Ocean Remote Sensing Project of Coastal Habitat Mapping

Transcription

1 Ocean Remote Sensing Project of Coastal Habitat Mapping 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 acoustic methods Introduction of our research Mapping by remote sensing Introduction of our research on seagrass mapping WESTPAC Ocean Remote Sensing Project of Coastal Habitat Mapping 1

2 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 is Ecological Engineer? Ecological engineer is defined as the plants and animals that create, modify, and maintain their environments. All organisms affect their environment in some small way, but ecological engineers make significant changes such as regulating the availability of essential resources (like food, water, light and shelter) or altering natural processes (such as water flow). By modifying their habitats, ecological engineers also influence the occurrence of plants and animals in natural communities. 2

3 What habitats exist in the sea? Seaweed Seagrass Coral reefs Mangrove Seaweed beds Sargassum horneri Macrocystis pyrifera 3

4 Seagrass Posidonia oceanica Zostera caulescens Zostera marina Primary productivity 4

5 Important ecological roles of seagrass and seaweed beds Herring eggs spawned on leaves of seagrass Cuttlefish eggs spawned on seaweed, Sargassum species Nursery grounds Juvenile fish in seagrass beds (left) and Sargassum beds (right) 5

6 Epiphytes on seagrass and seaweed: Substrate for plants and animals Feeding place for animals Turban shell on brown algae (left) and sea urchin in seagrass beds (right) 6

7 Feeding place for animals: Direct and indirect Biodiversity: rich species diversity 7

8 Buffering effect of wave and currents Disappearance of Zostera marina due to wasting disease Erosion of bottom substrate In summer (scanty growth season of Sargassum species) 8

9 In spring (luxuriant growth season of Sargassum species) 9

10 Komatsu, T., H. Ariyama, H. Nakahara and W. Sakamoto (1982) Spatial and temporal distributions of water temperature in a Sargassum forest, Journal of the Oceanographical Society of Japan, 38,

11 Percentage of downward irradiance of PAR in the luxuriant growth season of Saragssum species (Left) and scanty growth season (Right). Komatsu, T., H. Ariyama, H. Nakahara and W. Sakamoto (1982) Spatial and temporal distributions of water temperature in a Sargassum forest, Journal of the Oceanographical Society of Japan, 38, Komatsu, T., H. Ariyama, H. Nakahara and W. Sakamoto (1982) Spatial and temporal distributions of water temperature in a Sargassum forest, Journal of the Oceanographical Society of Japan, 38,

12 Komatsu, T. and H. Kawai (1986) Diurnal changes of ph distributions and the cascading of shore water in a Sargassum forest, Journal of the Oceanographical Society of Japan, 42, Komatsu, T. and H. Kawai (1986) Diurnal changes of ph distributions and the cascading of shore water in a Sargassum forest, Journal of the Oceanographical Society of Japan, 42,

13 Komatsu, T.: Day-night reversion in the horizontal distributions of dissolved oxygen content and ph in a Sargassum forest, Journal of Oceanographical Society of Japan, 45, , Measurement of water flow using plaster balls Komatsu and Kawai (1992) Measurements of time-averaged intensity of water motion with plaster balls, Journal of Oceanography, 48,

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15 Komatsu, T., S. Murakami and H. Kawai (1994) Some features of jump of water temperature in a Sargassum forest, Journal of Oceanography, 52, Komatsu, T., S. Murakami and H. Kawai (1994) Some features of jump of water temperature in a Sargassum forest, Journal of Oceanography, 52,

16 Important roles of seagrass and seaweed beds for a coastal ecosystem High primary production Spawning and nursery grounds for marine organisms Feeding places for marine animals Substrate for epiphytic flora and fauna Environment:Stabilization of substrate, production of oxygen, uptake of nutrients, buffering effect against currents, etc. Ecological importance of coral and mangroves are similar to that of seagrass and seaweed 16

17 Ecosystem services of habitats Gas regulation Climate regulation Disturbance regulation Erosion control and sediment retention Nutrient cycling Waste treatment 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 per ha ($ha -1 yr -1 ) Total global flow value($yr -1 x10 9 ) Estuaries ,832 4,110 Seagrass/seaweed beds ,004 3,801 Coral reefs 62 6, 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 forest 1,900 2,007 3,813 Temperate/boreal forests 2, Total forest biome 4, ,707 Constanza et al. (1997) 17

18 Coastal habitats at risk Threatened coastal ecosystems in the world Population Prospects: The 2004 Revision and World Urbanization Prospects: The 2003 Revision Three quarters of world population will live within 100 km from the coast till 2025 and threaten coastal ecosystems World Bank (2003) World Development Report 18

