Quantifying the influence of slope, aspect, crown shape and stem density on the estimation of tree height at plot level using lidar and InSAR data J. BREIDENBACH*{, B. KOCH{, G. KA ¨ NDLER{ and A. KLEUSBERG§ {Department of Biometrics, Forest Research Institute (FVA) Baden-Wuerttemberg, Freiburg, Germany {Department of Remote Sensing and Landscape Information Systems (FELIS), University of Freiburg, Freiburg, Germany §Institute for Navigation (INS), University of Stuttgart, Stuttgart, Germany This study compared the use of light detection and ranging (lidar) data and X- band interferometric synthetic aperture radar (InSAR) data for estimating Lorey’s height (H L ) within inventory plots. At plot level, H L can be estimated with a root mean squared error (RMSE) of 1.8 m or 6.0% using lidar data and 2.7 m or 9.0% using InSAR data. The most effective predictor variables were found to be the 75th percentile of the vegetation heights for lidar (p75 lidar ) and the 90th percentile for InSAR (p90 InSAR ). Estimation results can be improved considerably by incorporating ground slope into the models. An increase in slope, given the same field-measured H L , was associated with an upward shift in height percentiles. We assume that this is a cause of the asymmetrical crown shape on slopes. An interaction between slope and aspect was noted for InSAR data, with a tendency for the influence of slope to be stronger on upward (i.e. for slopes facing the InSAR sensor) than downward slopes. Comparison of the lidar- and InSAR-derived vegetation heights revealed that the p90 InSAR is more commonly below the p75 lidar . This difference was greater on upward than downward slopes. Estimations of H L can be further improved if additional information, such as the conifer proportion (CP) (as a surrogate for crown shape) or stem density (SD), is included in the models. It was possible to substitute the predictor variable CP by means of density metrics in the statistical models that depend on InSAR data. However, this was not possible for the statistical models depending on lidar data. Density metrics were also not able to explain the variability that was explained by the predictor variable SD (this was the case for models that depend on lidar and on InSAR data). 1. Introduction Tree height is an important forest parameter that can be used to estimate further parameters such as stem volume or biomass relevant to forestry. Moreover, tree height is a parameter that can be measured using elevation measuring systems such as light detection and ranging (lidar) and interferometric synthetic aperture radar (InSAR) systems. As a result, the measurement of tree height has often been the focus of scientific research. Previous research into the estimation of tree height using lidar data has involved either single tree measurements (e.g. Hyyppa ¨ et al. 2001, Persson et al. 2002, Leckie et al. 2003, Gaveau and Hill 2003) or the use of certain *Corresponding author. Email: Johannes.Breidenbach@forst.bwl.de International Journal of Remote Sensing Vol. 29, No. 5, 10 March 2008, 1511–1536 International Journal of Remote Sensing ISSN 0143-1161 print/ISSN 1366-5901 online # 2008 Taylor & Francis http://www.tandf.co.uk/journals DOI: 10.1080/01431160701736364