Prediction of species specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data

By Johannes Breidenbach, Erik Næsset, V. Lien, Terje Gobakken, Svein Solberg. 2010

271 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/123a7040d23f84097a1eaa9a1271b0774db6abc9

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In Environmental Engineering

In Insect Science

In Nature and Landscape Conservation

Citations in the corpus

Trends in Automatic Individual Tree Crown Detection and Delineation - Evolution of LiDAR Data (2016, methodology): “The potential for combining optical imagery and active remotely sensed data has also been exploited, especially for individual species identification [10,23].”
Prediction of Stem Attributes by Combining Airborne Laser Scanning and Measurements from Harvesters (2012, methodology): “In Norway, stem volume was estimated for all trees on plots and for trees of different tree species applying both the MSN-method and the RF method (Breidenbach et al. 2010).”
Evaluation of most similar neighbour and random forest methods for imputing forest inventory variables using data from target and auxiliary stands (2012, result): “In support of previous reports by Latifi et al. (2010a) and Breidenbach et al. (2010) concerning lower bias of MSN compared to RF predictions, some negligibly unbiased MSN estimates compared to RF for all response variables and neighbourhood sizes were achieved here.”
Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion (2018, background): “This is generally due to an inability to determine the appropriate kernel size to simultaneously minimize omission and commission error with respect to tree stem identification [36,37].”
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, methodology): “While the plot level methods are used in operational forest inventories in Norway since several years (Næsset 2004), adequate methods for using single tree data derived from ALS in forest inventories were proposed only recently (Lindberg et al. 2010; Breidenbach et al. 2010).”
Comparison of airborne laser scanning methods for estimating forest structure indicators based on Lorenz curves (2014, background): “Moreover, Breidenbach et al. (2010) introduced the idea of semi-ITD, in which all trees measured inside a given segment are considered to be represented by that segment, and not just the dominating tree.”
An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning (2012, background): “In the second phase, accurately located field trees are linked to the corresponding segments [53–56].”
A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems (2016, background): “…has been extensively used for land cover classification (McRoberts and Tomppo 2007; Latifi et al. 2010; Li et al. 2011) and estimation of forest stand parameters (Fazakas et al. 1999; Finley and McRoberts 2008; Fuchs et al. 2009; Breidenbach et al. 2010; Zhou et al. 2011; McRoberts et al. 2012).”
Variable selection strategies for nearest neighbor imputation methods used in remote sensing based forest inventory (2012, methodology): “Numerous distance metrics are used in NN imputation and many comparisons of them have been published recently (e.g., LeMay and Temesgen, 2005; Chirici et al., 2008; Hudak et al., 2008; Breidenbach et al., 2010; Latifi et al., 2010).”
Predicting wood quantity and quality attributes of balsam fir and black spruce using airborne laser scanner data (2014, background): “, 2009) and to predict species-specific stand attributes (Packalen and Maltamo, 2007; Hudak et al., 2008a,b; Hyyppä et al., 2008; Breidenbach et al., 2010).”
Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: application of multiple optical/LiDAR-derived predictors (2010, background): “Though Breidenbach et al. (2010) also reported a larger bias of RF compared with MSN models when modelling the total standing volume in plot scale, the slightly higher bias of the RF was overcompensated by significantly reduced RMSE of the RF method in this study.”
Comparative testing of single-tree detection algorithms under different types of forest (2011, methodology): “A plotwise regression model Hcandidate ~ f(Hfield) was constructed, removing those tree-candidate links which had a height difference larger than 2× standard error of the model (cf. Breidenbach et al., 2010).”