Remote Sensing of Submerged Aquatic Vegetation

2016 ◽  
pp. 125-140 ◽  
Author(s):  
Victor V. Klemas
Keyword(s):  
Remote Sensing ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation

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.

scholarly journals Spatiotemporal Dynamics of Submerged Aquatic Vegetation in a Deep Lake from Sentinel-2 Data

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 563 ◽  
Author(s):  
Nicola Ghirardi ◽  
Rossano Bolpagni ◽  
Mariano Bresciani ◽  
Giulia Valerio ◽  
Marco Pilotti ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
In Situ Measurements ◽  
Spatiotemporal Variability ◽  
Annual Variations ◽  
Bare Sediment ◽  
Sentinel 2

We mapped the extent of submerged aquatic vegetation (SAV) of Lake Iseo (Northern Italy, over the 2015–2017 period based on satellite data (Sentinel 2 A-B) and in-situ measurements; the objective was to investigate its spatiotemporal variability. We focused on the southern sector of the lake, the location of the shallowest littorals and the most developed macrophyte communities, mainly dominated by Vallisneria spiralis and Najas marina. The method made use of both in-situ measurements and satellite data (22 Sentinel 2 A-B images) that were atmospherically corrected with 6SV code and processed with the BOMBER (Bio-Optical Model-Based tool for Estimating water quality and bottom properties from Remote sensing images). This modeling system was used to estimate the different substrate coverage (bare sediment, dense stands of macrophytes with high albedo, and sparse stand of macrophytes with low albedo). The presented results substantiate the existence of striking inter- and intra-annual variations in the spatial-cover patterns of SAV. Intense uprooting phenomena were also detected, mainly affecting V. spiralis, a species generally considered a highly plastic pioneer taxon. In this context, remote sensing emerges as a very reliable tool for mapping SAV with satisfactory accuracy by offering new perspectives for expanding our comprehension of lacustrine macrophyte dynamics and overcoming some limitations associated with traditional field surveys.

scholarly journals A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists

2021 ◽  
Vol 13 (4) ◽  
pp. 623
Author(s):  
Gillian S. L. Rowan ◽  
Margaret Kalacska
Keyword(s):  
Remote Sensing ◽  
Water Column ◽  
Large Scale ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
Global Climate ◽  
Training Data ◽  
Suspended Particulate ◽  
The Subject

Submerged aquatic vegetation (SAV) is a critical component of aquatic ecosystems. It is however understudied and rapidly changing due to global climate change and anthropogenic disturbances. Remote sensing (RS) can provide the efficient, accurate and large-scale monitoring needed for proper SAV management and has been shown to produce accurate results when properly implemented. Our objective is to introduce RS to researchers in the field of aquatic ecology. Applying RS to underwater ecosystems is complicated by the water column as water, and dissolved or suspended particulate matter, interacts with the same energy that is reflected or emitted by the target. This is addressed using theoretical or empiric models to remove the water column effect, though no model is appropriate for all aquatic conditions. The suitability of various sensors and platforms to aquatic research is discussed in relation to both SAV as the subject and to project aims and resources. An overview of the required corrections, processing and analysis methods for passive optical imagery is presented and discussed. Previous applications of remote sensing to identify and detect SAV are briefly presented and notable results and lessons are discussed. The success of previous work generally depended on the variability in, and suitability of, the available training data, the data’s spatial and spectral resolutions, the quality of the water column corrections and the level to which the SAV was being investigated (i.e., community versus species.)

scholarly journals Multi-Scale Spectral Separability of Submerged Aquatic Vegetation Species in a Freshwater Ecosystem

2021 ◽  
Vol 9 ◽  
Author(s):  
Gillian S. L. Rowan ◽  
Margaret Kalacska ◽  
Deep Inamdar ◽  
J. Pablo Arroyo-Mora ◽  
Raymond Soffer
Keyword(s):  
Remote Sensing ◽  
Target Detection ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
Growing Season ◽  
Freshwater Ecosystems ◽  
Survey Method ◽  
Freshwater Ecosystem ◽  
Leaf Level ◽  
Spectral Separability

Optical remote sensing has been suggested as a preferred method for monitoring submerged aquatic vegetation (SAV), a critical component of freshwater ecosystems that is facing increasing pressures due to climate change and human disturbance. However, due to the limited prior application of remote sensing to mapping freshwater vegetation, major foundational knowledge gaps remain, specifically in terms of the specificity of the targets and the scales at which they can be monitored. The spectral separability of SAV from the St. Lawrence River, Ontario, Canada, was therefore examined at the leaf level (i.e., spectroradiometer) as well as at coarser spectral resolutions simulating airborne and satellite sensors commonly used in the SAV mapping literature. On a Leave-one-out Nearest Neighbor criterion (LNN) scale of values from 0 (inseparable) to 1 (entirely separable), an LNN criterion value between 0.82 (separating amongst all species) and 1 (separating between vegetation and non-vegetation) was achieved for samples collected in the peak-growing season from the leaf level spectroradiometer data. In contrast, samples from the late-growing season and those resampled to coarser spectral resolutions were less separable (e.g., inter-specific LNN reduction of 0.25 in late-growing season samples as compared to the peak-growing season, and of 0.28 after resampling to the spectral response of Landsat TM5). The same SAV species were also mapped from actual airborne hyperspectral imagery using target detection analyses to illustrate how theoretical fine-scale separability translates to an in situ, moderate-spatial scale application. Novel radiometric correction, georeferencing, and water column compensation methods were applied to optimize the imagery analyzed. The SAV was generally well detected (overall recall of 88% and 94% detecting individual vegetation classes and vegetation/non-vegetation, respectively). In comparison, underwater photographs manually interpreted by a group of experts (i.e., a conventional SAV survey method) tended to be more effective than target detection at identifying individual classes, though responses varied substantially. These findings demonstrated that hyperspectral remote sensing is a viable alternative to conventional methods for identifying SAV at the leaf level and for monitoring at larger spatial scales of interest to ecosystem managers and aquatic researchers.

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