Coveney, S.

English

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Coveney, S. (2010) Land-cover class as a qualifier for quoted elevation 
error in aerial LiDAR. In: 9th International Symposium on Spatial 
Accuracy Assessment in Natural Resources and Environmental Sciences, 
20-23 July 2010, University of Leicester, UK. 
 
Copyright © 2010 The Author 
 
 
A copy can be downloaded for personal non-commercial research or 
study, without prior permission or charge 
 
Content must not be changed in any way or reproduced in any format 
or medium without the formal permission of the copyright holder(s) 
 
 
When referring to this work, full bibliographic details must be given 
 
 
 
http://eprints.gla.ac.uk/87712/ 
 
 
 
 Deposited on: 15 November 2013 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Enlighten – Research publications by members of the University of Glasgow 
http://eprints.gla.ac.uk 
Land-cover class as a qualifier for quoted elevation 
error in aerial LiDAR. 
 
Seamus Coveney
 
National Centre for Geocomputation, NUI Maynooth Ireland 
Telephone: (00353-1-7086455) 
Fax: (00353-1-7086456) 
Email: seamus.coveney@nuim.ie 
 
Introduction 
LiDAR derived Digital Elevation data are used widely in the Geosciences to model 
topographically dependent environmental processes. High accuracies are not required for 
all applications, but modelling elevation sensitive processes such as flood or erosion risk 
generally requires data that accurately represent the ground surface. LiDAR Digital 
Surface Model (DSM) data do provide very high accuracies relative to many other 
classes of Digital Elevation Model (DEM). However, even LiDAR is subject to some 
error, and quite significant elevation errors can occur whenever dense ground vegetation 
cover interferes with laser illumination of a bare ground surface (Flood, 2004). 
Bare-earth LiDAR DEMs generated by reference to first and last laser returns (Lim et 
al., 2003, Hall et al., 2005; Webster, 2006) and from full waveform digitisation 
(Nayegandhi et al., 2006, Wagner et al., 2008) do facilitate the removal of much of the 
error that derives from the presence of vegetation. However, the efficiency of these 
methods tends to be reduced in densely vegetated areas (Flood, 2004). As a consequence 
of this, quoted LiDAR error may fail to account for actual error in all cases in natural 
areas (Hodgson and Bresnahan, 2004). This may be particularly problematic of the data 
are to be used to model elevation-sensitive environmental processes such as flood risk. 
The accuracy of three spatially-coincident aerial LiDAR datasets is examined in a 
low-lying coastal area (figure 2b) to clarify the relationship between generic land cover 
classes and LiDAR elevation error. Ground validation data are captured using FastStatic 
and RTK GPS data capture. The number of validation points used, the GPS survey 
methods employed, and the land-cover classes (table 1) assessed are designed to surpass 
the minimum requirements of the American Society of Photogrammetry and Remote 
Sensing (ASPRS) guidelines for the validation of LIDAR error (Flood, 2004).  
 
Generic class Land cover type 
Natural Open terrain (sand, rock, soil, ploughed 
fields, lawns, golf courses). 
Natural Brush lands and low trees. 
Natural / semi-natural Tall weeds and crops. 
Semi-natural Forested areas fully covered by trees. 
Anthropogenic Urban areas with dense man-made structures. 
Table 1. Land-cover classes evaluated (source ASPRS, 2004). 
 
Three aerial LiDAR datasets are used in the study. These include the onshore 
component of the INFOMAR (Integrated Mapping For the Sustainable Development of 
Ireland’s Marine Resource) bathymetric LiDAR dataset, the Office of Public Works 
(OPW) coastal management LiDAR dataset and the Ordnance Survey Ireland (OSI) bare-
earth LiDAR DEM dataset (figure 1). 
 
Methods 
Absolute elevation error is quantified for each dataset in idealised control areas prior 
to assessing the influence of ground vegetation in order to isolate LiDAR measurement 
error from residual ground vegetation error. LiDAR measurement error is determined 
using FastStatic GPS in open paved areas residual vegetation error is measured using a 
combination of FastStatic GPS (for urban and forested areas) and RTK GPS in Open 
terrain, Brush lands and cropland / weeded areas. 
 
 
   
Figure 1. OSi, OPW and INFOMAR LiDAR Coverages for Ireland 
 
 
  
Figure 2(a) Three LiDAR overlap areas and (b) optimal overlap area 
Three potential study areas are considered where all three LIDAR datasets overlap 
(figure 2a) and an area in the vicinity of Oranmore town (figure 2b) in Galway bay is 
selected due to the large overlap of all three datasets and the variable land cover present 
in the area. Data validation is carried out using ArcInfo Geostatistical Analyst, generating 
2.5D kriging prediction surfaces for each LiDAR dataset, and using the GPS data to 
validate data error in the five land cover classes tested. 
 
