scholarly journals Remote sensing of submerged aquatic vegetation: an introduction and best practices review

2020 ◽  
Author(s):  
Gillian Rowan ◽  
Margaret Kalacska
Keyword(s):  
Remote Sensing ◽  
Best Practices ◽  
Large Scale ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
Global Climate ◽  
Underwater Environment ◽  
Sensor Platform ◽  
Wide Range ◽  
Optical Imagery

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 can provide the efficient, accurate and large-scale monitoring needed to ensure proper SAV management. Our objective is to introduce remote sensing to researchers in the field of aquatic ecology. Applying remote sensing to the underwater environment is more complex in comparison to terrestrial studies due to the water column. A wide range of sensors and platforms from remotely operated vehicles to satellites are available for use in the underwater environment, a sample of which being presented herein. The utility of any sensor/platform combination varies depending on the aquatic conditions being observed. 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 detecting SAV are briefly presented and notable results and lessons are discussed.

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 Large-Scale Mapping and Predictive Modeling of Submerged Aquatic Vegetation in a Shallow Eutrophic Lake

2002 ◽  
Vol 2 ◽  
pp. 949-965 ◽  
Author(s):  
Karl E. Havens ◽  
Matthew C. Harwell ◽  
Mark A. Brady ◽  
Bruce Sharfstein ◽  
Therese L. East ◽  
...  
Keyword(s):  
Large Scale ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
Eutrophic Lake ◽  
Sediment Type ◽  
Gis Technology ◽  
General Utility ◽  
Sediment Types ◽  
Wide Range ◽  
Sampling Program

A spatially intensive sampling program was developed for mapping the submerged aquatic vegetation (SAV) over an area of approximately 20,000 ha in a large, shallow lake in Florida, U.S. The sampling program integrates Geographic Information System (GIS) technology with traditional field sampling of SAV and has the capability of producing robust vegetation maps under a wide range of conditions, including high turbidity, variable depth (0 to 2 m), and variable sediment types. Based on sampling carried out in AugustœSeptember 2000, we measured 1,050 to 4,300 ha of vascular SAV species and approximately 14,000 ha of the macroalga Chara spp. The results were similar to those reported in the early 1990s, when the last large-scale SAV sampling occurred. Occurrence of Chara was strongly associated with peat sediments, and maximal depths of occurrence varied between sediment types (mud, sand, rock, and peat). A simple model of Chara occurrence, based only on water depth, had an accuracy of 55%. It predicted occurrence of Chara over large areas where the plant actually was not found. A model based on sediment type and depth had an accuracy of 75% and produced a spatial map very similar to that based on observations. While this approach needs to be validated with independent data in order to test its general utility, we believe it may have application elsewhere. The simple modeling approach could serve as a coarse-scale tool for evaluating effects of water level management on Chara populations.

scholarly journals Novel approach for large‐scale monitoring of submerged aquatic vegetation – a nationwide example from Sweden

2021 ◽  
Author(s):  
Silvia Huber ◽  
Lars B. Hansen ◽  
Lisbeth T. Nielsen ◽  
Mikkel L. Rasmussen ◽  
Jonas Sølvsteen ◽  
...  
Keyword(s):  
Large Scale ◽  
Submerged Aquatic Vegetation ◽  
Aquatic Vegetation ◽  
Novel Approach

scholarly journals Detecting 2020 Coral Bleaching Event in the Northwest Hainan Island Using CoralTemp SST and Sentinel-2B MSI Imagery

2021 ◽  
Vol 13 (23) ◽  
pp. 4948
Author(s):  
Bailu Liu ◽  
Lei Guan ◽  
Hong Chen
Keyword(s):  
Coral Bleaching ◽  
Large Scale ◽  
Global Climate ◽  
Hainan Island ◽  
Threshold Value ◽  
Bleaching Event ◽  
Wide Range ◽  
Coral Reef Ecosystems ◽  
The Difference ◽  
Difference Images

In recent years, coral reef ecosystems have been affected by global climate change and human factors, resulting in frequent coral bleaching events. A severe coral bleaching event occurred in the northwest of Hainan Island, South China Sea, in 2020. In this study, we used the CoralTemp sea surface temperature (SST) and Sentinel-2B imagery to detect the coral bleaching event. From 31 May to 3 October, the average SST of the study area was 31.01 °C, which is higher than the local bleaching warning threshold value of 30.33 °C. In the difference images of 26 July and 4 September, a wide range of coral bleaching was found. According to the temporal variation in single band reflectance, the development process of bleaching is consistent with the changes in coral bleaching thermal alerts. The results show that the thermal stress level is an effective parameter for early warning of large-scale coral bleaching. High-resolution difference images can be used to detect the extent of coral bleaching. The combination of the two methods can provide better support for coral protection and research.

Surface reflectivity in polar regions retrieved from TDS-1 mission data

2021 ◽  
Author(s):  
Frederik Kreß ◽  
Maximilian Semmling ◽  
Estel Cardellach ◽  
Weiqiang Li ◽  
Mainul Hoque ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Roughness ◽  
Sea Ice ◽  
Large Scale ◽  
Global Climate ◽  
Path Loss ◽  
Signal Strength ◽  
Global Navigation Satellite Systems ◽  
The Arctic ◽  
Polar Regions

<p>In current times of a changing global climate, a special interest is focused on the<br>large-scale recording of sea ice. Among the existing remote sensing methods, bi-<br>statically reflected signals of Global Navigation Satellite Systems (GNSS) could<br>play an important role in fulfilling the task. Within this project, sensitivity of<br>GNSS signal reflections to sea ice properties like its occurrence, sea ice thick-<br>ness (SIT) and sea concentration (SIC) is evaluated. When getting older, sea<br>ice tends go get thicker. Because of decreasing salinity, i.e. less permittivity,<br>as well as relatively higher surface roughness of older ice, it can be assumed<br>that reflected signal strength decreases with increasing SIT. The reflection data<br>used were recorded in the years 2015 and 2016 by the TechDemoSat-1 (TDS-1)<br>satellite over the Arctic and Antarctic. It includes a down-looking antenna for<br>the reflected as well as an up-looking antenna dedicated to receive the direct sig-<br>nal. The raw data, provided by the manufacturer SSTL, were pre-processed by<br>IEEC/ICE-CSIC to derive georeferenced signal power values. The reflectivity<br>was estimated by comparing the power of the up- and down-looking links. The<br>project focuses on the signal link budget to apply necessary corrections. For this<br>reason, the receiver antenna gain as well as the Free-Space Path Loss (FSPL)<br>were calculated and applied for reflectivity correction. Differences of nadir and<br>zenith antenna FSPL and gain show influence of up to 6 dB and −9 dB to 9 dB<br>respectively on the recorded signal strength. All retrieved reflectivity values are<br>compared to model predictions based on Fresnel coefficients but also to avail-<br>able ancillary truth data of other remote sensing missions to identify possible<br>patterns: SIT relations are investigated using Level-2 data of the Soil Moisture<br>and Ocean Salinity (SMOS) satellite. The SIC comparison was done with an<br>AMSR-2 product. The results show sensitivity of the reflectivity value to both<br>SIT and SIC simultaneously, whereby the surface roughness is also likely to<br>have an influence. This on-going study aims at the consolidation of retrieval<br>algorithms for sea-ice observation. The resolution of different ice types and the<br>retrieval of SIT and SIC based on satellite data is a challenge for future work<br>in this respect.</p>

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