**Mapping the Invasive Species Chinese Tallow**

Elijah Ramsey III^1^, Gene Nelson^1^, Amina Rangoonwala^2^, and Robert
Ehrlich^3^

US Geological Survey Research Center^1^, Johnson Controls World Services
Inc.^2^, National Wetlands Research Center, 700 Cajundome Boulevard,
Lafayette, Louisiana 70506^1,2^,

C & E Enterprises, 1048 S. Oak Hills Way, Salt Lake City, UT 842108^3^

<elijah_ramsey@usgs.gov>

In a progression of papers, we described newly developed remote sensing
tools to map the localized occurrences and regional distribution of the
widespread and gregarious invasive species; Chinese tallow (*Sapium
sebiferum*) (Figure 1). These developments relied on newly available
Hyperion sensor hyperspectral data collected from the EO1 satellite
platform. Although directed at mapping and monitoring tallow, remote
sensing hyperspectral technologies advanced here have decreased
deficiencies that are at this time preventing widespread mapping of
invasive plants in the United States and internationally. We expect
methods developed as part of this project will provide a template for
mapping and monitoring other invasive species within the native
landscape in differing geographic areas.

Since introduction into the US in the late 1700's, Chinese tallow has
spread from South Carolina south to Florida and from Texas west to
Arizona and Southern California. It is continuing its rapid advance and
once it gains a foothold, removal is extraordinarily difficult. The best
defense is an aggressive offense, detect early and eradicate. Its spread
is creating extensive and irrevocable damage to native communities and
although of critical importance to the survival of many native habitats
and wildlife and the viable operations of many public and private lands,
the actual distribution of the Chinese tallow menace has yet to be
mapped. Not only would the ability to map the location of this invasive
be critical to documenting the spread of this invasive and to
eradication efforts, but monitoring capabilities would be essential for
assessing success of various controls. Nationally, identification by
regional mapping and a method of regular monitoring are keys to
addressing the infestation of all non-native plants within the United
States. Before this research, the technology for detecting and mapping
Chinese tallow and other non-native plants was not available for
operational use.

**Remote sensing of Chinese tallow**. By all accounts, except in
senescence, Chinese tallow mixes spectrally and structurally into the
matrix of native plants. In other words, it is hard to detect.
Furthermore, we found that in this period of infestation, pure stands of
tallow trees rarely occurred and where they did, the stands were narrow
and fragmented with dimensions rarely more than 5 m. Dominantly, tallow
trees are mixed intricately and at a low density within the matrix of
similar coastal and adjacent upland native vegetation. The necessary
trade-off is between the need for high spatial resolution and the need
for regional coverage.

**High spatial resolution photography: a proof of concept study**. The
first attempt to map Chinese tallow with remote sensing data used
airborne color-infrared photography (1:12,000) to simulate low spectral
but high spatial resolution satellite and airborne image and digital
video data (Ramsey III, E., G. Nelson, S. Sapkota, E. Seeger, and K.
Martella, 2002. Mapping Chinese tallow with color-infrared photography.
*Photogrammetric Engineering & Remote Sensing*, 68, 251-255). Mapping
occurred when the senescing Chinese tallow with bright red leaves
presented a high spectral contrast within the native landscapes (Figure
2). To simulate low spectral but high spatial resolution satellite and
airborne image and digital video data, CIR photography was scanned at a
1.0 m spatial resolution or less and classified for the occurrence of
bright red leaves associated with senescing Chinese tallow.
Classification accuracies higher than 95% confirmed high spatial
resolution data can be used to monitor Chinese tallow infestations
(Table 1) but the mapping was personnel intensive and spatial coverage
was severely limited (\<5 ha).

**EO1 Hyperion image data**. To overcome this challenge, high spectral
resolution data that offered subtle spectral discrimination was used to
compensate for the use of moderate spatial resolution data that offered
regional coverage. The Hyperion sensor onboard the EO1 satellite
provided the necessary high spectral resolution data at a reasonable
cost and a spectral unmixing tool provided the necessary subpixel
Chinese tallow detection. Even with the appropriate hyperspectral data
and spectral mapping tool, detecting the normally low percent
occurrences of Chinese tallow within the matrix of native plants
required identifying subtle spectral differences within the remote
sensing image data. Atmospheric spectral leveling diminishes the ability
to determine subtle spectral differences, and therefore, to determine
changes in canopy composition, in this case, tallow percent occurrences.
Atmospheric removal allows extraction of the canopy reflectance from the
sensor signal maximizing the potential spectral distinctiveness
(information, tallow percent occurrences) of the each reflectance
spectra.

**Generating canopy reflectance**. To obtain the necessary subtle
detection accuracy while maintaining spatially extensive coverage, a
model strategy was generated and implemented that transformed the
Hyperion image data into accurate renditions of the canopy reflectance
(Ramsey III, E., and G. Nelson, in review). The model strategy accounted
for atmospheric influences, the view and sun geometries, and
illumination of the target and used a whole image approach coupled with
site-specific canopy reflected radiance measurements to transform the
EO1 Hyperion apparent reflectance image data into highly accurate
reflectance data (Figure 3). Overall, hyperspectral reflectance spectra
generated with the atmospheric correction and normalization model
strategy followed expected spectral trends and contained expected
prominent features. Differences in the observed and model modeled canopy
reflectance spectra were most often low or nearly non existent, and
where relatively higher differences existed, these were most likely
associated with problems in the comparison not in the model generation.
Typical differences between the observed (site-specific) and modeled
(Hyperion) reflectances would be expected to range from ±1.0% to ±1.6%
in the visible and ±5% in the near infrared wavelengths in 95% of the
model simulations. These comparisons verified that our strategy
successfully produced highly accurate and detailed renditions of canopy
reflectances at the time the Hyperion data was collected. The high
accuracy supplied the highest spectral detail possible necessary for the
subtle spectral discrimination and detection of the red Chinese tallow
leaves in the matrix of native vegetation.

