Global Positioning System Survey Data Research Articles

Quality Assessment of Multiple UAV-SfM DEMs Derived for Impact Assessment of a Co-Seismic Avalanche in the Himalayas

Combined with the structure from motion (SfM) technique, unmanned aerial vehicles (UAVs) are powerful tools for generating high-resolution digital elevation models (DEMs) for application in hazard assessments. During our field observations in October 2015 at Langtang Village, which was destroyed by the Gorkha earthquake in April 2015, three different UAVs with mounted cameras were operated to evaluate the volume of the avalanche deposit covering the village. This study evaluated the performance of DEMs created from the different cameras on board those UAVs. Multiple DEMs for the different cameras, including Sony-α7R (PA7), Ricoh-GR (XGR), and Canon-IXUS (EIX), were created using SfM software. All DEMs were compared with a base DEM created from differential global positioning system survey data, which was obtained simultaneously with the UAV campaigns. The results show that the elevation difference of PA7-, XGR-, and EIX-DEMs are within ±0.14 m; the standard deviations of elevation difference range from 0.33 to 0.40 m. Although there were slightly larger differences in elevation on the southwest-to-west sides of the XGR- and EIX-DEMs, which can be attributed mainly to the flight paths and ground control point network, our DEMs are still of high enough quality to be used in hazard assessments.

Shallow Fault Slip of the 2020 M 5.1 Sparta, North Carolina, Earthquake

Abstract The 2020 M 5.1 Sparta, North Carolina, earthquake is the largest in the eastern United States since the 2011 M 5.8 Mineral, Virginia, earthquake and produced a ∼2.5-km-long surface rupture, unusual for an event of this magnitude. A geological field study conducted soon after the event indicates oblique slip along a east-southeast-trending fault with a consistently observed thrust component. My analysis of regional seismic waveforms, Interferometric Synthetic Aperture Radar, and Global Positioning System survey data yields a compact shallow rupture extending from Earth’s surface down-dip to the southwest over a ∼3 km fault length. The inferred kinematic rupture is primarily toward the up-dip and eastward along-strike directions and has predominantly thrust motion in the west, transitioning to roughly equal thrust and left-lateral strike-slip motion in the east. No normal faulting component, as proposed in an earlier geophysical study, is necessary to explain the data. The prevalence of only dip-slip motions observed at Earth’s surface may demand slip partitioning between dip slip and lateral motions at depth.

Identifying travel mode with GPS data

ABSTRACTTravel mode identification is an essential step in travel information detection with global positioning system (GPS) survey data. This paper presents a hybrid procedure for mode identification using large-scale GPS survey data collected in Beijing in 2010. In a first step, subway trips were detected by applying a GPS/geographic information system (GIS) algorithm and a multinomial logit model. A comparison of the identification results reveals that the GPS/GIS method provides higher accuracy. Then, the modes of walking, bicycle, car and bus were determined using a nested logit model. The combined success rate of the hybrid procedure was 86%. These findings can be used to identify travel modes based on GPS survey data, which will significantly improve the efficiency and accuracy of travel surveys and data analysis. By providing crucial travel information, the results also contribute to modeling and analyzing travel behaviors and are readily applicable to a wide range of transportation practices.

Global positioning system survey data for active seismic and volcanic areas of eastern Sicily, 1994 to 2013.

This work presents and describes a 20-year long database of GPS data collected by geodetic surveys over the seismically and volcanically active eastern Sicily, for a total of more than 6300 measurements. Raw data were initially collected from the various archives at the Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Catania—Osservatorio Etneo and organized in a single repository. Here, quality and completeness checks were performed, while all necessary supplementary information were searched, collected, validated and organized together with the relevant data. Once all data and information collections were completed, raw binary data were converted into the universal ASCII RINEX format; all data are provided in this format with the necessary information for precise processing. In order to make the data archive readily consultable, we developed software allowing the user to easily search and obtain the needed data by simple alphanumeric and geographic queries.

Present‐day Block Movement and Fault Activity on the Eastern Margin of the Tibetan Plateau

AbstractThe geology and tectonics in the eastern margin of Tibetan Plateau are complex. The main tectonic framework is composed of blocks and faults. Using discontinuous global positioning system survey data for 2008–2014, the velocity field for the Eurasia reference framework was obtained. Based on the velocity field, the present‐day velocities of the blocks and boundary faults were estimated. The results reveal that the movement rates of the Chuan‐Qing, South China, Chuan‐Dian and Indo‐China blocks are (17.02±0.60) mm/a, (8.77±.51) mm/a, (13.85±.31) mm/a and (6.84 ± 0.74) mm/a, respectively, and their movement directions are 99.5°, 120.3°, 142.9° and 153.3°, respectively. All blocks exhibit clockwise rotation. The displacement rates of the Xianshuihe, Longmenshan, Anninghe, Zemuhe, Xiaojiang and Red River faults are (7.30±1.25–8.30±1.26) mm/a, (10.07±0.97–11.79±0.89) mm/a, (0.96±0.74–2.98±1.73) mm/a, (2.03±0.49–3.20±0.73) mm/a, (3.45±0.40–6.02±0.50) mm/a and (6.23±0.56) mm/a, respectively. The Xianshuihe, Anninghe, Zemuhe and Xiaojiang faults show left‐lateral strike‐slip movement, while the Longmenshan and Red River faults show right‐lateral strike‐slip. These characteristics of the blocks and faults are related to the particular tectonic location and dynamic mechanism.

