Combining optical and radar satellite image time series to map natural vegetation: savannas as an example

Up‐to‐date land cover maps are important for biodiversity monitoring as they are central to habitat and ecosystem distribution assessments. Satellite remote sensing is a key technology for generating these maps. Until recently, land cover mapping has been limited to static approaches, which have pri...

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Vydáno v:Remote sensing in ecology and conservation Ročník 6; číslo 3; s. 316 - 326
Hlavní autoři: Lopes, Mailys, Frison, Pierre‐Louis, Durant, Sarah M., Schulte to Bühne, Henrike, Ipavec, Audrey, Lapeyre, Vincent, Pettorelli, Nathalie, Horning, Ned
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford John Wiley & Sons, Inc 01.09.2020
Wiley
Témata:
Biodiversity
Classification
Data combination
Ecosystem assessment
Environmental management
Grasslands
Image classification
Land cover
Life Sciences
Mapping
National parks
Natural vegetation
natural vegetation classification
Phenology
Protected areas
Radar
Radar data
Radar imaging
Radar satellites
Remote sensing
satellite image time series
Satellite imagery
Satellites
savanna
Savannahs
Sentinel‐1
Sentinel‐2
Spatial data
Time series
Vegetation mapping
Vegetation surveys
Benin
West Africa
satellite image time series
sentinel-1
natural vegetation classification
sentinel-2
data combination
savanna
ISSN:2056-3485, 2056-3485
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Shrnutí:Up‐to‐date land cover maps are important for biodiversity monitoring as they are central to habitat and ecosystem distribution assessments. Satellite remote sensing is a key technology for generating these maps. Until recently, land cover mapping has been limited to static approaches, which have primarily led to the production of either global maps at coarse spatial resolutions or geographically restricted maps at high spatial resolutions. The recent availability of optical (Sentinel‐2) and radar (Sentinel‐1) satellite image time series (SITS) which provide access to high spatial and very high temporal resolutions, is a game changer, offering opportunities to map land cover using both temporal and spatial information. These data moreover open interesting perspectives for land cover mapping based on data combination approach. However, the usefulness of combining dense time series (more than 30 images per year) and data combination approaches to map natural vegetation has so far not been assessed. To address this gap, this contribution tests the idea that the combined consideration of optical and radar data combination and time series analyses can significantly improve natural vegetation mapping in the Pendjari National Park, a Sahelian savanna protected area in Benin. Results highlight that the combination of Sentinel‐1 and Sentinel‐2 SITS performs as well as Sentinel‐2 SITS alone in terms of classification accuracy. Land cover maps are however qualitatively better when considering the data combination approach. Our results also clearly show that the use of dense/hypertemporal optical time series significantly improves classification outcomes compared to using multitemporal only a few images per year) or monotemporal data. Altogether, this work thus demonstrates the ability of dense SITS to improve discrimination of natural vegetation types using information on their phenology, leading to more detailed and more reliable maps for environmental management. This contribution tests the idea that the combined consideration of data combination and time series analyses can significantly improve natural vegetation mapping in savannas. Results demonstrate the ability of dense satellite image time series to improve discrimination of natural vegetation types using information on their phenology, leading to more detailed and more reliable maps for environmental management.
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ISSN:2056-3485
2056-3485
DOI:10.1002/rse2.139