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    Author(s): Krista Merry; Pete Bettinger; Jacek Siry; J. M. Bowker; Steven Weaver; Zennure Ucar
    Date: 2017
    Source: Landscape Research
    Publication Series: Scientific Journal (JRNL)
    Station: Southern Research Station
    PDF: View PDF  (1.0 MB)


    The recreational opportunities available across landscapes of the Southern United States can be broad and diverse, and collectively are considered a subset of cultural ecosystem services. In describing the settings of recreational opportunities, a number of methods have been proposed that are based in part on geographic information and that can be facilitated by geographical analyses. Presented here are two expedient and cost-effective methods for assessing the recreation supply potential of large, heavily-roaded areas that are situated mainly with privately-owned land in the Southern United States. One land classification process uses fine-scale aerial imagery and other geospatial data in a process that results in three recreational opportunity setting zones with a focus on motorised sightseeing: foreground, background, and remote areas. Within these zones, land cover was derived and aggregated into three major land cover classes, including forest, agriculture, and bare ground classes. Further, a second process uses fuzzy classification methods, and through this highly suitable recreation settings were identified. Each recreational opportunity zone is further subdivided by public- and privately-owned land. We feel these types of recreation setting models can allow managers and planners to gain an understanding of the passive recreation potential of heavily-roaded privately-owned landscapes typical of the Southern United States.

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    Merry, Krista; Bettinger, Pete; Siry, Jacek; Bowker, J. M.; Weaver, Steven; Ucar, Zennure. 2018. Mapping potential motorised sightseeing recreation supply across broad privately-owned landscapes of the Southern United States. Landscape Research. 43(5): 721-734.


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    Recreation, motorised sightseeing, private land, geographic information systems, supervised classification

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