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Knowledge-Based Raster Mapping Approach to Wetland Assessment: a Case Study in Suzhou, China
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  • 作者:Zhaohui Yang ; Xun Shi ; Qun Su
  • 关键词:Wetland assessment ; Wetland ; environment model ; Knowledge ; based ; Raster mapping ; Suzhou
  • 刊名:Wetlands
  • 出版年:2016
  • 出版时间:February 2016
  • 年:2016
  • 卷:36
  • 期:1
  • 页码:143-158
  • 全文大小:1,502 KB
  • 参考文献:Ausseil AE, Dymond JR, Shepherd JD (2007) Rapid mapping and prioritisation of wetland sites in the manawatu-wanganui region, New Zealand. Environ Manag 39(3):316–325CrossRef
    Austen E, Hanson A (2008) Identifying wetland compensation principles and mechanisms for Atlantic Canada using a Delphi method approach. Wetlands 28(3):640–655CrossRef
    Barbier EB, Koch EW, Silliman BR, Hacker SD, Wolanski E, Primavera J, Granek EF, Polasky S, Aswani S, Cramer LA, Stoms DM, Kennedy CJ, Bael D, Kappel CV, Perillo GME, Reed DJ (2008) Coastal ecosystem–based management with nonlinear ecological functions and values. Science 319(18):321–323CrossRef PubMed
    Brooks RP, Wardrop DH, Bishop JA (2004) Assessing wetland condition on a watershed basis in the Mid–Atlantic region using synoptic land-cover maps. Environ Monit Assess 94(1–3):9–22CrossRef PubMed
    Brown MT, Vivas MB (2005) Landscape development intensity index. Environ Monit Assess 101(1–3):289–309CrossRef PubMed
    Charnley S, Fischer AP, Jones ET (2007) Integrating traditional and local ecological knowledge into forest biodiversity conservation in the Pacific Northwest. For Ecol Manag 246:14–28CrossRef
    Chen T, Lin H (2011) Application of a landscape development intensity index for assessing wetlands in Taiwan. Wetlands 31(4):745–756CrossRef
    Chen T, Lin H (2013) Development of a framework for landscape assessment of Taiwanese wetlands. Ecol Indic 25:121–132CrossRef
    Chen SH, Jakeman AJ, Norton JP (2008) Artificial intelligence techniques: an introduction to their use for modelling environmental systems. Math Comput Simul 78:379–400CrossRef
    Cools J, Johnston R, Hattermann FF, Douven W, Zsuffa I (2013) Tools for wetland management: lessons learnt from a comparative assessment. Environ Sci Pol 34:138–145CrossRef
    Curtis IA (2004) Valuing ecosystem goods and services: a new approach using a surrogate market and the combination of a multiple criteria analysis and a Delphi panel to assign weights to attributes. Ecol Econ 50:163–194CrossRef
    Dalkey N, Helmer O (1963) An experimental application of the Delphi method to the use of experts. Manag Sci 9(3):458–467CrossRef
    De’ath G, Fabricius KE (2000) Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81(11):3178–3192CrossRef
    Debolini M, Marraccini E, Rizzo D, Galli M, Bonari E (2013) Mapping local spatial knowledge in the assessment of agricultural systems: a case study on the provision of agricultural services. Appl Geogr 42:23–33CrossRef
    Dong Z, Wang Z, Liu D, Song K, Li L, Jia M, Ding Z (2014) Mapping wetland areas using Landsat-derived NDVI and LSWI: a case study of West Songnen Plain, Northeast China. Journal of the Indian Society Of Remote Sensing 42(3):569–576CrossRef
    Fennessy MS, Jacobs AD, Kentula ME (2004) Review of rapid assessment methods for assessing wetland condition, Corvallis, OR: National Health and Environmental Effects Research Laboratory, Office of Research and Development, United States Environmental Protection Agency, EPA-620-R-04-009
    Fennessy MS, Jacobs AD, Kentula ME (2007) An evaluation of rapid methods for assessing the ecological condition of wetlands. Wetlands 27(3):543–560CrossRef
    Foody GM (2000) Mapping land cover from remotely sensed data with a softened feed forward neural network classification. J Intell Robot Syst 29(4):433–449CrossRef
    Government of Suzhou (2013) Inventory of 102 important wetlands in Suzhou. Available online at: <http://​www.​gusuwang.​com/​redirect.​php?​fid=​164&​tid=​1239137&​goto=​ nextnewset >. Accessed August 13, 2015
