On the use of Sentinel-2 for coastal habitat mapping and satellite-derived bathymetry estimation using downscaled coastal aerosol band

被引:93
作者
Poursanidis, Dimitris [1 ]
Traganos, Dimosthenis [2 ]
Reinartz, Peter [3 ]
Chrysoulakis, Nektarios [1 ]
机构
[1] Fdn Res & Technol Hellas, Inst Appl & Computat Math, Rslab Gr, Iraklion, Greece
[2] German Aerosp Ctr DLR, Remote Sensing Technol Inst, Rutherfordstr 2, D-12489 Berlin, Germany
[3] German Aerosp Ctr DLR, Earth Observat Ctr, D-82234 Wessling, Germany
关键词
Posidonia oceanica; Seagrass; Coastal habitat mapping; Satellite-derived bathymetry; Sentinel-2; Super-resolution; GOOGLE EARTH ENGINE; WATER DEPTH; POSIDONIA-OCEANICA; LAND-COVER; CLASSIFICATION; REFLECTANCE; SEAGRASSES; MEADOWS; SEA;
D O I
10.1016/j.jag.2019.03.012
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Coastal habitats provide a plethora of ecosystem services, yet they undergo continuous pressure and degradation due to the human-induced climate change. Conservation and management imply continuous monitoring and mapping of their spatial distribution at first. The present study explores the capabilities of the Copernicus Sentinel-2 mission and the contribution of its coastal aerosol band 1 (443 nm) for the mapping of the dominant Mediterranean coastal marine habitats and the bathymetry in three survey sites in the East Mediterranean. The selected sites have shallow to deep habitats and a high variability of oceanographic and seabed morphological conditions. The major findings of our study demonstrate the advantages of the downscaled Sentinel-2 coastal aerosol band 1 for both optically shallow habitat and satellite-derived bathymetry mapping due to its great water penetration. The use of Sentinel-2 band 1 allows detection of Posidonia oceanica seagrass beds down to 32.2 m of depth. Sentinel-2 constellation with its 10-m spatial resolution at most of the spectral bands, 5-day revisit frequency and open data policy can be an important tool to provide crucial missing information on the spatial distribution of coastal habitats and on their bathymetry distribution, especially in data-poor and/or remote areas with large gaps in a retrospective, rapid and non-intrusive manner. As such, it becomes a crucial ally for the conservation and management of coastal habitats globally.
引用
收藏
页码:58 / 70
页数:13
相关论文
共 48 条
[1]  
[Anonymous], 1995, NATURE STAT LEARNING, DOI DOI 10.1007/978-1-4757-2440-0
[2]   Random forest in remote sensing: A review of applications and future directions [J].
Belgiu, Mariana ;
Dragut, Lucian .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2016, 114 :24-31
[3]   Mapping Spatial Patterns of Posidonia oceanica Meadows by Means of Daedalus ATM Airborne Sensor in the Coastal Area of Civitavecchia (Central Tyrrhenian Sea, Italy) [J].
Borfecchia, Flavio ;
Micheli, Carla ;
Carli, Filippo ;
De Martis, Selvaggia Cognetti ;
Gnisci, Valentina ;
Piermattei, Viviana ;
Belmonte, Alessandro ;
De Cecco, Luigi ;
Martini, Sandro ;
Marcelli, Marco .
REMOTE SENSING, 2013, 5 (10) :4877-4899
[4]  
Boudouresque C F., 2012, Protection and conservation of Posidonia oceanica meadows
[5]  
Bramante James P., 2013, INT J REMOTE SENS, V34
[6]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[7]   Super-Resolving Multiresolution Images With Band-Independent Geometry of Multispectral Pixels [J].
Brodu, Nicolas .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2017, 55 (08) :4610-4617
[8]  
Chronis G, 1996, CINCS PELAGIC BENTHI, P82
[9]   Towards Deeper Measurements of Tropical Reefscape Structure Using the WorldView-2 Spaceborne Sensor [J].
Collin, Antoine ;
Hench, James L. .
REMOTE SENSING, 2012, 4 (05) :1425-1447
[10]   Seagrass Meadows, Ecosystem Services, and Sustainability [J].
Cullen-Unsworth, Leanne ;
Unsworth, Richard .
ENVIRONMENT, 2013, 55 (03) :14-27