NetMap's Technical Help Guide

Summary and References

For land use analysts and planners, it is important to understand the limits in accuracy and precision of GIS information, including data derived from remote sensing, field surveys, or digitizing, and from numerical models. Although GIS information is often approximate and coarse grained (particularly if derived from remote sensing and numerical model predictions), it offers unprecedented ability to plan (and evaluate through modeling) watershed-scale plans for forestry, restoration, road rehabilitation, conservation, wildfire planning, and to consider climate change impacts. For example, a GIS map of fish habitat quality can be used to prioritize where analysts will go into the field to plan inventory, monitoring, and restoration projects.
When implementing such plans at the scale of individual hillsides, stream reaches, or road segments (e.g., timber harvest, fuel treatments, forest restoration-thinning, placement of wood in streams for restoration, and road maintenance or abandonment), site-specific information should be obtained on the relevant parameters (e.g., forest stand condition, channel characteristics, hillslope conditions, and road attributes and conditions). Once field observations or data have been collected, site-specific management prescriptions can be tailored or modified as necessary from the original predictions made using GIS information. In that way, GIS information and field information are compatible, and when used together, they provide a robust method for implementing forest management or fishery management at scales ranging from the watershed down to the particular hillside, stream reach, or road segment.
Benda, L., D. J. Miller, K. Andras, P. Bigelow, G. Reeves, and D. Michael. 2007. NetMap: A new tool in support of watershed science and resource management. Forest Science 52:206-219.
Burnett, K. M., G. Reeves, D. Miller, S. Clarke, K. Vance-Borland, and K. Christiansen. 2007. Distribution of salmon-habitat potential relative to landscape characteristics and implications for conservation. Ecological Applications 17:66-80.
Dunne, T., J. K. Agee, S. Beissinger, W. E. Dietrich, D. Gray, M. Power, V. Resh, and K. Rodrigues. 2001. A scientific basis for the prediction of cumulative watershed effects. University of California Wildland Resource Center Report No. 46. University of California Wildland Resource Center, Berkeley, CA.
Miller, D. J. 2003. Programs for DEM Analysis. in Landscape Dynamics and Forest Management, General Technical Report RMRS-GTR-101CD. USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO, USA.
Miller, D. J., and K. M. Burnett. 2007. Effects of forest cover, topography, and sampling extent on the measured  density of shallow, translational landslides. Water  Resources Research 43:1-23.
Montgomery, D. R., and W. E. Dietrich. 1994. A physically based model for the topographic control on shallow landsliding. Water Resources Research 30:1153-1171.
Spies, T.A. and K.N. Johnson. 2007. Projecting Forest Policy and Management Effects across Ownerships in Coastal Oregon. Ecological Applications. 17(1):3-4.

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