The Experts below are selected from a list of 9105 Experts worldwide ranked by ideXlab platform
Achim Roth - One of the best experts on this subject based on the ideXlab platform.
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accuracy assessment of the global tandem x digital elevation model with gps data
Isprs Journal of Photogrammetry and Remote Sensing, 2018Co-Authors: Birgit Wessel, Martin Huber, Christian Wohlfart, Ursula Marschalk, Detlev Kosmann, Achim RothAbstract:Abstract The primary goal of the German TanDEM-X mission is the generation of a highly accurate and global Digital Elevation Model (DEM) with global accuracies of at least 10 m absolute height error (linear 90% error). The global TanDEM-X DEM acquired with single-pass SAR interferometry was finished in September 2016. This paper provides a unique accuracy assessment of the final TanDEM-X global DEM using two different GPS point reference data sets, which are distributed across all continents, to fully characterize the absolute height error. Firstly, the absolute vertical accuracy is examined by about three million globally distributed kinematic GPS (KGPS) points derived from 19 KGPS tracks covering a total length of about 66,000 km. Secondly, a comparison is performed with more than 23,000 “GPS on Bench Marks” (GPS-on-BM) points provided by the US National Geodetic Survey (NGS) scattered across 14 different land cover types of the US National Land Cover Data base (NLCD). Both GPS comparisons prove an absolute vertical mean error of TanDEM-X DEM smaller than ±0.20 m, a Root Means Square Error (RMSE) smaller than 1.4 m and an excellent absolute 90% linear height error below 2 m. The RMSE values are sensitive to land cover types. For low vegetation the RMSE is ±1.1 m, whereas it is slightly higher for developed areas (±1.4 m) and for forests (±1.8 m). This validation confirms an outstanding absolute height error at 90% confidence level of the global TanDEM-X DEM outperforming the requirement by a factor of five. Due to its extensive and globally distributed reference data sets, this study is of considerable interests for scientific and commercial applications.
Sainju, Arpan Man - One of the best experts on this subject based on the ideXlab platform.
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Flood Extent Mapping based on High Resolution Aerial Imagery and DEM: A Hidden Markov Tree Approach
'Informa UK Limited', 2021Co-Authors: Jiang Zhe, Sainju, Arpan ManAbstract:Flood extent mapping plays a crucial role in disaster management and national water forecasting. In recent years, high-resolution optical imagery becomes increasingly available with the deployment of numerous small satellites and drones. However, analyzing such imagery data to extract flood extent poses unique challenges due to the rich noise and shadows, obstacles (e.g., tree canopies, clouds), and spectral confusion between pixel classes (flood, dry) due to spatial heterogeneity. Existing machine learning techniques often focus on spectral and spatial features from raster images without fully incorporating the geographic terrain within classification models. In contrast, we recently proposed a novel machine learning model called geographical hidden Markov tree that integrates spectral features of pixels and topographic constraints from Digital Elevation Model (DEM) data (i.e., water flow directions) in a holistic manner. This paper evaluates the model through case studies on high-resolution aerial imagery from the National Oceanic and Atmospheric Administration (NOAA) National Geodetic Survey together with DEM. Three scenes are selected in heavily vegetated floodplains near the cities of Grimesland and Kinston in North Carolina during Hurricane Matthew floods in 2016. Results show that the proposed hidden Markov tree model outperforms several state of the art machine learning algorithms (e.g., random forests, gradient boosted model) by an improvement of F-score (the harmonic mean of the user's accuracy and producer's accuracy) from around 70% to 80% to over 95% on our datasets
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A Hidden Markov Tree Model for Flood Extent Mapping in Heavily Vegetated Areas based on High Resolution Aerial Imagery and DEM: A Case Study on Hurricane Matthew Floods
2020Co-Authors: Jiang Zhe, Sainju, Arpan ManAbstract:Flood extent mapping plays a crucial role in disaster management and national water forecasting. In recent years, high-resolution optical imagery becomes increasingly available with the deployment of numerous small satellites and drones. However, analyzing such imagery data to extract flood extent poses unique challenges due to the rich noise and shadows, obstacles (e.g., tree canopies, clouds), and spectral confusion between pixel classes (flood, dry) due to spatial heterogeneity. Existing machine learning techniques often focus on spectral and spatial features from raster images without fully incorporating the geographic terrain within classification models. In contrast, we recently proposed a novel machine learning model called geographical hidden Markov tree that integrates spectral features of pixels and topographic constraints from Digital Elevation Model (DEM) data (i.e., water flow directions) in a holistic manner. This paper evaluates the model through case studies on high-resolution aerial imagery from the National Oceanic and Atmospheric Administration (NOAA) National Geodetic Survey together with DEM. Three scenes are selected in heavily vegetated floodplains near the cities of Grimesland and Kinston in North Carolina during Hurricane Matthew floods in 2016. Results show that the proposed hidden Markov tree model outperforms several state of the art machine learning algorithms (e.g., random forests, gradient boosted model) by an improvement of F-score (the harmonic mean of the user's accuracy and producer's accuracy) from around 70% to 80% to over 95% on our datasets
Yan Ming Wang - One of the best experts on this subject based on the ideXlab platform.
