|Title:||Vegetation species mapping in a coastal-dune ecosystem using high resolution satellite imagery||Authors:||Medina Machín, Anabella
Hernández-Cordero, Antonio I.
Martín Abasolo, Javier
|UNESCO Clasification:||250616 Teledetección (Geología)||Keywords:||Automatic vegetation classification
Support Vector Machines, et al
|Issue Date:||2019||Publisher:||1548-1603||Journal:||GIScience and Remote Sensing||Abstract:||Vegetation mapping is a priority when managing natural protected areas. In this context, very high resolution satellite remote sensing data can be fundamental in providing accurate vegetation cartography at species level. In this work, a complete processing methodology has been developed and validated in a complex vulnerable coastal-dune ecosystem. Specifically, the analysis has been carried out using WorldView-2 imagery, which offers spatial and spectral resolutions. A thorough assessment of 5 atmospheric correction models has been performed using real reflectance measures from a field radiometry campaign. To select the classification methodology, different strategies have been evaluated, including additional spectral (23 vegetation indices) and spatial (4 texture parameters) information to the multispectral bands. Likewise, the application of linear unmixing techniques has been tested and abundance maps of each plant species have been generated using the library of spectral signatures recorded during the campaign. After the analysis conducted, a new methodology has been proposed based on the use of the 6S atmospheric model and the Support Vector Machine classification algorithm applied to a combination of different spectral and spatial input data. Specifically, an overall accuracy of 88,03% was achieved combining the corrected multispectral bands plus a vegetation index (MSAVI2) and texture information (variance of the first principal component). Furthermore, the methodology has been validated by photointerpretation and 3 plant species achieve significant accuracy: Tamarix canariensis (94,9%), Juncus acutus (85,7%) and Launaea arborescens (62,4%). Finally, the classified procedure comparing maps for different seasons has also shown robustness to changes in the phenological state of the vegetation.||URI:||http://hdl.handle.net/10553/42206||ISSN:||1548-1603||DOI:||10.1080/15481603.2018.1502910||Source:||Giscience & Remote Sensing [ISSN 1548-1603], v. 56 (2), p. 210-232|
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