Forest species mapping using airborne hyperspectral APEX data

Tagliabue Giulia (), Panigada Cinzia (), Colombo Roberto (), Fava Francesco (), Cilia Chiara (), Baret Frédéric (), Vreys Kristin (), Meuleman Koen () and Rossini Micol ()
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Tagliabue Giulia: Remote Sensing of Environmental Dynamics Laboratory (LTDA), Department of Sciences and Technologies for Environment and Landscape (DISAT), University of Milano-Bicocca, Italy
Panigada Cinzia: Remote Sensing of Environmental Dynamics Laboratory (LTDA), Department of Sciences and Technologies for Environment and Landscape (DISAT), University of Milano-Bicocca, Italy
Colombo Roberto: Remote Sensing of Environmental Dynamics Laboratory (LTDA), Department of Sciences and Technologies for Environment and Landscape (DISAT), University of Milano-Bicocca, Italy
Fava Francesco: Remote Sensing of Environmental Dynamics Laboratory (LTDA), Department of Sciences and Technologies for Environment and Landscape (DISAT), University of Milano-Bicocca, Italy
Cilia Chiara: Remote Sensing of Environmental Dynamics Laboratory (LTDA), Department of Sciences and Technologies for Environment and Landscape (DISAT), University of Milano-Bicocca, Italy
Baret Frédéric: Institut National de la Recherche Agronomique (INRA), France
Vreys Kristin: VITO Vlaamse Instelling voor Technologisch Onderzoek, Belgium
Meuleman Koen: VITO Vlaamse Instelling voor Technologisch Onderzoek, Belgium
Rossini Micol: Remote Sensing of Environmental Dynamics Laboratory (LTDA), Department of Sciences and Technologies for Environment and Landscape (DISAT), University of Milano-Bicocca, Italy

Miscellanea Geographica. Regional Studies on Development, 2016, vol. 20, issue 1, 28-33

Abstract: The accurate mapping of forest species is a very important task in relation to the increasing need to better understand the role of the forest ecosystem within environmental dynamics. The objective of this paper is the investigation of the potential of a multi-temporal hyperspectral dataset for the production of a thematic map of the dominant species in the Forêt de Hardt (France). Hyperspectral data were collected in June and September 2013 using the Airborne Prism EXperiment (APEX) sensor, covering the visible, near-infrared and shortwave infrared spectral regions with a spatial resolution of 3 m by 3 m. The map was realized by means of a maximum likelihood supervised classification. The classification was first performed separately on images from June and September and then on the two images together. Class discrimination was performed using as input 3 spectral indices computed as ratios between red edge bands and a blue band for each image. The map was validated using a testing set selected on the basis of a random stratified sampling scheme. Results showed that the algorithm performances improved from an overall accuracy of 59.5% and 48% (for the June and September images, respectively) to an overall accuracy of 74.4%, with the producer’s accuracy ranging from 60% to 86% and user’s accuracy ranging from 61% to 90%, when both images (June and September) were combined. This study demonstrates that the use of multi-temporal high-resolution images acquired in two different vegetation development stages (i.e., 17 June 2013 and 4 September 2013) allows accurate (overall accuracy 74.4%) local-scale thematic products to be obtained in an operational way.

Keywords: Vegetation map; Hyperspectral; Aerial; Supervised classification; Multi-temporal dataset; Forest ecosystem (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:mgrsod:v:20:y:2016:i:1:p:28-33:n:4

DOI: 10.1515/mgrsd-2016-0002

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