1 1.10 Application of estuarine and coastal classifications in marine ...

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andom and multi-stage random (Figure 16). Ultimately, the choice of which method to use depends on survey objectives and the types of data available. 1.10.7 SPATIAL CHANGE ANALYSIS The analysis of spatial and thematic changes based on a comparison of classified maps over time is a logical and efficient part of ecosystem-based management. A wide range of remote sensing technologies are applied to quantify environmental change, with many applications using satellite data due to a high frequency of repetitive coverage and consistency in image characteristics and processing techniques. Coastal change detection studies have involved the use of time series data from a wide range of sensors including Advanced Very High Resolution Radiometer (AVHRR), Landsat Thematic Mapper (TM), Multispectral Scanner (MSS), SPOT data, aerial photography and airborne laser altimetry. At finer spatial scales, subaquatic change detection has also been performed using underwater photography and video mosaicking. In addition to remote sensing techniques, a suite of pattern metrics and spatial statistics commonly applied in landscape ecology provide techniques to quantify the change in landscape or seascape composition and spatial configuration. Whatever the technique used for image acquisition, it is important that a standardized classification schema is applied in comparative analyses to maintain consistency in the types of classes discriminated. 1.10.7.1 NOAA C-CAP Change Detection 44
NOAA’s CoastWatch Change Analysis Program (C-CAP) uses digital remote sensing data from LandsatTM and aerial photography together with georeferenced in situ measurements to monitor change in coastal intertidal areas, wetlands, and adjacent uplands at five year intervals. C-CAP has developed a relatively coarse thematic resolution hierarchical classification scheme with 15 upland classes and 14 wetland classes, primarily to include key classes that can be accurately discriminated from multispectral satellite data (Dobson et al. 1995; http://www.csc.noaa.gov/crs/lca/tech_cls.html). The coastal classification system is a component of the National Land Cover Database (NLCD) and was designed to be compatible with the U.S. Geological Survey “Land Use and Land Cover Classification for use with remote sensing data”; 2) the U.S. Fish and Wildlife Service “Classification of Wetlands and Deepwater Habitats of the United States and 3) the U.S. Environmental Protection Agency Environmental Monitoring and Assessment Program (EMAP) classification system. In addition to depicting the land cover status for a given point in time, comparing maps from various years documents how land cover changes over time. With these maps users can examine how the land cover has changed (e.g., from forests to shrubs) and the amount of change can be easily quantified, as well as the change in spatial patterning (e.g., fragmentation) of individual land cover classes or entire landscape units. This information can be used to better understand the cumulative effects of environmental change, including the impacts of urban development and agriculture on the losses, gains and quality of wildlife habitat and indicators that link land cover change with ecosystem health can be developed and evaluated. 1.10.7.2 Tracking Coastal Habitat Change in New Jersey, USA 45
Page 1 and 2: 1.10 Application of estuarine and c
Page 3 and 4: SYNOPSIS Coastal and marine classif
Page 5 and 6: government and non-governmental man
Page 7 and 8: paucity of species and habitat data
Page 9 and 10: and many sub-catastrophic disturban
Page 11 and 12: conservation practitioners, industr
Page 13 and 14: levels of the hierarchy are defined
Page 15 and 16: distributions, both within and surr
Page 17 and 18: In a hierarchical framework for the
Page 19 and 20: 1.10.2.3 Australian Coastal Classif
Page 21 and 22: Ecological theory predicts that the
Page 23 and 24: outbreaks in the late 1970s (Green
Page 25 and 26: intertidal zones, and shallow coast
Page 27 and 28: 1.10.3.1 Identifying Priority Conse
Page 29 and 30: y the Massachusetts Oceans Act (200
Page 31 and 32: eight color coded zones to provide
Page 33 and 34: appropriate place to investigate fe
Page 35 and 36: 1.10.4.3 Massachusetts Ocean Plan T
Page 37 and 38: One of the foundational concepts un
Page 39 and 40: ecosystems has shown that considera
Page 41 and 42: In May 2004, Germany was the first
Page 43: improve the design and interpretati
Page 47 and 48: ground resolution of 30 m. Each cla
Page 49 and 50: also be used to assess the role of
Page 51 and 52: States (Gundlach and Hayes 1978). T
Page 53 and 54: information at the species level. I
Page 55 and 56: 1.10.8.4 Classifying and Mapping Hu
Page 57 and 58: map of the entire Australian shorel
Page 59 and 60: within 11 regions, leading to an ov
Page 61 and 62: enhancement of certain functions in
Page 63 and 64: achieved by identifying barriers to
Page 65 and 66: ecosystem service values extracted
Page 67 and 68: 1.10.12 FUTURE DIRECTIONS AND PRIOR
Page 69 and 70: 1.10.12.1 Linking Patterns and Proc
Page 71 and 72: approach. New classifications will
Page 73 and 74: Arundel, H. and Mount, R. 2007. Nat
Page 75 and 76: Connell, J. H. 1978. Diversity in t
Page 77 and 78: Duke. N.C., Meynecke, J.O., Dittman
Page 79 and 80: Green, A.L., C.E. Birkeland, R.H. R
Page 81 and 82: Hiddink, J. G., Jennings, S., Kaise
Page 83 and 84: Kostylev, V., Todd, B.J., Fader, G.
Page 85 and 86: Maxwell, D.L., Stelzenmüller, V.,
Page 87 and 88: Spatial and temporal patterns in fi
Page 89 and 90: Sharples, C. 2006. Indicative Mappi
Page 91 and 92: Wells, S., Ravilous, C., Corcoran,
Page 93 and 94: FIGURE LEGENDS Figure 1. A) Classes
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Figure 10. Ecological evaluation in
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Figure 21. Combining 'intolerance'
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selected planning units in gray ins
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Box 2. Classifications as thematic
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Figure 1. 1
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Figure 3. 3
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Figure 5. 5
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Figure 7. 7
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Figure 9. 9
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Figure 11. 11
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Figure 13. 13
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Figure 15. Fishing effort (hrs/day)
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Figure 17. 17
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Figure 19. 100% Conversion. 80% 60%
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Figure 21. 21
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Figure 23. 23
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Figure 25. 25
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Figure 27. 27
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Figure 29. 29
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Figure 31. 31
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Figure 33. 33
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