Accuracy assessment of georectified aerial photographs: Implications for measuring lateral channel movement in a GIS

Hughes, Michael L.; McDowell, P. F.; Marcus, W. Andrew

doi:10.1016/j.geomorph.2005.07.001

Cited by 263 publications

(193 citation statements)

References 25 publications

(36 reference statements)

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“…According to data herein described, the conventional and AUGEO system geo-referencing processes partially reduced the position error only to approximately 60 or 40 %, respectively; thus, additional geo-referencing is advisable to increase the positioning accuracy. These results are in accordance with previous geo-referencing studies by other authors who have used ''hard edge'' GCPs (Toutin and Chénier 2004;Hughes et al 2006) or ATTs in the case of the AUGEO system (Gómez-Candón et al 2011). However, both conventional and AUGEO geo-referencing processes require considerable human effort.…”

Section: Discussionsupporting

confidence: 92%

“…3 a The % of micro-plot non-overlapping area (%NOA), as affected by the RMSE and the micro-plot length (L from 1 to 40 m), assuming that the micro-plot width W is 6 m and the a, the geo-referencing error direction angle, as related to the field operating direction, is 0°. b The % of micro-plot non-overlapping area (%NOA), as affected by the micro-plot length (L from 1 to 40 m) and the geo-referencing error direction angle, as related to the field operating direction (a), assuming that the micro-plot width W is 6 m and the RMSE is 3 m procedure is in agreement with other authors, such as Toutin and Chénier (2004), Hughes et al (2006) and Weber et al (2008), who are very concerned about the need to use accurate training-site locations for geo-referencing remotely-sensed images. In general, with the current DGPS-technology, most remote images captured from satellites, conventional airplanes and UAV platforms hardly meet the position accuracy required and, consequently, additional geo-referencing effort is needed for site-specific management (Gómez-Candón et al 2011).…”

Section: Discussionsupporting

confidence: 89%

“…1b and then validating the results with 5 DGPS-assessed GCPs. A linear/first-order function was applied for the geo-referencing using the nearest neighbor re-sampling methods (Hughes et al 2006). The validation of the geo-referencing process was assessed for each GCP, and the PE/RMSE and U ge were calculated as previously described (Slama et al 1980;ERDAS Inc. 1999).…”

Section: Conventionalmentioning

confidence: 99%

“…The other six GeoEye-1 images of the studied series were co-registered through the ENVI menu ''image to image'' and taken as image references for the 4-May GeoEye-1. A linear/first-order function was applied for the geo-referencing using the nearest neighbor re-sampling methods (Hughes et al 2006). The validation of the geo-referencing process was assessed based on five DGPS-assessed GCPs; for each GCP, the total PE and error direction angle (U ge ) were estimated as previously described (Slama et al 1980;ERDAS Inc. 1999).…”

Section: Conventionalmentioning

confidence: 99%

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Understanding the errors in input prescription maps based on high spatial resolution remote sensing images

et al. 2012

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The aim of this study was to determine the positional accuracy of GeoEye-1 images and how it affects the delineation of the input prescription map (IPM) for sitespecific strategies. Seven panchromatic and multi-spectral GeoEye-1 satellite images were taken over the LaVentilla village area (Andalusia, Spain), from April to October 2010, at an interval of approximately 3-4 weeks. Sixteen hard-edge ground control points (GCPs) were geo-referenced using a sub-decimetre DGPS. Each DGPS-GCP position was compared with the corresponding co-ordinates for each image to determine the position error (PE) and error direction angle (U ge ). The PE and U ge for each GCP varied slightly for any given GeoEye-1 image and the overall PE among images estimated through the root mean square error (RMSE) varied considerably. RMSE ranged from approximately 2-9 m and from 3.5 to 9 m for the panchromatic and multi-spectral images studied, respectively, and the average was approximately 6.0 m for each of the series of images. Consequently, the geo-referencing of GeoEye-1 images is recommended to increase the positioning accuracy. Conventional geo-referencing using GCPs provided an average RMSE of 2 m for the panchromatic and 3.5 m for the multi-spectral images. The AUGEO System Ò geo-referencing of the 4-May GeoEye-1 image provided an RMSE of 0.75 m for the panchromatic and 2.70 ± 1.30 m for the multi-spectral images. The IPM delineated from remote-sensed images takes up the image geo-referencing error and, consequently, each micro-plot does not coincide with its corresponding ground-truth micro-plot. In this report, the percentage of non-overlapping area (%NOA) has been developed as a function of the PE/RMSE, a°(the angle between U ge and the operating direction, U op ), and the micro-plot size. The %NOA consistently increased as the RMSE and a°increased, and it decreased as the micro-plot width or length increased. The decision about micro-plot size should be based on the RMSE, a°, and the maximum admissible %NOA. In the case of the GeoEye-1 images studied with an average RMSE of 6 m, a micro-plot size of 6 9 30 m would have yielded an IPM inaccuracy (%NOA) of approximately 5 %, assuming an a°= 0°.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 89%

