U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2012–1215

Prepared in cooperation with the Toledo Bend Project

Remote Sensing Survey of Chinese Tallow Tree in the

Toledo Bend Reservoir Area, Louisiana and Texas
Cover:

Background, A mosaic of two scenes acquired by Earth Observing-1 Advanced Land Imager sensor (ALI): a false color composite (FCC) ALI image is 

shown on the left, and an ALI classified landcover map is shown on the right. White outlines overlain on the ALI mosaic contain an FCC Hyperion

image (left) and red tree occurrences calculated from Hyperion data (right).

Center, From left to right, red-leaf Chinese tallow (Triadica sebifera) shrubs in a pine plantation, trees along a field-forest edge, and shrubs along fence 

rows in the Toledo Bend Reservoir area, November 2010.
Remote Sensing Survey of Chinese Tallow 

Tree in the Toledo Bend Reservoir Area, 

Louisiana and Texas

By Elijah Ramsey III, Amina Rangoonwala, Terri Bannister, and Yukihiro Suzuoki

Prepared in cooperation with the Toledo Bend Project

Open-File Report 2012–1215

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2013

This and other USGS information products are available at http://store.usgs.gov/

U.S. Geological Survey

Box 25286, Denver Federal Center

Denver, CO 80225

Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. 

Government.

Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Suggested citation:

Ramsey, Elijah III; Rangoonwala, Amina; Bannister, Terri; and Suzuoki, Yukihiro, 2013, Remote sensing survey of 

Chinese tallow tree in the Toledo Bend Reservoir area, Louisiana and Texas: U.S. Geological Survey Open-File Report 

2012–1215, 74 p., http://pubs.usgs.gov/of/2012/1215/.
iii

Acknowledgments

We sincerely thank Elizabeth M. Middleton, Earth Observing-1 (EO-1) Satellite Mission 

Scientist for the National Aeronautics and Space Administration (NASA), who collaborated 

with us to carry out a challenging data collection strategy. The EO-1 satellite carries the high 

spectral resolution Hyperion sensor and the broadband Advanced Land Imager (ALI, which is 

an enhanced Thematic Mapper Imager). Dr. Middleton provided the support of Stuart Frye EO-1 

Chief Engineer, Stinger Ghaffarian Technologies, Inc., and Pat Cappelaere, Sensor Web Software 

Architect, Vightel Corporation who scheduled EO-1 Hyperion and ALI imagery collections amid 

dynamic limiting factors, including conflicting tasking requests, technical difficulties, and 

cancellation of collections due to the presence of widespread cloud cover. We would also 

thank Melvin Swoboda, Project Licensing Manager, Toledo Bend Project Joint Operation; Mark 

Howard, Geographic Information Specialist Administrator, SRA (Texas); and Carl L. Chance, 

Information Technology Geographic Project Supervisor, SRA (Louisiana). Gratitude is extended 

to Tom Zimmerman, Land and Minerals Program Manager of Sabine National Forest, and 

Tom Phillips, Biologist for the National Forests and Grasslands in Texas, for their help in tree 

identification. The research described in this report was supported in part by Toledo Bend 

Project (FERC-2305). 

We thank National Park Service Ranger Scott Taylor at the Big Bend National Park, Texas, for 

his excellent piloting during our prolonged aerial surveys. We acknowledge Dr. Robert Ehrlich of 

Residuum Energy for his contributions to the canopy spectral analyses and Dr. Daniel Schlapher 

of ReSe Applications for his help in implementing the ATCOR atmospheric correction software. 

We are grateful for the technical editing done by the USGS Lafayette Publishing Service Center. 

v

Contents

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................2

Objective..........................................................................................................................................................3

Background.....................................................................................................................................................3

Mapping Invasive Plants with Remote Sensing Data.....................................................................3

Spectral Contrast with Other Land Classes .....................................................................................3

Spectral Characteristics of Leaves ..........................................................................................3

Canopy Reflectance Spectra.....................................................................................................3

Spatial and Spectral Resolutions of Spatially Infrequent and Small Targets.............................4

High Spectral Resolution Offset of Coarse Spatial Resolution ............................................4

