Technical Note. Mapping seagrass beds using IKONOS satellite image and side scan sonar measurements: a Japanese case study

Sagawa, T.; Mikami, Akiko; Komatsu, Toshimitsu; Kosaka, Naoko; Kosako, A.; Miyazaki, Shuichi; Takahashi, Masaharu

doi:10.1080/01431160701269028

Cited by 37 publications

(15 citation statements)

References 17 publications

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“…The accuracy of seagrass mapping is also not consistent due to the differences in the classification scheme being used. Separating seagrass from sand or other habitats can be quite accurate at medium to high cover density [Mumby and Green, 2000;Pasqualini et al, 2005;Sagawa et al, 2007;Sagawa et al, 2008]. Unfortunately, further seagrass properties mapping such as percent cover and standing biomass are not as accurate [Phinn et al, 2008;Knudby and Nordlund, 2011;Lyons et al, 2011;Lyons et al, 2012].…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, even the accuracy of seagrass species map derived using high resolution data, especially in heterogeneous seagrass habitat is still relatively low and inconsistent [Phinn et al, 2008;Lyons et al, 2011]. Depending on the complexity of seagrass species classification scheme being used, the accuracy may vary from low to high [Sagawa et al, 2008;Yang and Yang, 2009]. In this study, we addressed the complexity of mapping continuous LAI at species and at community level.…”

Section: Discussionmentioning

confidence: 99%

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Mapping Seagrass from Space: Addressing the Complexity of Seagrass LAI Mapping

Wicaksono

Hafizt

2013

European Journal of Remote Sensing

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Information of seagrass LAI is still lacking in most parts of the world due to the high cost of comprehensive mapping. In this paper, we described the use of remote sensing as the cost and time effective solution to perform continuous seagrass LAI mapping, and discussed the issues and difficulties encountered during the mapping. ASTER VNIR and ALOS AVNIR-2 were used to perform the mapping. We proposed at life-form seagrass classification scheme to accommodate the low accuracy of at species level mapping. We also developed sampling mapping unit consist of several factors affecting the distribution of seagrass LAI. The results showed that sensor, method, and environmental limitation contribute to the low accuracy of seagrass LAI mapping.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Mapping Seagrass from Space: Addressing the Complexity of Seagrass LAI Mapping

Wicaksono

Hafizt

2013

European Journal of Remote Sensing

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“…In order to resolve these problems, Sagawa et al (2008) attempted a new strategy for collecting ground-truth data from hydroacoustic survey in which the overall accuracy showed 97.3% from the error matrix using aquatic video-camera data. Thus, the hydroacoustic survey method, using a DT-X digital echosounder (Biosonics Inc., Seattle, WA, USA) on January 17e20 and February 21, 2012, was used to generate an overall seagrass beds map (Montefalcone et al, 2013).…”

Section: In Situ Measurements Of Hydroacoustic and Spectral Reflectanmentioning

confidence: 99%

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

Kim

Choi

Ryu

et al. 2015

Estuarine, Coastal and Shelf Science

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“…Side scan sonar (SSS) image segmentation has wide applications in the detection of aircrafts, shipwrecks, torpedoes, mine-like objects and other submarine objects (Dorschel et al 2009;Kumagai et al 2010;De Beukelaer et al 2003;Sagawa et al 2008), and is a preparation step for the classification where it is very important to have a robust and high quality contour (Kohntopp et al 2015;Reed et al 2003). SSS image segmentation can be done according to different homogeneity criteria, such as gray level or texture feature.…”

Section: Introductionmentioning

confidence: 99%

Side scan sonar image segmentation based on neutrosophic set and quantum-behaved particle swarm optimization algorithm

et al. 2016

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To fulfill side scan sonar (SSS) image segmentation accurately and efficiently, a novel segmentation algorithm based on neutrosophic set (NS) and quantumbehaved particle swarm optimization (QPSO) is proposed in this paper. Firstly, the neutrosophic subset images are obtained by transforming the input image into the NS domain. Then, a co-occurrence matrix is accurately constructed based on these subset images, and the entropy of the gray level image is described to serve as the fitness function of the QPSO algorithm. Moreover, the optimal two-dimensional segmentation threshold vector is quickly obtained by QPSO. Finally, the contours of the interested target are segmented with the threshold vector and extracted by the mathematic morphology operation. To further improve the segmentation efficiency, the single threshold segmentation, an alternative algorithm, is recommended for the shadow segmentation by considering the gray level characteristics of the shadow. The accuracy and efficiency of the proposed algorithm are assessed with experiments of SSS image segmentation.

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Technical Note. Mapping seagrass beds using IKONOS satellite image and side scan sonar measurements: a Japanese case study

Cited by 37 publications

References 17 publications

Mapping Seagrass from Space: Addressing the Complexity of Seagrass LAI Mapping

Mapping Seagrass from Space: Addressing the Complexity of Seagrass LAI Mapping

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

Side scan sonar image segmentation based on neutrosophic set and quantum-behaved particle swarm optimization algorithm

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