Remote Sensing of Seagrass Ecosystems: Use of Spaceborne and Airborne Sensors

Dekker, A.G.; Brando, Vittorio; Anstee, Janet; Fyfe, S. K.; Malthus, Tim J.; Karpouzli, Evanthia

doi:10.1007/1-4020-2983-7_15

Cited by 31 publications

(48 citation statements)

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“…; Ventura et al., ), in temperate regions water clarity is a limiting factor in benthic habitat mapping (O'Neill et al. ) due to the presence of suspended material (organic detritus and inorganic sediments), phytoplankton, and CDOM causing reduced benthic visibility as is characterized by smaller Secchi depths (Babin et al., ; Dekker et al., ). When possible, mapping should be conducted during the lowest tides when turbidity levels are likely to be low (Fig.…”

Section: Discussionmentioning

confidence: 99%

“…4B), we demonstrate that tidal height interacts with local seagrass bed characteristics (i.e., how deep the eelgrass extends) and environmental conditions (i.e., water clarity at the time of acquisition) to determine the theoretical visibility for optimal mapping (Table 5). Unlike tropical regions where turbidity is generally low (Casella et al 2016;Ventura et al, 2016), in temperate regions water clarity is a limiting factor in benthic habitat mapping (O'Neill et al 2011) due to the presence of suspended material (organic detritus and inorganic sediments), phytoplankton, and CDOM causing reduced benthic visibility as is characterized by smaller Secchi depths (Babin et al, 2003;Dekker et al, 2006). When possible, mapping should be conducted during the lowest tides when turbidity levels are likely to be low ( Fig.…”

Section: Best Predictors Of Uas Mapping Outcomementioning

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: Best Predictors Of Uas Mapping Outcomementioning

confidence: 99%

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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“…Further, other fields within remote sensing, such as aquatic optics, take a process‐based modeling approach (Dekker et al. ). For example, Dekker et al.…”

Section: Discussionmentioning

confidence: 99%

“…The persistence could be affected by the user's accuracy but because this metric is generally large, we trust our measurements. Further, other fields within remote sensing, such as aquatic optics, take a process-based modeling approach (Dekker et al 2005). For example, Dekker et al (2011) presents a comparison of radiative transfer approaches that explicitly consider water depth and water column turbidity.…”

Section: Spread and Persistence Of Submerged Aquatic Vegetationmentioning

confidence: 99%

Measuring landscape‐scale spread and persistence of an invaded submerged plant community from airborne remote sensing

Santos

Khanna

Hestir

et al. 2016

Ecological Applications

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Processes of spread and patterns of persistence of invasive species affect species and communities in the new environment. Predicting future rates of spread is of great interest for timely management decisions, but this depends on models that rely on understanding the processes of invasion and historic observations of spread and persistence. Unfortunately, the rates of spread and patterns of persistence are difficult to model or directly observe, especially when multiple rates of spread and diverse persistence patterns may be co-occurring over the geographic distribution of the invaded ecosystem. Remote sensing systematically acquires data over large areas at fine spatial and spectral resolutions over multiple time periods that can be used to quantify spread processes and persistence patterns. We used airborne imaging spectroscopy data acquired once a year for 5 years from 2004 to 2008 to map an invaded submerged aquatic vegetation (SAV) community across 2220 km of waterways in the Sacramento-San Joaquin River Delta, California, USA, and measured its spread rate and its persistence. Submerged aquatic vegetation covered 13-23 km of the waterways (6-11%) every year. Yearly new growth accounted for 40-60% of the SAV area, ~50% of which survived to following year. Spread rates were overall negative and persistence decreased with time. From this dataset, we were able to identify both radial and saltatorial spread of the invaded SAV in the entire extent of the Delta over time. With both decreasing spread rate and persistence, it is possible that over time the invasion of this SAV community could decrease its ecological impact. A landscape-scale approach allows measurements of all invasion fronts and the spatial anisotropies associated with spread processes and persistence patterns, without spatial interpolation, at locations both proximate and distant to the focus of invasion at multiple points in time.

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“…Satellite images can also be used for analysis of the impacts of climate change and for tracking of anthropogenic pressures to biodiversity (Kachelriess, Wegmann, Gollock, & Pettorelli, 2014). There have been numerous notable recent reviews and books on the applications of satellite remote sensing for coastal management (Klemas, 2010;Miller, Del Castillo, & McKee, 2005;Weng, 2010), coastal biodiversity indicators (Strittholt, Miles, Horning, & Fosnight, 2007), mangrove ecosystems (Kuenzer, Bluemel, Gebhardt, Quoc, & Dech, 2011), seagrass meadows (Dekker et al, 2007;Kirkman, 1996), reef fish management (Hamel & Andréfouët, 2010), shark and ray ecology (Williamson, 2018) and fisheries science (Klemas, 2013).…”

Section: Global Remote Sensing With Satellitesmentioning

confidence: 99%

Towards a macroscope: Leveraging technology to transform the breadth, scale and resolution of macroecological data

Dornelas

Madin

Bunce

et al. 2019

Global Ecol Biogeogr

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The problem Earth‐based observations of the biosphere are spatially biased in ways that can limit our ability to detect macroecological patterns and changes in biodiversity. To resolve this problem, we need to supplement the ad hoc data currently collected with planned biodiversity monitoring, in order to approximate global stratified random sampling of the planet. We call this all‐encompassing observational system ‘the macroscope’. The solution With a focus on the marine realm, we identify seven main biosphere observation tools that compose the macroscope: satellites, drones, camera traps, passive acoustic samplers, biologgers, environmental DNA and human observations. By deploying a nested array of these tools that fills current gaps in monitoring, we can achieve a macroscope fit for purpose and turn these existing powerful tools into more than the sum of their parts. An appeal Building a macroscope requires commitment from many fields, together with coordinated actions to attract the level of funding required for such a venture. We call on macroecologists to become advocates for the macroscope and to engage with existing global observation networks.

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Remote Sensing of Seagrass Ecosystems: Use of Spaceborne and Airborne Sensors

Cited by 31 publications

References 0 publications

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

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

Measuring landscape‐scale spread and persistence of an invaded submerged plant community from airborne remote sensing

Towards a macroscope: Leveraging technology to transform the breadth, scale and resolution of macroecological data

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