Where is the loblolly pine?
The Savannah River Site in South Carolina is managed by the U. S.
Forest Service. Defined geographic units are planted with a single
timber species such as southern yellow pine and managed till maturity
when the timber is harvested and a new generation of trees is planted.
Some species, such as loblolly pine, don't always remain confined
to where they are planted. Can you distinguish loblolly pine from
other timber species in Landsat Thematic Mapper imagery?
Subpixel Classifier did!
Includes material © Space Imaging L.P.
Loblolly pine is an invasive species and the USFS wanted to determine
where the species was and how much was present.
Subpixel Classifier was used to develop a signature for loblolly
pine from Landsat TM imagery. The results were used in conjunction
with a USFS stand map to quantify the amount of loblolly in each
· Ground survey and aerial photography
were very time consuming
· Spectrally similar tree species were difficult
· Land cover classifiers did not provide sufficient
A signature developed using IMAGINE
Subpixel Classifier was used to process the Landsat imagery
to look for occurrences of loblolly pine. These detections were
aggregated according to the geographic units specified on USFS Savannah
River stand maps as shown on this map. Each stand is color-coded
to indicate what specie the unit is being managed for, and to indicate
how much loblolly is present.
For example, all light yellow areas are managed for longleaf pine
and small or non-existent amounts of loblolly were detected. The
gold areas are also managed for longleaf pine, but at least 20%
of the stand appears to be loblolly pine instead. The black dots
within each stand indicate the location of the loblolly detections.
They are frequently located along drainage areas within the stand.
This map can be used by resource managers to determine the location
of the densest loblolly stands and to see how much loblolly has
infiltrated non-loblolly stands. To measure the accuracy of the
classification, 170 pixels were randomly selected for field verification
to independently evaluate errors of omission and commission. The
total classification accuracy was 88% (91% omission accuracy and
85% commission accuracy).