Reliability of LiDAR derived predictors of forest inventory attributes: A case study with Norway spruce

By Steen Magnussen, Erik Næsset, Terje Gobakken. 2010

86 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/01179f608241ac118c7656adba8ca557b33a2807

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Terje Gobakken
2. Lauri Korhonen
3. Erik Næsset
4. Joanne C. White
5. Nicholas C. Coops
6. M. Wulder
7. C. Stepper
8. Richard A. Fournier

In Insect Science

1. Terje Gobakken
2. Lauri Korhonen
3. Erik Næsset
4. C. Stepper
5. Richard A. Fournier
6. M. Wulder
7. Joanne C. White
8. Nicholas C. Coops

In Nature and Landscape Conservation

1. Terje Gobakken
2. Lauri Korhonen
3. Erik Næsset
4. C. Stepper
5. Richard A. Fournier
6. Joanne C. White
7. M. Wulder
8. Nicholas C. Coops

Citations in the corpus

• A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach (2013, unknown): “Ground plots must represent the full range of variability of both response and predictor variables (Montgomery et al. 2006; Magnussen et al. 2010a; Frazer et al. 2011b), and be of sufficient size to avoid edge effects and minimize georeferencing errors (Gobakken and Næsset 2009; Frazer et al.…”
• Laser-assisted selection of field plots for an area-based forest inventory (2013, methodology): “ALS-based stem volume models usually have both height and density variables as predictors (e.g. Holmgren 2004; Magnussen et al. 2010) and using h70f (describes canopy height) and d0f (describes canopy density) to define the strata was therefore reasonable.”
• Predicting wood quantity and quality attributes of balsam fir and black spruce using airborne laser scanner data (2014, background): “more generalized applicability of ALS-based models (Hopkinson et al., 2006; Magnussen et al., 2010).”
• Comparing ALS and Image-Based Point Cloud Metrics and Modelled Forest Inventory Attributes in a Complex Coastal Forest Environment (2015, background): “Airborne laser scanning (ALS) is now widely acknowledged as an important data source for forest inventories [1].”
• Analysis of the Influence of Plot Size and LiDAR Density on Forest Structure Attribute Estimates (2014, background): “[35] found that a minimum point density is needed to reduce the replication variance in LiDAR derived predictions.”