Creation of Accurate Reflectance Images from Raw Hyperion Data .........................................4

Estimate of the Detection Limit for Percent Occurrence .....................................................4

Derivation of Canopy Reflectance Image from Hyperion Data............................................6

Creation of the Indicator Spectrum for Chinese Tallow Tree .......................................................6

Methods...........................................................................................................................................................8

The Strategy for Collecting Hyperion Imagery of the Toledo Bend Reservoir Area ................8

Simultaneously Collecting Hyperion and Advanced Land Imager Data.............................8

Creating Hyperion Reflectance Images............................................................................................8

Producing Digital Orthographic Mosaics of the Study Area.......................................................13

Classifying Land Cover of the Study Area ......................................................................................13

Purpose of Classification Map ................................................................................................13

Classification of Land Covers on Advanced Land Imager Data.........................................13

Creating Rectified Advanced Land Imager Images for

the Landcover Classification.............................................................................13

Conducting Field Reconnaissance ..................................................................................................17

Creating a Hyperlink for Interactive Query of Site-Based

Reconnaissance Information .....................................................................................17

Conducting the Helicopter Survey..........................................................................................17

Laboratory Measurements of Leaf Spectral Properties .....................................................19

Nonvegetated Calibration and Validation Targets ........................................................................19

Using Nonvegetated Targets for Calibration.........................................................................19

Creating Calibration Spectra from Nonvegetated Sources................................................19

Deriving Indicator Spectra for Chinese Tallow Tree ....................................................................19

Extracting Hyperion Spectral Data .........................................................................................22

Creating Indicator Spectra from the Helicopter-Based Reflectance Dataset ................22

Applying PolyVector Analysis-Derived Indicator Spectra to

the Hyperion Reflectance Image...............................................................................22

Overlaying Predicted Chinese Tallow Tree Occurrences

onto the Landcover Map.............................................................................................22

Results ...........................................................................................................................................................22

Hyperion and Advanced Land Imager Coverages ........................................................................22

Hyperion Coverages..................................................................................................................22

Advanced Land Imager Coverages ........................................................................................26
vi

Field Reconnaissance in Support of Chinese Tallow Tree Mapping..........................................26

Ground-Based Field Reconnaissance....................................................................................26

Observations from Fixed-Wing Aircraft .................................................................................26

Helicopter-Based Observations ..............................................................................................26

Spectral Properties of Leaves .................................................................................................32

Reflectance Spectra Used for Calibration and Validation

of Hyperion Reflectance Images ...............................................................................33

Non-Vegetated Reflectance Calibration and Validation Targets..............................33

Vegetated Reflectance Calibration and Validation Targets.......................................33

Field-Based Landcover Classes and Features Associated with Chinese

Tallow Tree and Red Trees..........................................................................................33

Land Features....................................................................................................................34

Chinese Tallow Trees on Edges.............................................................................34

Chinese Tallow Tree at Water Edges....................................................................34

Chinese Tallow Trees Along Fences.....................................................................34

Land Classes......................................................................................................................34

Pine Plantations .......................................................................................................34

Mature Pine Forest..................................................................................................34

Hardwood Forests ...................................................................................................34

Hardwood Fingers ...................................................................................................39

Pine and Hardwood Mixed Forests ......................................................................39

Grasslands ................................................................................................................39

Scrublands................................................................................................................41

Cypress Forests........................................................................................................41

Bare/Developed .......................................................................................................41

Interactive Query of Reconnaissance Site Information......................................................41

Landcover Classification of Advanced Land Imager Data ..........................................................41

Pine Plantation and Mature Pine Forests..............................................................................41

Hardwood Forests and Hardwood Fingers............................................................................47

Scrublands and Land Features................................................................................................47

Assessment of the Landcover Classification Map ..............................................................47

Helicopter Reflectance Data Spectral Dataset ....................................................................49

Creating Indicator Spectra from the Helicopter Reflectance Spectral Dataset.............49

PolyVector Analysis of Spectral Data ...........................................................................49

Percent Occurrences of Chinese Tallow Tree Indicated by

Hyperion Reflectance Images.............................................................................................51

PolyVector Analysis of Spectral Datasets.............................................................................51

