Spatial assessment of intertidal seagrass meadows using optical imaging systems and a lightweight drone

Duffy, James P.; Pratt, Laura R.; Anderson, Karen; Land, Peter E.; Shutler, Jamie D.

doi:10.1016/j.ecss.2017.11.001

Cited by 133 publications

(129 citation statements)

References 62 publications

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“…The omission of sparse eelgrass is a common issue in classification of optical imagery (e.g., Barrell and Grant ) , and it may be possible to reduce this issue by: obtaining finer resolution imagery at lower flight altitudes; using pixel‐based instead of object‐based classifications in cases where there is minimal non‐eelgrass SAV mixing (Duffy et al. ); or through the use of multispectral sensors mounted on UAS to utilize the spectral differences between eelgrass and macroalgae (O'Neill et al. ; Komárek et al.…”

Section: Discussionmentioning

confidence: 99%

“…Dense macroalgae exhibits textural differences that are generally distinguishable from dense eelgrass, whereas sparse eelgrass can easily be mistaken for sparse macroalgae, especially in areas where eelgrass is mixed with non-eelgrass SAV. The omission of sparse eelgrass is a common issue in classification of optical imagery (e.g., Barrell and Grant 2015) , and it may be possible to reduce this issue by: obtaining finer resolution imagery at lower flight altitudes; using pixel-based instead of object-based classifications in cases where there is minimal non-eelgrass SAV mixing (Duffy et al 2018); or through the use of multispectral sensors mounted on UAS to utilize the spectral differences between eelgrass and macroalgae (O'Neill et al 2011;Kom arek et al 2018). Cloud cover was not amongst the top predictors of mapping confidence as we hypothesized.…”

Section: Additional Influences On Mapping Outcomementioning

confidence: 99%

“…) or small subsets of exposed intertidal seagrass beds in temperate regions (Barrell and Grant ; Duffy et al. ).…”

Section: Introductionmentioning

confidence: 99%

“…These remote sensing platforms have the capacity for acquiring imagery of very fine spatial resolution (0.01-5 cm), have increased flexibility for image acquisition, and generally have lower operational costs. For seagrass habitat assessment, very fine resolution UAS imagery has been effective for density coverage mapping and detecting changes in small patch and landscape features that would not be possible with satellite or aerial photography (Barrell and Grant 2015;Duffy et al 2018). However, the application of UAS for mapping and monitoring submerged aquatic vegetation (SAV) has been limited by the challenges associated with the water overlying target benthic features and the environmental conditions at the time of image acquisition (Nahirnick et al in press).…”

Section: Introductionmentioning

confidence: 99%

“…However, the application of UAS for mapping and monitoring submerged aquatic vegetation (SAV) has been limited by the challenges associated with the water overlying target benthic features and the environmental conditions at the time of image acquisition (Nahirnick et al in press). As such, most research has been confined to clear shallow tropical waters (Ventura et al 2016;Casella et al 2017) or small subsets of exposed intertidal seagrass beds in temperate regions (Barrell and Grant 2015;Duffy et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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Mapping with confidence; delineating seagrass habitats using Unoccupied Aerial Systems (UAS)

Nahirnick

Reshitnyk

Campbell

et al. 2018

Remote Sens Ecol Conserv

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There is growing interest in the use of Unoccupied Aerial Systems (UAS) for mapping and monitoring of seagrass habitats. UAS provide flexibility with timing of imagery capture, are relatively inexpensive, and obtain very high spatial resolution imagery compared to imagery acquired from sensors mounted on satellite or piloted aircraft. However, research to date has focused on UAS applications for exposed intertidal areas or clear tropical waters. In contrast, submerged seagrass meadows in temperate regions are subject to high cloud cover and water column turbidity, which may limit the application of UAS imagery for coastal habitat mapping. To test the constraints on UAS seagrass mapping, we examined the effects of five environmental conditions at the time of UAS image acquisition (sun angle, tidal height, cloud cover, Secchi depth and wind speed) and five site characteristics (eelgrass patchiness and density, presence and density of non‐eelgrass submerged aquatic vegetation, sediment tone, eelgrass deep edge and site exposure) at 26 eelgrass (Zostera marina) monitoring sites in British Columbia, Canada. Eelgrass was delineated in UAS orthomosaics using object‐based image analysis, combining image segmentation with manual classification. Each site was ranked according to the analysts’ confidence in the delineated eelgrass. Robust Linear Regression revealed sun angle and ‘theoretical visibility’ (an aggregate of tidal height, Secchi depth, and eelgrass deep edge conditions) to be the most important variables affecting mapping confidence. In general, ideal environmental conditions to obtain high confidence eelgrass mapping included: (1) sun angles below 40°; (2) positive theoretical visibility with Secchi depths >5 m; (3) cloud cover conditions of <10% or >90%; and (4) wind speeds less than 5 km h−1. Additionally, high mapping confidence was achieved for sites with dense, continuous, and homogeneous eelgrass meadows. The results of this analysis will guide implementation of UAS mapping technologies in coastal temperate regions.

