The Accuracy and Coverage of Loran-C and of the Decca Navigator System — and the Fallacy of Fixed Errors

Last, D.

doi:10.1017/s0373463300010456

Cited by 6 publications

(6 citation statements)

References 2 publications

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“…The DECCA system was based on triangulation from land based radio transmitters. While it is difficult to estimate the magnitude of the positional error, as the accuracy of the DECCA system varies not only with distance from the land-based transmitting station but is also dependant on the season and the time of the day [50] , repeatable accuracies are assumed to be better than 400 m with 95% confidence [51] . With this positional accuracy indicated and the raster resolution of the acoustic data being only 10 m, it is likely that incorrect raster values will have been extracted for the ground-truth samples.…”

Section: Discussionmentioning

confidence: 99%

A Comparison of Supervised Classification Methods for the Prediction of Substrate Type Using Multibeam Acoustic and Legacy Grain-Size Data

2014

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Detailed seabed substrate maps are increasingly in demand for effective planning and management of marine ecosystems and resources. It has become common to use remotely sensed multibeam echosounder data in the form of bathymetry and acoustic backscatter in conjunction with ground-truth sampling data to inform the mapping of seabed substrates. Whilst, until recently, such data sets have typically been classified by expert interpretation, it is now obvious that more objective, faster and repeatable methods of seabed classification are required. This study compares the performances of a range of supervised classification techniques for predicting substrate type from multibeam echosounder data. The study area is located in the North Sea, off the north-east coast of England. A total of 258 ground-truth samples were classified into four substrate classes. Multibeam bathymetry and backscatter data, and a range of secondary features derived from these datasets were used in this study. Six supervised classification techniques were tested: Classification Trees, Support Vector Machines, k-Nearest Neighbour, Neural Networks, Random Forest and Naive Bayes. Each classifier was trained multiple times using different input features, including i) the two primary features of bathymetry and backscatter, ii) a subset of the features chosen by a feature selection process and iii) all of the input features. The predictive performances of the models were validated using a separate test set of ground-truth samples. The statistical significance of model performances relative to a simple baseline model (Nearest Neighbour predictions on bathymetry and backscatter) were tested to assess the benefits of using more sophisticated approaches. The best performing models were tree based methods and Naive Bayes which achieved accuracies of around 0.8 and kappa coefficients of up to 0.5 on the test set. The models that used all input features didn't generally perform well, highlighting the need for some means of feature selection.

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Section: Discussionmentioning

confidence: 99%

A Comparison of Supervised Classification Methods for the Prediction of Substrate Type Using Multibeam Acoustic and Legacy Grain-Size Data

2014

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“…In addition to control data sets, where point locations were unperturbed, the error treatments were 5, 25, 50, 200 and 400 m. The magnitude of error simulated in this study reflects the possible range of error that may be occurring during various sample techniques. While the smallest error margins are comparable to what might occur using modern positioning systems (Rattray et al 2014), locational uncertainty up to 400 m has been reported in historical data sets, such as where the Decca navigation system was used for positioning (Last 1992;Kubicki & Diesing 2006).…”

Section: Simulated Locational Uncertaintymentioning

confidence: 90%

“…While the smallest error margins are comparable to what might occur using modern positioning systems (Rattray et al . ), locational uncertainty up to 400 m has been reported in historical data sets, such as where the Decca navigation system was used for positioning (Last ; Kubicki & Diesing ).…”

Section: Methodsmentioning

confidence: 99%

“…When these records were digitized, geographic coordinates were often inferred from textual descriptions and may be substantially incorrect (Wieczorek, Guo & Hijmans ; Feeley & Silman ). Similarly, contemporary marine samples may have been positioned using outdated technology, such as the Decca navigation system, and may have positional errors on the order of hundreds of metres (Last ; Kubicki & Diesing ). This problem becomes important when the observation data are used to develop SDMs, as coordinates are used to extract the colocated environmental variables.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity of fine‐scale species distribution models to locational uncertainty in occurrence data across multiple sample sizes

2016

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Summary1. To generate realistic predictions, species distribution models require the accurate coregistration of occurrence data with environmental variables. There is a common assumption that species occurrence data are accurately georeferenced; however, this is often not the case. This study investigates whether locational uncertainty and sample size affect the performance and interpretation of fine-scale species distribution models. 2. This study evaluated the effects of locational uncertainty across multiple sample sizes by subsampling and spatially degrading occurrence data. Distribution models were constructed for kelp (Ecklonia radiata), across a large study site (680 km 2 ) off the coast of southeastern Australia. Generalized additive models were used to predict distributions based on fine-resolution (2Á5 m cell size) seafloor variables, generated from multibeam echosounder data sets, and occurrence data from underwater towed video. The effects of different levels of locational uncertainty in combination with sample size were evaluated by comparing model performance and predicted distributions. 3. While locational uncertainty was observed to influence some measures of model performance, in general this was small and varied based on the accuracy metric used. However, simulated locational uncertainty caused changes in variable importance and predicted distributions at fine scales, potentially influencing model interpretation. This was most evident with small sample sizes. 4. Results suggested that seemingly high-performing, fine-scale models can be generated from data containing locational uncertainty, although interpreting their predictions can be misleading if the predictions are interpreted at scales similar to the spatial errors. This study demonstrated the need to consider predictions across geographic space rather than performance alone. The findings are important for conservation managers as they highlight the inherent variation in predictions between equally performing distribution models, and the subsequent restrictions on ecological interpretations.

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“…The samples were collected prior to the introduction of the Global Positioning System (GPS) using the Decca Main Chain system and substantial positional errors are to be expected (Kubicki and Diesing, 2006). Repeatable accuracies are assumed to be better than 400 m with 95% confidence (Last, 1992). The samples were collected using a Shipek type sediment grab.…”

Section: Datamentioning

confidence: 99%

A multi-model ensemble approach to seabed mapping

Diesing

Stephens

2015

Journal of Sea Research

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The Accuracy and Coverage of Loran-C and of the Decca Navigator System — and the Fallacy of Fixed Errors

Cited by 6 publications

References 2 publications

A Comparison of Supervised Classification Methods for the Prediction of Substrate Type Using Multibeam Acoustic and Legacy Grain-Size Data

A Comparison of Supervised Classification Methods for the Prediction of Substrate Type Using Multibeam Acoustic and Legacy Grain-Size Data

Sensitivity of fine‐scale species distribution models to locational uncertainty in occurrence data across multiple sample sizes

A multi-model ensemble approach to seabed mapping

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