title: Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy.
abstract: Remote identification and mapping of canopy tree species can contribute valuable information towards our understanding of ecosystem biodiversity and function over large spatial scales. However, the extreme challenges posed by highly diverse, closed-canopy tropical forests have prevented automated remote species mapping of non-flowering tree crowns in these ecosystems. We set out to identify individuals of three focal canopy tree species amongst a diverse background of tree and liana species on Barro Colorado Island, Panama, using airborne imaging spectroscopy data. First, we compared two leading single-class classification methods--binary support vector machine (SVM) and biased SVM--for their performance in identifying pixels of a single focal species. From this comparison we determined that biased SVM was more precise and created a multi-species classification model by combining the three biased SVM models. This model was applied to the imagery to identify pixels belonging to the three focal species and the prediction results were then processed to create a map of focal species crown objects. Crown-level cross-validation of the training data indicated that the multi-species classification model had pixel-level producer's accuracies of 94-97% for the three focal species, and field validation of the predicted crown objects indicated that these had user's accuracies of 94-100%. Our results demonstrate the ability of high spatial and spectral resolution remote sensing to accurately detect non-flowering crowns of focal species within a diverse tropical forest. We attribute the success of our model to recent classification and mapping techniques adapted to species detection in diverse closed-canopy forests, which can pave the way for remote species mapping in a wider variety of ecosystems.
authors.affiliation: Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, United States of America.
authors.forename: Claire A
authors.initials: CA
authors.lastname: Baldeck
authors.affiliation: Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, United States of America.
authors.forename: Gregory P
authors.initials: GP
authors.lastname: Asner
authors.affiliation: Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, United States of America.
authors.forename: Robin E
authors.initials: RE
authors.lastname: Martin
authors.affiliation: Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, United States of America.
authors.forename: Christopher B
authors.initials: CB
authors.lastname: Anderson
authors.affiliation: Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, United States of America.
authors.forename: David E
authors.initials: DE
authors.lastname: Knapp
authors.affiliation: Department of Ecology and Evolutionary Biology, Brown University, Providence, RI, United States of America.
authors.forename: James R
authors.initials: JR
authors.lastname: Kellner
authors.affiliation: Smithsonian Tropical Research Institute, Washington, DC, United States of America.
authors.forename: S Joseph
authors.initials: SJ
authors.lastname: Wright
mesh_terms: D016001:Confidence Intervals; D065928:Forests; D005843:Geography; D021621:Imaging, Three-Dimensional / Q000379:methods*; D062312:Islands; D010176:Panama; D013045:Species Specificity; D013057:Spectrum Analysis / Q000379:methods*; D060388:Support Vector Machine; D014197:Trees / Q000502:physiology*; D014329:Tropical Climate