Abstract

BackgroundDespite 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.MethodsUsing 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.ResultsThe 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.DiscussionThis 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.

Highlights

  • IntroductionResearchers increasingly are developing and improving tools that can be applied to a range of topics

  • We present the reclaimed ecological information stored in images, providing an analysis of change in deer density over time and discussion of the potential effects of changing deer densities over time

  • In GIMP, processing time was minor compared to eCognition but more manual correction was required after automation to fill in areas without color

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Summary

Introduction

Researchers increasingly are developing and improving tools that can be applied to a range of topics. 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. 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. This method rapidly reclaimed informational value of deer density maps, enabling greater analysis, and may be applied to digitize a variety of published maps to geographic information system layers, which permit greater analysis

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