Evaluating remotely sensed techniques for mapping riparian vegetation
Riparian vegetation provides many important functions for fish and wildlife habitat, yet we lack the information about riparian condition needed for planning and policy decisions. In this project, we developed a more cost-effective method to classify riparian vegetation using aerial photography and compared the results with maps generated from classifying Landsat Thematic Mapper (TM) imagery. We used ortho-photography and other data freely available from the public domain, plus ArcInfo's capability to display stream buffers on top of photos, to identify the vegetation structure and location. Two buffers, one from 0 to 15.25 m and the other from 15.25 to 61 m on each side of the streams, were segmented rather than using the traditional method of drawing polygons around the vegetation types. This method was faster and cheaper than digitizing vegetation polygons, plus it allowed the photo interpreter's judgment to correct for stream location errors in the data without having to redraw the stream's location. Riparian vegetation structure was found to be far different from upland forest stands in the watershed. Hardwoods dominated the riparian zones. Overall, hardwood stands made up 59% of the vegetation within the first 15.25 m from the stream in a watershed where 75% of the upland area was conifer stands. Agricultural zones were dominated by hardwood stands and open conditions. Only 1–2% of the areas within 15.25 m of the streams had large conifer stands, while over 80% of these areas were hardwoods or brush. The ‘open area’ class comprised 13% of the total area within the first 15.25 m of the streams and increased to 49% in the area between 15.25 and 61 m from the streams. Current forest classifications based on Landsat TM imagery did not do a good job of identifying the structural characteristics of riparian vegetation. Our Landsat TM and photo classifications only agreed 25–30% of the time. The extreme diversity and linear arrangement of the riparian vegetation creates classification problems and results in the Landsat TM imagery being inadequate for use in policy decisions.
Natural Resources and the Environment
Computers and Electronics in Agriculture
Elsevier Science B.V.
Digital Object Identifier (DOI)
Congalton, R., K. Birch, R. Jones, and J. Schriever. 2002. Evaluating remotely sensed techniques for mapping riparian vegetation. Computers and Electronics in Agriculture. Vol. 37. pp. 113-126.
© 2002 Elsevier Science B.V.