Using bottom surface reflectance to map coastal marine areas: a new application method for Lyzenga's model

Sagawa, T.; Boisnier, Etienne; Komatsu, Toshimitsu; Mustapha, Karim Ben; Hattour, Abdalla; Kosaka, Naoko; Miyazaki, Shuichi

doi:10.1080/01431160903154341

Cited by 70 publications

(42 citation statements)

References 13 publications

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“…The relationship derived from in situ seagrass biomass and density coverage is very high, whilst the final BRI-seagrass biomass established for final empirical model for estimating seagrass biomass from satellite for the best bands (blue bands) is given in Figure 9 using another set of in situ for verification. The BRI of seagrass dominant pixel is relatively higher than previous study conducted by [19] in clear water. This indicates that the effect of high light scattering amount of total suspended sediments in water could increase BRI value of seagrass detected pixels due to low attenuation coefficient.…”

Section: Changes Of Stagb Between 2009 and 2013 On The Merambong Shoalcontrasting

confidence: 48%

“…The K i of each band was extracted from exponential graph between (L i and L si ) and corresponding depth, Z by dividing the extracted value with geometric factor, g. The g value was calculated in order to account for path length of light through water. This value is obtained through a series of mathematical solutions using the Snell's Law concept with information of sun elevation angle which is obtained from metadata of each scene, altitude and field of viewing angle of the sensor and the value is always 2 for all images of passive sensor including Landsat [19,32]. In this study, g value of Landsat TM 2009 is 2.15 while Landsat 8 OLI is 2.13.…”

Section: Retrieval Of Seagrass Featuresmentioning

confidence: 99%

“…It is 1.8 km in length from north to south and 200 m in width from east to west, covering latitude of 01˝19.979 1 North and longitude of 103˝35.965 1 East; total approximate area is 42.6 km 2 which covers Tanjung Kupang and Tanjung Adang. technique called bottom reflectance index (BRI) by [19] to detect the changes in submerged STAGB is tested on the multi-species seagrass meadow environment. In addition, we applied the empirical method on Landsat images with 30 m spatial resolution that have a large spatial coverage and are free to download.…”

Section: Study A-merambong Shoalmentioning

confidence: 99%

“…Prior to this, the robustness of water column correction technique called bottom reflectance index (BRI) by [19] to detect the changes in submerged STAGB is tested on the multi-species seagrass meadow environment. In addition, we applied the empirical method on Landsat images with 30 m spatial resolution that have a large spatial coverage and are free to download.…”

Section: Introductionmentioning

confidence: 99%

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Change Detection of Submerged Seagrass Biomass in Shallow Coastal Water

Misbari

Hashim

2016

Remote Sensing

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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.

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Section: Changes Of Stagb Between 2009 and 2013 On The Merambong Shoalcontrasting

confidence: 48%

Section: Retrieval Of Seagrass Featuresmentioning

confidence: 99%

Section: Study A-merambong Shoalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Change Detection of Submerged Seagrass Biomass in Shallow Coastal Water

Misbari

Hashim

2016

Remote Sensing

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“…Sidescan sonar can detect horizontal distribution of seagrass with a wide band width such as 30-50 m along a track (e.g. Sagawa et al, 2008;2010). Narrow multi-beam sonar can measure three-dimensional spatial distribution of seagrass (Komatsu et al, 2003).…”

Section: Wwwintechopencommentioning

confidence: 99%

Mapping is a Key for Sustainable Development of Coastal Waters: Examples of Seagrass Beds and Aquaculture Facilities in Japan with Use of ALOS Images

Komatsu¹,

Sagawa²,

Sawayama³

et al. 2012

Sustainable Development - Education, Business and Management - Architecture and Building Construction - Agriculture and Food Se

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Land‐based and climatic stressors of mangrove cover change in the Auckland Region, New Zealand

Suyadi

Gao

Lundquist

et al. 2019

Aquatic Conservation

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1. Changes in mangrove forest cover (increases and decreases) have been observed globally as a result of the influence of environmental changes and human impacts.Although mangrove forest increases are globally uncommon, in New Zealand mangroves are increasing in distribution at the expense of other intertidal estuarine habitats.2. This study quantified the change in mangrove area over a period of 74 years , and investigated the influence of land-based (e.g. land use conversion in the catchments), climatic, and oceanic factors on the change in temperate mangrove forest cover in the Auckland Region of New Zealand over that period.3. An improved mapping approach using aerial photographs and Landsat images was applied to quantify changes in tall and dwarf mangrove cover, and to identify the mechanism of change (expansion, gap filling, and loss), across 38 estuaries in the Auckland Region, New Zealand. 4. Mangrove area increased from 2313 ha in 1940 to 10 483 ha in 2014 (on average 3.2% yr −1 ), with higher rates in small estuaries (<1000 ha) than in large estuaries (>1000 ha). The area of the intertidal mudflat habitat, i.e. potential area for mangrove colonization, has doubled from 14 193 ha in 1974 to 28 764 ha in 2014.The increase in total mangrove area was explained primarily by gap filling followed by expansion. The proportion of expansion-related increase was predominantly seaward (82%) but mangroves also expanded upstream (16%). 5.Boosted regression tree analysis revealed that catchment forest cover, sea level, and mean annual air temperature explained around 85% of the variation in mangrove area. Periods of catchment forest clearing coincided with gains in mangrove forest area. Dwarf mangroves were more sensitive to changes in sea level, in particular over the past 25 years, when the annual sea-level rise exceeded the sediment accumulation rates. 6. Catchment land use, especially the clearing of forests, associated with increased sediment supply into estuaries is the strongest driver of mangrove forest gain.Thus, land-use management is a necessary aspect of the management of New Zealand estuaries to conserve the diversity of estuarine habitats (including mangroves) and the ecosystem services that each habitat provides.

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Using bottom surface reflectance to map coastal marine areas: a new application method for Lyzenga's model

Cited by 70 publications

References 13 publications

Change Detection of Submerged Seagrass Biomass in Shallow Coastal Water

Change Detection of Submerged Seagrass Biomass in Shallow Coastal Water

Mapping is a Key for Sustainable Development of Coastal Waters: Examples of Seagrass Beds and Aquaculture Facilities in Japan with Use of ALOS Images

Land‐based and climatic stressors of mangrove cover change in the Auckland Region, New Zealand

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