Seeding the PolyVector Analysis (PVA).................................................................................52

Seeding PolyVector Analysis (PVA) combined with Land Class Information..................52

Mapping Chinese Tallow Tree Occurrences in the Hardwoods Class.............................. 52

Extending the Seeded and PolyVector Analysis (PVA)

to Multiple Landcover Classes.....................................................................58

Seeding PolyVector Analysis (PVA) with Helicopter-Based

Indicator Spectra in Hardwood Landcovers ...........................................................58

Extending PolyVector Analysis (PVA) Mapping with Helicopter-Based Indicator 

Spectra to Multiple Landcover Clases and Hyperion Swaths.....................67
vii

Discussion and Conclusion........................................................................................................................71

Field Reconnaissance........................................................................................................................71

Creation of Workable Calibration Spectra......................................................................................71

Attempting the Detection of Chinese Tallow Tree with Calibration Spectra ............................72

Indicator Spectra Derived from Helicopter-Based Reflectance Spectra........................72

Implications for Future Mapping Efforts .........................................................................................72

References Cited..........................................................................................................................................73

Figures

1. Toledo Bend Reservoir study area (Louisiana and Texas).....................................................2

2. Reflectance spectra of leaves belonging to various tree species

imaged during fall senescence in the Toledo Bend Reservoir

(Louisiana and Texas) study area ..............................................................................................3

3. An idealized representation of a mixed pixel composition....................................................4

4. Color-infrared (CIR) photographs of a mixed hardwood and

pine forest in southwest Louisiana during fall senescence..................................................5

5. Correspondence (by percent occurrence in the pixel) between

field-observed and Hyperion-detected Chinese tallow trees

(Triadica sebifera) occurring in the top of canopy at 34 field sites......................................5

6. Reflectance spectra of two pine (Pinus L. spp) stands,

one with and one without Chinese tallow trees (Triadica sebifera) ....................................5

7. Downwelling sunlight (irradiance) illuminating the forest canopy,

upwelling spectra obtained from a raw Hyperion image, and

canopy reflectance (used for calibration in the atmospheric correction

of the Hyperion image data) obtained from a helicopter platform

with a handheld radiometer........................................................................................................6

8. A canopy reflectance spectrum obtained with a handheld radiometer

from a helicopter platform and a canopy reflectance spectrum derived

from a raw Hyperion image by using the simultaneous downwelling

sunlight recordings and an atmospheric correction model......................................................6

9. The characteristic components (indicator spectra) of the

composite spectrum of visible sunlight wherein the characteristic

component spectra are the blue, green, and red wavelengths

in each Hyperion pixel .................................................................................................................7

10. An illustration of how the linear addition of each component

or indicator spectrum (red, blue, and yellow) that best reproduces

the composite spectrum (green) of the Hyperion pixel..........................................................7

11. Illustration of the strategy for collecting Hyperion imagery for

coverage of lands lying adjacent to the Toledo Bend Reservoir

(Louisiana and Texas)...................................................................................................................9

12. Nominal coverage pattern and relative locations of coverage

provided by the Hyperion and Advanced Land Imager (ALI) sensors

onboard the Earth Observing-1 satellite .................................................................................10

13. Extents of 2004, 2009, and 2010 digital orthophoto quarter

quadrangle mosaics...................................................................................................................14

14. False-color composite mosaic, covering the Toledo Bend Reservoir

study area, created by using bands 6, 4, and 3 of two Advanced Land

Imager scenes.............................................................................................................................15
viii

15. Misalignment from band to band within a single bank of an

Advanced Land Imager image from October 17, 2009..........................................................16

16. Locations of Chinese tallow tree occurrences received from

personnel of the Toledo Bend Authority at the project initiation........................................18

17. Ground-based field sites visited or planned for use in calibrating

atmospherically corrected Hyperion reflectance images...................................................20

18. Oil-well platforms were used as nonvegetated calibration sites.......................................21

19. The number of useable or nearly cloud-free Hyperion images

collected per swath during the fall senescent period in 2009 to 2010...............................25

20. Locations of field sites surveyed during ground reconnaissance

for Chinese tallow trees in the Toledo Bend Reservoir study area....................................27

