Development and Evaluation of a Riparian Buffer Mapping Tool

Abstract

Land use and land cover within riparian areas greatly affect the conditions of adjacent water features. In particular, riparian forests provide many environmental benefits, including nutrient uptake, bank stabilization, steam shading, sediment trapping, aquatic and terrestrial habitat, and stream organic matter. In contrast, residential and commercial development and associated transportation infrastructure increase pollutant and nutrient loading and change the hydrologic characteristics of the landscape, thereby affecting both water quality and habitat. Restoring riparian areas is a popular and cost effective restoration technique to improve and protect water quality. Recognizing this, the Chesapeake Executive Council committed to restoring 10,000 miles of riparian forest buffers throughout the Chesapeake Bay watershed by the year 2010. In 2006, the Chesapeake Executive Council further committed to 'using the best available...tools to identify areas where retention and expansion of forests is most needed to protect water quality'. The Chesapeake Bay watershed encompasses 64,000 square miles, including portions of six States and Washington, D.C. Therefore, the interpretation of remotely sensed imagery provides the only effective technique for comprehensively evaluating riparian forest protection and restoration opportunities throughout the watershed. Although 30-meter-resolution land use and land cover data have proved useful on a regional scale, they have not been equally successful at providing the detail required for local-scale assessment of riparian area characteristics. Use of high-resolution imagery (HRI) provides sufficient detail for local-scale assessments, although at greater cost owing to the cost of the imagery and the skill and time required to process the data. To facilitate the use of HRI for monitoring the extent of riparian forest buffers, the U.S. Forest Service and the U.S. Geological Survey Eastern Geographic Science Center funded the development of a prototype semiautomated image classification tool, RBMapper, that is designed for use by technicians with limited image processing training. This document provides an overview of the RBMapper tool, includes instructions on how to obtain the RBMapper tool and tutorial datasets, and contains a summary evaluation of the tool