title: Biodiversity mapping in a tropical West African forest with airborne hyperspectral data.
abstract: Tropical forests are major repositories of biodiversity, but are fast disappearing as land is converted to agriculture. Decision-makers need to know which of the remaining forests to prioritize for conservation, but the only spatial information on forest biodiversity has, until recently, come from a sparse network of ground-based plots. Here we explore whether airborne hyperspectral imagery can be used to predict the alpha diversity of upper canopy trees in a West African forest. The abundance of tree species were collected from 64 plots (each 1250 m(2) in size) within a Sierra Leonean national park, and Shannon-Wiener biodiversity indices were calculated. An airborne spectrometer measured reflectances of 186 bands in the visible and near-infrared spectral range at 1 m(2) resolution. The standard deviations of these reflectance values and their first-order derivatives were calculated for each plot from the c. 1250 pixels of hyperspectral information within them. Shannon-Wiener indices were then predicted from these plot-based reflectance statistics using a machine-learning algorithm (Random Forest). The regression model fitted the data well (pseudo-R(2) = 84.9%), and we show that standard deviations of green-band reflectances and infra-red region derivatives had the strongest explanatory powers. Our work shows that airborne hyperspectral sensing can be very effective at mapping canopy tree diversity, because its high spatial resolution allows within-plot heterogeneity in reflectance to be characterized, making it an effective tool for monitoring forest biodiversity over large geographic scales.
authors.affiliation: Impacts on Agriculture, Forest, and Natural Ecosystems Division, Euro-Mediterranean Center on Climate Change, Viterbo, Italy; Department of Civil Engineering and Computer Sciences Engineering, Tor Vergata University, Rome, Italy.
authors.forename: Gaia
authors.initials: G
authors.lastname: Vaglio Laurin
authors.affiliation: Foxlab, Fondazione Edmund Mach, S. Michele all' Adige, Italy; Department of Electronics and Informatics, Vrije Universiteit Brussel, Brussels, Belgium.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Cheung-Wai Chan
authors.affiliation: Department of Geography, University of Hawai'i at Mānoa, Honolulu, Hawaii, United States of America.
authors.forename: Qi
authors.initials: Q
authors.lastname: Chen
authors.affiliation: The Royal Society for the Protection of Birds, Sandy, Bedfordshire, United Kingdom; A Rocha International, Cambridge, United Kingdom.
authors.forename: Jeremy A
authors.initials: JA
authors.lastname: Lindsell
authors.affiliation: Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom.
authors.forename: David A
authors.initials: DA
authors.lastname: Coomes
authors.affiliation: Department of Civil Engineering and Computer Sciences Engineering, Tor Vergata University, Rome, Italy.
authors.forename: Leila
authors.initials: L
authors.lastname: Guerriero
authors.affiliation: Department of Civil Engineering and Computer Sciences Engineering, Tor Vergata University, Rome, Italy.
authors.forename: Fabio
authors.initials: F
authors.lastname: Del Frate
authors.affiliation: Foxlab, Fondazione Edmund Mach, S. Michele all' Adige, Italy; Institute of Biometeorology, National Research Council, Firenze, Italy; Laboratory of Ecohydrology, Civil and Environmental Engineering, École Polytechnique Fédérale, Lausanne, Switzerland; MountFor Project Centre European Forest Institute, Fondazione E.Mach, San Michele all' Adige, Italy.
authors.forename: Franco
authors.initials: F
authors.lastname: Miglietta
authors.affiliation: Impacts on Agriculture, Forest, and Natural Ecosystems Division, Euro-Mediterranean Center on Climate Change, Viterbo, Italy; Department of Forest Resources and Environment, University of Tuscia, Viterbo, Italy.
authors.forename: Riccardo
authors.initials: R
authors.lastname: Valentini
mesh_terms: D001185:Artificial Intelligence; D044822:Biodiversity; D003198:Computer Simulation; D003247:Conservation of Natural Resources; D003625:Data Collection / Q000379:methods; D065947:Rainforest; D012044:Regression Analysis; D012807:Sierra Leone; D012815:Signal Processing, Computer-Assisted; D014197:Trees; D014329:Tropical Climate