ForestERA - Treatment modeling tools

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Treatment Modeling

We have developed a model-based procedure to predict and estimate changes in forest structure due to various forest management activities, including fuels and restoration treatments (e.g., thinning plus prescribed burning) and prescribed fire. This procedure is encoded as a spatial tool extension to the ArcGIS (ESRI, Redlands, CA) interface (see Figure 1).

Figure 1. Using an ArcGIS tool to model treatment effects on the landscape, users can select from 1 of 5 “hard-wired” treatment types, or, input specific parameters to create a unique alternative.

Selecting from 1 of 5 “hard-wired” treatment alternatives, or, by using custom input parameters to create a unique alternative, users can model changes in key forest structure attributes (e.g., canopy cover, stem density, basal area; see Figure 2).

Figure 2. Modeled change in basal area (m2/ha) before (left) and after (right) intermediate-level treatments (black perimeter) and high-level treatments (white perimeter).

To parameterize these hard-wired alternatives, and to characterize a realistic spectrum of treatment alternatives applied in ponderosa pine-dominated forests, we assembled empirical data from published studies designed to quantify post-treatment structure. The assumptions of our high-, intermediate-, and low-level treatment alternatives rely on data from experimental studies conducted in ponderosa pine-dominated stands (Scott 1998, Fulé et al. 2001a, Fulé et al. 2001b, Fulé et al 2002, Romme et al. 2003, Waltz et al. 2003).

The assumptions of our burn only treatments borrow from experiments that used differing levels of prescribed fire to manipulate ponderosa pine forest structure (Ffolliott 1977, Harrington 1981, Peterson et al. 1994, Roy and Vankat 1999, Fulé et al 2002). In addition to the application of empirical data from field studies, we integrated the expert knowledge of well-known forest and fire ecologists into our selection of reasonable treatment alternatives.

Challenges involved in this effort have included:

the relative scarcity of empirical data derived from forest treatment experiments;
the need to consider multiple structural variables simultaneously; dependence of post-treatment structure on initial conditions;
and traversing the gap between plot-level and landscape-scale analyses.

We attempted to reconcile these challenges, however, through a rigorous review of the literature and through collaborative efforts with many scientists and stakeholders.

Because the effects of fuels and restoration treatments on various ecosystem attributes are not well understood (Tiedemann et al. 2000, Wagner et al. 2000), managers will require improved information before recommending and implementing forest treatments on the landscape. By presenting a range of treatment options within a flexible user interface, ForestERA treatment tools will allow predictive models of forest fuels and restoration treatments to be overlaid with taxonomic distributions, inhabited areas, and other spatial data relevant to forest management.

References

Ffolliott, P. F., W. P. Clary, and F. Larson. 1977. Effects of a prescribed fire in an Arizona ponderosa pine forest. USDA Forest Service Research Note, RM-336.

Fulé, P.Z., A.E.M. Waltz, W.W. Covington, and T.A. Heinlein. 2001a. Measuring forest restoration effectiveness in hazardous fuels reduction. Journal of Forestry 99:24-29.

Fulé, P.Z., C. McHugh, T.A. Heinlein, and W.W. Covington. 2001b. Potential fire behavior is reduced following forest restoration treatments. Pages 28-35 in G. K. Vance, C.B. Edminster , W.W. Covington, and J.A. Blake (compilers), Ponderosa Pine Ecosystems Restoration and Conservation: Steps Toward Stewardship, RMRS-P-22.

Fulé, P. Z., W. W. Covington, H. B. Smith, J. D. Springer, T. A. Heinlein, K. D. Huisinga, and M. M. Moore. 2002. Comparing ecological restoration alternatives: Grand Canyon, Arizona. Forest Ecology and Management 170:19-41.

Harrington, M. G. 1981. Preliminary burning prescriptions for PIPO fuel reductions in southeastern AZ. USDA Forest Service Research Note, RM-402

Peterson, D. L., S. S. Sackett, L. J. Robinson, and S. M. Haase. 1994. The effects of repeated prescribed burning on Pinus ponderosa growth. International Journal of Wildland Fire 4:239-247.

Romme, W. H., M. Preston, D. L. Lynch, P. Kemp, M. L. Floyd, D. D. Hanna, and S. Burns. 2003. The Ponderosa Pine Forest Partnership: Ecology, economics, and community involvement in forest restoration. Pages 99-125 in P. Friederici (editor), Ecological restoration of southwestern ponderosa pine forests. Island Press, Washington D.C.

Roy, G. D., and J. L. Vankat. 1999. Reversal of human-induced vegetation changes in Sequoia National Park. Canadian Journal of Forest Research 29:399-412.

Scott, J. H. 1998. Fuel reduction in residential and scenic forests: a comparison of three treatments in a western Montana ponderosa pine stand. USDA Forest Service Rocky Mountain Forest and Range Experiment Station Research Paper RMRS-RP-5.

Tiedemann, A. R., J. O. Klemmedson, and E. L. Bull. 2000. Solution of forest health problems with prescribed fire: are forest productivity and wildlife at risk. Forest Ecology and Management 127:1-18.

Wagner, M. R., W. M. Block, B. W. Geils, and K. F. Wenger. 2000. Restoration ecology: a new forest paradigm or another merit badge for foresters? Journal of Forestry 98:22-27.

Waltz, A. E. M., P. Z. Fulé, W. W. Covington, and M. M. Moore. 2003. Diversity in ponderosa pine forest structure following ecological restoration treatments. Forest Science 49:885-900.

Treatment Modeling

References

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