The core/ periphery approach attempts to model vegetation by identifying
unique and non-unique environmental conditions. The unique areas
might be considered to define the core region of each vegetation class
where unique combinations of environmental conditions occur (using specific
temperature, precipitation and elevation values). These core areas
should provide a clear distinction among vegetation classes. Peripheral
areas outside the core region described by slightly different environmental
conditions that aren’t as unique as the environmental conditions that describe
the core area should represent potential transition zones between vegetation
classes.
Figure 8: The Core/ Periphery Approach – A profile of a distribution of Pine Oak, Grassland, and Desert Scrub, illustrates the core/ periphery concept. Each vegetation type has a core and periphery area. The core areas are associated with a unique combination of environmental values. The peripheral areas are transition areas between vegetation types and are associated with actual vegetation shared between two or more vegetation types and slightly different environmental values that are not unique.
Clustering with Imagine: ERDAS Imagine was used to cluster the
data to delineate the core and periphery areas of each vegetation class
and help prediction of the potential vegetation distribution. Imagine
was selected because of its geospatial clustering capabilities and to explore
the potential of using a remote sensing software traditionally used for
land cover classification to classify potential vegetation. Twenty
classifications or model runs were performed differing in either the variables
used or the number of clusters. Classifications were then compared
with Rzedowski’s Potential Vegetation of Mexico to assess the correspondence
or accuracy.
Using Higher vs. Lower Resolution Data Sets: Three sets of the
same variables were assembled using the same resolution for topographic
factors but differing actual and apparent resolutions for the climatic
variables. The classifications compared the use of higher vs. lower
resolution data sets for predicting the regional-scale distribution of
potential vegetation.
Accuracy Assessment: An accuracy assessment was performed to
analyze the similarity or correspondence between the classified grids and
Rzedowski’s Potential Vegetation map. Several remote sensing techniques
and statistics were used to assess accuracy including error matrices, producer’s
and user's accuracy, overall accuracy and the kappa statistic.
Fieldwork: Fieldwork was conducted around Durango, Mexico to
test my hypothesis that there are core and peripheral areas for each vegetation
class, that the core areas should exhibit the clearest distinction between
vegetation classes, and that peripheral areas should be associated with
transition zones between vegetation classes.