Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The Scanning Hydrographic Operational Airborne LiDAR Survey (SHOALS) consists of an ubiquitous topographic and bathymetric LiDAR system which provides high resolution measurements of emerged and immersed elevation. Besides, the return signals, i.e., waveforms, contain signatures of the benthic return for subtidal area and of the salt marsh cover for intertidal zone.  highlights the seamless classification of the coast from the upper marsh to the near-shore. The subtidal seafloor is characterized in applying a supervised classifier to 12 SHOALS-derived benthic variables, supporting by underwater image classification (78.6 %). The intertidal marsh is classified in respect to the combination of a synthetic Vegetation indice (based on Red and InfraRed) and the elevation (95.2 %). The merge of both classifications points out 16 classes with an overall accuracy of 86.9 %.
['Antoine Collin', 'Bernard Long', 'Phillippe Archambault']
I am writing a research paper on a idea The use of the SHOALS waveforms to mapping habitat within the seamless benthoscape
[{"from": "human", "value": "I am writing a research paper on a idea The use of the SHOALS waveforms to mapping habitat within the seamless benthoscape"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The Scanning Hydrographic Operational Airborne LiDAR Survey (SHOALS) consists of an ubiquitous topographic and bathymetric LiDAR system which provides high resolution measurements of emerged and immersed elevation. Besides, the return signals, i.e., waveforms, contain signatures of the benthic return for subtidal area and of the salt marsh cover for intertidal zone.  highlights the seamless classification of the coast from the upper marsh to the near-shore. The subtidal seafloor is characterized in applying a supervised classifier to 12 SHOALS-derived benthic variables, supporting by underwater image classification (78.6 %). The intertidal marsh is classified in respect to the combination of a synthetic Vegetation indice (based on Red and InfraRed) and the elevation (95.2 %). The merge of both classifications points out 16 classes with an overall accuracy of 86.9 %."}]