Quantifying the impact of woody material on leaf area index estimation from hemispherical photography using 3D canopy simulations

By William Woodgate, John Armston, Mathias I. Disney, Simon D. Jones, Lola Suárez, Michael J. Hill, P. Wilkes, M. Soto‐Berelov. 2016

55 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/28804039379f7b90dda4182fc87106940833eb78

How researchers see this paper

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

In Ecology

1. Mathias I. Disney
2. William Woodgate
3. John Armston
4. P. Wilkes
5. Matheus Boni Vicari

In Environmental Engineering

1. Mathias I. Disney
2. Kim Calders

Citations in the corpus

• Variability and bias in active and passive ground-based measurements of effective plant, wood and leaf area index (2018, methodology): “This model has been tested in previous studies against other models (Widlowski et al., 2015, 2007; Pinty et al., 2004), as well as against observations (Disney et al., 2006, 2011; Calders et al., 2013; Woodgate et al., 2016).”
• Leaf and wood classification framework for terrestrial LiDAR point clouds (2019, background): “Although many TLS ecological methodologies presented promising results, separating the mixture of materials in a point cloud, that is wood and leaf, remains one of the main challenges for the accuracy and reliability of these estimates (Ma et al., 2016; Woodgate et al., 2016; Zhu et al., 2018).”