Fusion of Small-Footprint Lidar and Multispectral Data to Estimate Plot- Level Volume and Biomass in Deciduous and Pine Forests in Virginia, USA

By Sorin C. Popescu, R. Wynne, John Scrivani. Forestry sciences, 2004

272 citations recorded by the source corpus.

The principal study objective was to explore the feasibility of using small-footprint lidar data and multispectral imagery to estimate forest volume and biomass on small (0.017-ha) plots. In addition, the spatial dependency of residuals between ground-measured and lidar-estimated variables was investigated. The lidar data set was acquired over deciduous and pine stands in the southeastern United States. Individual trees were identified on the lidar-derived canopy height model by local maximum focal filtering with both square and circular windows of variable size. The size of the dynamically varying window was based on the height of the canopy and the taxonomic group as derived from coregistered multispectral optical data. Lidar-measured parameters at an individual tree level (height, crown diameter) were used with regression models and cross validation to estimate plot-level field inventory data, including volume, biomass, basal area, and diameter at breast height (dbh). Maximum R2 values for estimating biomass were 0.32 for deciduous trees (RMSE 44 Mg/ha) and 0.82 for pines (RMSE 29 Mg/ha). When estimating volume, maximum R2 values for deciduous trees were 0.39 (RMSE 52.84 m3/ha) and 0.83 for pines (RMSE 47.9 m3/ha). Calculation of Moran's I coefficient for each regression model revealed a lack of significant autocorellation of the residuals at 0.05 level. Both model fit and prediction for volume and biomass models for deciduous and pine plots indicated that the circular window shape is more appropriate to locate individual trees with lidar. Using the fused data set, lidar and optical imagery, as opposed to using lidar data alone, always improved biomass and volume estimates for pines and in some cases for deciduous plots as well. FOR. SCI. 50(4):551–565.

Subfields: Environmental Engineering, Insect Science, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/5c7ce2b3c42405a7252cfd78a9973adec81135ab

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Citations in the corpus

The role of LiDAR in sustainable forest management (2008, background): “…the statistical properties of these grouped LiDAR pulses to a range of plot-level forestry attributes such as height, basal area, stem volume, above-ground biomass, and stem density (McCombs et al. 2003, Popescu et al. 2004, Reutebuch et al. 2005, van Aardt et al. 2006, Coops et al. 2007).”
A voxel-based lidar method for estimating crown base height for deciduous and pine trees (2008, methodology): “Lidar-derived tree dimensions: tree height and crown diameter Individual trees were located and measured on the lidarderived CHM by automated processing using the TreeVaW software, with algorithms described in Popescu and Wynne (2004) and Popescu et al. (2004).”
Landcover classification of small-footprint, full-waveform lidar data (2009, methodology): “However, elevations from lidar data are often utilized to provide an additional classification input or decision layer when combined with multispectral or hyperspectral imagery [8,9].”
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).”
Estimating stand volume in broad-leaved forest using discrete-return LiDAR: plot-based approach (2009, methodology): “…can be adopted with respect to the estimation using discrete-return lidar: (1) individual treebased (e.g., Persson et al. 2002; Maltamo et al. 2004; Popescu et al. 2003, 2004) and (2) stand/plot-based (e.g., Nelson et al. 1988; Næsset 1997, 2002; Means et al. 2000; Lim et al. 2003a; Lim and Treitz…”
Pine plantation structure mapping using WorldView-2 multispectral image (2013, background): “Transformed SAVI (TSAVI) Baret, Guyot, and Major (1989) Modified SAVI (MSAVI) Qi et al. (1994) Perpendicular vegetation index (PVI) Richardson and Weigand (1979) Modified chlorophyll absorption in reflectance index (MCARI) Daughtry et al.”
Comparison of small-footprint discrete return and full waveform airborne lidar data for estimating multiple forest variables (2016, background): “Such individual tree crown (ITC) metrics 111 have been identified as important inputs into predicative models of forest variables (e.g. 112 Hyyppä et al., 2001; Persson et al., 2002; Popescu et al., 2004).”
Advances in fusion of optical imagery and LiDAR point cloud applied to photogrammetry and remote sensing (2017, methodology): “Popescu et al. (2004) conclude that, using the fused data set, LiDAR and optical imagery, as opposed to using LiDAR data alone, always improved biomass and volume estimates for pines and in some cases for deciduous plots as well.”