bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatial distribution of the persistent organic pollutants across the Tibetan Plateau and its linkage with the climate systems: a 5-year air monitoring study
Authors: Wang Xiaoping , Ren Jiao , Gong Ping , Wang Chuanfei , Xue Yonggang , Yao Tandong , Lohmann Rainer
Published: June 2016

Abstract. The Tibetan Plateau (TP) has been contaminated by persistent organic pollutants (POPs), including legacy organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) through atmospheric transport. The exact source regions, transport pathways and time trends of POPs to the TP are not well understood. Here polystyrene–divinylbenzene copolymer resin (XAD)-based passive air samplers (PASs) were deployed at 16 Tibetan background sites from 2007 to 2012 to gain further insight into spatial patterns and temporal trends of OCPs and PCBs. The southeastern TP was characterized by dichlorodiphenyltrichloroethane (DDT)-related chemicals delivered by Indian monsoon air masses. The northern and northwestern TP displayed the greatest absolute concentration and relative abundance of hexachlorobenzene (HCB) in the atmosphere, caused by the westerly-driven European air masses. The interactions between the DDT polluted Indian monsoon air and the clean westerly winds formed a transition zone in central Tibet, where both DDT and HCB were the dominant chemicals. Based on 5 years of continuous sampling, our data indicated declining concentrations of HCB and hexachlorocyclohexanes (HCHs) across the Tibetan region. Inter-annual trends of DDT class chemicals, however, showed less variation during this 5-year sampling period, which may be due to the ongoing usage of DDT in India. This paper demonstrates the possibility of using POP fingerprints to investigate the climate interactions and the validity of using PAS to derive inter-annual atmospheric POP time trends.
Global distribution of CO2 in the Upper-Troposphere and Stratosphere
Authors: Diallo M. , Legras B. , Ray E. , Engel A. , Añel J. A.
Published: June 2016

Abstract. In this study, we aim to reconstruct a relevant and new database of monthly zonal mean distribution of carbon dioxide (CO2) at global scale extending from the upper-troposphere (UT) to stratosphere (S). This product can be used for model and satellite validation in the UT/S, as a prior for inversion modelling and mainly to analyse a plausible feature of the stratospheric-tropospheric exchange as well as the stratospheric circulation and its variability. To do so, we investigate the ability of a Lagrangian trajectory model guided by ERA-Interim reanalysis to construct the CO2 abundance in the UT/S. From 10 year backward trajectories and tropospheric observations of CO2, we reconstruct upper-tropospheric and stratospheric CO2 over the period 2000–2010. The inter-comparisons of the reconstructed CO2 with mid-latitude vertical profiles measured by balloon samples as well as quasi-horizontal air samples from ER-2 aircraft during SOLVE and CONTRAIL campaigns exhibit a remarkable agreement. That demonstrates the potential of Lagrangian model to reconstruct CO2 in the UT/S. The zonal mean distribution exhibits relatively large CO2 in the tropical stratosphere due to the seasonal variation of the tropical upwelling of Brewer-Dobson circulation. During winter and spring, the tropical pipe is relatively isolated but is less narrow during summer and autumn so that high CO2 values are more readily transported out of the tropics to the mid- and high latitude stratosphere. The shape of the vertical profiles suggests that relatively high CO2 above 20 km altitude mainly enter the stratosphere through tropical upwelling. CO2 mixing ratio is relatively low in the polar and tropical regions above 25 km. On average the CO2 mixing ratio decreases with altitude by 6–8 ppmv from the UT to stratosphere (e.g. up to 35 km) and is nearly constant with altitude.
Review of the state-of-the-art and future prospects of the ground-based GNSS meteorology in Europe
Authors: Guerova Guergana , Jones Jonathan , Dousa Jan , Dick Galina , de Haan Siebren , Pottiaux Eric , Bock Olivier , Pacione Rosa , Elgered Gunnar , Vedel Henrik , Bender Michael
Published: June 2016

Abstract. Global Navigation Satellite Systems (GNSS) have revolutionised positioning, navigation, and timing, becoming a common part of our everyday life. Aside from these well-known civilian and commercial applications, GNSS is now an established atmospheric observing system, which can accurately sense water vapour, the most abundant greenhouse gas, accounting for 60–70 % of atmospheric warming. In Europe, the application of GNSS in meteorology started roughly two decades ago and today it is a well-established research field. This review covers the state-of-the-art in GNSS meteorology in Europe. Discussed are the advances in GNSS processing for derivation of tropospheric products, application of GNSS tropospheric products in operational weather prediction and application of GNSS tropospheric products for climate monitoring. Reviewed are the GNSS processing techniques and tropospheric products. Given is a summary of the use of the products for validation and impact studies with operational Numerical Weather Prediction (NWP) models as well as very short weather prediction (nowcasting) case studies. Climate research with GNSS is an emerging field of research, the studies so far have been limited to comparison with the climate models and derivation of trends. More than 15 years of GNSS meteorology in Europe has already achieved outstanding cooperation between the atmospheric and geodetic communities. It is now feasible to develop next-generation GNSS tropospheric products and applications that can enhance the quality of weather forecasts and climate monitoring. This work is carried out within COST Action ES1206 "Advanced Global Navigation Satellite Systems tropospheric products for monitoring Severe Weather Events and Climate" (GNSS4SWEC, http://gnss4swec.knmi.nl ).
Modelling interannual variation in the spring and autumn land surface phenology of the European forest
Authors: Rodriguez-Galiano Victor F. , Sanchez-Castillo Manuel , Dash Jadunandan , Atkinson Peter M. , Ojeda-Zujar Jose
Published: June 2016

