Modern Applied Statistics with S
Modern Applied Statistics with S
Preface: 3D Remote Sensing in Forestry
International Journal of Remote Sensing - 3D Remote Sensing in Forestry
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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).