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

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of loss are through urbanization, conversion to agriculture and mariculture, human modifications to coastal hydrology, pollution, landfill, salt production and direct use of trees for timber, fuel and medicines (Valiela et al. 2001, Walters et al. 2008). Mangroves have also been destroyed and degraded by hurricanes and tsunamis (Alongi 2008, Giri et al. 2008) and now sea-level rise has been identified as a substantial cause of recent and predicted future reductions in the area and health of mangroves (Gilman et al. 2008). Classified remote sensing imagery such as generated from spectral interpretation of Landsat TM data provides a time series appropriate for a quantitative synoptic change detection to assess the distribution of mangroves and to document quantitatively how the spatial patterning of mangroves (losses, gains, contiguity/fragmentation) have changed. Combining map products with spatial analyses and data from investigation of the causal mechanisms that have led to changes and characterization of areas that have experienced most change will provide valuable information to support resource management decision-making, policy formulation and public education. Giri et al. (2008) interpreted a Landsat time series to estimate the extent of tsunamiaffected mangrove forests in SE Asia and to determine the rates and causes of deforestation from 1975 to 2005. Using a post classification change detection approach for coastal areas of Indonesia, Malaysia, Thailand, Burma (Myanmar), Bangladesh, India and Sri Lanka, the analyses determined that the region had lost 12% of its mangrove forests from 1975 to 2005 (Figure 19). Rates and causes of deforestation varied both spatially and temporally. Annual deforestation was highest in Burma (c. 1%) and lowest in Sri Lanka (0.1%). In contrast, mangrove forests in India and Bangladesh remained unchanged or gained a small percentage. Change detection revealed that the major causes of deforestation were agricultural expansion (81%), aquaculture (12%) and urban development (2%) (Figure 19). This type of application can 48
also be used to assess the role of mangroves in protecting the coastline from tsunamis and to identify possible areas for conservation, restoration and rehabilitation. 1.10.8 ENVIRONMENTAL RISK ASSESSMENT AND HUMAN IMPACTS Knowledge of the distribution of habitats, communities, and species and how they respond to the effects of human and non-human processes is fundamental to effective management of the marine environment. Spatially explicit risk assessments that link information on the sensitivity of the environment to the occurrence of a pressure are fundamental to the implementation of spatial management (Hope, 2006). In fact, the spatial distribution of habitats and the spatial distribution of human activities are in many cases interrelated. Many directives and strategies such as the EC Water Framework Directive and the EC Marine Strategy Framework Directive state that efficient management must identify anthropogenic pressures on the marine environment and assess their potential effects. Methods for assessing habitat sensitivity to human impacts are now urgently needed to measure impact sustainability, develop spatial management plans, and support sound environmental impact assessments. These procedures should be quantitative, validated, repeatable, and applicable at multiple spatial scales relevant to both the impact and management (Hiddink et al., 2007, Fraschetti et al. 2008). Marine species and habitats vary in their response to stressors (both anthropogenic and non-anthropogenic) and delineating and classifying areas or habitat classes based on their relative sensitivity and vulnerability to stressors is a valuable tool in managing and predicting disturbances to the marine environment. Zacharias and Gregr (2005) define “sensitivity” as the degree to which marine features respond to stresses, which are considered as deviations of 49
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 and 44: improve the design and interpretati
Page 45 and 46: NOAA’s CoastWatch Change Analysis
Page 47: ground resolution of 30 m. Each cla
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
Page 95 and 96: Figure 10. Ecological evaluation in
Page 97 and 98: Figure 21. Combining 'intolerance'
Page 99 and 100:
selected planning units in gray ins
Page 101 and 102:
Box 2. Classifications as thematic
Page 103 and 104:
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)
Page 119 and 120:
Figure 17. 17
Page 121 and 122:
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
Page 135:
Figure 33. 33
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