A Gis-derived integrated moisture index to predict forest composition and productivity of Ohio forests (U.S.A.)

A geographic information system (GIS) approach was used in conjunction with forest-plot data to develop an integrated moisture index (IMI), which was then used to predict forest productivity (site index) and species composition for forests in Ohio. In this region, typical of eastern hardwoods across the Midwest and southern Appalachians, topographic aspect and position (rather than elevation) change drastically at the fine scale and strongly influence many ecological functions. Elevational contours, soil series mapping units, and plot locations were digitized for the Vinton Furnace Experimental Forest in southeastern Ohio and gridded to 7.5-m cells for GIS modeling. Several landscape features (a slope-aspect shading index, cumulative flow of water downslope, curvature of the landscape, and water-holding capacity of the soil) were used to create the IMI, which was then statistically analyzed with site-index values and composition data for plots. On the basis of IMI values for forest land harvested in the past 30 years, we estimated oak site index and the percentage composition of two major species groups in the region: oak (Quercus spp.), and yellow poplar (Liriodendron tulipifera) plus black cherry (Prunus serotina). The derived statistical relationships were then applied in the GIS to create maps of site index and composition, and verified with independent data. The maps show the oaks will dominate on dry, ridge top positions (i.e., low site index), while the yellow poplar and black cherry will predominate on mesic sites. Digital elevation models with coarser resolution (1:24K, 1:100K, 1:250K) also were tested in the same manner. We had generally good success for 1:24K, moderate success for 1:100K, but no success for 1:250K data. This simple and portable approach has the advantage of using readily available GIS information which is time-invariant and requires no fieldwork. The IMI can be used to better manage forest resources where moisture is limiting and to predict how the resource will change under various forms of ecosystem management.