Abstract. This research reveals new insights into the weather drivers of interannual variation in land surface phenology (LSP) across the entire European forest, while at the same time establishes a new conceptual framework for predictive modelling of LSP. Specifically, the random-forest (RF) method, a multivariate, spatially non-stationary and non-linear machine learning approach, was introduced for phenological modelling across very large areas and across multiple years simultaneously: the typical case for satellite-observed LSP. The RF model was fitted to the relation between LSP interannual variation and numerous climate predictor variables computed at biologically relevant rather than human-imposed temporal scales. In addition, the legacy effect of an advanced or delayed spring on autumn phenology was explored. The RF models explained 81 and 62 % of the variance in the spring and autumn LSP interannual variation, with relative errors of 10 and 20 %, respectively: a level of precision that has until now been unobtainable at the continental scale. Multivariate linear regression models explained only 36 and 25 %, respectively. It also allowed identification of the main drivers of the interannual variation in LSP through its estimation of variable importance. This research, thus, shows an alternative to the hitherto applied linear regression approaches for modelling LSP and paves the way for further scientific investigation based on machine learning methods.
Oxidation of sulphides and rapid weathering in recent landslides
Authors: Emberson Robert , Hovius Niels , Galy Albert , Marc Odin
Published: June 2016

Abstract. Linking together the processes of rapid physical erosion to the resultant chemical dissolution of rock is a crucial step in building an overall deterministic understanding of weathering in mountain belts. Landslides, which are the most volumetrically important geomorphic process at these high rates of erosion, can generate extremely high rates of very localised weathering. To elucidate how this process works we have taken advantage of uniquely intense landsliding, resulting from Typhoon Morakot, in the Taimali river and surrounds in Southern Taiwan. Combining detailed analysis of seepage chemistry with estimates of catchment-by-catchment landslide volumes, we demonstrate that in this setting the primary role of landslides is to introduce fresh, highly labile mineral phases into the surface weathering environment. There, rapid weathering is driven by the oxidation of pyrite and the resultant sulphuric acid-driven dissolution of primarily carbonate rock. The total dissolved load correlates well with dissolved sulphate – the chief product of this style of weathering – in both landslides and streams draining the area (R2 = 0.841 and 0.929 respectively, p < 0.001 in both cases), with solute chemistry in seepage from landslides and catchments affected by significant landsliding governed by the same weathering reactions. Bedrock landslides create conditions for weathering where all mineral phase in a lithology are initially unweathered within landslide deposits, and therefore the most labile phases dominate the weathering at the outset and during a transient period of depletion. This mode of dissolution can strongly alter the overall output of solutes from catchments and their contribution to global chemical cycles if landslide-derived material is retained in catchments for extended periods after mass wasting.
Dynamic changes in terrestrial net primary production and their effects on evapotranspiration
Authors: Li Zhi , Chen Yaning , Wang Yang , Fang Gonghuan
Published: June 2016

Abstract. The dramatic increase of global temperature since the year 2000 has a considerable impact on the global water cycle and vegetation dynamics. Little has been done about recent feedback of vegetation to climate in different parts of the world, and land evapotranspiration (ET) is the means of this feedback. Here we used the global 1 km MODIS net primary production (NPP) and ET data sets (2000–2014) to investigate their temporospatial changes under the context of global warming. The results showed that global NPP slightly increased in 2000–2014 at a rate of 0.06 PgC yr−2. More than 64 % of vegetated land in the Northern Hemisphere (NH) showed increased NPP (at a rate of 0.13 PgC yr−2), while 60.3 % of vegetated land in the Southern Hemisphere (SH) showed a decreasing trend (at a rate of −0.18 PgC yr−2). Vegetation greening and climate change promote rises of global ET. Specially, the increased rate of land ET in the NH (0.61 mm yr−2) is faster than that in the SH (0.41 mm yr−2). Over the same period, global warming and vegetation greening accelerate evaporation in soil moisture, thus reducing the amount of soil water storage. Continuation of these trends will likely exacerbate regional drought-induced disturbances and point to an increased risk of ecological drought, especially during regional dry climate phases.
CLASSIFICATION OF INFORMAL SETTLEMENTS THROUGH THE INTEGRATION OF 2D AND 3D FEATURES EXTRACTED FROM UAV DATA
Authors: Gevaert C. M. , Persello C. , Sliuzas R. , Vosselman G.
Published: June 2016

