The document discusses mapping wetlands in North America using MODIS 500m imagery. It describes wetlands and existing global wetland databases. The methodology uses MODIS data from 2008, digital elevation models, and reference data to classify wetlands into three types - forest/shrub dominant wetlands, herbaceous dominant wetlands, and sea grass dominant wetlands. Training data is collected from existing land cover maps and Landsat imagery. A decision tree model and maximum likelihood classification are applied to extract wetlands from other land covers.

1. Wetlands mapping in North America using MODIS 500m imagery July 28, 2011 ○ Gegen Tana a , Ryutaro Tateishi b a Graduate Schools of Science, Chiba University b Center for Environmental Remote Sensing (CEReS), Chiba University

2. What is a wetland?

3. Background － Definition of wetlands (Broadly used) Ramsar Convention: The Convention on Wetlands, signed in Ramsar, Iran, in 1971, is an intergovernmental treaty which provides the framework for national action and international cooperation for the conservation and wise use of wetlands and their resources. (source – the Convention on Wetlands website) The Ramsar convention (Ramsar 2004) defined wetlands as areas of marsh, fen, peatland or water , whether natural or artificial, permanent or temporary, with water that is static or flowing, fresh, brackish or salt, including areas of marine water the depth of which at low tide does not exceed six meters.

4. － Global wetlands locations in the Ramar Convention 160 countries participate in and 1953 wetlands are contained. Total surface area of designated sites (hectares): 190,455,433 (source – the Convention on Wetlands website) The Ramsar definition of " wetlands " is a broad one, including not just marshes, fen and peatland, but also lakes, coral reefs, temporary pools, even underground caves, and all sorts of other systems everywhere from the mountains to the sea, including man-made habitats. Background

5. Background － The values of wetlands The provision of water A supplement of groundwater Regulation of water quantity & flood control Wildlife habitat Retention of nutrients and carbon Wetlands are one of the most important ecosystems in the world. It is important to inventory and monitor wetlands. A source of methane gas

6. Background － Global wetland databases Problems existed in the wetlands maps on large scale: Global wetland databases: Global land cover maps which include wetlands: Global wetland distribution ( http://data.giss.nasa.gov/landuse/wetland.html ) Global distribution of wetlands map ( http://soils.usda.gov/use/worldsoils/mapindex/wetlands.html ) Global lakes and wetlands database ( http://gcmd.nasa.gov/ ) GLC2000 GLCNMO GLOBCOVER Underestimate wetlands areas due to the spectral heterogeneity of wetlands. Difficult to separate wetlands from other vegetation types such as forest, herbaceous and shrub.

7. To extract wetlands in North America using MODIS 2008 data Objective Advantages of MODIS data: With high frequency repeat coverage Significant potential for mapping large wetland extent and dynamics Lower cost

8. Study area North America is defined in this study as Canada, United States , Mexico, the countries of Central America and the Caribbean Islands.

9. MCD43A4 : Terra+Aqua Nadir BRDF-Adjusted Reflectance 16-Day L3 Global 500m SIN Grid V005 (All 23 periods of 2008) Spectral bands (1-7): Data used － Nadir BRDF-Adjusted Reflectance

10. Data used － Digital elevation model and reference data Digital elevation model: Reference data: Landsat ETM+, 30m (Around 2008) Existing land cover maps (GLCNMO, GLC2000, GLOBCOVER) Google Earth images Ramsar Convention sites SRTM DEM, 90m (version 4.1)

11. － Land cover legend 20 land cover classes are defined by Land Cover Classification System (LCCS) Definition Code Class name R,G,B (color code) 1 Broadleaf e vergreen f orest 0,50,0 2 Broadleaf d eciduous f orest 60,150,0 3 Needleleaf e vergreen f orest 0,110,0 4 Needleleaf d eciduous f orest 85,110,25 5 Mixed f orest 0,200,0 6 Tree o pen 140,190,140 7 Shrub 190,190,0 8 Herbaceous 255,255,50 9 Herbaceous with s parse t ree / s hrub 180,230,100 10 Sparse vegetation 255,255,205 11 Cropland 255,175,80 12 P addy field 145,50,230 1 3 Cropland / o ther v egetation m osaic 220,160,255 1 4 Mangrove 155,130,230 15 Wetland 180,250,240 16 Bare Area,consolidated(gravel,rock) 150,150,150 17 Bare Area,unconsolidated (sand) 200,200,200 18 Urban 255,0,0 19 Snow / i ce 250,250,250 20 Water bodies 175,210,240