    Granados M, Mandrak NE, Jackson DA (2014) Synthesizing reference conditions for highly degraded areas through best professional judgment. J Great Lakes Res 40(S2):37–42CrossRef
    Hayati E, Majnounian B, Abdi E, Sessions J, Makhdoum M (2013) An expert-based approach to forest road network planning by combining Delphi and spatial multi-criteria evaluation. Environ Monit Assess 185:1767–1776CrossRef PubMed
    Host GE, Schuldt J, Ciborowski J, Johnson LB, Hollenhorst T, Richards C (2005) Use of GIS and remotely sensed data for a priori identification of reference areas for Great Lakes coastal ecosystems. Int J Remote Sens 26(23):5325–5342CrossRef
    Hudson BD (1992) The soil survey as paradigm-based science. Soil Sci Soc Am J 56:836–841CrossRef
    Irons JR, Dwyer JL, Barsi JA (2012) The next Landsat satellite: the Landsat data continuity mission. Remote Sens Environ 122:11–21CrossRef
    Klemas V (2011) Remote sensing of wetlands: case studies comparing practical techniques. J Coast Res 27(3):418–427CrossRef
    Lan J (2012) Comments on Suzhou city wetland protection ordinance and suggestions for improvement. Journal of Nanjing University of Aeronautics and Astronautics (Social Sciences) 14(3):36–40(in Chinese)
    Lane CR, Brown MT (2007) Diatoms as indicators of isolated herbaceous wetland condition in Florida, USA. Ecol Indic 7(3):521–540CrossRef
    Liu K, Li X, Shi X, Wang S (2008) Monitoring mangrove forest changes using remote sensing and GIS data with decision-tree learning. Wetlands 28(2):336–346CrossRef
    Mack JJ (2006) Landscape as a predictor of wetland condition an evaluation of the landscape development index (LDI) with a large reference wetland dataset from Ohio. Environ Monit Assess 120(1–3):221–241CrossRef PubMed
    Mamoun CM, Nigel R, Rughooputh S (2013) Wetlands’ inventory, mapping and land cover index assessment on Mauritius. Wetlands 33(4):585–595CrossRef
    Margules CR, Pressey RL (2000) Systematic conservation planning. Nature 405:243–253CrossRef PubMed
    Maynard CM (2015) Accessing the environment: delivering ecological and societal benefits through knowledge integration-the case of water management. Appl Geogr 58:94–104CrossRef
    Mitsch WJ, Gosselink JG (2000) Wetlands, third edn. John Wiley & Sons, Inc., New York, NY, USA
    Mwita E, Menz G, Misana S, Becker M, Kisanga D, Boehme B (2013) Mapping small wetlands of Kenya and Tanzania using remote sensing techniques. International Journal of Applied Earth Observation and Geoinformation 21:173–183CrossRef
    Nadeau TL, Leibowitz SG, Jr PJW, Ebersole JL, Fritz KM, Coulombe RA, Comeleo RL, Blocksom KA (2015) Validation of rapid assessment methods to determine streamflow duration classes in the Pacific Northwest, USA. Environ Manag 56:34–53CrossRef
    Pettorelli N, Ryan S, Mueller T, Bunnefeld N, Jedrzejewska B, Lima M, Kausrud K (2011) The normalized difference vegetation index (NDVI): unforeseen successes in animal ecology. Clim Res 46:15–27CrossRef
    Reiss KC, Brown MT (2007) Evaluation of Florida palustrine wetlands: application of USEPA levels 1, 2, and 3 assessment methods. EcoHealth 4:206–218CrossRef
    Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New York
    Salari A, Zakaria M, Nielsen CC, Boyce MS (2014) Quantifying tropical wetlands using field surveys, spatial statistics and remote sensing. Wetlands 34(3):565–574CrossRef
    Shi X, Zhu AX, Burt J, Qi F, Simonson D (2004) A case-based reasoning approach to fuzzy soil mapping. Soil Sci Soc Am J 68:885–894CrossRef
    Shi X, Long R, Dekett R, Philippe J (2009) Integrating different types of knowledge for digital soil mapping. Soil Sci Soc Am J 73:1682–1692CrossRef
    Silverman BW (1986) Density estimation for statistics and data analysis. Chapman and Hall, New YorkCrossRef
    Stein ED, Fetscher AE, Clark RP, Wiskind A, Grenier JL, Sutula M, Collins JN, Grosso C (2009) Validation of a wetland rapid assessment method: use of EPA’s level 1–2-3 framework for method testing and refinement. Wetlands 29(2):648–665CrossRef
    Sutherland WJ (2006) Predicting the ecological consequences of environmental change: a review of the methods. J Appl Ecol 43:599–616CrossRef
