Mapping seagrass and seaweed beds in NOWPAP Teruhisa Komatsu Atmosphere and Ocean Research Institute, The University of Tokyo
Contents What is coastal habitat? Important ecological roles of coastal habitats Mapping methods of coastal habitats Direct and indirect methods Mapping by remote sensing Introduction of seagrass mapping
What is habitat? Habitat is similar to biotope or biome: An area that is uniform in environmental conditions and in its distribution of animals and plants. On land, forests, meadows and marshes are habitats. They are called ecological engineers.
What habitats exist in the sea? Seaweed and seagrass beds Zostera marina Sargassum horneri Zostera caulescens
Ecosystem services of habitats Gas regulation Climate regulation Disturbance regulation Erosion control and sediment retention Nutrient cycling Waste treatmentt t Refugia (spawning and nursery grounds ) Food production Genetic resources Recreation Cultural Costanza et al. (1997)
Ecological services of coastal biomes in the world Biome Area (ha) Total value perha Total globalflow ($ha 1 yr 1 ) value($yr 1 x10 9 ) Estuaries 180 22,832 4,110 Seagrass/seaweed beds 200 19,004 3,801 Coral reefs 62 6,075 375 Shelf 2,660 1,610 4,283 Tidal marsh/mangroves 165 9,990 1,648 Total coastal biome 3,267 4,352 14,217 Tropical lforest 1,900 2,007 3,813 Temperate/boreal forests 2,955 302 894 Total forest biome 4,855 970 4,707 Constanza et al. (1997)
Coastal habitats at risk Agricultural pollution Industrial pollution Reclamation in Seto Inland Sea around 1960s Seagrass beds in Seto Inland Sea
Amelioration of water quality in Seto Inland Sea Komatsu (1997) Long term changes in the Zostera bed area in the Seto Inland Sea (Japan), especially along the coast of the Okayama Prefecture, Oceanologica. Acta, 20, 209 216.
Law on Environmental Conservation of the Seto Inland Sea Cumulative reclamation and Zostera marina Cumulative reclamation and Zostera marina Komatsu (1997) Long term changes in the Zostera bed area in the Seto Inland Sea (Japan), especially along the coast of the Okayama Prefecture, Oceanologica. Acta, 20, 209 216.
Counter measure against trawls in seagrass meadows in Okayama Prefecture, Japan Deployment of artificial reefs and columns obstructing trawl operation inside the seagrass beds of Ajino Bay in 1976 Deployment Komatsu (1997) Long term changes in the Zostera bed area in the Seto Inland Sea (Japan), especially along the coast of the Okayama Prefecture, Oceanologica. Acta, 20, 209 216.
Management of coastal habitats Monitoring of habitat distributions Protection of habitat areas Restoration of habitats Mapping coastal habitats is indispensable for sustainable use of coastal resources
Mapping coastal habitats by direct and indirect surveys Direct methods Walking Diving Observation from the ship Grabbing bottom sediments
Direct methods (ground survey) Characteristics density estimation, species identification assured method Problems low efficiency influence of turbidity of water and high waves on field survey
Mapping of habitats by indirect methods Acoustics Optics (satellite remote sensing)
Optical methods Introduction of seagrass mapping
Lyzenga s Model Sun E sun Lsi L i Satellite Sea surface Deep water Bottom surface Ki Z Ri Li = Lsi +Ai Ri exp( Ki F Z) (W/m 2 /sr sr) (Lyzenga 1978)
Lyzenga (1978, 1981) Depth invariant Index (DII) DII = ln( Li Lsi ) [( Ki/Kj )ln( Lj Lsj j )] DII = ln( Ai Ri ) ( Ki/Kj )ln( Aj Rj ) DII = c + ln (Ri/Rj) assuming that Ki/Kj=1 Lyzenga s model: Li = Lsi + Ai Ri exp( Ki f Z)
Sagawa et al. (2010) International Journal of Remote Sensing, 31, 3051 3064 Reflectance Index (RI) RI = ( Li Lsi )/( exp( Ki f Z ) ) RI = Ai Ri Lyzenga s model: Li = Lsi + Ai Ri exp( Ki f Z)
Example of attenuation coeeficient K= 0.093 m 1 F=2.18 Jerlov Water Type Ⅱ Ⅲ Rlti Relation bt between depth and radiance (green band) Lyzenga s model: Li = Lsi + Ai Ri exp( Ki F Z)) (W/m2/sr sr)
DI Index 誤差行列 (Error matrix) 全体の精度 (Overall accuracy) = 54 % Sagawa et al. (2010) International Journal of Remote Sensing, 31, 3051 3064
BR Index Sagawa et al. (2010) International Journal of Remote Sensing, 31, 3051 3064
Overall accuracy (DI) DI)(%) Overall accuracy (BR)(%) JWT Mahares 54.0 90.0 Ⅱ-Ⅲ Classification with BR Idex is more correct than DI Index Classification with BR Idex is more correct than DI Index (p < 0.05)
Sensors Aerial photography h Launch of Landsat satellite (1972) Sensors with spatial resolution (30~100 m) were most widely used (e.g. Landsat Multispectral Scanner, Landsat TM) long-time series/inexpensive data Sensors with better spatial resolution <10 m (e.g. IKONOS, SPOT, Worldview 2, Geo Eye etc) better spectral & spatial versatility/extremely expensive data Launch of ALOS (2006) and stop in April 2011 AVNIR-2 resolution: spatial (10 m)/multispectral (420~890nm)/radiometric (8 bits)
Characteristics of Landsat TM Band no. Spectral range (µm) Spatial resolution (m) 1 0.45-0.52 30 2 0.52-0.60 30 3 0.63-0.69 30 4 0.76-0.90 30 5 1.55-1.75 30 Bands 1, 2, 3 and 4 are nearly equivalent to those of ALOS. 6 10.4-12.5 120 We 7 can analyze 208-2.08 time 235 2.35 series data consisting 30 of LANDSAT TM and ALOS from 1972 to 2011.
Characteristics of AVNIR 2 sensor Swath Width Spatial Resolution Wavelength Quantization 70 km ( at nadir) 10 m (at nadir) Band 1: 0.42 0.50 µm (visible blue) Band 2: 0.52 0.60 µm (visible green) Band 3: 0.61 0.69 µm (visible red) Band 4: 0.76 0.89 µm (near infrared) 8 bits We can compare habitat distributions between images of ALOS AVNIR 2 and LANDSAT
Sibu Island, Johor Malaysia tudy S rea A
Raw ALOS AVNIR 2 Image (29 July 2008 11:54 am)
Change of coral reef to Sargassum beds from 2005 to 2008 Comparison result of ALOS (2008) and LANDSAT (2005) over Sibu Island Detection of succession from live coral to Sargassum forest Courtesy of Prof. Ibrahim Seeni at UTM, Malaysia 28
Tentative proposal to NOWPAP Temporal changes in spatial distributions ib ti of seaweed and seagrass beds with relation to environmental changes such as eutrophication or reclamation Focusing on changes in not only distributions of seagrass and seaweed beds but also land use Selection of test sites, where they are distributed broadly, with time series data of environmental parameters
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