21. Results of fixed-wing aerial reconnaissance for Chinese

tallow trees in the Toledo Bend Reservoir study area..........................................................28

22. Red-leafed trees and green-leafed Chinese tallow trees in mature pine forests ...........29

23. Aerial view of red trees in hardwood forests ........................................................................30

24. Photographs collected during the helicopter survey of the

Toledo Bend Reservoir study area...........................................................................................32

26. Representative reflectance spectra of green, red, and yellow Chinese

tallow tree leaves collected in the Toledo Bend Reservoir study area.............................32

27. Representative reflectance spectra of green, red, and yellow leaves

from Chinese tallow and sweetgum trees and green, orange, red, and

yellow leaves spectra from red maple trees in the Toledo Bend

Reservoir study area ..................................................................................................................32

28. Mean reflectance spectra of the ground surface within oil-platform

sites in the Toledo Bend Reservoir study area ......................................................................33

29. All field sites where Chinese tallow trees and red trees were observed

during ground-based and fixed-wing aerial reconnaissance in the

Toledo Bend Reservoir study area...........................................................................................35

30. Chinese tallow trees, or red trees and shrubs, observed during the

ground survey, and red trees observed during the aerial survey in

locations along forest edges ....................................................................................................36

31. Red- and green-leafed Chinese tallow trees and shrubs occurring

along fences ................................................................................................................................37

32. Pine plantations at various stages of growth and with variable

structural patterns were observed during the reconnaissance surveys..........................38

33. Red-leaf Chinese tallow trees occurring in pine plantations..............................................39

34. Photographs of grassland areas within the Toledo Bend Reservoir

study area ....................................................................................................................................40

35. Photographs of scrubland areas within the Toledo Bend Reservoir

study area ....................................................................................................................................42

36. Aerial photograph of a cypress forest within the Toledo Bend

Reservoir study area acquired during the fixed-wing airplane survey .............................43

37. Aerial photographs of newly clear-cut areas within the Toledo Bend

Reservoir study area ..................................................................................................................44

38. Photographs of developed areas containing red-leaf Chinese tallow tree

within the Toledo Bend Reservoir study area........................................................................44

39. An example display of the interactive reconnaissance site database .............................45

40. Landcover map of the Toledo Bend Reservoir study area based on data

acquired by the Earth Observing-1 Advanced Land Imager sensor..................................46
ix

41. Ground and aerial photographs of planted pine areas in the

Toledo Bend Reservoir study area...........................................................................................48

42. Canopy reflectance results for a mature pine and two young pine

plantations within the Toledo Bend Reservoir study area...................................................49

43. Aerial photograph of a mature pine stand in the Toledo Bend

Reservoir study area ..................................................................................................................50

44. Canopy reflectance spectra based on helicopter upwelling sunlight

recording of four pine forests and plantations and one scrub-shrub site

within the Toledo Bend Reservoir study area........................................................................50

45. Canopy reflectance of three indicator spectra used for detecting

Chinese tallow tree occurrences within the Toledo Bend

Reservoir study area ..................................................................................................................51

46. Spectral results of a PolyVector Analysis of an input indicator spectrum

for red-leaf Chinese tallow tree and Hyperion spectra from selected

locations within the Toledo Bend Reservoir study area.......................................................53

47. Mapped occurrences of red-leaf trees ..................................................................................55

48. A binomial representation of SPUNMIX-predicted occurrences

of red-leaf Chinese tallow trees and red trees in association with

hardwood forests and fingers in the Toledo Bend Reservoir study area..........................56

49. Spectral indicators resulting from a PolyVector Analysis of

helicopter-based indicator spectra and Hyperion-based spectra.....................................63

50. Maps representing percent occurrence of A, live vegetation,

B, senescent vegetation, and C, live vegetation with red-leaf

Chinese tallow trees...................................................................................................................65

51. Imagery showing the likelihood of Chinese tallow trees or red trees

occurring within hardwood forest stands in the Toledo Bend Reservoir

study area ....................................................................................................................................66

52. Illustrations detailing possible associations between spectrally

indicated predictions of likely occurrences of red-leaf Chinese

tallow trees and observed occurrences documented during aerial

and ground reconnaissance within the Toledo Bend Reservoir study area ....................68

