A REMOTE SENSING BASED ASSESSMENT OF LAND SUSCEPTIBILITY TO DEGRADATION: EXAMINING SEASONAL VARIATION IN LAND USE-LAND COVER FOR MODELLING LAND DEGRADATION IN A SEMI-ARID CONTEXT
Authors: Mashame Gofamodimo , Akinyemi Felicia
Published: June 2016

Abstract. Land degradation (LD) is among the major environmental and anthropogenic problems driven by land use-land cover (LULC) and climate change worldwide. For example, poor LULC practises such as deforestation, livestock overstocking, overgrazing and arable land use intensification on steep slopes disturbs the soil structure leaving the land susceptible to water erosion, a type of physical land degradation. Land degradation related problems exist in Sub-Saharan African countries such as Botswana which is semi-arid in nature. LULC and LD linkage information is still missing in many semi-arid regions worldwide.Mapping seasonal LULC is therefore very important in understanding LULC and LD linkages. This study assesses the impact of seasonal LULC variation on LD utilizing Remote Sensing (RS) techniques for Palapye region in Central District, Botswana. LULC classes for the dry and rainy seasons were classified using LANDSAT 8 images at Level I according to the Food and Agriculture Organization (FAO) International Organization of Standardization (ISO) code 19144. Level I consists of 10 LULC classes. The seasonal variations in LULC are further related to LD susceptibility in the semi-arid context. The results suggest that about 985 km² (22%) of the study area is susceptible to LD by water, major LULC types affected include: cropland, paved/rocky material, bare land, built-up area, mining area, and water body. Land degradation by water susceptibility due to seasonal land use-land cover variations is highest in the east of the study area where there is high cropland to bare land conversion.
OVERLAND FLOW ANALYSIS USING TIME SERIES OF SUAS-DERIVED ELEVATION MODELS
Authors: Jeziorska J. , Mitasova H. , Petrasova A. , Petras V. , Divakaran D. , Zajkowski T.
Published: June 2016

Abstract. With the advent of the innovative techniques for generating high temporal and spatial resolution terrain models from Unmanned Aerial Systems (UAS) imagery, it has become possible to precisely map overland flow patterns. Furthermore, the process has become more affordable and efficient through the coupling of small UAS (sUAS) that are easily deployed with Structure from Motion (SfM) algorithms that can efficiently derive 3D data from RGB imagery captured with consumer grade cameras. We propose applying the robust overland flow algorithm based on the path sampling technique for mapping flow paths in the arable land on a small test site in Raleigh, North Carolina. By comparing a time series of five flights in 2015 with the results of a simulation based on the most recent lidar derived DEM (2013), we show that the sUAS based data is suitable for overland flow predictions and has several advantages over the lidar data. The sUAS based data captures preferential flow along tillage and more accurately represents gullies. Furthermore the simulated water flow patterns over the sUAS based terrain models are consistent throughout the year. When terrain models are reconstructed only from sUAS captured RGB imagery, however, water flow modeling is only appropriate in areas with sparse or no vegetation cover.
APPLICATION OF GIS AND GROUNDWATER MODELLING TECHNIQUES TO IDENTIFY THE PERCHED AQUIFERS TO DEMARKATE WATER LOGGING CONDITIONS IN PARTS OF MEHSANA
Authors: Rawal D. , Vyas A. , Rao S. S.
Published: June 2016

Abstract. Groundwater is very important component of the hydrological cycle. It is an important source of water for drinking, domestic, industrial and agricultural uses. It plays a key role in meeting the water needs of various users sectors in India. Ground water resource is contributed by two major sources – rainfall and the other seepage from irrigation of the crops. A man-made effort through artificial recharge for water conservation structures adds to the ground water. The ground water behaviour in Indian sub-continent is highly complicated due to the occurrence of diversified geological formations with considerable lithological and chronological variations, complex tectonic framework, climatological dissimilarities and various hydro-chemical conditions. Assessment of ground water resources of an area requires proper identification and mapping of geological structures, geomorphic features along with sound information regarding slope, drainage, lithology, soil as well as thickness of the weathered zones. This study is to understand the ground water scenario in the water logged areas of Dharoi command while the surrounding areas showing continuous decline of water levels. The area falls in the command area of Dhorai dam and is in Mehsana District of Gujarat State. A part of northern command of Dharoi Command area falls in hard rock areas while the lower and southern portion fall in alluvial areas in the Dharoi Command (RBC) area in Mahsana Mehsana District of the Gujarat State. The study highlights the application of GIS in establishing the basic parameters of soil, land use and the distribution of water logging over a period of time and the groundwater modelling identifies the groundwater regime of the area and estimates the total recharge to the area due to surface water irrigation and rainfall and suggests suitable method to control water logging in the area.
TOWARDS "DRONE-BORNE" DISASTER MANAGEMENT: FUTURE APPLICATION SCENARIOS
Authors: Tanzi Tullio Joseph , Chandra Madhu , Isnard Jean , Camara Daniel , Sebastien Olivier , Harivelo Fanilo
Published: June 2016