19 Threatened coastal ecosystem Coral reef Blast coral fishing Devastated reef bottom Agricultural pollution Industrial pollution Reclamation Case of Seto Inland Sea in Japan Reclamation in Seto Inland Sea around 1960s Seagrass beds in Seto Inland Sea 19

20 Annual production per 1 ha (10,000yen) in Seto Inland Sea in 1971 Regions Seagrass/ seaweed beds Other waters Ratio Bungo Channel Suo-nada 45.0 Moba 3.7 Region 12.2 Iyo-nada Hiroshima Bay Bingo-nada Bisan-seto Harima-nada Osaka Bay Kii Channel Source: Nansei Regional Fisheries Laboratory, 1974 Law on Environmental Conservation of the Seto Inland Sea 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,

21 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, 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,

22 Restoration and creation of seagrass and seaweed beds Gradual sloped seawall at Kansai Airport 14 months after construction 22

23 Fishermen and citizens transplanting seagrass Education of fishermen and citizens Participation of children 23

24 Increase of seagrass beds Fishermen cooperative has practiced seeding the bottom with seagrass Catch (t) Crab Cuttle fish Sea bream Restoration ASEAN Case An example of Rayong Province, Thailand 24

25 Discharge from shrimp ponds pollute coastal waters Increase in POM and nutrients Frequent occurrence of red tide and decrease in transparency Decrease in seagrass and seaweed beds Intensifying fishing pressure on remained seagrass and seaweed beds Thai case Introduction of green seaweed into ponds Benthic animals increased due to seaweed Elevation of shrimps with animals and seaweed during first 3 months Elevation of shrimps with pellets after 3 month cultivation Harvesting shrimp Caulerpa cultivation after harvesting shrimp Zero emission culture of shrimp 25

26 Management of coastal habitats Monitoring of habitat distributions Protection of habitat areas Restoration of habitats Mapping coastal habitats is indispensable for sustainable use of cosatal resources Mapping coastal habitats by direct and indirect surveys Direct methods Walking Diving Observation from the ship Grabbing bottom sediments 26

27 Video camera observation Video camera observation 27

28 When wave is high, observation is difficult When turbidity is high, observation is difficult. 28

29 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) 29

30 Introduction of our research on Mapping of seagrass and seaweed beds with hydro-acoustic methods Echosounder Side-scan sonar Narrow multibeam sonar Echosounder Recorder Transducer 30

31 Zostera marina 31

32 Canopy height I L H High, intermediate and low densities We can estimate the biomass of the Zostera beds 32

33 Survaying GP S Cable PC Monitor for navigation Unit of signal processing 33

34 Towfish Transducer KLEIN SYSTEM m, 29kg Acquisition of data 34

35 2011/11/30 Zostera caulescense 左舷側 右舷側 37m Zostera asiatica 50m 35

36 2011/11/30 Sargassum horneri 25m Zostera caulescense rocks Sand waves rope 135k Hz 2004/6/29 繁 茂期 455k Hz 36

37 Narrow Multi-beam sonar: 3-dimensional image 37

38 Zostera caulescens 38

39 Leaf Flowering shoot Fruit Vegetative shoot Flowering shoot Stem Zostera caulescens Root Zostera marina Vegetative shoot Objective of the study To understand its ecological characteristics That will help to protect and restore seagrass bed To make precise cartography of the seagrass bed To make an attempt to examine shoot and biomass of the seagrass at different depths in Funakoshi Bay 39

40 R/V Rias Gyrocompass Motion sensor Transducer 40

41 D-GPS Monitor of Seabat 9001 Monitor for navigation guide 41

42 Monitor indicating navigation course 42

43 5 m 10 m Reflected beam Sea bottom 43

44 Seagrass 5 m 10 m Sea bottom Seagrass signals 44

45 Removing seagrass signals Bottom topography with seagrass 45

46 Bottom topography without seagrass Subtracting bottom topography without seagrass from bottom topography with seagrass 46

47 Lower depth limit Flowering shoots Vegetative shoots 47

48 Flowering shoots 80 cm Vegetative shoots Shoot height below 80 cm: vegetative shoots Flowering shoots Quadrat sampling by a diver 48

49 Volume Area (gray) Area and volume occupied by seagrass Above-ground biomass Biomass of vegetative shoots =Area of seagrass beds (m 2 ) x Mean biomass of vegetative shoots per unit square (g/m 2 ) Biomass of vegetative shoots Area & volume =Volume of seagrass beds(m 3 ) Quadrat sampling x Mean biomass of flowering shoots per unit volume (g/m 3 ) 49