Tables

1. Spectral band centers of imagery acquired by the Advanced Land

Imager sensor onboard the Earth Observing-1 satellite........................................................8

2. The set of wavelengths acquired by the Earth Observing-1 Hyperion

sensor that was used to map occurrences of Chinese tallow trees

within the Toledo Bend Reservoir study area........................................................................11

3. Characteristics of the digital orthophoto quarter quadrangles

spanning the Toledo Bend Reservoir study area that were used to map

occurrences of Chinese tallow trees ......................................................................................15

4. Orthorectification of banks and bands in Advanced Land Imager imagery.....................16

5. List of field reconnaissance surveys conducted for mapping

occurrences of Chinese tallow trees within the Toledo Bend Reservoir

study area ....................................................................................................................................18

6. List of image collections by the Advanced Land Imager and Hyperion

sensors aboard the Earth Observing-1 satellite that were used for

mapping Chinese tallow trees in the Toledo Bend Reservoir study area .........................24
x

7. Land classes identified within satellite imagery of the

Toledo Bend Reservoir study area...........................................................................................48

8. Results of PolyVector Analysis of spectra obtained during

helicopter reconnaissance for Chinese tallow tree in the Toledo

Bend Reservoir study area........................................................................................................52

9. Database input to the PolyVector Analysis of preliminary indicator

spectra created from leaf and canopy spectra and Hyperion

reflectance spectra....................................................................................................................54

10. PolyVector Analysis output indicator spectra. Correlations of the

three indicator spectra representing hardwood, seeded indicator,

and hardwood with all input spectra extracted from the Hyperion

reflectance image within the hardwood landcover..............................................................55

11. Summary of mapping results achieved by using various datasets

and combinations of seed spectra aimed at producing a unique set

of indicator spectra for detecting red-leaf Chinese tallow tree occurrences

in the Toledo Bend Reservoir study area................................................................................60

12. PolyVector Analysis input database—helicopter and hyperion spectra..........................64

13. PolyVector Analysis output indicator spectra correlations ................................................65

14. Mapping vegetation likely associated with red-leaf Chinese

tallow tree occurrences ............................................................................................................70
xi

Conversion Factors

Inch/Pound to SI

Multiply By To obtain

Length

inch (in.) 3.94E-8 nanometer (nm)

inch (in.) 2.54 centimeter (cm)

inch (in.) 25.4 millimeter (mm)

foot (ft.) 0.3048 meter (m)

mile (mi) 1.609 kilometer (km)

yard (yd) 0.9144 meter (m)

Area

acre 4,047 square meter (m2)

acre 0.4047 square hectometer (hm2) 

acre 0.004047 square kilometer (km2)

square foot (ft2) 929.0 square centimeter (cm2)

square foot (ft2) 0.09290 square meter (m2)

square inch (in2) 6.452 square centimeter (cm2)

section (640 acres or 1 square mile) 259.0 square hectometer (hm2) 

square mile (mi2) 2.590 square kilometer (km2) 

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F=(1.8×°C)+32

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows:

°C=(°F-32)/1.8

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 

(NAVD 88).

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Remote Sensing Survey of Chinese Tallow Tree in the 

Toledo Bend Reservoir Area, Louisiana and Texas

Abstract

We applied Hyperion sensor satellite data acquired 

by the National Aeronautics and Space Administration’s 

Earth Observing-1 (EO-1) satellite in conjunction with 

reconnaissance surveys to map the occurrences of the 

invasive Chinese tallow tree (Triadica sebifera) in the Toledo 

Bend Reservoir study area of northwestern Louisiana and 

northeastern Texas. The rationale for application of high 

spectral resolution EO-1 Hyperion data was based on the 

successful use of Hyperion data in the mapping of Chinese 

tallow tree in southwestern Louisiana in 2005. In contrast 

to the single Hyperion image used in the 2005 project, more 

than 20 EO-1 Hyperion and Advanced Land Imager (ALI) 

images of the study area were collected in 2009 and 2010 

during the fall senescence when Chinese tallow tree leaves 

turn red. Atmospherically corrected reflectance spectra of 

Hyperion imagery collected at ground and aerial observation 

locations provided the input datasets used in the program for 

spectral discrimination analysis. Discrimination analysis was 

used to identify spectral indicator sets to best explain variance 

contained in the input databases. The expectation was that 

at least one set of Hyperion-based indicator spectra would 

uniquely identify occurrences of red-leaf Chinese tallow 

tree; however, no combination of Hyperion-based reflectance 

datasets produced a unique identifier.