Abstract. Unmanned Aerial Vehicles (UAVs) are capable of providing very high resolution and up-to-date information to support informal settlement upgrading projects. In order to provide accurate basemaps, urban scene understanding through the identification and classification of buildings and terrain is imperative. However, common characteristics of informal settlements such as small, irregular buildings with heterogeneous roof material and large presence of clutter challenge state-of-the-art algorithms. Especially the dense buildings and steeply sloped terrain cause difficulties in identifying elevated objects. This work investigates how 2D radiometric and textural features, 2.5D topographic features, and 3D geometric features obtained from UAV imagery can be integrated to obtain a high classification accuracy in challenging classification problems for the analysis of informal settlements. It compares the utility of pixel-based and segment-based features obtained from an orthomosaic and DSM with point-based and segment-based features extracted from the point cloud to classify an unplanned settlement in Kigali, Rwanda. Findings show that the integration of 2D and 3D features leads to higher classification accuracies.
ENHANCEMENT OF GENERIC BUILDING MODELS BY RECOGNITION AND ENFORCEMENT OF GEOMETRIC CONSTRAINTS
Authors: Meidow J. , Hammer H. , Pohl M. , Bulatov D.
Published: June 2016

Abstract. Many buildings in 3D city models can be represented by generic models, e.g. boundary representations or polyhedrons, without expressing building-specific knowledge explicitly. Without additional constraints, the bounding faces of these building reconstructions do not feature expected structures such as orthogonality or parallelism. The recognition and enforcement of man-made structures within model instances is one way to enhance 3D city models. Since the reconstructions are derived from uncertain and imprecise data, crisp relations such as orthogonality or parallelism are rarely satisfied exactly. Furthermore, the uncertainty of geometric entities is usually not specified in 3D city models. Therefore, we propose a point sampling which simulates the initial point cloud acquisition by airborne laser scanning and provides estimates for the uncertainties. We present a complete workflow for recognition and enforcement of man-made structures in a given boundary representation. The recognition is performed by hypothesis testing and the enforcement of the detected constraints by a global adjustment of all bounding faces. Since the adjustment changes not only the geometry but also the topology of faces, we obtain improved building models which feature regular structures and a potentially reduced complexity. The feasibility and the usability of the approach are demonstrated with a real data set.
A GLOBAL SOLUTION TO TOPOLOGICAL RECONSTRUCTION OF BUILDING ROOF MODELS FROM AIRBORNE LIDAR POINT CLOUDS
Authors: Yan Jixing , Jiang Wanshou , Shan Jie
Published: June 2016

Abstract. This paper presents a global solution to building roof topological reconstruction from LiDAR point clouds. Starting with segmented roof planes from building LiDAR points, a BSP (binary space partitioning) algorithm is used to partition the bounding box of the building into volumetric cells, whose geometric features and their topology are simultaneously determined. To resolve the inside/outside labelling problem of cells, a global energy function considering surface visibility and spatial regularization between adjacent cells is constructed and minimized via graph cuts. As a result, the cells are labelled as either inside or outside, where the planar surfaces between the inside and outside form the reconstructed building model. Two LiDAR data sets of Yangjiang (China) and Wuhan University (China) are used in the study. Experimental results show that the completeness of reconstructed roof planes is 87.5%. Comparing with existing data-driven approaches, the proposed approach is global. Roof faces and edges as well as their topology can be determined at one time via minimization of an energy function. Besides, this approach is robust to partial absence of roof planes and tends to reconstruct roof models with visibility-consistent surfaces.
PALM TREE DETECTION USING CIRCULAR AUTOCORRELATION OF POLAR SHAPE MATRIX
Authors: Manandhar A. , Hoegner L. , Stilla U.
Published: June 2016

Abstract. Palm trees play an important role as they are widely used in a variety of products including oil and bio-fuel. Increasing demand and growing cultivation have created a necessity in planned farming and the monitoring different aspects like inventory keeping, health, size etc. The large cultivation regions of palm trees motivate the use of remote sensing to produce such data. This study proposes an object detection methodology on the aerial images, using shape feature for detecting and counting palm trees, which can support an inventory. The study uses circular autocorrelation of the polar shape matrix representation of an image, as the shape feature, and the linear support vector machine to standardize and reduce dimensions of the feature. Finally, the study uses local maximum detection algorithm on the spatial distribution of standardized feature to detect palm trees. The method was applied to 8 images chosen from different tough scenarios and it performed on average with an accuracy of 84% and 76.1%, despite being subjected to different challenging conditions in the chosen test images.
A SIMULATION TOOL ASSISTING THE DESIGN OF A CLOSE RANGE PHOTOGRAMMETRY SYSTEM FOR THE SARDINIA RADIO TELESCOPE
Authors: Buffa F. , Pinna A. , Sanna G.
Published: June 2016

