Geoscience Reference
In-Depth Information
16.3 METHODS
16.3.1
Precision of Landscape Change Metrics
To measure imprecision in metric values, overlapping Landsat Multi-Spectral Scanner (MSS)
path/row images were redundantly processed for two different study areas in the Upper Midwest
to create classifications representing forest, nonforest, water, and other and maps of the normalized
difference vegetation index (NDVI). Images on row 28 and paths 24-25 overlapped in the northern
Lower Peninsula of Michigan and on row 29 and paths 21-22 overlapped on the border between
northern Wisconsin and the western edge of Michigan's Upper Peninsula (Brown et al., 2000a).
The georeferenced MSS images at 60-m resolution were acquired from the North American
Landscape Characterization (NALC) project during the growing seasons corresponding to three
periods: 1973-1975, 1985-1986, and 1990-1991 (Lunetta et al., 1998). Subsequent LC classifica-
tions of the four images resulted in accuracies ranging from 72.5% to 91.2% (average 80.5%),
based on comparison with aerial photograph interpretations.
For landscape pattern analysis, the two study areas were partitioned into 5-
cells. A
total of 325 cells in the Michigan site and 250 in the Wisconsin-Michigan site were used in the
analysis. The partitions were treated as separate landscapes for calculating the landscape metric
values. The values of eight pattern metrics, four patch-based and four boundary-based, were
calculated for each partition using each of two overlapping images at each of three time periods
in both sites.
The precision of landscape metric values was calculated using the difference between metric
values calculated for the same landscape partition within the same time period. For each metric,
these differences were summarized across all landscape partitions using the root mean squared
difference (RMSD). To standardize the measure of error for comparison between landscape metrics,
the relative difference (RD) was calculated as the RMSD divided by the mean of the metric values
obtained in both images of a pair.
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5-km
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16.3.2
Comparing Class Definitions
16.3.2.1
Landsat Classifications
To evaluate the sensitivity of maps to differences in class definitions we calculated landscape
metric values from two independent LC classifications derived from Landsat Thematic Mapper
(TM) imagery of for the Huron River watershed located in southeastern Michigan. The only
significant difference between the two LC maps was the class definitions. Accuracy assessments
were not performed for either map. Therefore, the analysis serves only as an illustration for
evaluating the importance of class definitions.
For the first map, Level I LU/LC classes were mapped for the early 1990s using the National
Land Cover Data (NLCD) classification for the region. We developed the second data set using
TM imagery from July 24, 1988. It was classified to identify all areas of forest, defined as pixels
with
40% canopy cover, vs. nonforest. Spectral clusters, derived through unsupervised classifi-
cation (using the ISODATA technique), were labeled through visual interpretation of the image
and reclassified. Landscape metrics were computed using Fragstats applied to the forest class from
both data sets across the entire watershed. Also, the two data sets were overlaid to evaluate their
spatial correspondence.
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16.3.2.2
Aerial Photography Interpretations
We also compared two classifications of aerial photography over a portion of Livingston County
in southeastern Michigan. The first data set consisted of a manual interpretation of LU and LC