The inability to discover a unique spectral indicator 

resulted primarily from relatively sparse coverage by redleaf Chinese tallow tree within the study area (percentage 

of coverage was less than 5 percent per 30- by 30-meter 

Hyperion pixel). To enhance the performance of the 

spectral discrimination analysis, leaf and canopy spectra 

of Chinese tallow tree were added to the input datasets to 

By Elijah Ramsey III,1 Amina Rangoonwala,2 Terri Bannister,2 and Yukihiro Suzuoki3

guide the indicator selection. In addition, input databases 

were segregated by land class obtained from an ALI-based 

landcover classification in order to reduce the input variance 

and to promote spectral discrimination of red-leaf Chinese 

tallow tree. Although no unique spectral identifier for redleaf Chinese tallow tree was uncovered with these enhanced 

methods, in some cases predicted spatial patterns throughout 

the Hyperion images revealed alignment with vegetation 

associations within each land class that was often observed to 

contain Chinese tallow trees. These instances were associated 

particularly with the addition of helicopter-based spectra to the 

input databases. It was attempted to extend such predictions 

of likely occurrences of Chinese tallow tree by mapping six of 

the nine Hyperion swaths and four of the nine land classes, but 

this attempt produced uncertain results that could not be fully 

evaluated for accuracy. Even though the final mapping showed 

promise in identifying likely Chinese tallow tree occurrences, 

the low percentage of occurrences hindered mapping 

performance and validation. Results of the mapping suggested 

that successful detection of Chinese tallow tree in the study 

area would require a spectral sensor similar to the Hyperion 

but with a higher ground-level spatial resolution. 

Although the Hyperion-based spectral mapping did not 

provide the desired results, the associated field (ground and 

aerial) surveys did provide for a qualitative assessment of 

the overall Chinese tallow tree distribution within the study 

area. Ground and aerial surveys suggested that Chinese 

tallow tree occurrences were uncommon and were without an 

observed pattern in relation to proximity to the Toledo Bend 

Reservoir. Although uncommon and scattered, Chinese tallow 

trees and shrubs most commonly existed along forest edges, 

water edges, and fence lines, probably most in line with seed 

dispersal by birds. Chinese tallow trees were observed to be 

more densely dispersed within some scrublands and grasslands 

than were observed in pine, hardwood, and mixed forests. 

1

U.S. Geological Survey

2

Five Rivers Services, LLC, for the U.S. Geological Survey

3

ASci Corporation, Inc. (McLean, Virginia) for the U.S. Geological Survey
2 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

Introduction

Chinese tallow tree (Triadica sebifera) is a highly 

competitive, invasive tree that quickly takes advantage of 

any opportunity to become established in marsh, forested 

wetlands, agricultural fields, and upland forests (Ramsey and 

others, 2005a). Public and private lands are experiencing 

extensive losses of native habitats (for example, prairies, 

forests) and useable lands (for example, grazing, harvestable). 

Attempts to control Chinese tallow tree infestations are costly 

(The Nature Conservancy, 1998; Westbooks, 1998; U.S. 

Department of Agriculture, 2011), but the absolute costs have 

not been quantified. Based on the historical record, eradication 

of Chinese tallow tree or any other invasive species can 

never be fully successful (DeLoach and Tracy, 1997); this 

evidence emphasizes the need not only to map current 

occurrences but to also detect new occurrences and to monitor 

eradication efforts. 

