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RSS FeedsRemote Sensing, Vol. 11, Pages 1445: Object-Based Mapping of Coral Reef Habitats Using Planet Dove Satellites (Remote Sensing)

 
 

19 june 2019 05:00:23

 
Remote Sensing, Vol. 11, Pages 1445: Object-Based Mapping of Coral Reef Habitats Using Planet Dove Satellites (Remote Sensing)
 




High spatial resolution benthic habitat information is essential for coral reef protection and coastal environmental management. Satellite-based shallow benthic composition mapping offers a more efficient approach than traditional field measurements, especially given the advancements in high spatial and temporal resolution satellite imagery. The Planet Dove satellite constellation now has more than 150 instruments in orbit that offer daily coverage at high spatial resolution (3.7 m). The Dove constellation provides regularly updated imagery that can minimize cloud in tropical oceans where dense cloud cover persists. Daily image acquisition also provides an opportunity to detect time-sensitive changes in shallow benthic habitats following coral bleaching events, storms, and other disturbances. We developed an object-based coral reef habitat mapping approach for Dove and similar multispectral satellites that provides bathymetry estimation, bottom reflectance retrieval, and object-based classification to identify different benthic compositions in shallow coastal environments. We tested our approach in three study sites in the Dominican Republic using 18 Dove images. Benthic composition classification results were validated by field measurements (overall accuracy = 82%). Bathymetry and bottom reflectance significantly contributed to identifying benthic habitat classes with similar surface reflectance. This new object-based approach can be effectively applied to map and manage coral reef habitats.


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31 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 1446: Canopy Height Layering Biomass Estimation Model (CHL-BEM) with Full-Waveform LiDAR (Remote Sensing)
Remote Sensing, Vol. 11, Pages 1444: Newly Built Construction Detection in SAR Images Using Deep Learning (Remote Sensing)
 
 
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