Discussion 
LiDAR measurement error is found to be broadly in agreement with the statistics 
provided by data suppliers in all cases, confirming the reliability of each dataset in non-
vegetated areas. However, the magnitude of the errors associated with the presence or 
absence of vegetation in each of the landcover classes tested are found to vary in relation 
to vegetation canopy density and depth. Errors substantially larger than the global errors 
quoted by data suppliers were found in all three LiDAR datasets, highlighting the 
difficulties that may arise when LiDAR data are used to model elevation sensitive 
processes (such as coastal flood risk) in natural areas. 
 
References and Citations 
Flood, M. (2004), Vertical Accuracy Reporting for Lidar Data. ASPRS Lidar Committee 
(PAD). 
Hall, S.A., Burke, I.C., Box, D.O., Kaufmann, M.R. & Stoker, J.M., (2005), Estimating 
stand structure using discrete-return LiDAR: An example from low density, fire prone 
ponderosa pine forests. Forest Ecology and Management, 208, pp. 189-209. 
Hodgson, M.E. and Bresnahan, P. (2004), Accuracy of Airborne Lidar-Derived 
Elevation: Empirical Assessment and Error Budget. Photogrammetric Engineering & 
Remote Sensing, 70, 3, pp. 331–339. 
Lim, K., Treitz, P., Wulder, M., St-Onge, B. & Flood, M., 2003, LiDAR remote sensing 
of forest structure. Progress in Physical Geography, 27, 1, pp. 88-106. 
Nayegandhi, A., Brock, J.C., Wright, C.W. & O’Connell, M.J., (2006), Evaluating a 
small footprint, waveform-resolving LiDAR over coastal vegetation communities, 
Photogrammetric Engineering and Remote Sensing, 72, pp. 1407–1417. 
Wagner, W., Hollaus, M., Briese, C. & Ducic, V. (2008) 3D vegetation mapping using 
small-footprint full-waveform airborne laser scanners, International Journal of Remote 
Sensing, 29, 5, pp. 1433–1452. 
Webster, T.L., Forbes, D.L., Mackinnon, E. & Roberts, D. (2006), Flood-risk mapping 
for storm-surge events and sea-level rise using LIDAR for southeast New Brunswick. 
Canadian Journal of Remote Sensing, 32, 2, pp. 194-211 


