Real-time Kinematics Global Positioning System (GPS) Operation and Setup Method for the Synchronous Impulse Reconstruction (SIRE) Radar

Koenig, Francois; Wong, David C.

doi:10.21236/ada522208

Cited by 3 publications

(4 citation statements)

References 1 publication

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“…By measuring how many signal cycles occurred between the base station and receiver, the distance between the two is known with accuracy equal to the wavelength of the signal [114]. While GNSS uses only the coded signal, RTK and PPK use both the coded and carrier signal types and can thus obtain positional accuracy on the centimeter scale [115]. As RTK and PPK are dependent on base station corrections, their accuracy is limited by the accuracy of the base station position [114].…”

Section: Platformsmentioning

confidence: 99%

A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists

Rowan

Kalácska

2021

Remote Sensing

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Submerged aquatic vegetation (SAV) is a critical component of aquatic ecosystems. It is however understudied and rapidly changing due to global climate change and anthropogenic disturbances. Remote sensing (RS) can provide the efficient, accurate and large-scale monitoring needed for proper SAV management and has been shown to produce accurate results when properly implemented. Our objective is to introduce RS to researchers in the field of aquatic ecology. Applying RS to underwater ecosystems is complicated by the water column as water, and dissolved or suspended particulate matter, interacts with the same energy that is reflected or emitted by the target. This is addressed using theoretical or empiric models to remove the water column effect, though no model is appropriate for all aquatic conditions. The suitability of various sensors and platforms to aquatic research is discussed in relation to both SAV as the subject and to project aims and resources. An overview of the required corrections, processing and analysis methods for passive optical imagery is presented and discussed. Previous applications of remote sensing to identify and detect SAV are briefly presented and notable results and lessons are discussed. The success of previous work generally depended on the variability in, and suitability of, the available training data, the data’s spatial and spectral resolutions, the quality of the water column corrections and the level to which the SAV was being investigated (i.e., community versus species.)

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Section: Platformsmentioning

confidence: 99%

A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists

Rowan

Kalácska

2021

Remote Sensing

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“…RTK receives the base station corrections and applies them concurrently to data collection while PPK incorporates the corrections after data collection is complete in post-processing (Kalacska et al 2020). By measuring how many signal cycles occurred between the base station and receiver, the distance between the two is known with accuracy equal to the wavelength of the signal (Koenig and Wong 2010). While GNSS uses only the coded signal, RTK and PPK use both the coded and carrier signal types and can thus obtain positional accuracy on the centimetre scale (Koenig and Wong 2010).…”

Section: Basic Set-upmentioning

confidence: 99%

“…By measuring how many signal cycles occurred between the base station and receiver, the distance between the two is known with accuracy equal to the wavelength of the signal (Koenig and Wong 2010). While GNSS uses only the coded signal, RTK and PPK use both the coded and carrier signal types and can thus obtain positional accuracy on the centimetre scale (Koenig and Wong 2010). As RTK and PPK are dependent on base station corrections, their accuracy is limited by the accuracy of the base station position (Kalacska et al 2020).…”

Section: Basic Set-upmentioning

confidence: 99%

Remote sensing of submerged aquatic vegetation: an introduction and best practices review

Rowan¹,

Kalácska²

2020

Preprint

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Submerged aquatic vegetation (SAV) is a critical component of aquatic ecosystems. It is however understudied and rapidly changing due to global climate change and anthropogenic disturbances. Remote sensing can provide the efficient, accurate and large-scale monitoring needed to ensure proper SAV management. Our objective is to introduce remote sensing to researchers in the field of aquatic ecology. Applying remote sensing to the underwater environment is more complex in comparison to terrestrial studies due to the water column. A wide range of sensors and platforms from remotely operated vehicles to satellites are available for use in the underwater environment, a sample of which being presented herein. The utility of any sensor/platform combination varies depending on the aquatic conditions being observed. An overview of the required corrections, processing, and analysis methods for passive optical imagery is presented and discussed. Previous applications of remote sensing to identify and detecting SAV are briefly presented and notable results and lessons are discussed.

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“…As a result, we have implemented a real-time kinematic (RTK) global positioning system (GPS) survey procedure to locate points of interest with centimeter accuracy. Previously, we have discussed the advantages of the RTK method of Differential GPS (DGPS) operation using a local base station and a rover with an onsite radio frequency (RF) telemetry link (2). This report extends that concept using new GPS equipment, RF modems and a survey unit.…”

Section: Introductionmentioning

confidence: 96%

Real-time Kinematics Base Station and Survey Unit Setup Method for the Synchronous Impulse Reconstruction (SIRE) Radar

Koenig¹,

Kirose²

2012

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Real-time Kinematics Global Positioning System (GPS) Operation and Setup Method for the Synchronous Impulse Reconstruction (SIRE) Radar

Cited by 3 publications

References 1 publication

A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists

A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists

Remote sensing of submerged aquatic vegetation: an introduction and best practices review

Real-time Kinematics Base Station and Survey Unit Setup Method for the Synchronous Impulse Reconstruction (SIRE) Radar

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