Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data

By Hans Ole Ørka, Erik Næsset, Ole Martin Bollandsås. 2009

280 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/9a8c0e5e95b04e4c32175a3a2a51540bbc69bf24

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In Environmental Engineering

In Insect Science

In Nature and Landscape Conservation

Citations in the corpus

Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution (2013, background): “Complementary to spectral imagery, high-resolution airborne laser scanning (ALS) has become an operational tool in the last decade, which has rendered the classification of individual species more feasible (Brandtberg, 2007; Ørka et al., 2009; Heinzel and Koch, 2011).”
FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY (2016, methodology): “Also re-lated to that strategy are methods for classifying tree species in forestry, e.g. (Orka et al., 2012, Dalponte et al., 2012).”
Assessment of Low Density Full-Waveform Airborne Laser Scanning for Individual Tree Detection and Tree Species Classification (2014, result): “[38] used the RF method to classify tree species based on laser-derived features or a subset of features in Norway and compared the results with those of support vector machines (SVMs) and linear discriminant analysis (LDA).”
Comparison of nearest neighbour approaches for small area estimation of tree species-specific forest inventory attributes in central Europe using airborne laser scanner data (2010, background): “These variables are thus more related to the classification of tree species than to the estimation of timber volume (e.g., Ørka et al. 2009).”
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): “For example, Ørka et al. (2009) obtained an overall classification accuracy of 88% for birch and spruce, and Heinzel et al. (2008) of 84% for discriminating between coniferous trees, beech and oak/hornbeam, when LiDAR was combined with CIR true orthoimages.”
Tree Species Classification in Boreal Forests With Hyperspectral Data (2013, methodology): “using remote sensing data has mainly focused on the use of multispectral data [4], [5] or light detection and ranging (LiDAR) [6], [7] from airborne platforms.”
Classifying individual tree genera using stepwise cluster analysis based on height and intensity metrics derived from airborne laser scanner data (2011, methodology): “Recently, forest stand types or tree species classification have been studied using laser scanner datasets (Brantberg, 2007; Brandtberg et al., 2003; Brennan & Webster, 2006; Donoghue et al., 2007; Holmgren & Persson, 2004; Kim et al., 2009a, 2009b; Moffiet et al., 2005; Ørka et al., 2009).”