Use of Geographic Information Systems, Remote Sensing, and Suitability Modeling to Identify Conifer Restoration Sites with High Biological Potential for Anadromous Fish at the Cedar River Municipal Watershed in Western Washington, U.S.A

• Published in: Restoration ecology Vol. 16; no. 2; pp. 336 - 347
• Main Authors: Mollot, Lauren A, Bilby, Robert E
• Format: Journal Article
• Language: English
• Published: Malden, USA Malden, USA : Blackwell Publishing Inc 01.06.2008; Blackwell Publishing Inc; Blackwell Publishing Ltd
• Subjects: Environmental restoration; Geographic information systems; GIS modeling; habitat suitability mapping; hyperspectral high-resolution imagery; Integrated approach; LiDAR; Reforestation; remote sensing; Research methodology; Riparian ecology; riparian forest restoration; salmon and trout habitat; stream channel geomorphology; watershed surface analysis; Washington (state)
• ISSN: 1061-2971; 1526-100X
• DOI: 10.1111/j.1526-100X.2007.00340.x

We developed a methodology integrating several forms of remotely sensed data into a Geographic Information Systems (GIS) model that identifies suitable sites for riparian conifer restoration at the Cedar River Municipal Watershed in western Washington, U.S.A. The model integrates vegetative and geomorphic variables with information on the habitat preferences of anadromous fishes to identify riparian stands where conifer restoration would have the greatest biological benefit for salmon recovery. The high-resolution raster datasets used in our analysis were capable of characterizing the biophysical attributes of riparian areas at finer spatial scales than was previously possible. This model is intended to serve as a screening tool to identify candidate sites for riparian area restoration. The assessment approach described in this study can be applied not only to model salmonid habitat at the watershed scale but also to assess landscape patterns relevant to a wide range of restoration goals. This methodological framework offers several advantages over other approaches to restoration site selection and planning. First, the fine-scale spatial resolution of the GIS datasets (pixels =5 m). Therefore, the accuracy of site identification is greatly improved. Second, the quantitative nature of the model exercises greater objectivity than some other landscape-scale planning approaches. This regional planning tool could be replicated in other watersheds with comparable datasets and could be applied to identify habitat restoration sites for other species or guilds of species by simply altering the model criteria to match the habitat needs of the target organisms.