Forest species mapping using airborne hyperspectral APEX data
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...
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| Format: | Article |
| Language: | English |
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Sciendo
2016-03-01
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| Series: | Miscellanea Geographica: Regional Studies on Development |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/mgrsd-2016-0002 |
| _version_ | 1818733999771615232 |
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| author | Tagliabue Giulia Panigada Cinzia Colombo Roberto Fava Francesco Cilia Chiara Baret Frédéric Vreys Kristin Meuleman Koen Rossini Micol |
| author_facet | Tagliabue Giulia Panigada Cinzia Colombo Roberto Fava Francesco Cilia Chiara Baret Frédéric Vreys Kristin Meuleman Koen Rossini Micol |
| author_sort | Tagliabue Giulia |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T23:58:23Z |
| format | Article |
| id | doaj.art-f613cd3ca82643e88e3f03b45cfe899a |
| institution | Directory Open Access Journal |
| issn | 2084-6118 |
| language | English |
| last_indexed | 2024-12-17T23:58:23Z |
| publishDate | 2016-03-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Miscellanea Geographica: Regional Studies on Development |
| spelling | doaj.art-f613cd3ca82643e88e3f03b45cfe899a2022-12-21T21:28:01ZengSciendoMiscellanea Geographica: Regional Studies on Development2084-61182016-03-01201283310.1515/mgrsd-2016-0002mgrsd-2016-0002Forest species mapping using airborne hyperspectral APEX dataTagliabue Giulia0Panigada Cinzia1Colombo Roberto2Fava Francesco3Cilia Chiara4Baret Frédéric5Vreys Kristin6Meuleman Koen7Rossini Micol8Remote Sensing of Environmental Dynamics Laboratory (LTDA), Department of Sciences and Technologies for Environment and Landscape (DISAT), University of Milano-Bicocca, ItalyRemote Sensing of Environmental Dynamics Laboratory (LTDA), Department of Sciences and Technologies for Environment and Landscape (DISAT), University of Milano-Bicocca, ItalyRemote Sensing of Environmental Dynamics Laboratory (LTDA), Department of Sciences and Technologies for Environment and Landscape (DISAT), University of Milano-Bicocca, ItalyRemote Sensing of Environmental Dynamics Laboratory (LTDA), Department of Sciences and Technologies for Environment and Landscape (DISAT), University of Milano-Bicocca, ItalyRemote Sensing of Environmental Dynamics Laboratory (LTDA), Department of Sciences and Technologies for Environment and Landscape (DISAT), University of Milano-Bicocca, ItalyInstitut National de la Recherche Agronomique (INRA), FranceVITO Vlaamse Instelling voor Technologisch Onderzoek, BelgiumVITO Vlaamse Instelling voor Technologisch Onderzoek, BelgiumRemote Sensing of Environmental Dynamics Laboratory (LTDA), Department of Sciences and Technologies for Environment and Landscape (DISAT), University of Milano-Bicocca, ItalyThe 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.https://doi.org/10.1515/mgrsd-2016-0002vegetation maphyperspectralaerialsupervised classificationmulti-temporal datasetforest ecosystem |
| spellingShingle | Tagliabue Giulia Panigada Cinzia Colombo Roberto Fava Francesco Cilia Chiara Baret Frédéric Vreys Kristin Meuleman Koen Rossini Micol Forest species mapping using airborne hyperspectral APEX data Miscellanea Geographica: Regional Studies on Development vegetation map hyperspectral aerial supervised classification multi-temporal dataset forest ecosystem |
| title | Forest species mapping using airborne hyperspectral APEX data |
| title_full | Forest species mapping using airborne hyperspectral APEX data |
| title_fullStr | Forest species mapping using airborne hyperspectral APEX data |
| title_full_unstemmed | Forest species mapping using airborne hyperspectral APEX data |
| title_short | Forest species mapping using airborne hyperspectral APEX data |
| title_sort | forest species mapping using airborne hyperspectral apex data |
| topic | vegetation map hyperspectral aerial supervised classification multi-temporal dataset forest ecosystem |
| url | https://doi.org/10.1515/mgrsd-2016-0002 |
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