Abstract. Information plays a key role in crisis management and relief efforts for natural disaster scenarios. Given their flight properties, UAVs (Unmanned Aerial Vehicles) provide new and interesting perspectives on the data gathering for disaster management. A new generation of UAVs may help to improve situational awareness and information assessment. Among the advantages UAVs may bring to the disaster management field, we can highlight the gain in terms of time and human resources, as they can free rescue teams from time-consuming data collection tasks and assist research operations with more insightful and precise guidance thanks to advanced sensing capabilities. However, in order to be useful, UAVs need to overcome two main challenges. The first one is to achieve a sufficient autonomy level, both in terms of navigation and interpretation of the data sensed. The second major challenge relates to the reliability of the UAV, with respect to accidental (safety) or malicious (security) risks. This paper first discusses the potential of UAV in assisting in different humanitarian relief scenarios, as well as possible issues in such situations. Based on recent experiments, we discuss the inherent advantages of autonomous flight operations, both lone flights and formation flights. The question of autonomy is then addressed and a secure embedded architecture and its specific hardware capabilities is sketched out. We finally present a typical use case based on the new detection and observation abilities that UAVs can bring to rescue teams. Although this approach still has limits that have to be addressed, technically speaking as well as operationally speaking, it seems to be a very promising one to enhance disaster management efforts activities.
AUTOMATIC BUILDING DAMAGE DETECTION METHOD USING HIGH-RESOLUTION REMOTE SENSING IMAGES AND 3D GIS MODEL
Authors: Tu Jihui , Sui Haigang , Feng Wenqing , Song Zhina
Published: June 2016

Abstract. In this paper, a novel approach of building damaged detection is proposed using high resolution remote sensing images and 3D GIS-Model data. Traditional building damage detection method considers to detect damaged building due to earthquake, but little attention has been paid to analyze various building damaged types(e.g., trivial damaged, severely damaged and totally collapsed.) Therefore, we want to detect the different building damaged type using 2D and 3D feature of scenes because the real world we live in is a 3D space. The proposed method generalizes that the image geometric correction method firstly corrects the post-disasters remote sensing image using the 3D GIS model or RPC parameters, then detects the different building damaged types using the change of the height and area between the pre- and post-disasters and the texture feature of post-disasters. The results, evaluated on a selected study site of the Beichuan earthquake ruins, Sichuan, show that this method is feasible and effective in building damage detection. It has also shown that the proposed method is easily applicable and well suited for rapid damage assessment after natural disasters.
EMPIRICAL RELATIONSHIP BETWEEN LEAF BIOMASS OF RED PINE FORESTS AND ENHANCED VEGETATION INDEX IN SOUTH KOREA USING LANDSAT-5 TM
Authors: Gusso A. , Lee J. , Son Y. , Son Y. M.
Published: June 2016

Abstract. Research on forest carbon (C) dynamics has been undertaken due to the importance of forest ecosystems in national C inventories. Currently, the C sequestration of South Korean forests surpasses that of other countries. In South Korea, Pinus densiflora (red pine) is the most abundant tree species. Thus, understanding the growth rate and biomass evolution of red pine forest in South Korea is important for estimating the forest C dynamics. In this paper, we derived empirical relationship between foliage biomass and the no blue band enhanced vegetation index (EVI-2) profile using both field work and multi-temporal Landsat-5 TM remote sensing data to estimate the productivity of forest biomass in South Korea. Our analysis combined a set of 84 Landsat-5 TM images from 28 different dates between 1986 and 2008 to study red pine forest development over time. Field data were collected from 30 plots (0.04 ha) that were irregularly distributed over South Korea. Individual trees were harvested by destructive sampling, and the age of trees were determined by the number of tree rings. The results are realistic (R2&thinsp=&thinsp0.81, p
WETLAND ASSESSMENT USING UNMANNED AERIAL VEHICLE (UAV) PHOTOGRAMMETRY
Authors: Boon M. A. , Greenfield R. , Tesfamichael S.
Published: June 2016