12. － Definition of wetlands in LCCS Wetland formula in LCCS2 : Closed to Open Woody Vegetation Water Quality: Fresh Water // Closed to Open Woody Vegetation Water Quality: Brackish Water // Closed to Open Herbaceous Vegetation. Water Quality: Fresh Water or Brackish Water. Depending on the level of Total Dissolved Solids (TDS) expressed in parts per million (ppm), three classes are distinguished: fresh, brackish and saline water (Cowardin et al., 1979). 1) Fresh Water: Less than 1 000 ppm TDS. 2) Brackish Water: Between 1 000 and 10 000 ppm TDS. 3) Saline Water: More than 10 000 ppm TDS The main layer consists of closed to open woody vegetation. The crown cover is between 100 and 15%. The height is in the range of 7 - 2m. The main layer consists of closed to open herbaceous vegetation. The crown cover is between 100 and 15%. The height is in the range of 3 - 0.03m. Three main components : Hydrology, Soil and Vegetation LCCS definition : Land cover definition by Land Cover Classification System version 2 (LCCS2) developed by FAO ( http://www.glcn-lccs.org/ ) . Definition

13. Vegetated area Validation Wetland map Non-vegetated area Methodology － The flow of the study 23 period of MCD43A4(2008) NDWI Preprocessing Reference data Training data GLCNMO STRM 90m NDSI MODIS Tasseled Cap Indices Maximum likelihood classification Decision tree model III IV V I II II

14. Methodology － Part I: MODIS preprocessing Download the MCD43A4 Mosaic the tiles Resampling Modis Reprojection Tool (MRT) Cloud removal Reprojection Cloud free data

15. MODIS TC coefficients (Lobster et al. (2007)): MODIS Tasseled Cap Indices: The tasseled cap transformation was first developed in 1976 for Landsat MSS data. It is one of the available methods for enhancing spectral information of Landsat TM. The tasseled cap transformation was extended to MODIS data ( Zhang et al .(2002). Three of the six tasseled cap transform bands are often used. ZHANG, X.Y., SCHAAF, C.B., FRIEDL, M.A., STRAHLER, A.H., GAO, F. and HODGES, J.F.C.,2002, MODIS tasseled cap transformation and its utility. In Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS ’02), edited by, Toronto, Canada, 24–28 June (Piscataway, NJ: IEEE), pp. 1063–1065. LOBSER, S.E., COHEN, W.B., 2007, MODIS tasselled cap: land cover characteristics expressed through transformed MODIS data . International Journal of remote Sensing, 28, pp. 5079–5101. Brightness: Measure of vegetation and soil Greenness: Measure of vegetation Wetness: Interrelationship of soil and canopy moisture － Part II: MODIS Tasseled Cap Indices Methodology

16. Normalized Difference Water Index (NDWI): Normalized Difference Snow Index (NDSI): (Gao, 1996) － Part II: NDWI & NDSI Bo-Cai G.NDWI--A normalized difference water index for remote sensing of vegetation liquid water from space, Remote sensing of environment, 1996, pp257-266 Dorothy K. Halla, George A. Riggsb and Vincent V. Salomonsonc. NDSI:Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data Remote Sensing of Environment, 1995 (D.K Halla, 1996) Methodology

17. － Part III: Dominant wetland types in North America (source – the Convention on Wetlands website) O — Permanent freshwater lakes (over 8 ha) H — Intertidal marshes; includes salt marshes, salt meadows, saltings, raised salt marshes; includes tidal brackish and freshwater marshes. Tp - Permanent freshwater marshes/pools; ponds (below 8 ha), marshes and swamps on inorganic soils; with emergent vegetation water-logged for at least most of the growing season. F — Estuarine waters; permanent water of estuaries and estuarine systems of deltas. J — Coastal brackish/saline lagoons; brackish to saline lagoons with at least one relatively narrow connection to the sea. A — Permanent shallow marine waters in most cases less than six metres deep at low tide; includes sea bays and straits. E — Sand, shingle or pebble shores; includes sand bars, spits and sandy islets; includes dune systems and humid dune slacks. I — Intertidal forested wetlands; includes mangrove swamps, nipah swamps and tidal freshwater swamp forests. G — Intertidal mud, sand or salt flats. Xf - Freshwater, tree-dominated wetlands; includes freshwater swamp forests, seasonally flooded forests, wooded swamps on inorganic soils. Methodology

