KeywordsLittle Missouri National Grasslands
land use planning
geographic information systems
MetadataShow full item record
CitationJensen, M. E., Dibenedetto, J. P., Barber, J. A., Montagne, C., & Bourgeron, P. S. (2001). Spatial modeling of rangeland potential vegetation environments. Journal of Range Management, 54(5), 528-536.
PublisherSociety for Range Management
JournalJournal of Range Management
AbstractPotential vegetation environments (e.g., habitat types, range sites, ecological sites) are important to land managers because they provide a conceptual basis for the description of resource potentials and ecological integrity. Efficient use of potential vegetation classifications in regional or subregional scale assessments of ecosystem health has been limited to date, however, because traditional ecological unit mapping procedures often treat such classifications as ancillary information in the map unit description. Accordingly, it is difficult, if not impossible, to describe the precise location, patch size, and spatial arrangement of potential vegetation environments from most traditional ecological unit maps. Recent advances in remote sensing, geographic information systems (GIS), terrain modeling, and climate interpolation facilitate the direct mapping of potential vegetation through a predictive process based on gradient analysis and ecological niche theory. In this paper, we describe how a predictive vegetation mapping process was used to develop a 30 m raster-based map of 4 grassland, 5 shrubland, and 6 woodland habitat types across the Little Missouri National Grasslands, North Dakota. Discriminant analysis was used in developing this potential vegetation map based on 6 primary geographic information system themes. Geoclimatic subsections and remotely sensed vegetation lifeform maps were used in predictive model stratification. Terrain indices, LANDSAT satellite imagery, and interpolated climate information were used as independent (predictor) variables in model construction. A total of 616 field plots with known habitat type membership were used as dependent variables and assessed by a jackknife discriminant analysis procedure. Accuracy values of our map ranged from 54 to 77% in grasslands, 62 to 100% in shrublands, and 70 to 100% in woodlands dependent on geoclimatic subsection setting. Techniques are also described for generalizing the 30 m pixel resolution habitat type map to appropriate ecological unit maps (e.g., landtype associations) for use in ecosystem health assessments and land use planning.