Abstract. The use of Unmanned Arial Vehicle (UAV) photogrammetry is a valuable tool to enhance our understanding of wetlands. Accurate planning derived from this technological advancement allows for more effective management and conservation of wetland areas. This paper presents results of a study that aimed at investigating the use of UAV photogrammetry as a tool to enhance the assessment of wetland ecosystems. The UAV images were collected during a single flight within 2½ hours over a 100 ha area at the Kameelzynkraal farm, Gauteng Province, South Africa. An AKS Y-6 MKII multi-rotor UAV and a digital camera on a motion compensated gimbal mount were utilised for the survey. Twenty ground control points (GCPs) were surveyed using a Trimble GPS to achieve geometrical precision and georeferencing accuracy. Structure-from-Motion (SfM) computer vision techniques were used to derive ultra-high resolution point clouds, orthophotos and 3D models from the multi-view photos. The geometric accuracy of the data based on the 20 GCP’s were 0.018 m for the overall, 0.0025 m for the vertical root mean squared error (RMSE) and an over all root mean square reprojection error of 0.18 pixel. The UAV products were then edited and subsequently analysed, interpreted and key attributes extracted using a selection of tools/ software applications to enhance the wetland assessment. The results exceeded our expectations and provided a valuable and accurate enhancement to the wetland delineation, classification and health assessment which even with detailed field studies would have been difficult to achieve.
CLASSICAL PHOTOGRAMMETRY AND UAV – SELECTED ASCPECTS
Authors: Mikrut S.
Published: June 2016

Abstract. The UAV technology seems to be highly future-oriented due to its low costs as compared to traditional aerial images taken from classical photogrammetry aircrafts. The AGH University of Science and Technology in Cracow - Department of Geoinformation, Photogrammetry and Environmental Remote Sensing focuses mainly on geometry and radiometry of recorded images. Various scientific research centres all over the world have been conducting the relevant research for years. The paper presents selected aspects of processing digital images made with the UAV technology. It provides on a practical example a comparison between a digital image taken from an airborne (classical) height, and the one made from an UAV level. In his research the author of the paper is trying to find an answer to the question: to what extent does the UAV technology diverge today from classical photogrammetry, and what are the advantages and disadvantages of both methods? The flight plan was made over the Tokarnia Village Museum (more than 0.5 km2) for two separate flights: the first was made by an UAV - System FT-03A built by FlyTech Solution Ltd. The second was made with the use of a classical photogrammetric Cesna aircraft furnished with an airborne photogrammetric camera (Ultra Cam Eagle). Both sets of photographs were taken with pixel size of about 3 cm, in order to have reliable data allowing for both systems to be compared. The project has made aerotriangulation independently for the two flights. The DTM was generated automatically, and the last step was the generation of an orthophoto. The geometry of images was checked under the process of aerotriangulation. To compare the accuracy of these two flights, control and check points were used. RMSE were calculated. The radiometry was checked by a visual method and using the author's own algorithm for feature extraction (to define edges with subpixel accuracy). After initial pre-processing of data, the images were put together, and shown side by side. Buildings and strips on the road were selected from whole data for the comparison of edges and details. The details on UAV images were not worse than those on classical photogrammetric ones. One might suppose that geometrically they also were correct. The results of aerotriangulation prove these facts, too. Final results from aerotriangulation were on the level of RMS = 1 pixel (about 3 cm). In general it can be said that photographs from UAVs are not worse than classic ones. In the author's opinion, geometric and radiometric qualities are at a similar level for this kind of area (a small village). This is a very significant result as regards mapping. It means that UAV data can be used in mapping production.
THE NEED OF NESTED GRIDS FOR AERIAL AND SATELLITE IMAGES AND DIGITAL ELEVATION MODELS
Authors: Villa G. , Mas S. , Fernández-Villarino X. , Martínez-Luceño J. , Ojeda J. C. , Pérez-Martín B. , Tejeiro J. A. , García-González C. , López-Romero E. , Soteres C.
Published: June 2016