**Generating characteristic spectra**. Once accurate spectral detail was
obtained, a spectral subpixel extraction algorithm operating on the EO1
Hyperion site-specific corrected and normalized canopy reflectance data
was used to provide realistic and predictive characteristic spectra that
represented the percent occurrence of pertinent and dominant landcover
spectral characteristics occurring at the time of the Hyperion image
collection (Ramsey III, E., G. Nelson, R. Ehrlich, A. Rangoonwala and K.
Martella, in review, Generation and validation of characteristic spectra
from EO1 Hyperion image data for detecting the percent occurrence of
invasive species, specifically Chinese Tallow). Collected leaf spectra
provided evidence for the expected variance and variety of forms of
canopy reflectance spectra characterizing the pertinent land covers. In
addition to their use in atmospheric correction, site-specific canopy
reflectance spectra provided a direct link between the site composition
variance directly influencing the canopy reflectance and the canopy
reflected radiance measured at the sensor. Simultaneous collection and
subsequent classification of 35-mm slides provided dominant
site-specific compositions at the time of the Hyperion image data
collection. Mixing proportions associating the modeled characteristic
spectra with the site compositions were compared with compositions
generated from the site-specific slide classifications. This final
comparison insured that the modeled characteristic spectra adequately
portrayed the dominant canopy reflectance variance existent during the
image collection.

Using the input site-specific reflectance spectra sets (n=34, about 400
nm to 925 nm), the sub pixel extraction model found 5 characteristic
spectra that adequately represented the site-specific and also the
corresponding Hyperion imaged canopy reflectance spectra data sets. Both
sets of site-specific and Hyperion characteristic spectra were similar
(Figures 4 and 5). One characteristic spectrum represented percent
occurrences of senescing cypress-tupelo and leaf-off and brown foliage.
Two others represented shadows and green vegetation percent occurrences.
The shadow and green vegetation variances were correlated. The fourth
characteristic spectrum was somewhat related to the percent occurrence
of yellow tallow and yellowing foliage. The final characteristic
spectrum was visually and statistically associated with the percent
occurrence of red Chinese tallow. About 81% (n=34) and 78% (n=33, one
site excluded) of the composition variances associated with the red
tallow site-specific and imaged characteristic spectra, respectively
were explained by the site-specific compositions generated in the
site-specific slide classification (Figures 6 and 7). The implemented
methodology including validation and verification insured the modeled
characteristic spectra were both physically realistic and predictive,
and thereby these spectra could more likely be spatially and temporally
extended, and used with other sources of hyperspectral sensor image
data.

**Mapping the percent occurrence of Chinese tallow**. The final
objective was to implement methods designed and tested in the previous
studies and provide a realistic and accurate representation of the
spatial distribution of the percent occurrence of Chinese tallow within
the Hyperion coverage. Using results generated in the previous studies
(accurate canopy reflectance spectra and realistic and predictive
characteristic spectra) we transformed corrected and normalized Hyperion
reflectance image data into composition images with a subpixel
extraction model (Ramsey III, E. A. Rangoonwala, G. Nelson, and R.
Ehrlich, in review. Mapping the invasive species, Chinese Tallow with
EO1 satellite Hyperion hyperspectral image data and relating tallow
percent occurrences to a classified Landsat Thematic Mapper landcover
map). Composition images were related to green vegetation (combining the
green and shadow characteristic spectra), senscent foliage and senescing
cypress-tupelo forest, red Chinese tallow, and a final composition image
that was slightly related to yellow tallow but mostly did not correspond
to any observed land cover feature. Statistical and visual comparisons
confirmed successful portrayal of these landscape features at the time
of the Hyperion image collection (Figures 8 to 13) .

In a final analysis, the percent occurrence of red tallow as a surrogate
for Chinese tallow was linked to land covers generated with Landsat
Thematic Mapper image data as a first step to establish the sensitive
and susceptible of various ecosystems to risk of invasive establishment
(Figure 14) . Results suggested the highest percent occurrences and
widest distribution of tallows were located (1) on the numerous and
ubiquitous topographic highs and levies within and lining the ubiquitous
coastal canals, (2) scattered throughout fallow fields or along fence
rows separating active and non active fields, and (3) in disturbed or
more open canopy woody wetland deciduous forests (including
cypress-tupelo forests), upland woody land evergreen forests (dominantly
pines), and upland deciduous and mixed forests. Additionally, pine and
in one case cypress plantations were often highly contaminated by tallow
trees. In all, narrow linear features (levies, fencelines, cheniers,)
and scattered trees and small isolated stands (cultivated and fallow
fields, topographic highs in marshes, disturbed and open forests
canopies) are mixed with the native vegetation in the TM pixel and
amalgamated within the landcover classification. Newly created remote
sensing techniques relying on Hyperion hyperspectral image data and a
specialized subpixels extraction model provided identification of subtle
spectral anomalies caused by the occurrence of red tallow that was not
possible with the coarser spectral resolution Landsat Thematic Mapper
image data. Linkage of these occurrences with the Landsat Thematic
Mapper landcover map dramatized those ecosystems containing the highest
levels of tallow and those activities that enhanced tallow
establishment.

![](media/image1.wmf){width="5.993055555555555in"
height="5.34375in"}Table 1. Accuracy assessment of classified 0.5 and
1.0 m CIR photography