The Working Draft of the Chicago Wilderness Biodiversity Recovery Plan calls
for creation of large complexes and mosaics of several different natural community
types (forest, savanna, prairie and wetlands). It also recommends activities
to improve the scientific basis for ecological monitoring and management of
the recovery and status of natural communities in the region. The use of hyperspectral
scanning (HSS) remote sensing offers the potential of being a cost effective
method of monitoring large-scale community restoration efforts in the Chicago
region. Data recently collected as part of an intensive study to characterize
dolomite prairie communities in the lower Des Plaines River valley (Sluis and
Tandarich, 2000) provided an opportunity to 1) test the use of HSS in mapping
native /natural sites on and near Midewin National Tallgrass Prairie (Midewin)
and to 2) develop and verify this approach as a monitoring tool. This approach
has the potential for significant savings in time and money for monitoring at
several points during the restoration effort and for long-term monitoring and
management of natural communities.
The characterization of vegetation and soils of reference deep and shallow soil
grassland -- the latter type locally called dolomite prairie -- sites was recently
completed under a Chicago Wilderness grant (Sluis and Tandarich, 2000). Ten
grassland sites within the Des Plaines and Kankakee River watersheds within
and adjacent to Midewin were studied. Recent wetland delineation and ecological
surveys of Midewin revealed less than 100 acres of dolomite prairie. According
to the Natural Resources Conservation Service (NRCS) soil map, there are approximately
2000 acres of soils on Midewin which may have supported some type of dolomite
prairie in the past and which could be successfully restored to this uncommon
prairie type. However, a restoration of a large areal scope as Midewin requires
cost-effective monitoring techniques. Ground methods only would be both time-consuming
and costly.
This study used an aerial component incorporating the new hyperspectral scanning
(HSS) remote sensing imagery to characterize grassland environments on Midewin.
This is a necessary step prior to the ability to monitor ongoing prairie restoration
efforts. This method, which has been proven to work effectively with agronomic
crop plants, could significantly reduce ground time and enable a synoptic, yet
detailed view of restoration areas. Ultimately, this method could be expanded
to large-scale restoration efforts at Midewin, and elsewhere in the Chicago
region.
Spectral resolution is a problem when attempting to map native plant groupings.
Color infrared (CIR) film is emulsion-based and has the attendant problems of
exposure, processing, enlargement and grain. Multispectral scanning (MSS), being
digitally based, has fewer problems than film. However, both CIR film and MSS
images show a blending of reflectance, called spectral response, within broad
spectral bands covering a limited portion of the electromagnetic spectrum. This
means that the images contain the standard wavelength band separations, blue
(0.4-0.5 µm), green (0.5-0.6 µm), red (0.6-0.7 µm), and near
infrared (0.7-1.3 µm) (µm or micrometer is a unit of wavelength
of 0.000001 meter) (Figure
1). Due to the digital nature of MSS, these bands can be viewed and analyzed
uniquely. While these bands may be useful for broad-scale vegetation mapping,
such as trees versus grass, there is a problem when trying to discriminate between
many specific plant species. However, it is known that the spectral response
in the blue, green and red regions is sensitive to the chlorophyll content,
and the response in the near infrared region is sensitive to the plant and leaf
structure (Figure 2).
The advantage of HSS is its ability to break the spectral bands into smaller,
more discrete units to better capture these unique responses.
The imagery scale of CIR and MSS is an additional problem. The enlargement of
CIR film results in "graininess" (the silver grains coating the film
surface becoming visible) that inhibits pattern recognition. While this is less
of a problem for the digital imagery, the most common and readily available
spacecraft MSS spatial resolution is 10-30 meters. This means that the smallest
area that can be scanned on the ground and resolved by the sensor on a digital
image is a square area 10-30 meters on a side that is 0.025 to 0.25 acres. This
has made mapping complex native plant community groupings very difficult.
Within the last 15 years, a remote sensing system has been developed that allows
the spectrum to be separated into 120 to 240 discrete bands, depending upon
the sensor. This system is called hyperspectral (literally, beyond the normal
spectrum) remote sensing (HSS). The sensor in our study, the ITD - Spectral
Visions Sensor, has 120 bands (Appendix
B). This gives spectral bandwidths of 0.03-0.05 µm that allows many
discrete spectral response separations. In addition, the spatial resolution
of this sensor produces l m picture elements (pixels). When used at this large-scale
resolution, HSS enables discrimination of small areas of vegetation types based
on their unique spectral responses not possible with a multispectral scanning
(MSS) sensor such as carried on the Landsat 7 satellite.