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A MODELING APPROACH FOR ESTIMATING WATERSHED IMPERVIOUS SURFACE AREA FROM NATIONAL LAND COVER DATA 92
Citation:
Jennings, D B., S T. Jarnagin, AND D W. Ebert. A MODELING APPROACH FOR ESTIMATING WATERSHED IMPERVIOUS SURFACE AREA FROM NATIONAL LAND COVER DATA 92. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING 70(11):1295-1307, (2004).
Impact/Purpose:
Overarching Objectives and Links to Multi-year Planning
This research directly supports long-term goals established in ORD's multi-year research plans related to GPRA Goal 2 (Water Quality) and Long Term Goal WQ-2 Assessment of aquatic systems impairment. Relative to the GRPA Goal 2 Water Quality multi-year plan, this research will "provide tools to assess and diagnose impairment in aquatic systems and the sources of associated stressors" and "provide the tools to restore and protect aquatic ecosystems and to forecast the ecological, economic, and human health outcomes of alternative solutions" (Water Quality Long Term Research Goals 2 and 3).
Subtask 1 - Impervious Surface Evaluation
This subtask addresses the development of impervious surfaces estimators for local to regional scale assessments of watersheds and their landscape relationship to stream ecology. The amount of impervious surface area in a watershed is a key indicator of landscape change. As a single variable, it serves to integrate a number of concurrent interactions that directly influence a watershed's hydrology, stream chemical quality, and in-stream habitat. It is our working hypothesis that impervious surface area within a watershed, as an independently mapped predictor variable, can be used to generally track a range of watershed ecological parameters (e.g., NPS pollution, biological integrity, TMDLs) that are of concern to local, state and federal environmental managers. The specific objectives of this research are: 1) to quantitatively evaluate the varying remote sensing methods used in mapping impervious surfaces at multiple scales (local to regional), and 2) to relate the varying levels of impervious surface area in watersheds to the environmental condition of multiple water resource endpoints such as streamflow, temperature, and biota.
Subtask 2 -- Landscape Assessments and Evaluations of Best Management Practices: Watershed Demonstrations
Best Management Practices (BMP) encompass a range of strategies to reduce water pollution related to urban and agricultural activities. EPA, through Section 319(h) of the Clean Water Act [PL 92-500], provides grants to states to implement BMPs in areas with suspected or known water-quality problems. Grants for implementation of BMPs have not been tracked or monitored to document their effectiveness. Although effectiveness can be measured in many different ways, one straightforward but important measure is existence. Implementation of BMPs is a voluntary process and actual implementation is not always executed (Nowak 1992). The primary objective of this project is to assess the feasibility of using high-resolution aerial photography and other remotely sensed data to identify the existence of BMPs that were planned under the 319 program. An additional objective is to evaluate the effectives of BMPs implemented by examining monitoring data from about 5 sites in the OW National NPS monitoring system.
There are several potential benefits to determining the feasibility of using the aerial photography for identifying BMPs: 1) since BMP implementation is voluntary and some may not be implemented due to a variety of social and economic factors (Nowak 1992), remote detection of BMPs can provide data to estimate the ratio of BMPs implemented to BMPs planned; 2) remote detection of BMPs provides validation data that can be input into EPA's Grants Reporting and Tracking System (GRTS), and 3) remote monitoring of BMPs over time could be used to develop data on BMP lifespans, providing important data related to social- and cost-effectiveness.
Subtask 3 -- TMDL Non-point Source Assessment Tool
This subtask involves the development of a software tool to assess the potential risks of water bodies to exceed TMDL threshold values established by States. When completed, the tool will allow the user to evaluate watersheds over entire regions. The too
Description:
We used National Land Cover Data 92 (NLCD92), vector impervious surface data, and raster GIS overlay methods to derive impervious surface coefficients per NLCD92 class in portions of the Nfid-Atlantic physiographic region. The methods involve a vector to raster conversion of the impervious surface data and subsequent overlay comparison with the raster NLCD92 data to derive a per-pixel amount of impervious surface for each NLCD92 cell. Sample areas for the study were twenty-seven small sub-watersheds ranging in size from three km2 to 100 km2. A three-category rural-to-urban gradient study design was utilized due to the changing sub-pixel relationship of impervious surfaces within developed/non-developed areas. We used ten "rural" (< 9 percent impervious surface) sub-watersheds, seven "intermediate" (9-18 percent impervious surface) sub-watersheds and ten "dense suburban" (18-35 percent impervious surface) sub-watersheds to produce three separate coefficient models per NLCD92 class. Results show distinct per-class coefficient groupings based on the rural-to-urban gradient with impervious surface coefficients being directly related to the increasing level of impervious surface percent in the watershed. To determine gradient type for any given watershed, we developed an a priori indicator based on the percentage of NLCD92 developed pixels (21, 22, and 23) within a watershed. The subsequent regression formula, y--0.3966(x) + 2.4977, provided an approximation of watershed impervious surface percent which then allowed one of the three coefficient models to be applied. Results from the application of coefficients to all 27 watersheds shows a relative accuracy for the impervious surface prediction of 85% and a mean absolute impervious surface percent error of 1.4% & 0.5% at both the Anderson Level I coefficients and the Anderson Level 2 coefficients.