Rapid digitization to reclaim thematic maps of white-tailed deer density from 1982 and 2003 in the conterminous US

• Main Authors: Brice Hanberry, Phillip Hanberry
• Format: Article
• Language: English
• Published: PeerJ Inc. 2020-01-01
• Series: PeerJ
• Subjects: eCognition; GIMP; Herbivory; Historical geography; Object-based image analysis
• Online Access: https://peerj.com/articles/8262.pdf

Background Despite availability of valuable ecological data in published thematic maps, manual methods to transfer published maps to a more accessible digital format are time-intensive. Application of object-based image analysis makes digitization faster. Methods Using object-based image analysis followed by random forests classification, we rapidly digitized choropleth maps of white-tailed deer (Odocoileus virginianus) densities in the conterminous US during 1982 and 2001 to 2005 (hereafter, 2003), allowing access to deer density information stored in images. Results The digitization process took about one day each per deer density map, of which about two hours was computer processing time, which will differ due to factors such as resolution and number of objects. Deer were present in 4.75 million km2 (60% of the area) and 5.56 million km2 (70%) during 1982 and 2003, respectively. Population and density in areas with deer presence were 17.15 million and 3.6 deer/km2 during 1982 and 29.93 million and 5.4 deer/km2 during 2003. Greatest densities were 7.2 deer/km2 in Georgia during 1982 and 14.6 deer/km2 in Wisconsin during 2003. Six states had deer densities ≥9.8 deer/km2 during 2003. Colorado, Idaho, and Oregon had greatest increases in population and area of deer presence, and deer expansion is likely to continue into western states. Error in these estimates may be similar to error resulting from differential reporting by state agencies. Deer densities likely are within historical levels in most of the US. Discussion This method rapidly reclaimed informational value of deer density maps, enabling greater analysis, and similarly may be applied to digitize a variety of published maps to geographic information system layers, which permit greater analysis.