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Calle M.,CSIC - National Museum of Natural Sciences | Alho P.,University of Turku | Alho P.,Finnish Geospatial Research Institute | Benito G.,CSIC - National Museum of Natural Sciences
Geomorphology | Year: 2017

Gravel mining has been a widespread activity in ephemeral rivers worldwide whose long-lasting hydrogeomorphological impacts preclude effective implementation of water and environmental policies. This paper presents a GIS-based method for temporal assessment of morphosedimentary changes in relation to in-channel gravel mining in a typical ephemeral Mediterranean stream, namely the Rambla de la Viuda (eastern Spain). The aims of this work were to identify morphosedimentary changes and responses to human activities and floods, quantify river degradations and analyze factors favoring fluvial recovery for further applications in other rivers. Aerial photographs and LiDAR topography data were studied to analyze geomorphic evolution over the past 70 years along a 7.5-km reach of an ephemeral gravel stream that has been mined intensively since the 1970s. To evaluate changes in the riverbed, we mapped comparable units applying morphological, hydraulic, and stability (based on vegetation density and elevation) criteria to 13 sets of aerial photographs taken from 1946 to 2012. A detailed spatiotemporal analysis of comparable units revealed a 50% reduction in the active section and a 20% increase in stable areas, compared to the conditions observed prior to gravel mining. Instream mining was first observed in 1976 aerial photograph covering already up to 50% of the 1956 riverbed area. River degradation since then was quantified by means of a LiDAR DTM and RTK-GPS measurements, which revealed a 3.5-m incision that had started simultaneously with gravel mining. Climate and land use changes were present but the effects were completely masked by changes produced by instream gravel mining. Therefore, river incision/degradation was triggered by scarcity of sediment and lack of longitudinal sedimentary connection, creating an unbalanced river system that is still adjusting to the present hydrosedimentary conditions. © 2017 Elsevier B.V.

Borio D.,European Commission - Joint Research Center Ispra | Dovis F.,Polytechnic University of Turin | Kuusniemi H.,Finnish Geospatial Research Institute | Lo Presti L.,Polytechnic University of Turin
Proceedings of the IEEE | Year: 2016

Jamming is the act of intentionally directing powerful electromagnetic waves toward a victim receiver with the ultimate goal of denying its operations. This paper describes the main types of Global Navigation Satellite System (GNSS) jammers and reviews their impact on GNSS receivers. A survey of state-of-the-art methods for jamming detection is also provided. Different detection approaches are investigated with respect to the receiver stage where they can be implemented. © 1963-2012 IEEE.

Fedorets G.,University of Helsinki | Granvik M.,University of Helsinki | Granvik M.,Finnish Geospatial Research Institute
Planetary and Space Science | Year: 2015

We obtained observations and performed rotation period, pole orientation and convex shape model analysis for the slowly rotating Hungaria asteroid (39420) Elizabethgaskell as a follow-up to the Thousand Asteroid Light Curve Survey (TALCS, Masiero et al., 2009). The TALCS observations combined with our follow-up observations did not allow for an unambiguous spin and shape solution. To reject the possibility of a methodological failure in the analysis, we simulated a lightcurve of an elongated object by generating synthetic detections with the same cadence as in the real observations and added random noise. The same period, pole and shape analysis was then successfully performed for a simulated object. Thus, we conclude that (39420) Elizabethgaskell is either a binary or, more likely, a non-principal-axis rotator. Being one of only two Hungarias observed in an untargeted survey, the properties of (39420) Elizabethgaskell suggest that binaries and/or non-principal-axis rotators are common in the Hungaria population. © 2015 Elsevier Ltd.

