Reprint

Novel Advances in Aquatic Vegetation Monitoring in Ocean, Lakes and Rivers

Edited by
August 2019
132 pages
  • ISBN978-3-03921-205-7 (Paperback)
  • ISBN978-3-03921-206-4 (PDF)

This book is a reprint of the Special Issue Novel Advances in Aquatic Vegetation Monitoring in Ocean, Lakes and Rivers that was published in

Engineering
Environmental & Earth Sciences
Summary

In recent decades, there has been an increase in the development of strategies for water ecosystem mapping and monitoring. Overall, this is primarily due to legislative efforts to improve the quality of water bodies and oceans. Remote sensing has played a key role in the development of such approaches—from the use of drones for vegetation mapping to autonomous vessels for water quality monitoring. Within the specific context of vegetation characterization, the wide range of available observations—from satellite imagery to high-resolution drone aerial imagery—has enabled the development of monitoring and mapping strategies at multiple scales (e.g., micro- and mesoscales).

 

This Special Issue, entitled “Novel Advances in Aquatic Vegetation Monitoring in Ocean, Lakes and Rivers”, collates recent advances in remote sensing-based methods applied to ocean, river, and lake vegetation characterization, including seaweed, kelp, submerged and emergent vegetation, and floating-leaf and free-floating plants. A total of six manuscripts have been compiled in this Special Issue, ranging from area mapping substrates in riverine environments to the identification of macroalgae in marine environments. The work presented leverages current state-of-the-art methods for aquatic vegetation monitoring and will spark further research within this field.

Format
  • Paperback
License
© 2019 by the authors; CC BY-NC-ND license
Keywords
freshwater wetland; Lake Baikal; methodological comparison; Selenga River Delta; WorldView-2; aquatic vegetation; concave–convex decision function; remote sensing extraction; GF-1 satellite; Lake Ulansuhai; China; invasive plants; Spartina alterniflora; CAS S. alterniflora; object-based image analysis; Landsat OLI; substrate; aquatic vegetation; bottom reflectance; water-column correction; river; spectroscopy; radiative transfer; WorldView-3; macroalgae; reflectance; 1st derivative; species discrimination; unmanned aerial vehicle; nuclear power station; floating algae index (FAI); normalized difference vegetation index (NDVI); remote sensing; seaweed enhancing index (SEI); seaweed