4.7 Article

An assessment of landscape characteristics affecting estuarine nitrogen loading in an urban watershed

期刊

JOURNAL OF ENVIRONMENTAL MANAGEMENT
卷 94, 期 1, 页码 50-60

出版社

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jenvman.2011.07.025

关键词

Landscape metrics; Total nitrogen load; Geographic information systems; Remote sensing; Stepwise multivariate regression; Urban sprawl; Pensacola estuarine drainage area

资金

  1. Florida State University
  2. University of West Florida
  3. US Environmental Protection Agency
  4. CEER-GOM
  5. US EPA Agreement [R829458]
  6. Chinese Academy of Sciences and China's State Administration of Foreign Experts Affairs through an international research partnership
  7. Ecosystem Processes and Ecosystem Services
  8. EPA [1099802, R829458] Funding Source: Federal RePORTER

向作者/读者索取更多资源

Exploring the quantitative association between landscape characteristics and the ecological conditions of receiving waters has recently become an emerging area for eco-environmental research. While the landscape water relationship research has largely targeted on inland aquatic systems, there has been an increasing need to develop methods and techniques that can better work with coastal and estuarine ecosystems. In this paper, we present a geospatial approach to examine the quantitative relationship between landscape characteristics and estuarine nitrogen loading in an urban watershed. The case study site is in the Pensacola estuarine drainage area, home of the city of Pensacola, Florida, USA, where vigorous urban sprawling has prompted growing concerns on the estuarine ecological health. Central to this research is a remote sensor image that has been used to extract land use/cover information and derive landscape metrics. Several significant landscape metrics are selected and spatially linked with the nitrogen loading data for the Pensacola bay area. Landscape metrics and nitrogen loading are summarized by equal overland flow-length rings, and their association is examined by using multivariate statistical analysis. And a stepwise model-building protocol is used for regression designs to help identify significant variables that can explain much of the variance in the nitrogen loading dataset. It is found that using landscape composition or spatial configuration alone can explain most of the nitrogen loading variability. Of all the regression models using metrics derived from a single land use/cover class as the independent variables, the one from the low density urban gives the highest adjusted R-square score, suggesting the impact of the watershed-wide urban sprawl upon this sensitive estuarine ecosystem. Measures towards the reduction of non-point source pollution from urban development are necessary in the area to protect the Pensacola bay ecosystem and its ecosystem services. (C) 2011 Elsevier Ltd. All rights reserved.

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