Observation of typhoon-induced seagrass die-off using remote sensing

Kim, Keunyong; Choi, Jong-Kuk; Ryu, Joo-Hyung; Jeong, Hae Jin; Lee, Kitack; Park, Myung Gil; Kim, Kwang Young

doi:10.1016/j.ecss.2014.12.036

Cited by 40 publications

(21 citation statements)

References 44 publications

(56 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, multiple studies have observed pronounced changes in seagrass area over time, both increases [254,261] and decreases [253,258]. These changes in seagrass extent have consistently been reported as consequences of abrupt external events (e.g., increased pollution, land use change, disturbance, etc.).…”

Section: Seagrass Meadowsmentioning

confidence: 99%

See 1 more Smart Citation

Multiple Stable States and Catastrophic Shifts in Coastal Wetlands: Progress, Challenges, and Opportunities in Validating Theory Using Remote Sensing and Other Methods

et al. 2015

View full text Add to dashboard Cite

Multiple stable states are established in coastal tidal wetlands (marshes, mangroves, deltas, seagrasses) by ecological, hydrological, and geomorphological feedbacks. Catastrophic shifts between states can be induced by gradual environmental change or by disturbance events. These feedbacks and outcomes are key to the sustainability and resilience of vegetated coastlines, especially as modulated by human activity, sea level rise, and climate change. Whereas multiple stable state theory has been invoked to model salt marsh responses to sediment supply and sea level change, there has been comparatively little empirical verification of the theory for salt marshes or other coastal wetlands. Especially lacking is long-term evidence documenting if or how stable states are established and maintained at ecosystem scales. Laboratory and field-plot studies are informative, but of necessarily limited spatial and temporal scope. For the purposes of long-term, coastal-scale monitoring, remote sensing is the best viable option. This review summarizes the above topics and highlights the emerging promise and challenges of using remote sensing-based analyses to validate coastal OPEN ACCESS Remote Sens. 2015, 7 10185 wetland dynamic state theories. This significant opportunity is further framed by a proposed list of scientific advances needed to more thoroughly develop the field.

show abstract

Section: Seagrass Meadowsmentioning

confidence: 99%

“…Several recent studies also focused on the spatial and temporal variability of seagrasses [252][253][254][255][256][257][258][259]. Some studies document remarkable stability of seagrass meadows over long time periods.…”

Section: Seagrass Meadowsmentioning

confidence: 99%

Multiple Stable States and Catastrophic Shifts in Coastal Wetlands: Progress, Challenges, and Opportunities in Validating Theory Using Remote Sensing and Other Methods

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Seagrass habitats along the Straits of Johor become vulnerable due to the southwest monsoon. However, the Sumatera Island reduces the wind velocity, evades extreme speed of water current towards the confined areas of Merambong and often causes uprooting of seagrass roots from the ground [35,36], especially to the Halophila ovalis. However, the impact of monsoon seasons on seagrass growth and decrement is not significant and prominent due to areas being sheltered by Malaysia Peninsula and Singapore Islands, located in both north and south, respectively.…”

Section: Changes On the Seagrass Distribution Map Between 2009 And 20mentioning

confidence: 99%

Change Detection of Submerged Seagrass Biomass in Shallow Coastal Water

Misbari

Hashim

2016

Remote Sensing

View full text Add to dashboard Cite

Satellite remote sensing is an advanced tool used to characterize seagrass biomass and monitor changes in clear to less-turbid waters by analyzing multi-temporal satellite images. Seagrass information was extracted from the multi-temporal satellite datasets following a two-step procedure: (i) retrieval of substrate-leaving radiances; and (ii) estimation of seagrass total aboveground biomass (STAGB). Firstly, the substrate leaving radiances is determined by compensating the water column correction of the pre-processed data because of the inherent errors associated with the geometric and radiometric fidelities including atmospheric perturbations. Secondly, the seagrass leaving radiances were correlated to the corresponding in situ STAGB to predict seagrass biomass. The relationship between STAGB and cover percentage was then established for seagrass meadows occurring in Merambong, Straits of Johor, Malaysia. By applying the above-mentioned approach on Landsat Thematic Mapper (TM) acquired in 2009 and Operational Land Imager (OLI) data acquired in 2013, the resulting maps indicated that submerged STAGB in less clear water can be successfully quantified empirically from Landsat data, and can be utilized in STAGB change detection over time. Data validation showed a good agreement between in situ STAGB and Landsat TM (R 2 = 0.977, p < 0.001) and OLI (R 2 = 0.975, p < 0.001) derived water leaving radiances for the studied seagrass meadows. The STAGB was estimated as 803˘0.47 kg in 2009, while it was 752.3˘0.34 kg in 2013, suggesting a decrease of 50.7 kg within the four-year interval. This could be mainly due to land reclamation in the intertidal mudflat areas performed, with a view to increase port facilities and coastal landscape development. Statistics on dugong sightings also supports changes in STAGB.

show abstract

“…Time series maps of seagrass abundance have recently been produced from Landsat 5 data and pixelbased classification [18][19][20] using image-specific training areas so that each date in the series is associated with a unique classifier. The "quality" and relevance of classes derived from satellite data are variable across images [21] so that transferring pixel-based definitions of class spectra to other images has been considered impractical [11].…”

Section: Introductionmentioning

confidence: 99%

Supervised Classification of Benthic Reflectance in Shallow Subtropical Waters Using a Generalized Pixel-Based Classifier across a Time Series

2015

View full text Add to dashboard Cite

Abstract:We tested a supervised classification approach with Landsat 5 Thematic Mapper (TM) data for time-series mapping of seagrass in a subtropical lagoon. Seagrass meadows are an integral link between marine and inland ecosystems and are at risk from upstream processes such as runoff and erosion. Despite the prevalence of image-specific approaches, the classification accuracies we achieved show that pixel-based spectral classes may be generalized and applied to a time series of images that were not included in the classifier training. We employed in-situ data on seagrass abundance from 2007 to 2011 to train and validate a classification model. We created depth-invariant bands from TM bands 1, 2, and 3 to correct for variations in water column depth prior to building the classification model. In-situ data showed mean total seagrass cover remained relatively stable over the study area and period, with seagrass cover generally denser in the west than the east. Our approach achieved mapping accuracies (67% and 76% for two validation years) comparable with those attained using spectral libraries, but was simpler to implement. We produced a series of annual maps illustrating inter-annual variability in seagrass occurrence. Accuracies may be improved in future work by better addressing the spatial mismatch between pixel size of remotely sensed data and footprint of field data and by employing atmospheric correction techniques that normalize reflectances across images. OPEN ACCESSRemote Sens. 2015, 7 5099

show abstract

Observation of typhoon-induced seagrass die-off using remote sensing

Cited by 40 publications

References 44 publications

Multiple Stable States and Catastrophic Shifts in Coastal Wetlands: Progress, Challenges, and Opportunities in Validating Theory Using Remote Sensing and Other Methods

Multiple Stable States and Catastrophic Shifts in Coastal Wetlands: Progress, Challenges, and Opportunities in Validating Theory Using Remote Sensing and Other Methods

Change Detection of Submerged Seagrass Biomass in Shallow Coastal Water

Supervised Classification of Benthic Reflectance in Shallow Subtropical Waters Using a Generalized Pixel-Based Classifier across a Time Series

Contact Info

Product

Resources

About