    Suzhou Municipal Agricultural Committee (2010) Investigation report on wetland resources in Suzhou. Suzhou, Jiangsu
    US EPA (2006) Application of elements of a state water monitoring and assessment program for wetlands. Wetlands Division, Office of Wetlands, Oceans, and Watersheds, U.S. Environmental Protection Agency, Washington, DC
    Vivas MB (2007) Development of an index of landscape development intensity for predicting the ecological condition of aquatic and small isolated palustrine wetland systems in Florida. University of, Florida
    Weisberg SB, Thompson B, Ranasinghe JA, Montagne DE, Cadien DB, Dauer DM, Diener D, Oliver J, Reish DJ, Velarde RG, Word JQ (2008) The level of agreement among experts applying best professional judgment to assess the condition of benthic infaunal communities. Ecol Indic 8(4):389–394CrossRef
    Weller DE, Snyder MN, Whigham DF, Jacobs AD, Jordan TE (2007) Landscape indicators of wetland condition in the Nanticoke river watershed, Maryland and Delaware, USA. Wetlands 27(3):498–514CrossRef
    White DC, Lewis MM, Green G, Gotch TB (2015) A generalizable NDVI-based wetland delineation indicator for remote monitoring of groundwater flows in the Australian Great Artesian Basin. Ecological Indicators, http://​dx.​doi.​org/​10.​1016/​j.​ecolind.​2015.​01.​032
    Xu H (2008) A new index for delineating built-up land features in satellite imagery. Int J Remote Sens 29(14):4269–4276CrossRef
    Zhang C, Xie Z (2013) Object-based vegetation mapping in the Kissimmee River watershed using HyMap data and machine learning techniques. Wetlands 33(2):233–244CrossRef
    Zhu AX (2000) Mapping soil landscape as spatial continua: the neural network approach. Water Resour Res 36(3):663–677CrossRef
    Zhu AX, Hudson B, Burt J, Lubich K, Simonson D (2001) Soil mapping using GIS, expert knowledge, and fuzzy logic. Soil Sci Soc Am J 65(5):1463–1472CrossRef
    Zsuffa I, Dam AA, Kaggwa RC, Namaalwa S, Mahieu M, Cools J, Johnston R (2014) Towards decision support-based integrated management planning of papyrus wetlands: a case study from Uganda. Wetl Ecol Manag 22:199–213CrossRef
  • 作者单位:Zhaohui Yang (1)
    Xun Shi (2)
    Qun Su (1)

    1. School of Environmental Science & Engineering, Suzhou University of Science and Technology, 1 Kerui Road, Suzhou, Jiangsu, 215009, China
    2. Department of Geography, Dartmouth College, 6017 Fairchild, Hanover, NH, 03755, USA
  • 刊物主题:Freshwater & Marine Ecology; Environmental Management; Ecology; Hydrogeology; Coastal Sciences; Landscape Ecology;
  • 出版者:Springer Netherlands
  • ISSN:1943-6246
文摘
We present a knowledge-based raster mapping (KBRM) approach to assessing wetlands based on the wetland-environment model, which is built by local experts using their knowledge and experience. The Delphi method and the Analytic Hierarchy Process (AHP) were employed to improve the consistency and the quality of the elicited expert knowledge. The environmental factors selected by local experts include edge density, aquatic connectivity, water-body density, road density, urban area density, vegetation screening index, and slope gradient. The experts assigned weights to those environmental factors and created optimality function curves to characterize the relationships between individual factors and the wetland condition. At each raster cell, an optimality value for each environmental factor was calculated based on the local value of that factor, and an overall optimality score was then calculated as the weighted average of the optimality values for individual factors. As a case study, we implemented this approach in assessing the wetlands in Suzhou, China. While our approach does not directly require field information, its result is comparable with the result of the rapid field method that is directly based on field data. In addition, our approach is advantageous in representing interior details of a wetland. Keywords Wetland assessment Wetland-environment model Knowledge-based Raster mapping Suzhou

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