Abstract. The Sardinia Radio Telescope (SRT) is a 64 m diameter antenna, whose primary mirror is equipped with an active surface capable to correct its deformations by means of a thick network of actuators. Close range photogrammetry (CRP) was used to measure the self-load deformations of the SRT primary reflector from its optimal shape, which are requested to be minimized for the radio telescope to operate at full efficiency. In the attempt to achieve such performance, we conceived a near real-time CRP system which requires the cameras to be installed in fixed positions and at the same time to avoid any interference with the antenna operativeness. The design of such system is not a trivial task, and to assist our decision we therefore developed a simulation pipeline to realistically reproduce and evaluate photogrammetric surveys of large structures. The described simulation environment consists of (i) a detailed description of the SRT model, included the measurement points and the camera parameters, (ii) a tool capable of generating realistic images accordingly to the above model, and (iii) a self-calibrating bundle adjustment to evaluate the performance in terms of RMSE of the camera configurations.
REVIEW OF ADVANCES IN COBB ANGLE CALCULATION AND IMAGE-BASED MODELLING TECHNIQUES FOR SPINAL DEFORMITIES
Authors: Giannoglou V. , Stylianidis E.
Published: June 2016

Abstract. Scoliosis is a 3D deformity of the human spinal column that is caused from the bending of the latter, causing pain, aesthetic and respiratory problems. This internal deformation is reflected in the outer shape of the human back. The golden standard for diagnosis and monitoring of scoliosis is the Cobb angle, which refers to the internal curvature of the trunk. This work is the first part of a post-doctoral research, presenting the most important researches that have been done in the field of scoliosis, concerning its digital visualisation, in order to provide a more precise and robust identification and monitoring of scoliosis. The research is divided in four fields, namely, the X-ray processing, the automatic Cobb angle(s) calculation, the 3D modelling of the spine that provides a more accurate representation of the trunk and the reduction of X-ray radiation exposure throughout the monitoring of scoliosis. Despite the fact that many researchers have been working on the field for the last decade at least, there is no reliable and universal tool to automatically calculate the Cobb angle(s) and successfully perform proper 3D modelling of the spinal column that would assist a more accurate detection and monitoring of scoliosis.
COMBINING PUBLIC DOMAIN AND PROFESSIONAL PANORAMIC IMAGERY FOR THE ACCURATE AND DENSE 3D RECONSTRUCTION OF THE DESTROYED BEL TEMPLE IN PALMYRA
Authors: Wahbeh W. , Nebiker S. , Fangi G.
Published: June 2016

Abstract. This paper exploits the potential of dense multi-image 3d reconstruction of destroyed cultural heritage monuments by either using public domain touristic imagery only or by combining the public domain imagery with professional panoramic imagery. The focus of our work is placed on the reconstruction of the temple of Bel, one of the Syrian heritage monuments, which was destroyed in September 2015 by the so called "Islamic State". The great temple of Bel is considered as one of the most important religious buildings of the 1st century AD in the East with a unique design. The investigations and the reconstruction were carried out using two types of imagery. The first are freely available generic touristic photos collected from the web. The second are panoramic images captured in 2010 for documenting those monuments. In the paper we present a 3d reconstruction workflow for both types of imagery using state-of-the art dense image matching software, addressing the non-trivial challenges of combining uncalibrated public domain imagery with panoramic images with very wide base-lines. We subsequently investigate the aspects of accuracy and completeness obtainable from the public domain touristic images alone and from the combination with spherical panoramas. We furthermore discuss the challenges of co-registering the weakly connected 3d point cloud fragments resulting from the limited coverage of the touristic photos. We then describe an approach using spherical photogrammetry as a virtual topographic survey allowing the co-registration of a detailed and accurate single 3d model of the temple interior and exterior.
COARSE POINT CLOUD REGISTRATION BY EGI MATCHING OF VOXEL CLUSTERS
Authors: Wang Jinhu , Lindenbergh Roderik , Shen Yueqian , Menenti Massimo
Published: June 2016

Abstract. Laser scanning samples the surface geometry of objects efficiently and records versatile information as point clouds. However, often more scans are required to fully cover a scene. Therefore, a registration step is required that transforms the different scans into a common coordinate system. The registration of point clouds is usually conducted in two steps, i.e. coarse registration followed by fine registration. In this study an automatic marker-free coarse registration method for pair-wise scans is presented. First the two input point clouds are re-sampled as voxels and dimensionality features of the voxels are determined by principal component analysis (PCA). Then voxel cells with the same dimensionality are clustered. Next, the Extended Gaussian Image (EGI) descriptor of those voxel clusters are constructed using significant eigenvectors of each voxel in the cluster. Correspondences between clusters in source and target data are obtained according to the similarity between their EGI descriptors. The random sampling consensus (RANSAC) algorithm is employed to remove outlying correspondences until a coarse alignment is obtained. If necessary, a fine registration is performed in a final step. This new method is illustrated on scan data sampling two indoor scenarios. The results of the tests are evaluated by computing the point to point distance between the two input point clouds. The presented two tests resulted in mean distances of 7.6 mm and 9.5 mm respectively, which are adequate for fine registration.
RESEARCH AND TECHNOLOGY DEVELOPMENT FOR CONSTRUCTION OF 3D VIDEO SCENES
Authors: Khlebnikova Tatyana A.
Published: June 2016

