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Photogrammetric and laser altimetric reconstruction of water levels for extreme flood event analysis
Author(s) -
Lane Stuart N.,
James Timothy D.,
Pritchard Hamish,
Saunders Mark
Publication year - 2003
Publication title -
the photogrammetric record
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 51
eISSN - 1477-9730
pISSN - 0031-868X
DOI - 10.1046/j.0031-868x.2003.00022.x
Subject(s) - photogrammetry , flood myth , elevation (ballistics) , lidar , remote sensing , digital elevation model , event (particle physics) , aerial survey , scale (ratio) , geology , cartography , geography , engineering , physics , archaeology , quantum mechanics , structural engineering
This paper assesses the feasibility of estimating water levels using digital photogrammetry. A common problem during an extreme flood event is that automated water level recorders do not record the highest water levels, as a result of instrument malfunctioning. This paper explores two possible solutions to this problem based upon data acquired using synoptic remote sensing methods. The first method requires: (a) high‐resolution elevation data (for example, in the form of a digital elevation model for the floodplain); and (b) information on the planimetric position of the maximum flood extent, such as from debris lines (known as wrack lines) visible on aerial imagery flown after the event. The planimetric data can then be used to segment the topographic data in order to identify water level elevations. The second method uses a digitial photogrammetric approach and is suitable where no topographic data are available, but aerial imagery is available, flown after the event. Provided this imagery is of the right scale, digital photogrammetric analysis may be used to identify the elevations of wrack lines visible on the imagery. In this paper, the second of these options is compared with the first. The research shows that desktop photogrammetric methods, using 1:4500 scale imagery, can yield water level estimates that are precise to ±0·147 m, on the basis of check data obtained from lidar data. This is a worst possible estimate of the acquired precision given uncertainties in the lidar data. When compared with the first option, based upon segmenting lidar data using flood outlines, the photogrammetric approach was found to be preferable given both the quality of the lidar and uncertainties over how to segment it.