Authors: Brendan Cullen*, SUNY Geneseo, Stephen J. Tulowiecki, SUNY Geneseo, Noah Haber, SUNY Geneseo, Peter Scilla, SUNY Geneseo, David Robertson , SUNY Geneseo , Chris P.S. Larsen, University at Buffalo
Topics: Historical Geography, Biogeography, Human-Environment Geography
Keywords: Historical GIS, Biogeography, Forests, Native Americans, Landscape Reconstruction, New York
Session Type: Paper
Start / End Time: 8:00 AM / 9:40 AM
Room: Bonaparte, Marriott, River Tower Elevators, 4th Floor
Presentation File: No File Uploaded
Previous research has sought to reconstruct pre-European forest conditions in North America, including forest composition and structure. Studies have often used “witness-tree” records from original land surveys to quantitatively estimate forest density, such as for use in restoring landscapes to previous conditions. However, while land survey records containing forest descriptions are present for many areas throughout Eastern North America, not all land survey records contain witness-tree data, motivating other means of estimating forest density. This study develops a methodology for creating a forest density index from qualitative forest descriptions in land survey records. The study area comprises three original land purchases in western New York State (25,000 km2). First, field notes from township surveys were mapped in a GIS. Second, qualitative descriptions of forest density within the transcribed records were grouped into three categories, then given a rank of 1 (e.g. “open fields”), 2 (e.g. “thinly timbered”, “oak openings”), or 3 (e.g. “timbered”). Third, the ranked descriptions of forest density were used with interpolation methods to generate a continuous surface of forest density across the study area. Results suggest that qualitative density descriptions are spatially autocorrelated, supporting the validity of this study’s methodology. Furthermore, results also manifest that areas with no forest cover or lower tree density correspond with Native American settlement. This methodology, with few modifications to account for differences in vocabulary among surveys, can be used to produce an index of forest density and supplement other forest reconstruction methods.