50 Acoustic methods Detection of seagrass, sand, rocks and seaweed are possible. Sidescansonr permits us to measure area of coastal habitats. Narrow multibeam sonar can estimate volume and area of seagrass and seaweed beds 50

51 Optical methods Introduction of our research on seagrass mapping Color of object Sun Observer Red band light Reflection and absorption Surface of red object 51

52 Measurement of spectral reflectance 52

53 Refrectance 2011/11/ Posidonia oceanica Sand Wavelength (m) Spectral reflectance of Posidonia oceanica and sand 53

54 Spectral sensitivity of IKONOS 54

55 Water well absorbs red and infrared bands Spectral reflectance of Zostera caulescens in a black aquarium with (red line) and without seawater (blue line) 55

56 Sea truth Collecting habitat distributions in situ to use for classification and evaluate classification results Diving, observation from the boat, walking with GPS Problem Point data don t have area corresponding to that of a pixel 4 m 4 m ピクセル Pixel True color image of IKONOS (left) and its close-up (right). Right rectangular show a pixel of 4 x 4 m composing the image. The size of pixel corresponds to spatial resolution of satellite image. 56

57 Mixel effect In situ pixel Area corresponding to a pixel of image where two classes are distributed (left) and a pixel of image with blended reflectance (right) Our idea is to use sidescan data as sea truth data Examples of Zostera asiatica (left) and Zostera caulescens (right) 57

58 We checked 150 points by underwater camera (left) in Funakoshi Bay (right) in Sanriku Coast, Japan Classification Zostera caulescense Zostera caulescense Underwater camera Zostera asiatica Sand Total User accuracy % Zostera asiatica % Sand % Total Producer % 96.0% accuracy % Total accuracy 97.3% Tau coefficient

59 Digital number (DN) of pixel to spectral irradiance Example of IKONOS (mw / cm 2 /sr) Lyzenga s Model Sun E sun Lsi L i Satellite Sea surface Bottom surface Ri Ki Li = Lsi + Ai Ri exp( Ki F Z) (W/m 2 /sr) (Lyzenga 1978) Z 59

60 ( 1981 (1978, Lyzenga Depth-invariant Index (DII) DII = ln( Li-Lsi )-[( Ki/Kj )ln( Lj-Lsj )] DII = ln( Ai Ri )-( Ki/Kj )ln( Aj Rj ) ( Ri/Rj ) DII = c + ln assuming that Ki/Kj=1 Lyzenga s model: Li = Lsi + Ai Ri exp( Ki f Z) Sagawa et al. (2010) International Journal Sensing, 31, of Remote Reflectance Index (RI) RI = ( Li -Lsi )/( exp( -Ki f Z ) ) RI = Ai Ri Lyzenga s model: Li = Lsi + Ai Ri exp( Ki f Z) 60

61 Example of attenuation coeeficient K= m -1 F=2.18 Jerlov Water Type Ⅱ-Ⅲ Relation between depth and radiance (green band) Lyzenga s model: Li = Lsi + Ai Ri exp( Ki F Z) (W/m2/sr) DI Index 誤差行列 (Error matrix) Sagawa et al. (2010) International Journal of Remote Sensing, 31, 全体の精度 (Overall accuracy )= 61.7 % 61

62 BR index 誤差行列 (Error matrix) Sagawa et al. (2010) International Journal of Remote Sensing, 31, 全体の精度 (Overall accuracy )= 83.8 % 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,

63 BR Index 誤差行列 (Error matrix) 全体の精度 (Overall accuracy )= 90 % Sagawa et al. (2010) International Journal of Remote Sensing, 31, Funakoshi Bay Overall accuracy (DI)(%) Overall accuracy (BR)(%) JWT Ⅱ Mahares Ⅱ-Ⅲ BR Idex is more correct classificatin than DI Index(p < 0.05) 63