Through a progression of studies, we have demonstrated 

that Chinese tallow tree can be mapped during fall senescence 

when their red leaves contrast with the matrix of native 

vegetation (Ramsey and others, 2002; Ramsey and Nelson, 

2005; Ramsey and others, 2005a, b). To obtain regional 

mapping coverage at reasonable costs, we used high spectral 

(242 bands in 400 to 2,500 nanometers [nm]) and moderate 

30-meter (m) spatial resolution Hyperion image data acquired 

by the National Aeronautics and Space Administration’s 

(NASA) Earth Observing-1 (EO-1) satellite. The challenge 

was to detect low-subpixel occurrences of Chinese tallow tree 

with Hyperion image data at a 30-m spatial resolution. Using 

specialized processing of the high spectral resolution data 

(restricted to 400 to 1,000 nm) and ground-based datasets, 

we formed a spectral indicator that successfully mapped 

subpixel occurrences of Chinese tallow tree present at the 

time of the Hyperion data collection within all dominant land 

classes (Ramsey and others, 2005a). The indicator spectrum 

for red-leaf Chinese tallow tree explained 78 percent of the 

identified occurrences. Individual confidence limits associated 

with the mapping suggested that red-leaf Chinese tallow tree, 

when occurring at 10 percent of 30- by 30-m pixel area, was 

detected 68 percent of the time and, when occurring at 15 

percent of total land cover, was detected 85 percent of the time 

(Ramsey and others, 2005b). 

Successful mapping of Chinese tallow tree in 

southwestern Louisiana with Hyperion data indicated that 

senescing Chinese tallow tree could perhaps be similarly 

mapped in the lands surrounding the Toledo Bend Reservoir 

project area (farther north in Louisiana) (fig. 1). Although 

dominant land classes differ somewhat between the study 

area in southwestern Louisiana and the current study area (in 

northwestern Louisiana and northeastern Texas), in both areas 

there exist grasslands, pine forests, hardwood forests, and pine 

plantations. In the southwestern Louisiana Chinese tallow tree 

mapping, Chinese tallow tree had been successfully detected 

in all four of these land classes; therefore, we implemented the 

mapping approach that had proven successful for southwestern 

Louisiana to detect and map the occurrences of Chinese tallow 

tree in the lands surrounding the Toledo Bend Reservoir.

Figure 1. Toledo Bend Reservoir study 

area (Louisiana and Texas), as defined by 

the Sabine River Authority (Mark Howard, 

GIS Administrator, Sabine River Authority 

of Texas, written commun., October 2009).

TEXAS LOUISIANA

ARKANSAS

MISSISSIPPI

OKLAHOMA

Toledo Bend Reservoir

study site

0 50 100 KILOMETERS

0 50 100 MILES
Background 3

Objective

The goals of this project were (1) to provide a realistic 

and accurate study to determine the current extent and 

distribution of Chinese tallow tree infestations within the 

Toledo Bend Reservoir project boundary and adjacent lands 

affected by project operations and maintenance, and (2) to 

determine the extent and distribution of Chinese tallow tree 

in the general region surrounding the Toledo Bend Reservoir 

project area (fig. 1).

Background

Mapping Invasive Plants with

Remote Sensing Data

In all remote sensing optical mapping, the ability to 

consistently identify targets as represented in the image is 

related to the spectral contrast of the target feature within 

its surrounding landscape. Increasing the spectral detail of 

the image data promotes the ability to exploit even subtle 

differences in contrast so that the target can be detected 

within a spectrally varied landscape. Our previous mapping of 

Chinese tallow tree in southwestern Louisiana exploited the 

high spectral resolution offered by the Hyperion sensor and 

the stark spectral contrast between senescing Chinese tallow 

tree and the surrounding vegetation. The Chinese tallow tree 

mapping in the Toledo Bend Reservoir area also relied on the 

use of Hyperion data and the senescing Chinese tallow tree 

spectral uniqueness within this particular landscape.

Spectral Contrast with Other Land Classes

During the initial calibration of a remote sensing project 

dealing with detection and mapping of selected targets, 

the spectral contrast of the target within its surroundings is 

estimated. In the terrestrial environment involving plants (for 

example, trees, shrubs, and grasses), spectral contrast would be 

ascertained first at the leaf level and then at the canopy level. 

Spectral Characteristics of Leaves

The spectral properties of individual leaves are first 

inspected because leaves are the dominant control of canopy 

reflectance as imaged by an optical remote-sensing system (fig. 