Cellino A.,National institute for astrophysics | Muinonen K.,University of Helsinki | Muinonen K.,Finnish Geospatial Research Institute | Hestroffer D.,IMCCE | Carbognani A.,Osservatorio Astronomico della Valle dAosta
Planetary and Space Science | Year: 2015

The inversion of sparse photometric data of asteroids to derive from them information about the spin and shape properties of the objects is a hot topic in the era of the Gaia space mission. We have used a rigorous analytical treatment of the Lommel-Seeliger light-scattering law computed for the particular case of bodies having the shapes of ideal triaxial ellipsoids, and we have implemented this in the software developed for the treatment of Gaia photometric data for asteroids. In a set of numerical simulations, the performances of the photometry inversion code improve significantly with respect to the case in which purely geometric scattering is taken into account. When applied to real photometric data of asteroids obtained in the past by the Hipparcos satellite, however, we do not see any relevant improvement of the performances, due to the poor accuracy of these measurements. These results suggest that the role played by the light-scattering properties of asteroid surfaces is indeed relevant. On the other hand, any refined treatment of light-scattering effects cannot improve the reliability of photometric inversion when the quantity and quality of available data are much worse than what we expect to obtain from Gaia. © 2015 Elsevier Ltd.

Penttila A.,University of Helsinki | Shevchenko V.G.,University of Kharkiv | Wilkman O.,University of Helsinki | Muinonen K.,University of Helsinki | Muinonen K.,Finnish Geospatial Research Institute
Planetary and Space Science | Year: 2016

We introduce a constrained nonlinear least-squares algorithm to be used in estimating the parameters in the H, G1, G2 phase function. As the algorithm works directly in the magnitude space, it will surpass the possible bias problem that may be present in the existing H,G1,G2 fit procedure when applied to low-accuracy observations with large magnitude variations. With constraints on the photometric phase-curve shape parameters G1 and G2, it guarantees a physically reasonable phase-curve estimate. With a new data set of 93 asteroids, we re-assess the two-parameter version of the H,G1,G2 function. Finally, we introduce a one-parameter version of the phase function that can give a suggestion of the asteroids taxonomic group based only on its phase curve. A statistical model selection procedure is presented that can automatically select between the different versions of the photometric phase functions. An online tool that implements these algorithms is introduced. © 2015 Elsevier Ltd. All rights reserved.

Virkki A.,University of Helsinki | Muinonen K.,University of Helsinki | Muinonen K.,Finnish Geospatial Research Institute
Icarus | Year: 2016

We model radar scattering by planetary surfaces using a ray-optics algorithm that includes Fresnelian reflection and refraction, diffuse scattering, and coherent backscattering. We enhance the realism of the ray-optics algorithm by using scattering particles that are geometrically representative of the surfaces and interiors of planetary bodies. The shapes as well as the dielectric properties of the scattering particles have been characterized in laboratory. The results demonstrate the effects of various physical parameters on radar scattering with an emphasis on asteroids. We present the effects of number density, size distribution, and dielectric and geometric properties of scattering particles on the radar reflectivity and circular-polarization ratio of planetary surfaces. We also briefly discuss applications to the Galilean Moon Europa and comets. © 2016 Elsevier Inc.

Sainio J.,Åbo Akademi University | Westerholm J.,Åbo Akademi University | Oksanen J.,Finnish Geospatial Research Institute
ISPRS International Journal of Geo-Information | Year: 2015

The breakthrough of GPS-equipped smartphones has enabled the collection of track data from human mobility on massive scales that can be used in route recommendation, urban planning and traffic management. In this work we present a fast map server that can generate and visualize heat maps of popular routes online from massive sports track data based on client preferences, e.g., running routes lasting less than an hour. The heat maps shown respect user privacy by not showing routes with less than a predefined number of different users, for instance five. The results are represented to the client using a dynamic tile layer. The current implementation uses data collected by the Sports Tracker mobile application with over 800,000 different tracks and 2.8 billion GPS data points. Stress tests indicate that the server can handle hundreds of simultaneous client requests in a single server configuration. © 2015 by the authors; licensee MDPI, Basel, Switzerland.

