Automated estimation of individual conifer tree height and crown diameter via two-dimensional spatial wavelet analysis of lidar data

Abstract

We describe and evaluate a new analysis technique, spatial wavelet analysis (SWA), to automatically estimate the location, height, and crown diameter of individual trees within mixed conifer open canopy stands from light detection and ranging (lidar) data. Two-dimensional Mexican hat wavelets, over a range of likely tree crown diameters, were convolved with lidar canopy height models. Identification of local maxima within the resultant wavelet transformation image then allowed determination of the location, height, and crown diameters of individual trees. In this analysis, which focused solely on individual trees within open canopy forests, 30 trees incorporating seven dominant North American tree species were assessed. Two-dimensional (2D) wavelet-derived estimates were well correlated with field measures of tree height (r = 0.97) and crown diameter (r = 0.86). The 2D wavelet-derived estimates compared favorably with estimates derived using an established method that uses variable window filters (VWF) to estimate the same variables but relies on a priori knowledge of the tree height-crown diameter relationship. The 2D spatial wavelet analysis presented herein could potentially allow automated, large-scale, remote estimation of timber board feet, foliar biomass, canopy volume, and aboveground carbon, although further research testing the limitations of the method in a variety of forest types with increasing canopy closures is warranted. Résumé. Nous décrivons et évaluons une nouvelle technique d'analyse, l'analyse spatiale en ondelettes (ASO) pour estimer automatiquement la localisation, la hauteur et le diamètre de la couronne des arbres individuels dans des peuplements de conifères mixtes à couvert ouvert à partir de données lidar (light detection and ranging). Des ondelettes à deux dimensions de type chapeau mexicain pour une variété de diamètres de couronnes d'arbres potentiels ont été convoluées avec des modèles de hauteur de couvert lidar. L'identification de maximums locaux à l'intérieur de l'image de transformation en ondelettes résultante a permis par la suite la détermination de la localisation, de la hauteur et des diamètres des couronnes des arbres individuels. Dans cette analyse, qui s'est concentrée seulement sur les arbres individuels à l'intérieur de forêts à couvert ouvert, 30 arbres représentant sept espèces dominantes d'arbres d'Amérique du Nord ont été évalués. Les estimations 2-D dérivées des ondelettes étaient bien corrélées avec les mesures de terrain de la hauteur des arbres (r = 0,97) et du diamètre de la couronne (r = 0,86). Les estimations 2-D dérivées des ondelettes se comparaient avantageusement aux estimations dérivées à l'aide d'une méthode établie utilisant des filtres de fenêtre variable (FFV) pour l'estimation de ces mêmes variables, mais qui est basée sur la connaissance a priori de la relation hauteur des arbres/diamètre de la couronne. L'analyse spatiale en ondelettes 2-D présentée ici pourrait potentiellement permettre l'estimation automatisée à grande échelle par télédétecti...

Estimation of the mean tree height of forest stands by photogrammetric measurement using digital aerial images of high spatial resolution

“…According to such studies, tree height underestimation occurs because tree tops cannot be accurately determined in analogue aerial photographs of low spatial resolution and because terrain elevations (next to the tree) cannot be read because of dense crown canopies. The underestimation of tree heights also commonly occurs with other remote sensing methods, such as the most up-to-date LiDAR technology, whether it is the height estimation of individual trees (e.g., Falkowski et al 2006, Heurich 2008, Lin et al 2012, Hunter et al 2012 or of the mean stand heights (e.g., Naesset 2002b, Coops et al 2007, Hyyppä et al 2008. The underestimations of LiDAR-estimated individual tree heights reported by above-mentioned studies ranged from 0.43 to 1.20 m, whereas underestimations of LiDAR-estimated mean stand heights ranged up to 3.30 m. The magnitude of underestimation, i.e.…”

Extraction of Mangrove Biophysical Parameters Using Airborne LiDAR

“…However, the results indicate overestimation, as illustrated by the high commission errors. There are several factors that may influence tree detection overestimates, including CHM pixel size [29], canopy shape, and canopy density [43,58]. For this study, the overestimation is mainly due to the effect of the multiple treetops, which relate to the canopy characteristics and the performance of segmented algorithm.…”

“…The IWS method involves the extended application of watershed segmentation to individual tree crown delineation by inverting a raster surface to the equivalent of individual hydrologic drainage basins [29,43,[54][55][56][57]. In this study, the CHM was inverted, which resulted in turning the peaks upside down into depressions.…”

“…Variable Window Filtering (VWF) and Inverse Watershed Segmentation (IWS) are LiDAR processing techniques that are typically applied in a variety of applications. Various studies have applied both algorithms to derive forest attributes [29,32,42,43]. VWF is an adaptive local maximum filtering approach with a circular window of variable window size, which is useful for individual tree crown delineation.…”

“…VWF is an adaptive local maximum filtering approach with a circular window of variable window size, which is useful for individual tree crown delineation. IWS initially aims to segment the tree crown over the high-spatial-resolution image [44] and has been recently applied to the LiDAR-derived CHM [43,45]. IWS starts with the inversion of the raster canopy surface into the equivalent of individual hydrologic drainage basins [29,45].…”