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the geoid slope validation Survey 2014 and grav d airborne gravity enhanced geoid comparison results in iowa
Journal of Geodesy, 2017Co-Authors: Yan Ming Wang, Colin Becker, Gerald L Mader, Dan Martin, Tao Jiang, Steven E Breidenbach, Charles Geoghegan, Daniel Winester, Sebastien Guillaume, Beat BurkiAbstract:Three Geoid Slope Validation Surveys were planned by the National Geodetic Survey for validating geoid improvement gained by incorporating airborne gravity data collected by the “Gravity for the Redefinition of the American Vertical Datum” (GRAV-D) project in flat, medium and rough topographic areas, respectively. The first Survey GSVS11 over a flat topographic area in Texas confirmed that a 1-cm differential accuracy geoid over baseline lengths between 0.4 and 320 km is achievable with GRAV-D data included (Smith et al. in J Geod 87:885–907, 2013). The second Survey, Geoid Slope Validation Survey 2014 (GSVS14) took place in Iowa in an area with moderate topography but significant gravity variation. Two sets of geoidal heights were computed from GPS/leveling data and observed astroGeodetic deflections of the vertical at 204 GSVS14 official marks. They agree with each other at a \({\pm }1.2\,\, \hbox {cm}\) level, which attests to the high quality of the GSVS14 data. In total, four geoid models were computed. Three models combined the GOCO03/5S satellite gravity model with terrestrial and GRAV-D gravity with different strategies. The fourth model, called xGEOID15A, had no airborne gravity data and served as the benchmark to quantify the contribution of GRAV-D to the geoid improvement. The comparisons show that each model agrees with the GPS/leveling geoid height by 1.5 cm in mark-by-mark comparisons. In differential comparisons, all geoid models have a predicted accuracy of 1–2 cm at baseline lengths from 1.6 to 247 km. The contribution of GRAV-D is not apparent due to a 9-cm slope in the western 50-km section of the traverse for all gravimetric geoid models, and it was determined that the slopes have been caused by a 5 mGal bias in the terrestrial gravity data. If that western 50-km section of the testing line is excluded in the comparisons, then the improvement with GRAV-D is clearly evident. In that case, 1-cm differential accuracy on baselines of any length is achieved with the GRAV-D-enhanced geoid models and exhibits a clear improvement over the geoid models without GRAV-D data. GSVS14 confirmed that the geoid differential accuracies are in the 1–2 cm range at various baseline lengths. The accuracy increases to 1 cm with GRAV-D gravity when the west 50 km line is not included. The data collected by the Surveys have high accuracy and have the potential to be used for validation of other Geodetic techniques, e.g., the chronometric leveling. To reach the 1-cm height differences of the GSVS data, a clock with frequency accuracy of \(10^{-18}\) is required. Using the GSVS data, the accuracy of ellipsoidal height differences can also be estimated.
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geodesy geoids and vertical datums a perspective from the u s national Geodetic Survey
2010Co-Authors: Daniel R Roman, Yan Ming Wang, J Saleh, L I XiaopengAbstract:• An Office with the National Ocean Service in the National Oceanic and Atmospheric Administration • Responsible for maintaining elements of and access to the National Spatial Reference System including: – North American Datum of 1983 (NAD 83) – North American Vertical Datum of 1988 (NAVD 88) • NGS developed geoid height models for easier access – GEOID90 the earliest regional gravimetric geoid heights – USGG2009 – most recent gravimetric heights (ITRF00) – GEOID09 – “hybrid” geoid heights (NAD 83 to NAVD 88) – Model development paralleled evolution of GNSS
Andras Molna - One of the best experts on this subject based on the ideXlab platform.
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accurate non disturbance population Survey method of nesting colonies in the reedbed with georeferenced aerial imagery
Sensors, 2020Co-Authors: Gabo Ako, Zsol Molna, Zsofia Szilagyi, Csaba Iro, Edina Morvai, Ors Abram, Andras MolnaAbstract:High altitude aerial Surveys have the potential to improve disturbance-free data collection in wildlife research, but previously, bird species were not recognizable in high-altitude orthophotos. This method of aerial Surveying is effective and can be repeated frequently due to its low cost; it also has the additional advantage of being able to monitor the status of protected areas. In the case of waterbirds, due to the low vegetation coverage, aerial remote sensing is an exceptionally effective technique for Surveying populations and detecting nests. Aerial Surveys made at low altitudes can cause serious stress for birds. The method we developed and employed is unlikely to be detected by either ground-based or nesting birds but is far more reliable compared to the low-resolution imaging methods and to the evaluation of non-georeferenced photo series. The modern sensors and photogrammetric procedures enable the use of the present method worldwide; furthermore, the large-scale ortho image-derived information has become obtainable more frequently. Direct georeferencing makes the field Geodetic Survey unnecessary. Orthophotos with a 0.7 cm spatial resolution allow us to reliably identify even the individuals of smaller species, and by the use of oblique images, they can be tracked from two or four different directions.
Mark Simons - One of the best experts on this subject based on the ideXlab platform.
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A satellite Geodetic Survey of large-scale deformation of volcanic centres in the central Andes
Nature, 2002Co-Authors: Matthew E. Pritchard, Mark SimonsAbstract:Surface deformation in volcanic areas usually indicates movement of magma or hydrothermal fluids at depth. Stratovolcanoes tend to exhibit a complex relationship between deformation and eruptive behaviour. The characteristically long time spans between such eruptions requires a long time series of observations to determine whether deformation without an eruption is common at a given edifice. Such studies, however, are logistically difficult to carry out in most volcanic arcs, as these tend to be remote regions with large numbers of volcanoes (hundreds to even thousands). Here we present a satellite-based interferometric synthetic aperture radar (InSAR) Survey of the remote central Andes volcanic arc, a region formed by subduction of the Nazca oceanic plate beneath continental South America. Spanning the years 1992 to 2000, our Survey reveals the background level of activity of about 900 volcanoes, 50 of which have been classified as potentially active. We find four centres of broad (tens of kilometres wide), roughly axisymmetric surface deformation. None of these centres are at volcanoes currently classified as potentially active, although two lie within about 10 km of volcanoes with known activity. Source depths inferred from the patterns of deformation lie between 5 and 17 km. In contrast to the four new sources found, we do not observe any deformation associated with recent eruptions of Lascar, Chile.