Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics

By Jari Vauhkonen, Ilkka Korpela, Matti Maltamo, Timo Tokola. 2010

239 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/5c8029103513c998cddfa628a165f7b402c49c68

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In Environmental Engineering

In Insect Science

In Nature and Landscape Conservation

Citations in the corpus

Estimation of tree crown volume from airborne lidar data using computational geometry (2013, methodology): “However, the volume metrics based on the alpha shapes that we used are also useful for purposes other than CV estimation (Vauhkonen et al. 2010)”
Comparison of airborne laser scanning methods for estimating forest structure indicators based on Lorenz curves (2014, background): “Maltamo et al. (2004), Lindberg et al. (2010), Vauhkonen et al. (2010) and Vastaranta et al. (2012) combined ABA and ITD with the purpose of overcoming this difficulty and improving estimation accuracy.”
Estimation of stem attributes using a combination of terrestrial and airborne laser scanning (2012, methodology): “Previous studies have achieved slightly higher accuracy for estimation of tree level DBH, tree height, and stem volume from ALS data (e.g., Persson et al. 2002; Solberg et al. 2006; Vauhkonen et al. 2008, 2010).”
A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems (2016, methodology): “The random forest algorithm is now widely used for biomass estimation (Baccini et al. 2008; Eskelson et al. 2009; Vauhkonen et al. 2010; Avitabile et al. 2012; Hudak et al. 2012; Pflugmacher et al. 2014; Tanase et al. 2014).”
Evaluation of most similar neighbour and random forest methods for imputing forest inventory variables using data from target and auxiliary stands (2012, background): “In the forestry domain, previous studies such as Vauhkonen et al. (2010) and Latifi et al. 160180 140 120 RMSE (m3 ha–1) 100 80 60 N on e 10 20 30 40 50 60 70 80 90 10 0 11 0 12 0 13 0 S am pl es fr om K A 14 0 15 0 16 0 17 0 18 0 19 0 20 0 A ll 140160 120 100 80 60 40 N on e 10 20 30 40 50 60…”
Predicting the spatial pattern of trees by airborne laser scanning (2013, methodology): “Volume and area were derived from the convex hull of the ALS data, calculated in either horizontal two-dimensional space (2D, area), or by including a third, height, dimension (3D, volume); computation of the other ALS features mentioned above is explained in detail by Vauhkonen et al. (2010).”
Tree species classification and estimation of stem volume and DBH based on single tree extraction by exploiting airborne full-waveform LiDAR data (2012, methodology): “Moreover, Vauhkonen et al. (2010) adopted k-Most Similar Neighbor (k-MSN) and the Random Forest (RF) methods to simultaneously estimate species, DBH, height and stem volume using airborne LiDAR data based on a larger number of predictors, the respective RMSEs for volume and DBH were achieved with 31”
An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning (2012, methodology): “Nearest-neighbor methods applicable to single-tree interpretation are, therefore, currently under development [23,50,51].”
Trends in Automatic Individual Tree Crown Detection and Delineation - Evolution of LiDAR Data (2016, methodology): “[85–88] applied the alpha shape technique to select tree objects for predicting a range of parameters including crown base height, species and crown volume, and found that the approach is sensitive to the applied point density.”
Prediction of Stem Attributes by Combining Airborne Laser Scanning and Measurements from Harvesters (2012, methodology): “In Finland, tree species, tree height, stem diameter, and stem volume have been estimated simultaneously using both the MSN-method and RF with explanatory variables derived with alpha-shape models and the height and intensity distribution of laser data within manually delineated tree crowns (Vauhkon”
Individual tree detection from Unmanned Aerial Vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest (2017, background): “Imaging technology has been found to be more reliable in capturing spectral information of the upper canopy while LiDAR—due to its ability to penetrate into the canopy—is more efficient for vertical stratification of vegetation layers and terrain associated with dense canopies [58,96].”