Species identification of individual trees by combining high resolution LiDAR data with multi‐spectral images

By Johan Holmgren, Å. Persson, Ulf Söderman. 2008

339 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/0016ec7e0b270c75a96d7f71787c44cdf5e3f8be

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In Environmental Engineering

In Insect Science

In Nature and Landscape Conservation

Citations in the corpus

Predicting wood quantity and quality attributes of balsam fir and black spruce using airborne laser scanner data (2014, methodology): “In boreal forests with relatively few tree species, ALS data have been used to predict the species of individual trees (Holmgren et al., 2008; Orka et al., 2009) and to predict species-specific stand attributes (Packalen and Maltamo, 2007; Hudak et al.”
Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution (2013, positive): “A similar idea is to combine LiDAR and multi-spectral data, the former to delineate individual trees and the later to distinguish among the different species (Holmgren et al., 2008; Chubey et al., 2009; Ke et al., 2010), or data from sensors providing additional spectral or 3-”
Estimating crown base height for Scots pine by means of the 3D geometry of airborne laser scanning data (2010, methodology): “The methods based on return frequencies (Holmgren and Persson 2004, Solberg et al. 2006, Popescu and Zhao 2008), cross-sectional area (Holmgren et al. 2008) and linear regression (Maltamo et al. 2006, Popescu and Zhao 2008) were adapted slightly for the present purpose.”
Semi-automatic classification of tree species in different forest ecosystems by spectral and geometric variables derived from Airborne Digital Sensor (ADS40) and RC30 data (2011, methodology): “…the extraction of trees (Straub, 2003; St-Onge et al., 2004; Hirschmugl et al., 2007; Waser et al., 2008a & 2008b) or tree species classification lead to better accuracies than using only a single data input (Heinzel et al., 2008; Holmgren et al., 2008; Lamonaca et al., 2008; Chubey et al., 2009).”
Effects of pulse density on predicting characteristics of individual trees of Scandinavian commercial species using alpha shape metrics based on airborne laser scanning data (2008, background): “Furthermore, according to Holmgren et al. (2008), the economically most important Scandinavian species groups (pine, spruce, and deciduous) can be classified with an accuracy of 95% by combining ALS data with multispectral image material.”
Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data (2012, background): “Such datasets can either be derived from LiDAR data [52,61,69,71,73] or from the spectral images themselves [74] providing that they cover the area of interest stereoscopically.”
Estimation of tree lists from airborne laser scanning by combining single-tree and area-based methods (2010, background): “A mixture of tree species within field plots is also a motivation for tree crown segmentation because tree species information may be derived from the tree crown segments (Brandtberg et al. 2003, Holmgren and Persson 2004, Holmgren et al. 2008).”
Tree Species Classification in Boreal Forests With Hyperspectral Data (2013, methodology): “In Finland, Korpela et al. [7] classified Spruce, Pine, and Birch using LiDAR data, obtaining a best kappa accuracy of 0.84.”
Prediction of Stem Attributes by Combining Airborne Laser Scanning and Measurements from Harvesters (2012, methodology): “High accuracy tree species classification results have been obtained in earlier studies using variables extracted from ALS data within crown segments (Holmgren and Persson 2004; Holmgren et al. 2008).”
Contemporary forest restoration: A review emphasizing function (2014, background): “Advances in the use of Light Detection and Ranging (LiDAR) when coupled with multi-spectral data, however, are providing land managers ways to discern species composition and structure in a variety of ecosystems (e.g., Hill and Thomson, 2005; Bork and Su, 2007; Holmgren et al., 2008), occurrence of ”