Garcia J.M.V.,Finnish Geospatial Research Institute
Proceedings of 2015 International Conference on Localization and GNSS, ICL-GNSS 2015 | Year: 2015

This paper addresses the problem of model-based source localization using spatially autocorrelated received signal strength (RSS) measurements when the model parameters are not known a priori. This combined problem arises typically in situations in which a large number of observations are collected in positions close to each other in unknown environments. In our approach we model the RSS as a spatial Gaussian process characterized by an autocorrelation function. The position is then estimated by maximizing the log-likelihood after a previous analytical optimization over some of the model parameters. This technique allows the estimation of position without prior knowledge of these parameters. Although very convenient, we show experimentally that this method can be very sensitive to the geometry of the problem. As a solution we propose maximum a posteriori (MAP) estimation with indirect priors on the parameters affecting the mean of the predictions. Using data gathered from three different environments we demonstrate the effectiveness of the approach in real scenarios. On average, the localization errors achieved are 0.53, 0.99 and 0.86 m in a basketball field, a lobby and an office respectively. © 2015 IEEE.

Bilker-Koivula M.,Finnish Geospatial Research Institute | Bilker-Koivula M.,Aalto University
International Association of Geodesy Symposia | Year: 2014

The two high-resolution global gravity field models, EGM2008 and EIGEN-6C, are compared with ground truth in Finland and surrounding areas. Thereafter, the models are used as background models in the calculation of a quasi-geoid model for Finland. The differences between height anomalies calculated from the globalmodels and from two GPSlevelling datasets for Finland show standard deviations between 5 and 7 cm. Comparisons with free-air anomalies show small and homogeneously distributed differences over most of the area. In both comparisons the largest discrepancies are found close to the Russian border east of the 29° longitude line. This is most probably due to lower resolution Russian data used in the global models. When the global models are used as background models in the calculation of a quasi-geoid model for Finland, the problems around the 29° longitude line disappear. Comparison between the final quasi-geoid models and GPS-levelling data show an improvement over earlier models for Finland. © Springer International Publishing Switzerland 2014.

Guinness R.E.,Finnish Geospatial Research Institute
Sensors (Switzerland) | Year: 2015

This paper presents the results of research on the use of smartphone sensors (namely, GPS and accelerometers), geospatial information (points of interest, such as bus stops and train stations) and machine learning (ML) to sense mobility contexts. Our goal is to develop techniques to continuously and automatically detect a smartphone user’s mobility activities, including walking, running, driving and using a bus or train, in real-time or near-real-time (<5 s). We investigated a wide range of supervised learning techniques for classification, including decision trees (DT), support vector machines (SVM), naive Bayes classifiers (NB), Bayesian networks (BN), logistic regression (LR), artificial neural networks (ANN) and several instance-based classifiers (KStar, LWLand IBk). Applying ten-fold cross-validation, the best performers in terms of correct classification rate (i.e., recall) were DT (96.5%), BN (90.9%), LWL (95.5%) and KStar (95.6%). In particular, the DT-algorithm RandomForest exhibited the best overall performance. After a feature selection process for a subset of algorithms, the performance was improved slightly. Furthermore, after tuning the parameters of RandomForest, performance improved to above 97.5%. Lastly, we measured the computational complexity of the classifiers, in terms of central processing unit (CPU) time needed for classification, to provide a rough comparison between the algorithms in terms of battery usage requirements. As a result, the classifiers can be ranked from lowest to highest complexity (i.e., computational cost) as follows: SVM, ANN, LR, BN, DT, NB, IBk, LWL and KStar. The instance-based classifiers take considerably more computational time than the non-instance-based classifiers, whereas the slowest non-instance-based classifier (NB) required about five-times the amount of CPU time as the fastest classifier (SVM). The above results suggest that DT algorithms are excellent candidates for detecting mobility contexts in smartphones, both in terms of performance and computational complexity. © 2015 by the authors; licensee MDPI, Basel, Switzerland.

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