64 IOC/WESTPAC Ocean Remote Sensing of Coastal Habitat Mapping To assist the sound integrated coastal zone management, it is necessary to grasp present spatial distributions of habitats with standardized mapping methods to provide baseline information for managers at different levels and to enhance the awareness of general public on how their coastal habitats are changing under human activities and climate change. Japan Aerospace Exploration Agency (JAXA) provides non-commercial satellite images with ultra-high spatial resolution optical sensors (10 m), AVNIR 2. It succeeded to American satellite, LANDSAT TM with high spatial resolution optical sensors (30 m) that have been used for coastal mapping and been out of use since 2003 due to scan line corrector failure of LANDSAT 7. However, a large number of LANDSAT TM images from 1982 to 2003 had been archived. With ALOS AVNIR2 images in combination with available LANDSAT TM images, it becomes possible to analyze temporal and spatial changes in habitat distributions. The IOC Sub-Commission for the Western Pacific (WESTPAC) reformed its Ocean Remote Sensing Project (ORSP) at the Eighth Intergovernmental Session (10-13 May 2010, Bali, Indonesia), with emphasis on the application of remote sensing for integrated coastal area management. Sensors Aerial photography ( 1972 ) Launch of Landsat satellite Sensors with spatial resolution (30~100 m) were most widely used ( TM (e.g. Landsat Multispectral Scanner, Landsat long-time series/inexpensive data Sensors with better spatial resolution <10 m ( etc (e.g. IKONOS, SPOT, Worldview 2, Geo Eye better spectral & spatial versatility/extremely expensive data Launch of ALOS ( 2006 ) and stop in April 2011 AVNIR-2 ( bits resolution: spatial (10 m)/multispectral (420~890nm)/radiometric (8 64

65 Characteristics of Landsat TM Band no. Spectral range (µm) Spatial resolution (m) Bands 5 1, 2, 3 and are nearly equivalent 30 to those of ALOS We 7 can analyze 2.08 time series data consisting 30 of LANDSAT TM and ALOS from 1972 to Characteristics of AVNIR-2 sensor Swath Width Spatial Resolution Wavelength Quantization 70 km ( at nadir) 10 m (at nadir) Band 1: µm (visible blue) Band 2: µm (visible green) Band 3: µm (visible red) Band 4: µm (near infrared) 8 bits 65

66 Monitoring of habitats in Malaysia Target sites: Sibu Island, Sg. Pulai Estuary and Langkawi Island Seagrass, seaweed and coral habitats ALOS AVNIR-2 (2008) Courtesy of Prof. Ibrahim Seeni at UTM, Malaysia Comparison result of ALOS (2008) and LANDSAT (2005) over Sibu Island LANDSAT-5 TM (2005) No seagrass Sargassum Live coral 131 Seaweed beds around Sibu Island 66

67 Study Site Ben Rohmdane (2009) Sulawesi Spermonde Archipelago west coast of south Sulawesi Visual observation-based seatruth data set July-August 2008 Quadrat (1m 2 ) From a motor boat equipped with GPS By snorkelling & walking 67

68 Observed Seagrass species Enhalus acroides Thalassia hemprichii Syringodium isoetifolium Halodule uninervis Cymodocea rotundata Halophila ovalis Observed coral classes Live corals Damaged corals Bleached corals Damaged coral consisting of reef rubble 68

69 Unsupervised Classification Based on the image statistics only Information about benthic distribution in 2006 ISODATA (Iterative Self-Organizing Data Analysis Technique) Accuracy assessment Error matrix By selecting a sample of reference locations & comparing the classification at these locations Damaged Live corals corals Seagrass Sand Total User accuracy Live corals % Damaged corals % Seagrass % Sand % Total Producer accuracy 66.9% 10.8% 19.4% 22.8% Overall accuracy 37.2% Tau coefficient

70 Examination of seatruth data Map Information on seabottom in 2006 Live corals Focus on coral classes solely Collected seatruth data in 2008 Damaged corals Bathymetric modelling Land Digital elevation model was obtained from the sounded & corrected bottom depths RI-method 70

71 DII-derived Supervised classification using radiometrically corrected data RI-derived Error matrix DII-derived bands supervised classification Live corals Damaged corals Seagrass Sand Total User accuracy Live corals % Damaged corals % Seagrass % Sand % Total Producer accuracy 97.3% 80.9% 70.7% 59.0% Overall accuracy Tau 77.7% coefficient

72 Sources of misclassification among seagrass & damaged corals The presence of macroalgae on the surface of the corals: common limitation of remote sensing in tropical waters (Mumby et ( 1997 al., Sargassum sp. Enteromorpha sp. & Dictyota sp. AVNIR-2 as monitoring tool for habitats in a coral system Detection of loss in live corals confirmed by the enhancement of the overall accuracy after the removal of erroneous damaged corals 72

73 Our activity Project aims to map seagrass beds in each member state from various viewpoints such as important ecosystems, vulnerability of ecosystem, their distributions, human impacts including fishing activities and reclamation, contrasting areas with and without conservation measures. The project publish appropriate outreach products on seagrass bed mapping for integrated coastal area management. Thank you very much for your attention! Я вам очень признательна! 73