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Section: Discussionmentioning

confidence: 99%

Section: Additional Influences On Mapping Outcomementioning

confidence: 99%

“…) or small subsets of exposed intertidal seagrass beds in temperate regions (Barrell and Grant ; Duffy et al. ).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mapping with confidence; delineating seagrass habitats using Unoccupied Aerial Systems (UAS)

Nahirnick

Reshitnyk

Campbell

et al. 2018

Remote Sens Ecol Conserv

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Application of multiplatform, multispectral remote sensors for mapping intertidal macroalgae: A comparative approach

Rossiter

Furey

McCarthy

et al. 2020

Aquatic Conservation

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1. Intertidal macroalgal communities are economically and ecologically important and, with a likely increase in anthropogenic pressures, there is need to evaluate and monitor these diverse habitats. Efforts to conserve and sustainably manage these habitats must be underpinned by accurate, cost-effective, and efficient data collection methods. The high spatial and temporal resolution of unmanned aerial vehicles (UAVs), compared with satellites and aircraft, combined with the development of lightweight sensors, provides researchers with a valuable set of tools to research intertidal macroalgal communities.2. The ability of multispectral sensors, mounted on a satellite, an aircraft, and a UAV, to identify and accurately map the intertidal brown fucoid Ascophyllum nodosum (Fucales, Ochrophyta) at a site with a low species diversity of macroalgae were compared.3. Visual analysis confirmed that the spatial resolution of satellite imagery was too coarse to map intertidal macroalgae as it could not capture the fine spatial patterns of the macroalgal community. High-resolution RGB (colour) imagery, taken during the aircraft and UAV surveys, was used to collect training and reference data through the visual identification and digital delineation of species. Classes were determined based on the level of taxonomic detail that could be observed, with higher levels of taxonomic detail observed in the UAV imagery over the aircraft imagery. Data from both were used to train a maximum-likelihood classifier (MLC).4. The UAV imagery was able to more accurately classify a distinct A. nodosum class, along with other macroalgal and substratum classes (overall accuracy, OA, 92%), than the aerial imagery, which could only identify a lower taxonomic resolution of mixed A. nodosum and fucoid class, achieving a lower OA (78.9%). This study has demonstrated that in a coastal site with low macroalgal species diversity, and despite the spectral similarity of macroalgal species, UAV-mounted multispectral sensors proved the most accurate for focused assessments of individual canopyforming species.

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Evaluating UAV‐based multispectral imagery for mapping an intertidal seagrass environment

Elma,

Gaulton,

Chudley

et al. 2024

Aquatic Conservation

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Worldwide seagrass habitats are under severe anthropogenic threat. In the United Kingdom (UK), the health of habitats of the widely distributed Zostera species is particularly threatened by eutrophication that can lead to detrimental macroalgae overgrowth. To manage and conserve seagrass habitats, effective monitoring tools are required. We use an off‐the‐shelf consumer‐grade multispectral (RGB, red edge, and near‐infrared) camera mounted on an unoccupied aerial vehicle (UAV) to map an intertidal multispecies seagrass environment in Lindisfarne National Nature Reserve, Northumberland, UK. Field surveys were undertaken of three seagrass areas, including those dominated by Zostera noltii, Zostera marina and macroalgae. Using the Maximum Likelihood Classifier (MLC), results indicated an overall accuracy (OA) between 84% and 91% across classified habitat maps. As expected, the red edge and near‐infrared bands offered an advantage beyond RGB imagery to discriminate between the vegetation types for accurate habitat mapping. Our research provides a foundation for accurately mapping a complex intertidal seagrass environment through the utilisation of an off‐the‐shelf multispectral UAV. The study may aid the implementation and development of effective monitoring programmes for the management of Zostera spp. decline and macroalgae proliferation to prevent seagrass degradation and conserve these valuable yet fragile ecosystems.

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Spatial assessment of intertidal seagrass meadows using optical imaging systems and a lightweight drone

Cited by 133 publications

References 62 publications

Mapping with confidence; delineating seagrass habitats using Unoccupied Aerial Systems (UAS)

Mapping with confidence; delineating seagrass habitats using Unoccupied Aerial Systems (UAS)

Application of multiplatform, multispectral remote sensors for mapping intertidal macroalgae: A comparative approach

Evaluating UAV‐based multispectral imagery for mapping an intertidal seagrass environment

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