Abstract. For the last two decades surface information in the form of conventional digital and analogue topographic maps has been being supplemented by new digital geospatial products, also known as 3D models of real objects. It is shown that currently there are no defined standards for 3D scenes construction technologies that could be used by Russian surveying and cartographic enterprises. The issues regarding source data requirements, their capture and transferring to create 3D scenes have not been defined yet. The accuracy issues for 3D video scenes used for measuring purposes can hardly ever be found in publications. Practicability of development, research and implementation of technology for construction of 3D video scenes is substantiated by 3D video scene capability to expand the field of data analysis application for environmental monitoring, urban planning, and managerial decision problems. The technology for construction of 3D video scenes with regard to the specified metric requirements is offered. Technique and methodological background are recommended for this technology used to construct 3D video scenes based on DTM, which were created by satellite and aerial survey data. The results of accuracy estimation of 3D video scenes are presented.
NOVEL APPROACH FOR ESTIMATING NITROGEN CONTENT IN PADDY FIELDS USING LOW ALTITUDE REMOTE SENSING SYSTEM
Authors: Saberioon M. M. , Gholizadeh A.
Published: June 2016

Abstract. Concerns over the use of nitrogen have been increasing due to the high cost of fertilizers and environmental pollutions caused by excess nitrogen application in agricultural fields. Several methods are available to assess the amount of nitrogen in crops, however, they are expensive, time-consuming, inaccurate, and/or require specialists to operate the tools. Researcher recently suggested remote sensing and specifically Low Altitude Remote Sensing (LARS) system of chlorophyll content in crop canopies as a low-cost alternative to estimate plant nitrogen status. The main objective of this study was to develop and test a new Vegetation Index (VI) to determine the status of nitrogen and chlorophyll content in rice leaf by analysing and considering all Visible (Vis) bands. Besides, capability of introduced VI has compared with all known VIs in both Vis and Near Infrared (NIR) bands in canopy scale. To develop the VI, images from 6-pannel leaf colour chart were acquired using Basler Scout scA640-70fc under light-emitting diode lighting, in which principal component analysis was used to retain the lower order principal component to develop a new index called IPCA. A conventional digital camera mounted to an Unmanned Aerial Vehicle (UAV) was also used to acquire images over the rice canopy in Vis bands. Simultaneously, Tetracam agriculture digital camera was employed to acquire rice canopy image in Vis-NIR bands. The results indicated that the proposed index at canopy (r = 0.78) scale could be used as a sensor to determine the status of chlorophyll content consequently for monitoring nitrogen in rice plant through different growth stages. Moreover, results confirmed that a lowcost LARS system would be suited for high spatial and temporal resolution images and data analysis for proper assessment of key nutrients in crop farming in a fast, inexpensive and non-destructive way.
DESIGN AND IMPLEMENTATION OF A LOW-COST UAV-BASED MULTI-SENSOR PAYLOAD FOR RAPID-RESPONSE MAPPING APPLICATIONS
Authors: Sakr M. , Lari Z. , El-Sheimy N.
Published: June 2016

Abstract. The main objective of this paper is to investigate the potential of using Unmanned Aerial Vehicles (UAVs) as a platform to collect geospatial data for rapid response applications, especially in hard-to-access and hazardous areas. The UAVs are low-cost mapping vehicles, and they are easy to handle and deploy in-field. These characteristics make UAVs ideal candidates for rapid-response and disaster mitigation scenarios. The majority of the available UAV systems are not capable of real-time/near real-time data processing. This paper introduces a low-cost UAV-based multi-sensor mapping payload which supports real-time processing and can be effectively used in rapid-response applications. The paper introduces the main components of the system, and provides an overview of the proposed payload architecture. Then, it introduces the implementation details of the major building blocks of the system. Finally, the paper presents our conclusions and the future work, in order to achieve real-time/near real-time data processing and product delivery capabilities.
UAV MULTISPECTRAL SURVEY TO MAP SOIL AND CROP FOR PRECISION FARMING APPLICATIONS
Authors: Sona Giovanna , Passoni Daniele , Pinto Livio , Pagliari Diana , Masseroni Daniele , Ortuani Bianca , Facchi Arianna
Published: June 2016

Abstract. New sensors mounted on UAV and optimal procedures for survey, data acquisition and analysis are continuously developed and tested for applications in precision farming. Procedures to integrate multispectral aerial data about soil and crop and ground-based proximal geophysical data are a recent research topic aimed to delineate homogeneous zones for the management of agricultural inputs (i.e., water, nutrients). Multispectral and multitemporal orthomosaics were produced over a test field (a 100 m x 200 m plot within a maize field), to map vegetation and soil indices, as well as crop heights, with suitable ground resolution. UAV flights were performed in two moments during the crop season, before sowing on bare soil, and just before flowering when maize was nearly at the maximum height. Two cameras, for color (RGB) and false color (NIR-RG) images, were used. The images were processed in Agisoft Photoscan to produce Digital Surface Model (DSM) of bare soil and crop, and multispectral orthophotos. To overcome some difficulties in the automatic searching of matching points for the block adjustment of the crop image, also the scientific software developed by Politecnico of Milan was used to enhance images orientation. Surveys and image processing are described, as well as results about classification of multispectral-multitemporal orthophotos and soil indices.
ARCHAEOLOGICAL DOCUMENTATION OF A DEFUNCT IRAQI TOWN
Authors: Šedina J. , Pavelka K. , Housarová E.
Published: June 2016