Identifying Travel Mode with GPS Data Using Support Vector Machines and Genetic Algorithm

Travel mode identification is one of the essential steps in travel information detection with Global Positioning System (GPS) survey data. This paper presents a Support Vector Classification (SVC) model for travel mode identification with GPS data. Genetic algorithm (GA) is employed for optimizing the parameters in the model. The travel modes of walking, bicycle, subway, bus, and car are recognized in this model. The results indicate that the developed model shows a high level of accuracy for mode identification. The estimation results also present GA’s contribution to the optimization of the model. The findings can be used to identify travel mode based on GPS survey data, which will significantly enhance the efficiency and accuracy of travel survey and data processing. By providing crucial trip information, the results also contribute to the modeling and analyzing of travel behavior and are readily applicable to a wide range of transportation practices.

Association of elevation error with surface type, vegetation class and data origin in discrete-returns airborne LiDAR

Airborne LiDAR (light detection and ranging) data are now commonly regarded as the most accurate source of elevation data for medium-scale topographical modelling applications. However, quoted LiDAR elevation error may not necessarily represent the actual errors occurring across all surfaces, potentially impacting the reliability of derived predictions in Geographical Information Systems (GIS). The extent to which LiDAR elevation error varies in association with land cover, vegetation class and LiDAR data source is quantified relative to dual-frequency global positioning system survey data captured in a 400-ha area in Ireland, where four separate classes of LiDAR point data overlap. Quoted elevation errors are found to correspond closely with the minimum requirement recommended by the American Society of Photogrammetry and Remote Sensing for the definition of 95% error in urban areas only. Global elevation errors are found to be up to 5 times the quoted error, and errors within vegetation areas are found to be even larger, with errors in individual vegetation classes reaching up to 15 times the quoted error. Furthermore, a strong skew is noted in vegetated areas within all the LiDAR data sets tested, pushing errors in some cases to more than 25 times the quoted error. The skew observed suggests that an assumption of a normal error distribution is inappropriate in vegetated areas. The physical parameters that were found to affect elevation error most fundamentally were canopy depth, canopy density and granularity. Other factors observed to affect the degree to which actual errors deviate from quoted error included the primary use for which the data were acquired and the processing applied by data suppliers to meet these requirements.

Automated Identification and Extraction of Horizontal Curve Information from Geographic Information System Roadway Maps

Roadway horizontal alignment has long been recognized as one of the most significant contributing factors to lane departure crashes. Knowledge of the location and geometric information of horizontal curves can greatly facilitate the development of appropriate countermeasures. When curve information is unavailable, obtaining curve data in a cost-effective way is of great interest to practitioners and researchers. To date, many approaches have been developed to extract curve information from commercial satellite imagery, Global Positioning System survey data, laser-scanning data, and AutoCAD digital maps. As geographic information system (GIS) roadway maps become more accessible and more widely used, they become another cost-effective source for extraction of curve data. This paper presents a fully automated method for the extraction of horizontal curve data from GIS roadway maps. A specific curve data–extraction algorithm was developed and implemented as a customized add-in tool in ArcMap. With this tool, horizontal curves could be automatically identified from GIS roadway maps. The length, radius, and central angle of the curves were also computed automatically. The only input parameter of the proposed algorithm was calibrated to have the least curve identification errors. Finally, algorithm validation was conducted through a comparison of the algorithm-extracted curve data with the ground truth curve data for 76 curves that were obtained from Bing aerial maps. The validation results indicated that the proposed algorithm was very effective and that it identified completely 96.7% of curves and computed accurately their geometric information.

Ethnogeography and Resource Use among the Yanomami

Study of the spatial patterns of land use and forest resource exploitation by a Yanomami community in northern Amazonia (Brazil) combining high‐resolution satellite imagery and global positioning system survey data with ethnogeographic fieldwork shows that its natural‐resource use is configured in terms of “reticular space” rather than a set of discrete concentric exploitation “zones.” This model of reticular space has general relevance for the Yanomami and other Amazonian indigenous groups. The research reported demonstrates the value of a multidisciplinary methodology for gathering data on ethnogeographic practices and knowledge that are essential for the appropriate demarcation of indigenous protected areas and the long‐term sustainable management of their environment in the Amazon and other tropical‐forest environments.

Characterization of the diminishing accuracy in detecting forest insect damage over time

The goal of this project was to determine the ability to detect forest insect disturbances occurring over a 6 year period using a remote sensing change detection approach. The study area in central British Columbia, Canada, has been experiencing an epidemic outbreak of bark beetles. The actual location and number of trees attacked by mountain pine beetle (Dentroctonus ponderosae Hopkins) were determined by annual surveys using a helicopter and a global positioning system (GPS). In this study, mountain pine beetle red-attack trees, infested between 1995 and 2001, were detected with an enhanced wetness difference index (EWDI), which was created using a 1995 and 2001 Landsat image pair. Red-attack damage was detected with an accuracy (true positive rate) of 74% using all years of the helicopter GPS survey data as validation. Assessments of the classification were subsequently undertaken that compared the EWDI-derived red-attack locations to each year of available validation data. The results of this analysis showed that recent red-attack damage was detected with greater accuracy than older red-attack damage (with an accuracy decline of approximately 15% over the 6 year period). The greatest accuracy was obtained for the most recent 2 years of attack, namely 2000 and 2001, with a red-attack detection accuracy of 81%.

A combination of GPS and conventional terrestrial survey data

Abstract Traditionally, Global Positioning System (GPS) data and conventional terrestrial survey data have been separately processed prior to being merged to form a combined data set. This paper addresses the issues relating to simultaneously processing the two data sets. It intends to demonstrate that the combination of GPS data and conventional terrestrial survey data provides more flexible and efficient survey procedures and that terrestrial data can be used to enhance GPS ambiguity resolution schemes. The ability to combine GPS carrier phase data and terrestrial directions and distances enables some of the inherent shortfalls in both systems to be overcome. The paper demonstrates that the combination of the two technologies equates to more than the sum of the two parts.