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Remote Sens. 2012, 4(8), 2457-2476; doi:10.3390/rs4082457

Semi-Supervised Methods to Identify Individual Crowns of Lowland Tropical Canopy Species Using Imaging Spectroscopy and LiDAR

Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, USA
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Received: 24 June 2012 / Revised: 3 August 2012 / Accepted: 13 August 2012 / Published: 20 August 2012
(This article belongs to the Special Issue Remote Sensing of Biological Diversity)
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Abstract

Our objective is to identify and map individuals of nine tree species in a Hawaiian lowland tropical forest by comparing the performance of a variety of semi-supervised classifiers. A method was adapted to process hyperspectral imagery, LiDAR intensity variables, and LiDAR-derived canopy height and use them to assess the identification accuracy. We found that semi-supervised Support Vector Machine classification using tensor summation kernel was superior to supervised classification, with demonstrable accuracy for at least eight out of nine species, and for all combinations of data types tested. We also found that the combination of hyperspectral imagery and LiDAR data usually improved species classification. Both LiDAR intensity and LiDAR canopy height proved useful for classification of certain species, but the improvements varied depending upon the species in question. Our results pave the way for target-species identification in tropical forests and other ecosystems. View Full-Text
Keywords: biodiversity; Carnegie Airborne Observatory; species mapping; hyperspectral imagery; canopy height; LiDAR intensity; tropical forests; semi-supervised classification biodiversity; Carnegie Airborne Observatory; species mapping; hyperspectral imagery; canopy height; LiDAR intensity; tropical forests; semi-supervised classification
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Féret, J.-B.; Asner, G.P. Semi-Supervised Methods to Identify Individual Crowns of Lowland Tropical Canopy Species Using Imaging Spectroscopy and LiDAR. Remote Sens. 2012, 4, 2457-2476.

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