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Miscellanea Geographica

Regional Studies on Development

4 Issues per year


CiteScore 2016: 0.40

SCImago Journal Rank (SJR) 2015: 0.170
Source Normalized Impact per Paper (SNIP) 2015: 0.230

14 points in the Ministerial journal value rating scale.

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Online
ISSN
2084-6118
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Forest species mapping using airborne hyperspectral APEX data

Giulia Tagliabue
  • Remote Sensing of Environmental Dynamics Laboratory (LTDA), Department of Sciences and Technologies for Environment and Landscape (DISAT), University of Milano-Bicocca, Italy
  • Email:
/ Cinzia Panigada
  • Corresponding author
  • Remote Sensing of Environmental Dynamics Laboratory (LTDA), Department of Sciences and Technologies for Environment and Landscape (DISAT), University of Milano-Bicocca, Italy
  • Email:
/ Roberto Colombo
  • Remote Sensing of Environmental Dynamics Laboratory (LTDA), Department of Sciences and Technologies for Environment and Landscape (DISAT), University of Milano-Bicocca, Italy
  • Email:
/ Francesco Fava
  • Remote Sensing of Environmental Dynamics Laboratory (LTDA), Department of Sciences and Technologies for Environment and Landscape (DISAT), University of Milano-Bicocca, Italy
  • Email:
/ Chiara Cilia
  • Remote Sensing of Environmental Dynamics Laboratory (LTDA), Department of Sciences and Technologies for Environment and Landscape (DISAT), University of Milano-Bicocca, Italy
  • Email:
/ Frédéric Baret
  • Institut National de la Recherche Agronomique (INRA), France
  • Email:
/ Kristin Vreys
  • VITO Vlaamse Instelling voor Technologisch Onderzoek, Belgium
  • Email:
/ Koen Meuleman
  • VITO Vlaamse Instelling voor Technologisch Onderzoek, Belgium
  • Email:
/ Micol Rossini
  • Remote Sensing of Environmental Dynamics Laboratory (LTDA), Department of Sciences and Technologies for Environment and Landscape (DISAT), University of Milano-Bicocca, Italy
  • Email:
Published Online: 2016-04-20 | DOI: https://doi.org/10.1515/mgrsd-2016-0002

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

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About the article

Received: 2015-06-10

Accepted: 2015-12-15

Published Online: 2016-04-20

Published in Print: 2016-03-01



Citation Information: Miscellanea Geographica, ISSN (Online) 2084-6118, DOI: https://doi.org/10.1515/mgrsd-2016-0002. Export Citation

© by Cinzia Panigada. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)

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