Abstract. The subject of this article is the possibilities of the documentation of a defunct town from the Pre-Islamic period to Early Islamic period. This town is located near the town Makhmur in Iraq. The Czech archaeological mission has worked at this dig site. This Cultural Heritage site is threatened by war because in the vicinity are positions of ISIS. For security reasons, the applicability of Pleiades satellite data has been tested. Moreover, this area is a no-fly zone. However, the DTM created from stereo-images was insufficient for the desired application in archeology. The subject of this paper is the testing of the usability of RPAS technology and terrestrial photogrammetry for documentation of the remains of buildings. RPAS is a very fast growing technology that combines the advantages of aerial photogrammetry and terrestrial photogrammetry. A probably defunct church is a sample object.
UAV ONBOARD GPS IN POSITIONING DETERMINATION
Authors: Tahar K. N. , Kamarudin S. S.
Published: June 2016

Abstract. The establishment of ground control points is a critical issue in mapping field, especially for large scale mapping. The fast and rapid technique for ground control point’s establishment is very important for small budget projects. UAV onboard GPS has the ability to determine the point positioning. The objective of this research is to assess the accuracy of unmanned aerial vehicle onboard global positioning system in positioning determination. Therefore, this research used UAV onboard GPS as an alternative to determine the point positioning at the selected area. UAV is one of the powerful tools for data acquisition and it is used in many applications all over the world. This research concentrates on the error contributed from the UAV onboard GPS during observation. There are several points that have been used to study the pattern of positioning error. All errors were analyzed in world geodetic system 84- coordinate system, which is the basic coordinate system used by the global positioning system. Based on this research, the result of UAV onboard GPS positioning could be used in ground control point establishment with the specific error. In conclusion, accurate GCP establishment could be achieved using UAV onboard GPS by applying a specific correction based on this research.
NON-DESTRUCTIVE MONITORING OF RICE BY HYPERSPECTRAL IN-FIELD SPECTROMETRY AND UAV-BASED REMOTE SENSING: CASE STUDY OF FIELD-GROWN RICE IN NORTH RHINE-WESTPHALIA, GERMANY
Authors: Willkomm M. , Bolten A. , Bareth G.
Published: June 2016

Abstract. In the context of an increasing world population, the demand for agricultural crops is continuously rising. Especially rice plays a key role in food security, not only in Asia. To increase crop production of rice, either productivity of plants has to be improved or new cultivation areas have to be found. In this context, our study investigated crop growth of paddy rice (Oryza Sativa J.) in Germany. An experimental field in the vegetation period of 2014 with two nitrogen treatments was conducted using remote sensing methods. The research project focussed on two main aspects: (1) the potential of UAV-based and hyperspectral remote sensing methods to monitor selected growth parameters at different phenological stages; (2) the potential of paddy rice cultivation under the present climate condition in western Germany. We applied a low-cost UAV-system (Unmanned Aerial Vehicle) to generate high resolution Crop Surface Models (CSM). These were compared with hyperspectral in-field measurements and directly measured agronomic parameters (fresh and dry aboveground biomass (AGB), leaf-area-index (LAI) and plant nitrogen concentration (PNC)). For all acquisition dates we could determine single in-field structures in the CSM (e.g. distribution of hills) and different growth characteristics between the nitrogen treatments. Especially in the second half of the growing season, the plants with higher nitrogen availability were about 25 – 30 % larger. The plant height in the CSM correlates particularly with fresh AGB and the LAI (R2 > 0.8). Thus, the conducted methods for plant growth monitoring can be a contribution for precision agriculture approaches.
A WORKFLOW FOR UAV’s INTEGRATION INTO A GEODESIGN PLATFORM
Authors: Anca P. , Calugaru A. , Alixandroae I. , Nazarie R.
Published: June 2016

Abstract. This paper presents a workflow for the development of various Geodesign scenarios. The subject is important in the context of identifying patterns and designing solutions for a Smart City with optimized public transportation, efficient buildings, efficient utilities, recreational facilities a.s.o.. The workflow describes the procedures starting with acquiring data in the field, data processing, orthophoto generation, DTM generation, integration into a GIS platform and analyzing for a better support for Geodesign. Esri’s City Engine is used mostly for 3D modeling capabilities that enable the user to obtain 3D realistic models. The workflow uses as inputs information extracted from images acquired using UAVs technologies, namely eBee, existing 2D GIS geodatabases, and a set of CGA rules. The method that we used further, is called procedural modeling, and uses rules in order to extrude buildings, the street network, parcel zoning and side details, based on the initial attributes from the geodatabase. The resulted products are various scenarios for redesigning, for analyzing new exploitation sites. Finally, these scenarios can be published as interactive web scenes for internal, groups or pubic consultation. In this way, problems like the impact of new constructions being build, re-arranging green spaces or changing routes for public transportation, etc. are revealed through impact and visibility analysis or shadowing analysis and are brought to the citizen’s attention. This leads to better decisions.
FROM UAS DATA ACQUISITION TO ACTIONABLE INFORMATION – HOW AN END-TO-END SOLUTION HELPS OIL PALM PLANTATION OPERATORS TO PERFORM A MORE SUSTAINABLE PLANTATION MANAGEMENT
Authors: Hoffmann C. , Weise C. , Koch T. , Pauly K.
Published: June 2016

