Optimization of Data Collection and Refinement of Post-processing Techniques for Maritime Canada's First Shallow Water Topographic-bathymetric Lidar Survey

Webster, Tim; McGuigan, Kevin G.; Crowell, Nathan; Collins, Kate; MacDonald, Candace

doi:10.2112/si76-004

Cited by 17 publications

(4 citation statements)

References 26 publications

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“…Several applications of LiDAR bathymetry for the European sea show potential of this technology being operational method for surveying shallow water of coastal zone [20,23]. Limitation effects caused by waves [23,24], water transparency [24,25], and algae development [20,26] have been noticed.…”

Section: Introductionmentioning

confidence: 99%

Coupling of Dual Channel Waveform ALS and Sonar for Investigation of Lake Bottoms and Shore Zones

et al. 2021

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In this work, we proposed to include remote sensing techniques as a part of the methodology for natural lake bottom mapping, with a focus on the littoral zone. Due to the inaccessibility of this zone caused by dense vegetation, measurements of the lake bottom and the coastline are also difficult to perform using traditional methods. The authors of this paper present, discuss and verify the applicability of remote sensing active sensors as a tool for measurements in the shore zone of a lake. The single-beam Lowrance HDS-7 ComboGPS echosounder with an 83/200 kHz transducer and a two-beam LiDAR RIEGL VQ-1560i-DW scanner have been used for reservoir bottom measurements of two neighboring lakes, which differ in terms of water transparency. The research has found a strong correlation between both sonar and LiDAR for mapping the bottom depth in a range up to 1.6 m, and allowed LiDAR mapping of approximately 20% of the highly transparent lake, but it has not been found to be useful in water with low transparency. In the light of the conducted research, both devices, sonar and LiDAR, have potential for complementary use by fusing both methods: the sonar for mapping of the sublittoral and the pelagic zone, and the LiDAR for mapping of the littoral zone, overcoming limitation related to vegetation in the lake shore zone.

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Section: Introductionmentioning

confidence: 99%

Coupling of Dual Channel Waveform ALS and Sonar for Investigation of Lake Bottoms and Shore Zones

et al. 2021

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“…The average biomass measured in situ in September 2018 was not significantly different from the value obtained from remote sensing (Table 7). Lately, active remote sensing data from aircrafts or UAVs have been used to map seagrass cover, mainly LiDAR [78][79][80][81][82][83][84][85][86]; hyperspectral sensors such as CASI-2, PHILLS, and HyMap [78,[87][88][89][90]; or photographs [91][92][93][94][95]. The efficiency of remotely sensed data has been proven through many studies involving the estimation of seagrass cover; nevertheless, most studies are still reliant on field measurements when estimating shoot density and canopy height (e.g., [96]).…”

Section: Discussionmentioning

confidence: 99%

Mapping and Quantification of the Dwarf Eelgrass Zostera noltei Using a Random Forest Algorithm on a SPOT 7 Satellite Image

Benmokhtar

Robin

Maanan

et al. 2021

IJGI

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The dwarf eelgrass Zostera noltei Hornemann (Z. noltei) is the most dominant seagrass in semi-enclosed coastal systems of the Atlantic coast of Morocco. The species is experiencing a worldwide decline and monitoring the extent of its meadows would be a useful approach to estimate the impacts of natural and anthropogenic stressors. Here, we aimed to map the Z. noltei meadows in the Merja Zerga coastal lagoon (Atlantic coast of Morocco) using remote sensing. We used a random forest algorithm combined with field data to classify a SPOT 7 satellite image. Despite the difficulties related to the non-synchronization of the satellite images with the high tide coefficient, our results revealed, with an accuracy of 95%, that dwarf eelgrass beds can be discriminated successfully from other habitats in the lagoon. The estimated area was 160.76 ha when considering mixed beds (Z. noltei-associated macroalgae). The use of SPOT 7 satellite images seems to be satisfactory for long-term monitoring of Z. noltei meadows in the Merja Zerga lagoon and for biomass estimation using an NDVI–biomass quantitative relationship. Nevertheless, using this method of biomass estimation for dwarf eelgrass meadows could be unsuccessful when it comes to areas where the NDVI is saturated due to the stacking of many layers.

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“…In the subtidal zone, completely submerged kelp habitat has been quantified with Landsat along the Gaspé Peninsula in Quebec (Simms and Dubois, 2001), with SPOT-7 along the Mingan Archipelago also in Québec (St-Pierre and Gagnon, 2020) and with lidar along the south shore of Nova Scotia (Webster, 2018). Eelgrass habitat has been quantified with lidar (Webster et al, 2016), sonar (Vandermeulen, 2014;Barrell et al, 2015), and a variety of multispectral sensors including Worldview, Quickbird, and SPOT (Milton et al, 2009;Barrell et al, 2015; at various sites along the exposed Atlantic coast of Nova Scotia and along the Northumberland Straight. The above listed studies used either an image-based classification generally based on the maximum likelihood classifier St-Pierre and Gagnon, 2020;Webster et al, 2020), or object-based classification (Milton et al, 2009;Barrell et al, 2015) to quantify marine macrophyte habitat at bay-wide scales with commercial satellites.…”

Section: Introductionmentioning

confidence: 99%

Branching Algorithm to Identify Bottom Habitat in the Optically Complex Coastal Waters of Atlantic Canada Using Sentinel-2 Satellite Imagery

Wilson

Wong

Devred

2020

Front. Environ. Sci.

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Optimization of Data Collection and Refinement of Post-processing Techniques for Maritime Canada's First Shallow Water Topographic-bathymetric Lidar Survey

Abstract: BioOne Complete (complete.BioOne.org) is a full-text database of 200 subscribed and open-access titles in the biological, ecological, and environmental sciences published by nonprofit societies, associations, museums, institutions, and presses.

Cited by 17 publications

References 26 publications

Coupling of Dual Channel Waveform ALS and Sonar for Investigation of Lake Bottoms and Shore Zones

Coupling of Dual Channel Waveform ALS and Sonar for Investigation of Lake Bottoms and Shore Zones

Mapping and Quantification of the Dwarf Eelgrass Zostera noltei Using a Random Forest Algorithm on a SPOT 7 Satellite Image

Branching Algorithm to Identify Bottom Habitat in the Optically Complex Coastal Waters of Atlantic Canada Using Sentinel-2 Satellite Imagery

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