title: A macroecological analysis of SERA derived forest heights and implications for forest volume remote sensing.
abstract: Individual trees have been shown to exhibit strong relationships between DBH, height and volume. Often such studies are cited as justification for forest volume or standing biomass estimation through remote sensing. With resolution of common satellite remote sensing systems generally too low to resolve individuals, and a need for larger coverage, these systems rely on descriptive heights, which account for tree collections in forests. For remote sensing and allometric applications, this height is not entirely understood in terms of its location. Here, a forest growth model (SERA) analyzes forest canopy height relationships with forest wood volume. Maximum height, mean, H₁₀₀, and Lorey's height are examined for variability under plant number density, resource and species. Our findings, shown to be allometrically consistent with empirical measurements for forested communities world-wide, are analyzed for implications to forest remote sensing techniques such as LiDAR and RADAR. Traditional forestry measures of maximum height, and to a lesser extent H₁₀₀ and Lorey's, exhibit little consistent correlation with forest volume across modeled conditions. The implication is that using forest height to infer volume or biomass from remote sensing requires species and community behavioral information to infer accurate estimates using height alone. SERA predicts mean height to provide the most consistent relationship with volume of the height classifications studied and overall across forest variations. This prediction agrees with empirical data collected from conifer and angiosperm forests with plant densities ranging between 10²-10⁶ plants/hectare and heights 6-49 m. Height classifications investigated are potentially linked to radar scattering centers with implications for allometry. These findings may be used to advance forest biomass estimation accuracy through remote sensing. Furthermore, Lorey's height with its specific relationship to remote sensing physics is recommended as a more universal indicator of volume when using remote sensing than achieved using either maximum height or H₁₀₀.
authors.affiliation: Department of Geographical Sciences, University of Maryland, College Park, Maryland, United States of America. M.Brolly@umd.edu
authors.forename: Matthew
authors.initials: M
authors.lastname: Brolly
authors.forename: Iain H
authors.initials: IH
authors.lastname: Woodhouse
authors.forename: Karl J
authors.initials: KJ
authors.lastname: Niklas
authors.forename: Sean T
authors.initials: ST
authors.lastname: Hammond
mesh_terms: D044822:Biodiversity; D018533:Biomass; D014197:Trees