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Miscellanea Geographica

Regional Studies on Development

4 Issues per year


CiteScore 2016: 0.40

SCImago Journal Rank (SJR) 2016: 0.227
Source Normalized Impact per Paper (SNIP) 2016: 0.404

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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
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/ 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
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/ Roberto Colombo
  • Remote Sensing of Environmental Dynamics Laboratory (LTDA), Department of Sciences and Technologies for Environment and Landscape (DISAT), University of Milano-Bicocca, Italy
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/ Francesco Fava
  • Remote Sensing of Environmental Dynamics Laboratory (LTDA), Department of Sciences and Technologies for Environment and Landscape (DISAT), University of Milano-Bicocca, Italy
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/ Chiara Cilia
  • Remote Sensing of Environmental Dynamics Laboratory (LTDA), Department of Sciences and Technologies for Environment and Landscape (DISAT), University of Milano-Bicocca, Italy
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/ Frédéric Baret / Kristin Vreys / Koen Meuleman / Micol Rossini
  • Remote Sensing of Environmental Dynamics Laboratory (LTDA), Department of Sciences and Technologies for Environment and Landscape (DISAT), University of Milano-Bicocca, Italy
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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

References

  • Alonzo, M, Bookhagen, B & Roberts, DA 2014, ‘Urban tree species mapping using hyperspectral and LiDAR data fusion’, Remote Sensing of Environment, vol. 148, pp. 70-83.Google Scholar

  • Baldeck, CA, Asner, GP, Martin, RE, Anderson, CB, Knapp, DE, Kellner, JR & Wright, SJ 2015, ‘Operational Tree Species Mapping in a Diverse Tropical Forest with Airborne Imaging Spectroscopy’, PLOS ONE, vol. 10, no. 7, pp. 1-21.Google Scholar

  • Boschetti, M, Boschetti, L, Oliveri, S, Casati, L & Canova, I 2007, ‘Tree species mapping with airborne hyperspectral MIVIS data: the Ticino Park study case’, International Journal of Remote Sensing, vol. 28, no. 6, pp. 1251-1261.CrossrefGoogle Scholar

  • Cho, MA, Mathieu, R, Asner, GP, Naidoo, L, van Aardt, J, Ramoelo, A, Debba, P, Wessels, K, Main, R, Smit, IPJ & Erasmus, B 2012, ‘Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system’, Remote Sensing of Environment, vol. 125, pp. 214-226.Web of ScienceCrossrefGoogle Scholar

  • Clark, ML, Roberts, DA & Clark, DB 2005, ‘Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales’, Remote Sensing of Environment, vol. 96, pp. 375-398.CrossrefGoogle Scholar

  • Colgan, M, Baldeck, C, Féret, JB & Asner, GP 2012, ‘Mapping Savanna Tree Species at Ecosystem Scales Using Support Vector Machine Classification and BRDF Correction on Airborne Hyperspectral and LiDAR Data’, Remote Sensing, vol. 4, no. 11, pp. 3462-3480.Google Scholar

  • Dalponte, M, Bruzzone, L & Gianelle, D 2008, ‘Fusion of Hyperspectral and LiDAR Remote Sensing Data for Classification of Complex Forest Areas’, IEEE Transactions on Geoscience and Remote Sensing, vol. 46, no. 5, pp. 1416-1427.CrossrefGoogle Scholar

  • Dalponte, M, Bruzzone, L & Gianelle, D 2012, ‘Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data’, Remote Sensing of Environment, vol. 123, pp. 258-270.Google Scholar

  • Dalponte, M, Orka, HO, Gobakken, T, Gianelle, D & Naesset, E 2013, ‘Tree Species Classification in Boreal Forests With Hyperspectral Data’, IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 5, pp. 2632-2645.CrossrefGoogle Scholar

  • Filella, I & Penuelas, J 1994, ‘The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status’, International Journal of Remote Sensing, vol. 15, no. 7, pp. 1459-1470.CrossrefGoogle Scholar

  • Franklin, SE 2001, Remote Sensing for Sustainable Forest Management, CRC Press, Boca Raton.Google Scholar

  • Gao, BC, Montes, MJ & Davis, CO 2004, ‘Refinement of wavelength calibrations of hyperspectral imaging data using a spectrum-matching technique’, Remote Sensing of Environment, vol. 90, no. 4, pp. 424-433.CrossrefGoogle Scholar

  • Ghosh, A, Fassnacht, FE, Joshi, PK & Koch, B 2014, ‘A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales’, International Journal of Applied Earth Observation and Geoinformation, vol. 26, pp. 49-63.Web of ScienceCrossrefGoogle Scholar

  • Horler, DNH, Dockray, M & Barber, J 1983, ‘The red edge of plant leaf reflectance’, International Journal of Remote Sensing, vol. 4, no. 2, pp. 273-288.CrossrefGoogle Scholar

  • Hu, B, Miller, JR, Zarco-Tejada, PJ, Freemantle, J & Zwick, H 2008, ‘Boreal forest mapping at the BOREAS study area using seasonal optical indices sensitive to plant pigment content’, Canadian Journal of Remote Sensing, vol. 34, pp. 158-171.CrossrefGoogle Scholar

  • Hughes, G 1968, ‘On the mean accuracy of statistical pattern recognizers’, IEEE Transactions on Information Theory, vol. 14, no. 1, pp. 55-63.CrossrefGoogle Scholar

  • Jones, TG, Coops, NC, & Sharma, T 2010, ‘Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada’, Remote Sensing of Environment, vol. 114, no. 12, pp. 2841-2852.Web of ScienceCrossrefGoogle Scholar

  • Kempeneers, P, Van Coillie, F, Liao, W, Devriendt, F & Vandekerkhove, K 2014, ‘Tree species mapping by combining hyperspectral with LiDAR data’, Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Québec.Google Scholar

  • Linke, J, Betts, MG, Lavigne, MB & Franklin, SE 2006, ‘Introduction: structure, function and change of forest landscapes’ in Understanding forest disturbance and spatial pattern: Remote sensing and GIS approaches, eds. M Wulder & SE Franklin, Taylor & Francis Group, Abingdon, pp. 1-29.Google Scholar

  • Marcinkowska, A, Zagajewski, B, Ochtyra, A, Jarocińska, A, Raczko, E, Kupková, L, Stych, P & Meuleman, K 2014, ‘Mapping vegetation communities of the Karkonosze National Park using APEX hyperspectral data and Support Vector Machines’, Miscellanea Geographica, vol. 18, no. 2, pp. 23-29.Google Scholar

  • Pandey, PC, Tate, NJ & Balzter, H 2014, ‘Mapping Tree Species in Coastal Portugal Using Statistically Segmented Principal Component Analysis and Other Methods’, IEEE Sensors Journal, vol. 14, no. 12, pp. 4434-4441.Web of ScienceCrossrefGoogle Scholar

  • Panigada, C, Rossini, M, Busetto, L, Meroni, M, Fava, F & Colombo, R 2010, ‘Chlorophyll concentration mapping with MIVIS data to assess crown discoloration in the Ticino Park oak forest’, International Journal of Remote Sensing, vol. 31, no. 12, pp. 3307-3332.CrossrefWeb of ScienceGoogle Scholar

  • Zarco-Tejada, PJ & Miller, JR 1999, ‘Land cover mapping at BOREAS using red edge spectral parameters from CASI imagery’, Journal of Geophysical Research, vol. 104, no. D22, pp. 27921-27933.CrossrefGoogle Scholar

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.

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

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