18. － Part III: Types of wetlands and Landsat ETM+ 1. Training data should satisfy the LCCS definition. Principles of training data collection: Totally 31 scenes of Landsat ETM+ images were used for collecting training sites. Methodology Forest/Shrub dominant wetland (Inland) Herbaceous dominant wetland (Inland) Sea grass dominant wetland (Coastal) According to the vegetation types described in the LCCS, wetlands in North America were classified into three types. 2. MCD43A4 is MODIS data with the spatial resolution of 500m. The pure training site area should be selected larger than 250ha (3 ×3 pixels ).

19. Methodology － Part III: Training data collection Common part of existing maps Google Earth Ramsar Convention Landsat ETM+ Google Earth Non-vegetated* 1 and vegetated * 2 land cover types Wetland Collection of training data *1: Water, Snow, Urban, Bare (Rock&Sand) *2: Broadleaf evergreen forest, Broadleaf deciduous forest, Needleleaf evergreen forest, Needleleaf deciduous forest, Mixed forest, Tree open, Shrub, Herbaceous, Herbaceous with sparse tree/shrub, Sparse vegetation, Cropland, Paddy field, Cropland/other vegetation mosaic, Mangrove,

20. Methodology － Part IV: Decision tree model STRM 90m <1000m NDWI_8<0.2013 Water Snow Wetland&Other vagetation NDSI_P10<0.1391 Mask of Urban&Bare area (GLCNMO) Resampling No Yes Yes No

21. Methodology － Part V: Maximum likelihood method Input satellite data: MODIS Tasseled Cap Indices (Brightness, Greenness, Wetness) Period of data: Totally 12 periods, 36 scenes. (1,4, 8, 11-18, 20,22) Training data: Wetland (3 types) and other vegetated land cover types (According to the monthly changed Tasseled Cap _Greenness pattern, each land cover type was subclassified. ) Integration: After classification, vegetated land cover types were integrated as “Others”, three types of wetlands were integrated as “wetland”.

22. Result Wetlands Others Water

23. Comparison (1) － Everglades National Park (United States) Google Earth image Result of this study GLOBCOVER GLC2000

24. Comparison (2) Result of this study GLOBCOVER GLC2000 － Reserva de la Biosfera Ría Celestún (Mexico) Google Earth image

25. Conclusions and future works In this study, wetlands defined in the LCCS were classified into three types and wetlands in North America with large spatial extent were successfully extracted. MODIS t asseled Cap Indices (brightness, greenness and wetness), SRTM 90m, NDWI and NDSI were confirmed useful for extracting wetlands. However, because of the spectral heterogeneity of wetlands, some small extent of wetlands and narrow wetlands were failed to be extracted in this study. Other reference data like Landsat ETM+ should be considered for mapping small and narrow wetlands. Quantitative validation should be also performed by using ground truth data. (National Wetland Inventory: http://www.fws.gov/wetlands/ ) Subclasses of land cover types especially for wetlands were very effective for classification in this study. Develop a wetland map of 2008 for global scale.

26. Thank you for your attention!

28. － Characteristics of remote sensing data for mapping wetlands Advantages: With high frequency repeat coverage Significant potential for mapping large wetland extent and dynamics Lower cost Benefit on mapping wetlands in local and regional scales With high accuracy in mapping small extent of wetlands Advantages: Disadvantages: Disadvantages: Unavoidably underestimate wetland area due to the small and fragmented nature of many wetlands Lower map accuracy Difficult to get global or continent range of wetlands map Time consuming for mapping large wetlands Higher cost (SAR data) High spatial resolution remote sensing data: Moderate spatial resolution remote sensing data: Background

29. Background － Global wetland databases Name:Global wetland distribution Resolution: Year:1987 Name:Distribution of wetlands Resolution: Year: Name: GLC2000 Resolution: 1km Year:2000 Name: GLCNMO Resolution: 1km Year:2003 Name: GLOBCOVER Resolution: 300m Year:2005 Name:Global lakes and wetlands database Resolution:1km Year:2004 Wetlands Wetlands Wetlands