Name: Alder Site Suitability Model

Display Field: VALUE

Type: Raster Layer

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Description: <DIV STYLE="text-align:Left;"><DIV><DIV><P STYLE="margin:0 0 6 0;"><SPAN>The land base of the Pacific Northwest includes large areas that could support hardwoods or a hardwood component. Often, however, site index, the most commonly used measure of a site's potential productivity, is not available for red alder as other species occupy the site. In order to make site-specific management decisions, the suitability for red alder production can be assessed by geographic and topographic position, soil moisture and aeration during the growing season, and soil fertility and physical condition (Harrington 1986). The difficulty of weighing these physical factors to determine site suitability appears to be a major impediment to the establishment of red alder plantations. Additionally, forest managers are lacking a planning tool that would consider red alder in the landscape for long term management plans. </SPAN></P><P STYLE="margin:0 0 6 0;"><SPAN><SPAN>To assist forest managers in their planning and site selection efforts, we developed a GIS-based Red Alder Site Suitability Model based on physical criteria identified by Harrington (1986) as most influential on the productivity of red alder. The major components of the model are elevation, topographic position, slope, aspect, soil type, and soil depth. The model was implemented in a GIS (ESRI ArcPro v.3.0) raster environment with topographic position, slope, aspect, and elevation derived from a 10-meter digital elevation model (DEM), using lidar data where available. Topographic position class of valley, lower slope, flat slope, middle slope, upper slope, or ridgetop was derived from the topographic position index (TPI) using standard deviation thresholds as described by Weiss (2001). The soil texture and depth were derived from Washington DNR</SPAN></SPAN><SPAN><SPAN>’</SPAN></SPAN><SPAN><SPAN>s corporate soil data layer. Each pixel was then classified and assigned one of four suitability categories: High, Medium, Low, and No Potential. </SPAN></SPAN></P><P STYLE="margin:0 0 6 0;"><SPAN><SPAN>Because of the level of spatial detail of the model, forest managers can assess the potential of red alder management on any given site, such as planned timber harvest. Additionally, the model can be used at a larger scale, i.e. planning for future product diversification in a watershed.</SPAN></SPAN></P><P STYLE="margin:0 0 6 0;"><SPAN><SPAN>The model has been cursorily field-verified on existing red alder plantations and compared with locations and site index of natural red alder stands for DNR's forest inventory system. Initial results indicate that the model is accurate in identifying sites with potential for intensive red alder management. Local knowledge will still be an important factor in the application of the model. Frost pockets or areas susceptible to other physical damage such as ice damage (i.e. within the east wind drafts of the Columbia River Gorge) are not identified in by this model. The usefulness of this model will be determined by the experience of the field staff over time. </SPAN></SPAN></P><P STYLE="margin:0 0 6 0;"><SPAN><SPAN>References:</SPAN></SPAN></P><P STYLE="margin:0 0 6 0;"><SPAN><SPAN>Harrington, Constance A. 1986. A method of site quality evaluation for red alder. Gen. Tech. Rep. PNW-GTR-192. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 22 p. </SPAN></SPAN><A href="https://doi.org/10.2737/PNW-GTR-192" STYLE="text-decoration:underline;"><SPAN><SPAN>https://doi.org/10.2737/PNW-GTR-192</SPAN></SPAN></A></P><P STYLE="margin:0 0 6 0;"><SPAN>Weiss, A. 2001. Topographic position and landforms analysis. In Poster presentation, ESRI user conference, San Diego, CA (Vol. 200). http://www.jennessent.com/downloads/tpi-poster-tnc_18x22.pdf</SPAN></P></DIV></DIV></DIV>

Service Item Id: 881c1dfe302c4b898a0ac8d050dd05a6

Copyright Text: Florian Deisenhofer (Florian.Deisenhofer@dnr.wa.gov) and Allison Bailey (Allison.Bailey@dnr.wa.gov), 2023 model updates. Florian Deisenhofer, Rebecca Niggemann, and Eric Aubert, 2010 Initial model development. Constance Harrington, Emeritus Scientist, US Forest Service, conceptual foundation for the model design.

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Fields:

• OBJECTID ( type: esriFieldTypeOID, alias: OBJECTID )
• VALUE ( type: esriFieldTypeInteger, alias: VALUE )
• COUNT ( type: esriFieldTypeDouble, alias: COUNT )
• ALDER_INDX ( type: esriFieldTypeString, alias: ALDER_INDX, length: 20 )
• ELEV_FT ( type: esriFieldTypeString, alias: ELEV_FT, length: 20 )
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