Classification of Seagrass Beds by Coupling Airborne LiDAR Bathymetry Data and Digital Aerial Photographs

Ishiguro, Satoshi; Yamada, Katsumasa; Yamakita, Takehisa; Yamano, Hiroya; Oguma, Hiroyuki; Matsunaga, Tsuneo

doi:10.1007/978-981-10-0780-4_5

Cited by 3 publications

(2 citation statements)

References 18 publications

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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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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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“…Manned aerial vehicles (MAV) serve as a robust alternative that leverages passive sensors with a basic red-green-blue (RGB) spectrum and sometimes the infrared spectrum [6] or an active light detection and ranging (LiDAR) sensor [7]. The complete solution makes it difficult to plan missions, and sensors such as LiDAR are rather expensive.…”

Section: Landuse/landcover Observation Techniquesmentioning

confidence: 99%

Satellite–Derived Topography and Morphometry for VHR Coastal Habitat Mapping: The Pleiades–1 Tri–Stereo Enhancement

et al. 2022

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The evolution of the coastal fringe is closely linked to the impact of climate change, specifically increases in sea level and storm intensity. The anthropic pressure that is inflicted on these fragile environments strengthens the risk. Therefore, numerous research projects look into the possibility of monitoring and understanding the coastal environment in order to better identify its dynamics and adaptation to the major changes that are currently taking place in the landscape. This new study aims to improve the habitat mapping/classification at Very High Resolution (VHR) using Pleiades–1–derived topography, its morphometric by–products, and Pleiades–1–derived imageries. A tri–stereo dataset was acquired and processed by image pairing to obtain nine digital surface models (DSM) that were 0.50 m pixel size using the free software RSP (RPC Stereo Processor) and that were calibrated and validated with the 2018–LiDAR dataset that was available for the study area: the Emerald Coast in Brittany (France). Four morphometric predictors that were derived from the best of the nine generated DSMs were calculated via a freely available software (SAGA GIS): slope, aspect, topographic position index (TPI), and TPI–based landform classification (TPILC). A maximum likelihood classification of the area was calculated using nine classes: the salt marsh, dune, rock, urban, field, forest, beach, road, and seawater classes. With an RMSE of 4 m, the DSM#2–3_1 (from images #2 and #3 with one ground control point) outperformed the other DSMs. The classification results that were computed from the DSM#2–3_1 demonstrate the importance of the contribution of the morphometric predictors that were added to the reference Red–Green–Blue (RGB, 76.37% in overall accuracy, OA). The best combination of TPILC that was added to the RGB + DSM provided a gain of 13% in the OA, reaching 89.37%. These findings will help scientists and managers who are tasked with coastal risks at VHR.

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High-resolution, precision mapping of seagrass blue carbon habitat using multi-spectral imaging and aerial LiDAR

Ekelund,

Waddington,

Harris

et al. 2024

Estuarine, Coastal and Shelf Science

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Classification of Seagrass Beds by Coupling Airborne LiDAR Bathymetry Data and Digital Aerial Photographs

Cited by 3 publications

References 18 publications

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

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

Satellite–Derived Topography and Morphometry for VHR Coastal Habitat Mapping: The Pleiades–1 Tri–Stereo Enhancement

High-resolution, precision mapping of seagrass blue carbon habitat using multi-spectral imaging and aerial LiDAR

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