Study of NSSDA Variability by Means of Automatic Positional Accuracy Assessment Methods

Ruiz-Lendínez, Juan José; López, Francisco Javier Ariza; Ureña-Cámara, Manuel Antonio

doi:10.3390/ijgi8120552

Cited by 6 publications

(4 citation statements)

References 30 publications

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“…PAAT is a point-based standard methodology (PBSM) which uses a series of measures to characterise the positional difference between two sets of geospatial data. It computes the random and systematic errors between a reference dataset and a target dataset, expressing differences through three metrics: mean residual, root mean square error (RMSE), and standard deviation [72][73][74]. Likewise, the degree of uncertainty is calculated using two metrics: absolute circular error (ACE) and relative circular error (RCE) at a 90% confidence level.…”

Section: Positional Accuracy Cum Vector-based Assessmentmentioning

confidence: 99%

Shoreline Delineation from Synthetic Aperture Radar (SAR) Imagery for High and Low Tidal States in Data-Deficient Niger Delta Region

Dike,

Oyetunji,

Amaechi

2023

JMSE

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Satellite image analysis is a potentially powerful tool for monitoring coastal shoreline positions. This study explores the use of multi-temporal, dual-polarised Sentinel-1 GRD synthetic aperture radar (SAR) imagery with a spatial resolution of 10 m for delineating shorelines. It was conducted in a data-deficient and complex environment (the Niger delta of Nigeria), in a developing country with a cloud-heavy climate. The study focuses on exploring and testing the capability of using multitemporal waterlines from SAR images to derive shoreline positions at high and low tidal states. From 54 Sentinel-1 images recorded in 2017, the study selected 12 images to represent both high and low tidal states. These were spread across the wet and dry seasons in order to account for seasonal differences. Shoreline positions were obtained by identifying the land–water boundary via segmentation using histogram-minimum thresholding, vectorizing and smoothing that boundary, and averaging its position over multiple waterlines. The land–water segmentation had an overall accuracy of 95–99%. It showed differences between wet and dry season shoreline positions in areas dominated by complex creek networks, but similarities along open coasts. The SAR-derived shorelines deviated from the reference lines by a maximum of 43 m (approximately four pixels), and often less than 10 m (one pixel) in most locations (open coast, estuarine, complex creek networks) at high and low tides, except low tide lines in areas with extensive inter-tidal flats at shorelines 70 m to 370 m from the reference lines. However, for applications such as coastal vulnerability assessment, the high tide shoreline is of greater importance. Thus, depending on the application of interest, problems with low tide shoreline delineation may be irrelevant. Despite limitations, notably the relatively small number of images available that were recorded at high or low tide, the method provides a simple, objective, and cost-effective approach to monitoring shorelines at high and low tide.

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Section: Positional Accuracy Cum Vector-based Assessmentmentioning

confidence: 99%

Shoreline Delineation from Synthetic Aperture Radar (SAR) Imagery for High and Low Tidal States in Data-Deficient Niger Delta Region

Dike,

Oyetunji,

Amaechi

2023

JMSE

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“…The Federal Geographic Data Committee recently proposed the National Standard for Spatial Data Accuracy (NSSDA), which is applicable to both analog and digital cartographic data [41]. This standard assumes a normal distribution of ε and uses the root mean square error (RMSE) as the most common and valid statistical measure for the evaluation of products obtained via photogrammetry and remote sensing.…”

Section: Determination Of the Errors Obtainedmentioning

confidence: 99%

Analysis of Topographic Surveys with RPAS in Steep Coastal Dunes

Contreras-de-Villar,

García,

Muñoz-Perez

et al. 2023

Land

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The current use of photogrammetric systems with RPAS (remotely piloted aircraft systems) for the acquisition of topographic data in every type of coastal area has the benefit of a low risk for the personnel involved, good precision, increased productivity, and lower costs. However, their accuracy has not yet been researched in areas with steep terrain. In this paper, we study dune areas with slopes between 20 and 30%. The objective of this work is to examine the influence of the inclination of both the flight and the camera and to compare the results with those obtained using constant-height flights. With the data collected during three field campaigns, a total of 18 cases were studied. Among the results obtained, in the case of the horizontal flights, a vertical error of 0.048 m was detected for a 0° inclination of the camera versus an error of 0.086 m for a 10° inclination, thus an increase in the camera inclination decreased the accuracy by 44%. Moreover, the flight inclination did not lead to a significant reduction in the error. Therefore, as a main conclusion, the planning of horizontal flights as well as the non-tilt of the camera are recommended.

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“…The National Standard for Spatial Data Accuracy (NSSDA) is a recent standard proposed by the Federal Geographic Data Committee (1998) [32] and can be used for both analog and digital cartographic data [33]. This standard assumes a normal distribution of ε and uses the root-mean-square error (RMSE) as the most common and valid statistic for the evaluation of products obtained by photogrammetry and remote sensing.…”

Section: Calculation Of the Errormentioning

confidence: 99%

Beach Leveling Using a Remotely Piloted Aircraft System (RPAS): Problems and Solutions

Contreras-de-Villar

García²,

Muñoz-Pérez

et al. 2020

JMSE

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The size and great dynamism of coastal systems require faster and more automated mapping methods like the use of a remotely piloted aircraft system (RPAS) or unmanned aerial vehicle (UAV). This method allows for shorter intervals between surveys. The main problem for surveying using low-altitude digital photogrammetry in beach areas is their visual homogeneity. Obviously, the fewer the homologous points defined by the program, the lower the accuracy. Moreover, some factors influence the error performed in photogrammetric techniques, such as flight height, flight time, percentage of frame overlap (side and forward), and the number of ground control points (GCPs). A total of 72 different cases were conducted varying these factors, and the results were analyzed. Among the conclusions, it should be highlighted that the error for noon flights is almost double that for the early morning flights. Secondly, there is no appreciable difference regarding the side overlap. But, on the other side, RMSE increased to three times (from 0.05 to 0.15 m) when forward overlap decreased from 85% to 70%. Moreover, relative accuracy is 0.05% of the flying height which means a significant increase in error (66%) between flights performed at 60 and 100 m height). Furthermore, the median of the error for noon flights (0.12 m) is almost double that for the early morning flights (0.07 m) because of the higher percentage of grids with data for early flights. Therefore, beach levelings must never be performed at noon when carried out by RPAS. Eventually, a new parameter has been considered: the relationship between the number of GCPs and the surface to be monitored. A minimum value of 7 GCP/Ha should be taken into account when designing a beach leveling campaign using RPAS.

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Study of NSSDA Variability by Means of Automatic Positional Accuracy Assessment Methods

Cited by 6 publications

References 30 publications

Shoreline Delineation from Synthetic Aperture Radar (SAR) Imagery for High and Low Tidal States in Data-Deficient Niger Delta Region

Shoreline Delineation from Synthetic Aperture Radar (SAR) Imagery for High and Low Tidal States in Data-Deficient Niger Delta Region

Analysis of Topographic Surveys with RPAS in Steep Coastal Dunes

Beach Leveling Using a Remotely Piloted Aircraft System (RPAS): Problems and Solutions

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