Section: Conventionalmentioning

confidence: 99%

Section: Conventionalmentioning

confidence: 99%

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Understanding the errors in input prescription maps based on high spatial resolution remote sensing images

et al. 2012

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“…On the other hand, the minimization of distortions in historical aerial photographs is achieved by scanning and georeferencing processes. This procedure provides a common geographic reference system that enables digital photograph overlapping and thus coastal change measurements, and the process results in a geometric correction of most of the aforementioned distortions (Hughes et al 2006), except the highest relief displacements, that can only be removed by orthorectification (Moore and Griggs 2002). Therefore, geometric correction is generally considered adequate for coastal areas without high cliffs (Hughes et al 2006).…”

mentioning

confidence: 99%

Error determination in the photogrammetric assessment of shoreline changes

Río

Prieto

2012

Nat Hazards

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The evaluation of error or uncertainty in shoreline change studies is an issue of prime importance for providing an adequate framework for calculated rates of change, and to allow the establishment of threshold values above which the rates would be significant. In this note a practical, easy-to-use method is presented to estimate error involved in the calculation of shoreline changes on aerial photographs, including the three most used types of shoreline indicators: high water line, dune/cliff toe and cliff top. This approach takes into account the specific characteristics of each shoreline proxy, such as relief in the case of the cliff top or tidal oscillations in the case of the high water line. At the same time it includes the error components that are independent from the proxy, basically related to the technical aspects of the process such as photo scanning and georeferencing. A practical example of application of the method is provided for several types of data inputs, based on shoreline changes around the Bay of Cadiz (SW Spain).

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Investigating decadal‐scale geomorphic dynamics in an alpine mountain setting

2015

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We know little about the effect of recent climate variability upon landscapes at the timescale of decades because of (1) the complex, nonlinear, and path‐dependent nature of the response of a landscape to climate forcing and (2) the difficulty of quantifying spatially distributed impacts at the timescale of decades to centuries, despite this being the timescale over which significant hypotheses have been raised over human impacts upon climate change and hence geomorphic systems. A unique resource to investigate the linkages between climatic variability and geomorphic response is provided by the extensive coverage of aerial imagery commonly available since the 1950s. Here we use archival digital photogrammetry to produce high‐precision digital elevation models over large spatial scales, and so to reconstruct the quantitative history of surface downwasting and sediment flux in a high mountain alpine system, over the timescales of decades. Propagation of error methods is used to identify locations of significant landscape response and to compute volumes of significant surface change. Orthorectified aerial images are used in an image correlation framework to detect horizontal and vertical displacements of components of the landscape. Results are coupled to extant climate data and modeled snow cover to show how the landscape responds to climate forcing and to geomorphological maps to understand how this response varies between landscape elements. The results show distinct landscape response to both warming and cooling periods and a tendency for the acceleration of surface displacement under warming conditions. Precipitation and snow cover are critical in controlling glacier dynamics and rock glacier displacement velocities. However, while some landforms might lead to locally high sediment flux, landscape heritage can disconnect zones of high change rates from the valley bottom. Hence, the landscape response to climate forcing is not necessarily reflected in valley system processes or sediment deposits.

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Accuracy assessment of georectified aerial photographs: Implications for measuring lateral channel movement in a GIS

Cited by 263 publications

References 25 publications

Understanding the errors in input prescription maps based on high spatial resolution remote sensing images

Understanding the errors in input prescription maps based on high spatial resolution remote sensing images

Error determination in the photogrammetric assessment of shoreline changes

Investigating decadal‐scale geomorphic dynamics in an alpine mountain setting

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