Abstract. Palm oil represents the most efficient oilseed crop in the world but the production of palm oil involves plantation operations in one of the most fragile environments - the tropical lowlands. Deforestation, the drying-out of swampy lowlands and chemical fertilizers lead to environmental problems that are putting pressure on this industry. Unmanned aircraft systems (UAS) together with latest photogrammetric processing and image analysis capabilities represent an emerging technology that was identified to be suitable to optimize oil palm plantation operations. This paper focuses on two key elements of a UAS-based oil palm monitoring system. The first is the accuracy of the acquired data that is necessary to achieve meaningful results in later analysis steps. High performance GNSS technology was utilized to achieve those accuracies while decreasing the demand for cost-intensive GCP measurements. The second key topic is the analysis of the resulting data in order to optimize plantation operations. By automatically extracting information on a block level as well as on a single-tree level, operators can utilize the developed application to increase their productivity. The research results describe how operators can successfully make use of a UAS-based solution together with the developed software solution to improve their efficiency in oil palm plantation management.
QUALITY ANALYSIS ON 3D BUIDLING MODELS RECONSTRUCTED FROM UAV IMAGERY
Authors: Jarzabek-Rychard M. , Karpina M.
Published: June 2016

Abstract. Recent developments in UAV technology and structure from motion techniques have effected that UAVs are becoming standard platforms for 3D data collection. Because of their flexibility and ability to reach inaccessible urban parts, drones appear as optimal solution for urban applications. Building reconstruction from the data collected with UAV has the important potential to reduce labour cost for fast update of already reconstructed 3D cities. However, especially for updating of existing scenes derived from different sensors (e.g. airborne laser scanning), a proper quality assessment is necessary. The objective of this paper is thus to evaluate the potential of UAV imagery as an information source for automatic 3D building modeling at LOD2. The investigation process is conducted threefold: (1) comparing generated SfM point cloud to ALS data; (2) computing internal consistency measures of the reconstruction process; (3) analysing the deviation of Check Points identified on building roofs and measured with a tacheometer. In order to gain deep insight in the modeling performance, various quality indicators are computed and analysed. The assessment performed according to the ground truth shows that the building models acquired with UAV-photogrammetry have the accuracy of less than 18 cm for the plannimetric position and about 15 cm for the height component.
UAS BASED TREE SPECIES IDENTIFICATION USING THE NOVEL FPI BASED HYPERSPECTRAL CAMERAS IN VISIBLE, NIR AND SWIR SPECTRAL RANGES
Authors: Näsi R. , Honkavaara E. , Tuominen S. , Saari H. , Pölönen I. , Hakala T. , Viljanen N. , Soukkamäki J. , Näkki I. , Ojanen H. , Reinikainen J.
Published: June 2016

Abstract. Unmanned airborne systems (UAS) based remote sensing offers flexible tool for environmental monitoring. Novel lightweight Fabry-Perot interferometer (FPI) based, frame format, hyperspectral imaging in the spectral range from 400 to 1600 nm was used for identifying different species of trees in a forest area. To the best of the authors’ knowledge, this was the first research where stereoscopic, hyperspectral VIS, NIR, SWIR data is collected for tree species identification using UAS. The first results of the analysis based on fusion of two FPI-based hyperspectral imagers and RGB camera showed that the novel FPI hyperspectral technology provided accurate geometric, radiometric and spectral information in a forested scene and is operational for environmental remote sensing applications.
GEODETIC REFERENCE SYSTEM TRANSFORMATIONS OF 3D ARCHIVAL GEOSPATIAL DATA USING A SINGLE SSC TERRASAR-X IMAGE
Authors: Vassilaki D. I. , Stamos A. A.
Published: June 2016

Abstract. Many older maps were created using reference coordinate systems which are no longer available, either because no information to a datum was taken in the first place or the reference system is forgotten. In other cases the relationship between the map’s coordinate system is not known with precision, meaning that its absolute error is much larger than its relative error. In this paper the georeferencing of medium-scale maps is computed using a single TerraSAR-X image. A single TerraSAR-X image has high geolocation accuracy but it has no 3D information. The map, however, provides the missing 3D information, and thus it is possible to compute the georeferencing of the map using the TerraSAR-X geolocation information, assembling the information of both sources to produce 3D points in the reference system of the TerraSAR-X image. Two methods based on this concept are proposed. The methods are tested with real world examples and the results are promising for further research.
LIDAR-INCORPORATED TRAFFIC SIGN DETECTION FROM VIDEO LOG IMAGES OF MOBILE MAPPING SYSTEM
Authors: Li Y. , Fan J. , Huang Y. , Chen Z.
Published: June 2016

