A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach
By Joanne C. White, M. Wulder, Andrés Varhola, Mikko Vastaranta, Nicholas C. Coops, Bruce Douglas Cook, Doug Pitt, Murray E. Woods. 2013
341 citations recorded by the source corpus.
Subfields: Environmental Engineering, Insect Science, Nature and Landscape Conservation
View Paper: https://www.semanticscholar.org/paper/9c670a9f604fe52124dcc3e7d90a9c4eb99ade60
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
- Matti Maltamo
- Petteri Packalen
- Erik Næsset
- Terje Gobakken
- Lauri Mehtätalo
- Ruben Valbuena
- Timothy G. Gregoire
- Ross Nelson
In Insect Science
- Petteri Packalen
- Matti Maltamo
- Terje Gobakken
- Erik Næsset
- Lauri Mehtätalo
- Ruben Valbuena
- Timothy G. Gregoire
- Ross Nelson
In Nature and Landscape Conservation
- Matti Maltamo
- Petteri Packalen
- Erik Næsset
- Terje Gobakken
- Lauri Mehtätalo
- Ruben Valbuena
- Timothy G. Gregoire
- Ross Nelson
Citations in the corpus
- Key structural features of Boreal forests may be detected directly using L-moments from airborne lidar data (2017, methodology): “These descriptors of ALS return height distributions are metrics commonly employed as auxiliary variables in forest assessment (e.g., Nassset, 2002; White et al., 2013; Asner and Mascaro, 2014).”
- Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations (2017, methodology): “, 90 percentile height used here) are largely unaffected by pulse densities above 1 pulse m−2 (Jakubowski et al., 2013; White et al., 2013).”