Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection
AbstractLand cover and land use maps derived from satellite remote sensing imagery are critical to support biodiversity and conservation, especially over large areas. With its 10 m to 20 m spatial resolution, Sentinel-2 is a promising sensor for the detection of a variety of landscape features of ecological relevance. However, many components of the ecological network are still smaller than the 10 m pixel, i.e., they are sub-pixel targets that stretch the sensor’s resolution to its limit. This paper proposes a framework to empirically estimate the minimum object size for an accurate detection of a set of structuring landscape foreground/background pairs. The developed method combines a spectral separability analysis and an empirical point spread function estimation for Sentinel-2. The same approach was also applied to Landsat-8 and SPOT-5 (Take 5), which can be considered as similar in terms of spectral definition and spatial resolution, respectively. Results show that Sentinel-2 performs consistently on both aspects. A large number of indices have been tested along with the individual spectral bands and target discrimination was possible in all but one case. Overall, results for Sentinel-2 highlight the critical importance of a good compromise between the spatial and spectral resolution. For instance, the Sentinel-2 roads detection limit was of 3 m and small water bodies are separable with a diameter larger than 11 m. In addition, the analysis of spectral mixtures draws attention to the uneven sensitivity of a variety of spectral indices. The proposed framework could be implemented to assess the fitness for purpose of future sensors within a large range of applications. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Radoux, J.; Chomé, G.; Jacques, D.C.; Waldner, F.; Bellemans, N.; Matton, N.; Lamarche, C.; d’Andrimont, R.; Defourny, P. Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection. Remote Sens. 2016, 8, 488.
Radoux J, Chomé G, Jacques DC, Waldner F, Bellemans N, Matton N, Lamarche C, d’Andrimont R, Defourny P. Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection. Remote Sensing. 2016; 8(6):488.Chicago/Turabian Style
Radoux, Julien; Chomé, Guillaume; Jacques, Damien C.; Waldner, François; Bellemans, Nicolas; Matton, Nicolas; Lamarche, Céline; d’Andrimont, Raphaël; Defourny, Pierre. 2016. "Sentinel-2’s Potential for Sub-Pixel Landscape Feature Detection." Remote Sens. 8, no. 6: 488.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.