Abstract. Mobile Mapping System (MMS) simultaneously collects the Lidar points and video log images in a scenario with the laser profiler and digital camera. Besides the textural details of video log images, it also captures the 3D geometric shape of point cloud. It is widely used to survey the street view and roadside transportation infrastructure, such as traffic sign, guardrail, etc., in many transportation agencies. Although many literature on traffic sign detection are available, they only focus on either Lidar or imagery data of traffic sign. Based on the well-calibrated extrinsic parameters of MMS, 3D Lidar points are, the first time, incorporated into 2D video log images to enhance the detection of traffic sign both physically and visually. Based on the local elevation, the 3D pavement area is first located. Within a certain distance and height of the pavement, points of the overhead and roadside traffic signs can be obtained according to the setup specification of traffic signs in different transportation agencies. The 3D candidate planes of traffic signs are then fitted using the RANSAC plane-fitting of those points. By projecting the candidate planes onto the image, Regions of Interest (ROIs) of traffic signs are found physically with the geometric constraints between laser profiling and camera imaging. The Random forest learning of the visual color and shape features of traffic signs is adopted to validate the sign ROIs from the video log images. The sequential occurrence of a traffic sign among consecutive video log images are defined by the geometric constraint of the imaging geometry and GPS movement. Candidate ROIs are predicted in this temporal context to double-check the salient traffic sign among video log images. The proposed algorithm is tested on a diverse set of scenarios on the interstate highway G-4 near Beijing, China under varying lighting conditions and occlusions. Experimental results show the proposed algorithm enhances the rate of detecting traffic signs with the incorporation of the 3D planar constraint of their Lidar points. It is promising for the robust and large-scale survey of most transportation infrastructure with the application of MMS.
VEHICLE LOCALIZATION BY LIDAR POINT CORRELATION IMPROVED BY CHANGE DETECTION
Authors: Schlichting A. , Brenner C.
Published: June 2016

Abstract. LiDAR sensors are proven sensors for accurate vehicle localization. Instead of detecting and matching features in the LiDAR data, we want to use the entire information provided by the scanners. As dynamic objects, like cars, pedestrians or even construction sites could lead to wrong localization results, we use a change detection algorithm to detect these objects in the reference data. If an object occurs in a certain number of measurements at the same position, we mark it and every containing point as static. In the next step, we merge the data of the single measurement epochs to one reference dataset, whereby we only use static points. Further, we also use a classification algorithm to detect trees. For the online localization of the vehicle, we use simulated data of a vertical aligned automotive LiDAR sensor. As we only want to use static objects in this case as well, we use a random forest classifier to detect dynamic scan points online. Since the automotive data is derived from the LiDAR Mobile Mapping System, we are able to use the labelled objects from the reference data generation step to create the training data and further to detect dynamic objects online. The localization then can be done by a point to image correlation method using only static objects. We achieved a localization standard deviation of about 5 cm (position) and 0.06° (heading), and were able to successfully localize the vehicle in about 93 % of the cases along a trajectory of 13 km in Hannover, Germany.
A LIGHT-WEIGHT LASER SCANNER FOR UAV APPLICATIONS
Authors: Tommaselli A. M. G. , Torres F. M.
Published: June 2016

Abstract. Unmanned Aerial Vehicles (UAV) have been recognized as a tool for geospatial data acquisition due to their flexibility and favourable cost benefit ratio. The practical use of laser scanning devices on-board UAVs is also developing with new experimental and commercial systems. This paper describes a light-weight laser scanning system composed of an IbeoLux scanner, an Inertial Navigation System Span-IGM-S1, from Novatel, a Raspberry PI portable computer, which records data from both systems and an octopter UAV. The performance of this light-weight system was assessed both for accuracy and with respect to point density, using Ground Control Points (GCP) as reference. Two flights were performed with the UAV octopter carrying the equipment. In the first trial, the flight height was 100 m with six strips over a parking area. The second trial was carried out over an urban park with some buildings and artificial targets serving as reference Ground Control Points. In this experiment a flight height of 70 m was chosen to improve target response. Accuracy was assessed based on control points the coordinates of which were measured in the field. Results showed that vertical accuracy with this prototype is around 30 cm, which is acceptable for forest applications but this accuracy can be improved using further refinements in direct georeferencing and in the system calibration.
RAPID INSPECTION OF PAVEMENT MARKINGS USING MOBILE LIDAR POINT CLOUDS
Authors: Zhang Haocheng , Li Jonathan , Cheng Ming , Wang Cheng
Published: June 2016

Abstract. This study aims at building a robust semi-automated pavement marking extraction workflow based on the use of mobile LiDAR point clouds. The proposed workflow consists of three components: preprocessing, extraction, and classification. In preprocessing, the mobile LiDAR point clouds are converted into the radiometrically corrected intensity imagery of the road surface. Then the pavement markings are automatically extracted with the intensity using a set of algorithms, including Otsu’s thresholding, neighbor-counting filtering, and region growing. Finally, the extracted pavement markings are classified with the geometric parameters using a manually defined decision tree. Case studies are conducted using the mobile LiDAR dataset acquired in Xiamen (Fujian, China) with different road environments by the RIEGL VMX-450 system. The results demonstrated that the proposed workflow and our software tool can achieve 93% in completeness, 95% in correctness, and 94% in F-score when using Xiamen dataset.