title: A hyperspectral image can predict tropical tree growth rates in single-species stands.
abstract: Remote sensing is increasingly needed to meet the critical demand for estimates of forest structure and composition at landscape to continental scales. Hyperspectral images can detect tree canopy properties, including species identity, leaf chemistry and disease. Tree growth rates are related to these measurable canopy properties but whether growth can be directly predicted from hyperspectral data remains unknown. We used a single hyperspectral image and light detection and ranging-derived elevation to predict growth rates for 20 tropical tree species planted in experimental plots. We asked whether a consistent relationship between spectral data and growth rates exists across all species and which spectral regions, associated with different canopy chemical and structural properties, are important for predicting growth rates. We found that a linear combination of narrowband indices and elevation is correlated with standardized growth rates across all 20 tree species (R2= 53.70%). Although wavelengths from the entire visible-to-shortwave infrared spectrum were involved in our analysis, results point to relatively greater importance of visible and near-infrared regions for relating canopy reflectance to tree growth data. Overall, we demonstrate the potential for hyperspectral data to quantify tree demography over a much larger area than possible with field-based methods in forest inventory plots.
authors.affiliation: School of Forest Resources and Conservation, University of Florida, Gainesville, Florida 32601, USA.
authors.forename: T Trevor
authors.initials: TT
authors.lastname: Caughlin
authors.affiliation: School of Forest Resources and Conservation, University of Florida, Gainesville, Florida 32601, USA.
authors.forename: Sarah J
authors.initials: SJ
authors.lastname: Graves
authors.affiliation: Department of Global Ecology, Carnegie Institution for Science, Stanford, California 94305, USA.
authors.forename: Gregory P
authors.initials: GP
authors.lastname: Asner
authors.affiliation: Yale-NUS College, Singapore, Singapore.|Smithsonian Tropical Research Institute, Apartado, Panama, 0843-03092 Panama.
authors.forename: Michiel
authors.initials: M
authors.lastname: van Breugel
authors.affiliation: Smithsonian Tropical Research Institute, Apartado, Panama, 0843-03092 Panama.
authors.forename: Jefferson S
authors.initials: JS
authors.lastname: Hall
authors.affiliation: Department of Global Ecology, Carnegie Institution for Science, Stanford, California 94305, USA.
authors.forename: Roberta E
authors.initials: RE
authors.lastname: Martin
authors.affiliation: Yale School of Forestry & Environmental Studies, New Haven, Connecticut 06511, USA.
authors.forename: Mark S
authors.initials: MS
authors.lastname: Ashton
authors.affiliation: School of Forest Resources and Conservation, University of Florida, Gainesville, Florida 32601, USA.|Smithsonian Tropical Research Institute, Apartado, Panama, 0843-03092 Panama.
authors.forename: Stephanie A
authors.initials: SA
authors.lastname: Bohlman
mesh_terms: D003710:Demography; D065928:Forests; D018515:Plant Leaves; D014197:Trees; D014329:Tropical Climate
keywords: Panama; canopy biology; field planting trials; forest dynamics; hyperspectral; light detection and ranging; plantation; precision forestry; reforestation; remote sensing; tree demography; tropical forest