Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction

By Felix Morsdorf, B. Kötz, Erich H. Meier, Klaus I. Itten, Britta Allgöwer. 2006

477 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/a0dfa395af943c1f304fde8c9a27f483ea18fdfb

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In Ecology

In Environmental Engineering

In Nature and Landscape Conservation

Citations in the corpus

The role of LiDAR in sustainable forest management (2008, methodology): “Morsdorf et al. (2006) used small-footprint discrete return LiDAR to estimate Leaf Area Index (LAI) and fractional cover (fraction of ground covered by vegetation over uncovered ground), with the latter computed as the fraction of laser vegetation returns over the total num- NOVEMBER/DECEMBER 2008,…”
Development of a UAV-LiDAR System with Application to Forest Inventory (2012, background): “The maximum scanning range of the Ibeo LUX is 110◦, although this is restricted to±30◦ as large scan angles have been shown to have a significant impact on the derivation of key metrics used for forest investigation [30].”
Mapping gap fraction, LAI and defoliation using various ALS penetration variables (2010, background): “…an underestimation of gap fraction, and hence an underestimation of the defoliation, because the footprint size of a laser pulse might be too large to produce first echoes after penetrating down small canopy gaps produced by defoliating insect larvae (Lovell et al. 2003, Morsdorf et al. 2006).”
Extracting leaf area index using viewing geometry effects—A new perspective on high-resolution unmanned aerial system photography (2018, background): “…Schurr, 2013; Walter et al., 2015).34 Especially plant growth is a trait of enduring interest—the ability to grow is a central driver of yield35 in crops (White et al., 2016) and an important parameter in assessments in plant ecology (Asner et al.,36 2003; Moser et al., 2007; Clark et al., 2008).”
Testing LiDAR models of fractional cover across multiple forest ecozones (2009, methodology): “Morsdorf et al. (2006) found that the best FCLiDAR estimate was returned when only the first and single return data were used to predict FCHP for the first two overhead HP annulus rings.”
Improving leaf area index (LAI) estimation by correcting for clumping and woody effects using terrestrial laser scanning (2018, methodology): “Various studies have used DHP as validation for gap fraction and LAI measurements (Hopkinson and Chasmer, 2009; Korhonen et al., 2011; Morsdorf et al., 2006).”
Modeling approaches to estimate effective leaf area index from aerial discrete-return LIDAR (2009, methodology): “These estimates were obtained in order to find the best relationship between the conical view of the hemispherical photograph and LIDAR metrics obtained from a cylinder, as has been performed previously (Morsdorf et al., 2006).”
Generalizing predictive models of forest inventory attributes using an area-based approach with airborne LiDAR data (2015, methodology): “The gap fraction was estimated as previously described, by the ratio between the number of returns below a specified height threshold and the total number of returns, as suggested by Morsdorf et al. (2006) and Hopkinson et al. (2013).”