Abstract. Usual workflows for production, archiving, dissemination and use of Earth observation images (both aerial and from remote sensing satellites) pose big interoperability problems, as for example: non-alignment of pixels at the different levels of the pyramids that makes it impossible to overlay, compare and mosaic different orthoimages, without resampling them and the need to apply multiple resamplings and compression-decompression cycles. These problems cause great inefficiencies in production, dissemination through web services and processing in “Big Data” environments. Most of them can be avoided, or at least greatly reduced, with the use of a common “nested grid” for mutiresolution production, archiving, dissemination and exploitation of orthoimagery, digital elevation models and other raster data. “Nested grids” are space allocation schemas that organize image footprints, pixel sizes and pixel positions at all pyramid levels, in order to achieve coherent and consistent multiresolution coverage of a whole working area. A “nested grid” must be complemented by an appropriate “tiling schema”, ideally based on the “quad-tree” concept. In the last years a “de facto standard” grid and Tiling Schema has emerged and has been adopted by virtually all major geospatial data providers. It has also been adopted by OGC in its “WMTS Simple Profile” standard. In this paper we explain how the adequate use of this tiling schema as common nested grid for orthoimagery, DEMs and other types of raster data constitutes the most practical solution to most of the interoperability problems of these types of data.
RESEARCH ON VISUAL ANALYSIS METHODS OF TERRORISM EVENTS
Authors: Guo Wenyue , Liu Haiyan , Yu Anzhu , Li Jing
Published: June 2016

Abstract. Under the situation that terrorism events occur more and more frequency throughout the world, improving the response capability of social security incidents has become an important aspect to test governments govern ability. Visual analysis has become an important method of event analysing for its advantage of intuitive and effective. To analyse events’ spatio-temporal distribution characteristics, correlations among event items and the development trend, terrorism event’s spatio-temporal characteristics are discussed. Suitable event data table structure based on “5W” theory is designed. Then, six types of visual analysis are purposed, and how to use thematic map and statistical charts to realize visual analysis on terrorism events is studied. Finally, experiments have been carried out by using the data provided by Global Terrorism Database, and the results of experiments proves the availability of the methods.
IMPLEMENTATION OF KRIGING METHODS IN MOBILE GIS TO ESTIMATE DAMAGE TO BUILDINGS IN CRISIS SCENARIOS
Authors: Laun S. , Rösch N. , Breunig M. , Al Doori M.
Published: June 2016

Abstract. In the paper an example for the application of kriging methods to estimate damage to buildings in crisis scenarios is introduced. Furthermore, the Java implementations for Ordinary and Universal Kriging on mobile GIS are presented. As variogram models an exponential, a Gaussian and a spherical variogram are tested in detail. Different test constellations are introduced with various information densities. As test data set, public data from the analysis of the 2010 Haiti earthquake by satellite images are pre-processed and visualized in a Geographic Information System. As buildings, topography and other external influences cannot be seen as being constant for the whole area under investigation, semi variograms are calculated by consulting neighboured classified buildings using the so called moving window method. The evaluation of the methods shows that the underlying variogram model is the determining factor for the quality of the interpolation rather than the choice of the kriging method or increasing the information density of a random sample. The implementation is completely realized with the programming language Java. Thereafter, the implemented software component is integrated into GeoTech Mobile, a mobile GIS Android application based on the processing of standardized spatial data representations defined by the Open Geospatial Consortium (OGC). As a result the implemented methods can be used on mobile devices, i.e. they may be transferred to other application fields. That is why we finally point out further research with new applications in the Dubai region.
CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL
Authors: Yang X. , Tang L.
Published: June 2016

