Hyperspectral remote sensing protocol development for submerged aquatic vegetation in shallow waters

2004 ◽  
Author(s):  
Charles R. Bostater, Jr. ◽  
Teddy Ghir ◽  
Luce Bassetti ◽  
Carlton Hall ◽  
E. Reyeier ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
Hyperspectral Remote Sensing ◽  
Shallow Waters ◽  
Protocol Development

Hyperspectral remote sensing for shallow waters I A semianalytical model

1998 ◽  
Vol 37 (27) ◽  
pp. 6329 ◽  
Author(s):  
Zhongping Lee ◽  
Kendall L. Carder ◽  
Curtis D. Mobley ◽  
Robert G. Steward ◽  
Jennifer S. Patch
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Remote Sensing ◽  
Shallow Waters

scholarly journals Open-Source Analysis of Submerged Aquatic Vegetation Cover in Complex Waters Using High-Resolution Satellite Remote Sensing: An Adaptable Framework

2022 ◽  
Vol 14 (2) ◽  
pp. 267
Author(s):  
Arthur de Grandpré ◽  
Christophe Kinnard ◽  
Andrea Bertolo
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
High Resolution ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
Spatial Scales ◽  
Atmospheric Correction ◽  
Source Analysis ◽  
Radiometric Calibration ◽  
Ecological Data

Despite being recognized as a key component of shallow-water ecosystems, submerged aquatic vegetation (SAV) remains difficult to monitor over large spatial scales. Because of SAV’s structuring capabilities, high-resolution monitoring of submerged landscapes could generate highly valuable ecological data. Until now, high-resolution remote sensing of SAV has been largely limited to applications within costly image analysis software. In this paper, we propose an example of an adaptable open-sourced object-based image analysis (OBIA) workflow to generate SAV cover maps in complex aquatic environments. Using the R software, QGIS and Orfeo Toolbox, we apply radiometric calibration, atmospheric correction, a de-striping correction, and a hierarchical iterative OBIA random forest classification to generate SAV cover maps based on raw DigitalGlobe multispectral imagery. The workflow is applied to images taken over two spatially complex fluvial lakes in Quebec, Canada, using Quickbird-02 and Worldview-03 satellites. Classification performance based on training sets reveals conservative SAV cover estimates with less than 10% error across all classes except for lower SAV growth forms in the most turbid waters. In light of these results, we conclude that it is possible to monitor SAV distribution using high-resolution remote sensing within an open-sourced environment with a flexible and functional workflow.

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