Influence of Fusing Lidar and Multispectral Imagery on Remotely Sensed Estimates of Stand Density and Mean Tree Height in a Managed Loblolly Pine Plantation

By J. McCombs, S. Roberts, David L. Evans. Forestry sciences, 2003

151 citations recorded by the source corpus.

Stereo aerial photography has long been used to measure tree density and height photogrammetrically. Recent attempts have been made to locate and measure trees automatically in high-resolution digital imagery. This study used small-footprint lidar (1.057 µm, 1 mrad divergence, 0.67 m footprint) and high-resolution (0.61 m) multispectral (550, 675, 700, and 800 nm) data sets to estimate stem counts and tree heights in 15-yr-old loblolly pine stands. A data fusion process was used to combine the datasets. Tree identification accuracy and mean height estimation derived from the separate and fused data sets were compared against field data. Tree identification was more accurate using spectral data (78.6% and 92.4%) than lidar data (64.7% and 87.3%) within the two planting densities, respectively. The fused dataset improved accuracy of tree identification over the single-dataset approaches (83.5% and 94.8%). Plot-level mean height of lidar-located trees provided the best estimates of mean field height (average difference = 0.15 m). Missed trees for all methods were shorter than mean field height by up to 3.03 m (fused data). These results indicate fusion of spectral and lidar data will likely improve estimates of mean tree height and stem density. Increased lidar posting density is identified as a key factor to improve tree recognition and measurement. FOR. SCI.49(3):457–466.

Subfields: Ecology, Environmental Engineering, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/ff18c261a73fe8e1b2b8b4337b4bcb9c3d081bc6

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Citations in the corpus

A voxel-based lidar method for estimating crown base height for deciduous and pine trees (2008, methodology): “…discrete-returns systems were used to estimate canopy characteristics , with many studies focusing on tree height (Magnussen & Boudewyn, 1998; Magnussen et al., 1999; Maltamo et al., 2004; McCombs et al., 2003; Naesset, 1997; Naesset & Økland, 2002; Popescu & Wynne, 2004; Popescu et al., 2002).”
Predicting individual stem volumes of sugi (Cryptomeria japonica D. Don) plantations in mountainous areas using small-footprint airborne LiDAR (2005, background): “…airborne LiDAR data offered the possibility to detect most canopy trees and hardly any subcanopy trees of smaller height, such as suppressed or intermediate trees (Holmgren et al. 2003a; Maltamo et al. 2004; McCombs et al. 2003; Persson et al. 2002; Takahashi et al. 2005; Yone et al. 2002).”
Species identification of individual trees by combining high resolution LiDAR data with multi‐spectral images (2008, methodology): “Fusion of LIDAR data and multi-spectral aerial images has been studied for tree detection and measurement of tree heights (e.g. Leckie et al. 2003, McCombs et al. 2003, St-Onge et al. 2004), and for estimation of volume and biomass (e.g. Popescu et al. 2004).”
Fusion of Small-Footprint Lidar and Multispectral Data to Estimate Plot- Level Volume and Biomass in Deciduous and Pine Forests in Virginia, USA (2004, background): “…very intense and resulted in a series of studies that demonstrated that lidar technology is well suited for providing estimates of forest biophysical parameters (Næsset and Bjerknes 2001, Næsset and Økland 2002, Persson et al. 2002, Popescu et al. 2002, Holmgren et al. 2003, McCombs et al. 2003).”