Abstract. GPS traces collected via crowdsourcing way are low-cost and informative and being as a kind of new big data source for urban geographic information extraction. However, the precision of crowdsourcing traces in urban area is very low because of low-end GPS data devices and urban canyons with tall buildings, thus making it difficult to mine high-precision geographic information such as lane-level road information. In this paper, we propose an efficient partition-and-filter model to filter trajectories, which includes trajectory partitioning and trajectory filtering. For the partition part, the partition with position and angle constrain algorithm is used to partition a trajectory into a set of sub-trajectories based on distance and angle constrains. Then, the trajectory filtering with expected accuracy method is used to filter the sub-trajectories according to the similarity between GPS tracking points and GPS baselines constructed by random sample consensus algorithm. Experimental results demonstrate that the proposed partition-and-filtering model can effectively filter the high quality GPS data from various crowdsourcing trace data sets with the expected accuracy.
STUDY ON MODELING AND VISUALIZING THE POSITIONAL UNCERTAINTY OF REMOTE SENSING IMAGE
Authors: Jiao Weili , Long Tengfei , Ling Saiguang , He Guojin
Published: June 2016

Abstract. It is inevitable to bring about uncertainty during the process of data acquisition. The traditional method to evaluate the geometric positioning accuracy is usually by the statistical method and represented by the root mean square errors (RMSEs) of control points. It is individual and discontinuous, so it is difficult to describe the error spatial distribution. In this paper the error uncertainty of each control point is deduced, and the uncertainty spatial distribution model of each arbitrary point is established. The error model is proposed to evaluate the geometric accuracy of remote sensing image. Then several visualization methods are studied to represent the discrete and continuous data of geometric uncertainties. The experiments show that the proposed evaluation method of error distribution model compared with the traditional method of RMSEs can get the similar results but without requiring the user to collect control points as checkpoints, and error distribution information calculated by the model can be provided to users along with the geometric image data. Additionally, the visualization methods described in this paper can effectively and objectively represents the image geometric quality, and also can help users probe the reasons of bringing the image uncertainties in some extent.
WILL IT BLEND? VISUALIZATION AND ACCURACY EVALUATION OF HIGH-RESOLUTION FUZZY VEGETATION MAPS
Authors: Zlinszky A. , Kania A.
Published: June 2016

Abstract. Instead of assigning every map pixel to a single class, fuzzy classification includes information on the class assigned to each pixel but also the certainty of this class and the alternative possible classes based on fuzzy set theory. The advantages of fuzzy classification for vegetation mapping are well recognized, but the accuracy and uncertainty of fuzzy maps cannot be directly quantified with indices developed for hard-boundary categorizations. The rich information in such a map is impossible to convey with a single map product or accuracy figure. Here we introduce a suite of evaluation indices and visualization products for fuzzy maps generated with ensemble classifiers. We also propose a way of evaluating classwise prediction certainty with “dominance profiles” visualizing the number of pixels in bins according to the probability of the dominant class, also showing the probability of all the other classes. Together, these data products allow a quantitative understanding of the rich information in a fuzzy raster map both for individual classes and in terms of variability in space, and also establish the connection between spatially explicit class certainty and traditional accuracy metrics. These map products are directly comparable to widely used hard boundary evaluation procedures, support active learning-based iterative classification and can be applied for operational use.
LATERAL FLOODING ASSOCIATED TO WAVE FLOOD GENERATION ON RIVER SURFACE
Authors: Ramírez-Núñez C. , Parrot J.-F.
Published: June 2016

Abstract. This research provides a wave flood simulation using a high resolution LiDAR Digital Terrain Model. The simulation is based on the generation of waves of different amplitudes that modify the river level in such a way that water invades the adjacent areas. The proposed algorithm firstly reconstitutes the original river surface of the studied river section and then defines the percentage of water loss when the wave floods move downstream. This procedure was applied to a gently slope area in the lower basin of Coatzacoalcos river, Veracruz (Mexico) defining the successive areas where lateral flooding occurs on its downstream movement.
3D NEAREST NEIGHBOUR SEARCH USING A CLUSTERED HIERARCHICAL TREE STRUCTURE
Authors: Suhaibah A. , Uznir U. , Anton F. , Mioc D. , Rahman A. A.
Published: June 2016

Abstract. Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D) method is prominently required in order to locate and identify the surrounding information such as at which level of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN) analysis. It uses a point location and identifies the surrounding neighbours. However, with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results are presented in this paper. Another advantage of this structure is that it also offers a minimal overlap and coverage among nodes which can reduce repetitive data entry.