Land-cover class as a qualifier for quoted elevation error in aerial LiDAR

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,          Coveney, S. (2010) Land-cover class as a qualifier for quoted elevation error in aerial LiDAR. In: 9th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, 20-23 July 2010, University of Leicester, UK.  Copyright © 2010 The Author   A copy can be downloaded for personal non-commercial research or study, without prior permission or charge  Content must not be changed in any way or reproduced in any format or medium without the formal permission of the copyright holder(s)   When referring to this work, full bibliographic details must be given    http://eprints.gla.ac.uk/87712/     Deposited on: 15 November 2013                 Enlighten – Research publications by members of the University of Glasgow http://eprints.gla.ac.uk Land-cover class as a qualifier for quoted elevation error in aerial LiDAR.  Seamus Coveney National Centre for Geocomputation, NUI Maynooth Ireland Telephone: (00353-1-7086455) Fax: (00353-1-7086456) Email: seamus.coveney@nuim.ie  Introduction LiDAR derived Digital Elevation data are used widely in the Geosciences to model topographically dependent environmental processes. High accuracies are not required for all applications, but modelling elevation sensitive processes such as flood or erosion risk generally requires data that accurately represent the ground surface. LiDAR Digital Surface Model (DSM) data do provide very high accuracies relative to many other classes of Digital Elevation Model (DEM). However, even LiDAR is subject to some error, and quite significant elevation errors can occur whenever dense ground vegetation cover interferes with laser illumination of a bare ground surface (Flood, 2004). Bare-earth LiDAR DEMs generated by reference to first and last laser returns (Lim et al., 2003, Hall et al., 2005; Webster, 2006) and from full waveform digitisation (Nayegandhi et al., 2006, Wagner et al., 2008) do facilitate the removal of much of the error that derives from the presence of vegetation. However, the efficiency of these methods tends to be reduced in densely vegetated areas (Flood, 2004). As a consequence of this, quoted LiDAR error may fail to account for actual error in all cases in natural areas (Hodgson and Bresnahan, 2004). This may be particularly problematic of the data are to be used to model elevation-sensitive environmental processes such as flood risk. The accuracy of three spatially-coincident aerial LiDAR datasets is examined in a low-lying coastal area (figure 2b) to clarify the relationship between generic land cover classes and LiDAR elevation error. Ground validation data are captured using FastStatic and RTK GPS data capture. The number of validation points used, the GPS survey methods employed, and the land-cover classes (table 1) assessed are designed to surpass the minimum requirements of the American Society of Photogrammetry and Remote Sensing (ASPRS) guidelines for the validation of LIDAR error (Flood, 2004).   Generic class Land cover type Natural Open terrain (sand, rock, soil, ploughed fields, lawns, golf courses). Natural Brush lands and low trees. Natural / semi-natural Tall weeds and crops. Semi-natural Forested areas fully covered by trees. Anthropogenic Urban areas with dense man-made structures. Table 1. Land-cover classes evaluated (source ASPRS, 2004).  Three aerial LiDAR datasets are used in the study. These include the onshore component of the INFOMAR (Integrated Mapping For the Sustainable Development of Ireland’s Marine Resource) bathymetric LiDAR dataset, the Office of Public Works (OPW) coastal management LiDAR dataset and the Ordnance Survey Ireland (OSI) bare-earth LiDAR DEM dataset (figure 1).  Methods Absolute elevation error is quantified for each dataset in idealised control areas prior to assessing the influence of ground vegetation in order to isolate LiDAR measurement error from residual ground vegetation error. LiDAR measurement error is determined using FastStatic GPS in open paved areas residual vegetation error is measured using a combination of FastStatic GPS (for urban and forested areas) and RTK GPS in Open terrain, Brush lands and cropland / weeded areas.      Figure 1. OSi, OPW and INFOMAR LiDAR Coverages for Ireland     Figure 2(a) Three LiDAR overlap areas and (b) optimal overlap area Three potential study areas are considered where all three LIDAR datasets overlap (figure 2a) and an area in the vicinity of Oranmore town (figure 2b) in Galway bay is selected due to the large overlap of all three datasets and the variable land cover present in the area. Data validation is carried out using ArcInfo Geostatistical Analyst, generating 2.5D kriging prediction surfaces for each LiDAR dataset, and using the GPS data to validate data error in the five land cover classes tested.  Discussion LiDAR measurement error is found to be broadly in agreement with the statistics provided by data suppliers in all cases, confirming the reliability of each dataset in non-vegetated areas. However, the magnitude of the errors associated with the presence or absence of vegetation in each of the landcover classes tested are found to vary in relation to vegetation canopy density and depth. Errors substantially larger than the global errors quoted by data suppliers were found in all three LiDAR datasets, highlighting the difficulties that may arise when LiDAR data are used to model elevation sensitive processes (such as coastal flood risk) in natural areas.  References and Citations Flood, M. (2004), Vertical Accuracy Reporting for Lidar Data. ASPRS Lidar Committee (PAD). Hall, S.A., Burke, I.C., Box, D.O., Kaufmann, M.R. & Stoker, J.M., (2005), Estimating stand structure using discrete-return LiDAR: An example from low density, fire prone ponderosa pine forests. Forest Ecology and Management, 208, pp. 189-209. Hodgson, M.E. and Bresnahan, P. (2004), Accuracy of Airborne Lidar-Derived Elevation: Empirical Assessment and Error Budget. Photogrammetric Engineering & Remote Sensing, 70, 3, pp. 331–339. Lim, K., Treitz, P., Wulder, M., St-Onge, B. & Flood, M., 2003, LiDAR remote sensing of forest structure. Progress in Physical Geography, 27, 1, pp. 88-106. Nayegandhi, A., Brock, J.C., Wright, C.W. & O’Connell, M.J., (2006), Evaluating a small footprint, waveform-resolving LiDAR over coastal vegetation communities, Photogrammetric Engineering and Remote Sensing, 72, pp. 1407–1417. Wagner, W., Hollaus, M., Briese, C. & Ducic, V. (2008) 3D vegetation mapping using small-footprint full-waveform airborne laser scanners, International Journal of Remote Sensing, 29, 5, pp. 1433–1452. Webster, T.L., Forbes, D.L., Mackinnon, E. & Roberts, D. (2006), Flood-risk mapping for storm-surge events and sea-level rise using LIDAR for southeast New Brunswick. Canadian Journal of Remote Sensing, 32, 2, pp. 194-211 

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Land-cover class as a qualifier for quoted elevation error in aerial LiDAR

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