Modernization of the Estonian National GNSS Reference Station Network

Metsar, Jaanus; Kollo, Karin; Ellmann, Artu

doi:10.3846/gac.2018.2023

Cited by 28 publications

(14 citation statements)

References 3 publications

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“…The airborne GNSS and inertial measurement unit datasets were utilised for flight trajectory calculations with respect to the nearby Estonian national GNSS reference stations (Metsar et al, 2018). The RIEGL RiProcess software and standard workflow (e.g., Gruno et al, 2013) were used to compute 3D coordinated point clouds that depict instantaneous SSH (with respect to the reference ellipsoid GRS-80).…”

Section: )mentioning

confidence: 99%

Applications of airborne laser scanning for determining marine geoid and surface waves properties

Varbla

Ellmann

Delpeche-Ellmann

2021

European Journal of Remote Sensing

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Marine and coastal applications require now more than ever accurate and expansive understanding of the marine surface topography in the offshore domain, with the relationship of sea surface heights and geoid (equipotential surface of the Earth) being the key components. This study demonstrates some of the under-utilised and unexplored marine applications of airborne laser scanning (ALS) with respect to geoid heights verification and validation, as well as identification of surface waves properties. A synergistic methodology was developed that combines ALS measurements, hydrodynamic models and tide gauge records in conjunction with geoid models. Examination of the determined discrepancies with respect to the ALSderived sea surface heights reveals concealed characteristics of the geoid and dynamic topography. A sea surface height accuracy of 1.7 cm in terms of standard deviation was achieved using a high-resolution regional geoid model. Furthermore, ALS point cloud data can be used to retrieve surface wavefield properties (waves heights, wavelengths and directions). In this study a direct method approach is presented. Such a deeper insight into the wave dynamics plays an enormous contribution in the understanding and quantification of coastal processes (erosion and sediment transport), as well as validation and calibration of wave models and relevant sensors.

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Section: )mentioning

confidence: 99%

Applications of airborne laser scanning for determining marine geoid and surface waves properties

Varbla

Ellmann

Delpeche-Ellmann

2021

European Journal of Remote Sensing

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“…The fixed-wing platform also had an on-board Global Navigation Satellite System Real-time Kinematic/Post-Processed Kinematic (GNSS RTK/PPK) receiver activated. Data recorded during the flights were subsequently post-processed using differential correction data (RINEX) provided by the Estonian Land Board's virtual reference stations [27]. We opted for this approach based on Tomaštík et al [21] findings, which reported higher horizontal and vertical accuracies using PPK than using ground control points (GCP).…”

Section: Data Acquisitionmentioning

confidence: 99%

An Evaluation of the Effects of UAS Flight Parameters on Digital Aerial Photogrammetry Processing and Dense-Cloud Production Quality in a Scots Pine Forest

et al. 2021

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The application of unmanned aerial systems (UAS) in forest research includes a wide range of equipment, systems, and flight settings, creating a need for enhancing data acquisition efficiency and quality. Thus, we assessed the effects of flying altitude and lateral and longitudinal overlaps on digital aerial photogrammetry (DAP) processing and the ability of its products to provide point clouds for forestry inventory. For this, we used 18 combinations of flight settings for data acquisition, and a nationwide airborne laser scanning (ALS) dataset as reference data. Linear regression was applied for modeling DAP quality indicators and model fitting quality as the function of flight settings; equivalence tests compared DAP- and ALS-products. Most of DAP-Digital Terrain Models (DTM) showed a moderate to high agreement (R2 > 0.70) when fitted to ALS-based models; nine models had a regression slope within the 1% region of equivalence. The best DAP-Canopy Height Model (CHM) was generated using ALS-DTM with an R2 = 0.42 when compared with ALS-CHM, indicating reduced similarity. Altogether, our results suggest that the optimal combination of flight settings should include a 90% lateral overlap, a 70% longitudinal overlap, and a minimum altitude of 120 m above ground level, independent of the availability of an ALS-derived DTM for height normalization. We also provided insights into the effects of flight settings on DAP outputs for future applications in similar forest stands, emphasizing the benefits of overlaps for comprehensive scene reconstruction and altitude for canopy surface detection.

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“…The UAS data was post-processed in SenseFly eMotion 3 [40] using receiver independent exchange (RINEX) format data provided by the GNSS CORS (Continuously Operating Reference Station) of Estonia [41] for post-processing kinematics (PPK) corrections. This post-process provided an increase in the geotagging accuracy [42] of the UAS images from 5 m error to under 0.06 m, where the method and accuracy obtained is similar to [43]; and thus less than the one-pixel size in our study.…”

Section: Image Processing and Analysismentioning

confidence: 99%

The Application of an Unmanned Aerial System and Machine Learning Techniques for Red Clover-Grass Mixture Yield Estimation under Variety Performance Trials

Burnside

Lima

et al. 2021

Remote Sensing

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A significant trend has developed with the recent growing interest in the estimation of aboveground biomass of vegetation in legume-supported systems in perennial or semi-natural grasslands to meet the demands of sustainable and precise agriculture. Unmanned aerial systems (UAS) are a powerful tool when it comes to supporting farm-scale phenotyping trials. In this study, we explored the variation of the red clover-grass mixture dry matter (DM) yields between temporal periods (one- and two-year cultivated), farming operations [soil tillage methods (STM), cultivation methods (CM), manure application (MA)] using three machine learning (ML) techniques [random forest regression (RFR), support vector regression (SVR), and artificial neural network (ANN)] and six multispectral vegetation indices (VIs) to predict DM yields. The ML evaluation results showed the best performance for ANN in the 11-day before harvest category (R² = 0.90, NRMSE = 0.12), followed by RFR (R² = 0.90 NRMSE = 0.15), and SVR (R² = 0.86, NRMSE = 0.16), which was furthermore supported by the leave-one-out cross-validation pre-analysis. In terms of VI performance, green normalized difference vegetation index (GNDVI), green difference vegetation index (GDVI), as well as modified simple ratio (MSR) performed better as predictors in ANN and RFR. However, the prediction ability of models was being influenced by farming operations. The stratified sampling, based on STM, had a better model performance than CM and MA. It is proposed that drone data collection was suggested to be optimum in this study, closer to the harvest date, but not later than the ageing stage.

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Modernization of the Estonian National GNSS Reference Station Network

Cited by 28 publications

References 3 publications

Applications of airborne laser scanning for determining marine geoid and surface waves properties

Applications of airborne laser scanning for determining marine geoid and surface waves properties

An Evaluation of the Effects of UAS Flight Parameters on Digital Aerial Photogrammetry Processing and Dense-Cloud Production Quality in a Scots Pine Forest

The Application of an Unmanned Aerial System and Machine Learning Techniques for Red Clover-Grass Mixture Yield Estimation under Variety Performance Trials

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