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The Effects of Spatial Resolution on Impervious Cover Classifications in Watersheds and Riparian Zones in Vermont
Citation:
Morgan, J., Y. Wang, AND N. Detenbeck. The Effects of Spatial Resolution on Impervious Cover Classifications in Watersheds and Riparian Zones in Vermont. Association of American Geographers, Boston, MA, April 05 - 09, 2017.
Impact/Purpose:
Polluted stormwater runoff contributes to the decline of fish, aquatic insects, and other stream inhabitants and has led to the degradation of many of the nation’s waterways. During a rain storm, water that runs off of impervious cover (roads, rooftops, parking lots, etc.) drains into storm sewers and is then piped to the nearest waterway, carrying with it all of the oil, grease, and heavy metals deposited on the road by vehicles. Stormwater runoff also contributes to flooding and can increase the temperature of cold water streams. Green infrastructure can mitigate the impacts of stormwater runoff by intercepting and infiltrating rainfall before it reaches areas of impervious cover. The modeling and management of water resources using green infrastructure requires accurate estimates of impervious cover. We developed a quick, cost-effective method for identifying impervious cover from high spatial resolution data, which is often costly and time consuming. We compared our impervious cover estimates to coarser resolution, freely available national data and developed a model for determining where the national data is adequate for analysis and where higher resolution data may be needed. Our study showed that the national data overestimate impervious cover in more urbanized areas and underestimate impervious cover in less developed areas. This information can be used by municipalities and watershed managers to determine where limited monetary and geospatial resources can be spent to map impervious cover and to develop more accurate water quality improvement plans.
Description:
Impervious cover (roads, rooftops, etc.) is a known stressor on stream biota and habitat and is often used as an indicator for assessing the effects of urbanization on stream health. Understanding how spatial data resolution impacts estimates of impervious cover is important for effective modeling and management of water resources at multiple scales. However, broad scale classifications of high spatial resolution data can be both time consuming and expensive. Using National Agriculture Imagery Program (NAIP) imagery, we developed a quick and cost- effective method for characterizing impervious cover. The National Land Cover Database (NLCD) was compared to NAIP imagery across a range of scales, from the riparian zone to the Hydrologic Unit Code 10 level for 888 catchments in Vermont. We determined that the NLCD data are underestimating impervious surface areas in less developed sub-watersheds and overestimating impervious surface areas in more densely populated sub-watersheds. A Bayesian classification and regression tree model, based on readily available land use/land cover and U.S. Census Bureau housing data, is proposed for identifying areas where NLCD data are adequate for ecological analysis or where higher spatial resolution data may be required. The ability to predict where high spatial resolution data may be required, based on derived relationships from readily available national scale data, will decrease the cost and resources required for broad scale classifications of impervious cover estimates.