2). If contrast does not exist at the leaf level between the target 

plant and the other plants immediately around it, detection of 

the target plant is unlikely unless other factors (for example, 

canopy structure or background) promote differentiation of the 

target plant from all other spatially co-occurring plant species. 

If spectral contrast can be assured, the next step is to estimate 

limitations for detecting the target plant at the canopy level.

Leaf reflectance, in percent

Figure 2. Reflectance spectra of leaves belonging to various 

tree species imaged during fall senescence in the Toledo Bend 

Reservoir (Louisiana and Texas) study area. Tree varieties include 

pine (Pinus L. spp.), Chinese tallow tree (Triadica sebifera), 

cypress (Cupressus L. spp.), red maple (Acer rubrum), southern 

red oak (Quercus falcata Michx.), and sweetgum (Liquidambar 

styraciflua). The reflectance spectra were derived from laboratory 

spectral recordings described in Ramsey and others (2005). 

A minimum of three reflectance spectra from each of three 

leaves per tree variety were used in the calculation of mean leaf 

reflectance per tree variety.

Canopy Reflectance Spectra

Canopy reflectance comprises all compositions within 

the instantaneous field of view (IFOV) or image pixel. In 

terrestrial environments, these compositions include the 

plants and the soil background. In canopies where the plants 

form full or nearly full cover (little sunlight reaches the soil 

surface), the background contributes little to the canopy 

reflectance. Excluding the background contribution, the 

canopy reflectance is related to the position and percent 

occurrence of each plant type within the IFOV. If the plant 

occurs in the subcanopy, its contribution to the canopy 

reflectance will be diminished by an amount related to the 

sunlight reduction from the canopy overstory (the amount of 

plant material between the subcanopy and the top of canopy 

[TOC]) to the subcanopy plants. In most cases, plants in 

the subcanopy are not detectable by optical sensors. If a 

plant reaches the TOC, the plant contribution (to the canopy 
4 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

reflectance) will depend on what proportion of the IFOV 

(for example the 30- by 30-m Hyperion pixel) the plant 

(or multiple plants of the same type) makes up the pixel 

composition. In most circumstances, the ability to detect a 

plant having spectrally contrasting leaves depends on the 

proportion (percent occurrence) of that plant observable at the 

TOC within the IFOV (fig. 3). This detection limit is normally 

estimated by field calibrations.

Spatial and Spectral Resolutions of Spatially 

Infrequent and Small Targets

There is a tradeoff between the size of the IFOV and 

the overall sensor coverage. Sensors with higher spatial 

resolutions (and smaller IFOVs) typically image (capture 

spectral data) less extensive ground areas. For instance, a 

sensor with a 1-m IFOV would image a smaller area than 

a sensor with a 30-m IFOV. Coverage differences have 

implications for monitoring and detection. The more spatially 

extensive the coverage, the higher potential for more frequent 

image collections. A higher collection frequency increases 

the possibility of collecting images when and where needed. 

In situations where collection timing is critical (for example, 

during fall senescence), higher frequency collection increases 

the chance for acquiring appropriately timed images. Thus, 

sensors with coarser IFOVs or larger pixels are beneficial 

in monitoring and detection mapping because they allow 

for enhanced collection frequency and regional mapping; 

however, the spatial resolution may not allow detection of 

spatially infrequent and small target occurrences.

High Spectral Resolution Offset of

Coarse Spatial Resolution

One method used to provide subpixel detection (of targets 

smaller than the IFOV; see fig. 4) is to increase the spectral 

resolution. Most resource-mapping sensors operate with a 

limited number (8 to 15) of spectral bands (for example, colorinfrared [CIR] photography relies on three spectral bands). 

In contrast, sensors with high spectral resolution (operating 

with tens to hundreds of spectral bands) can provide 

subpixel detection of targets exhibiting a spectral contrast 

within a landscape. 

In a joint project of the U.S. Geological Survey (USGS) 

and NASA in southwestern Louisiana, TOC Chinese tallow 

trees were detected at subpixel occurrences within the 30-m 

IFOV of the EO-1 Hyperion sensor (Ramsey and others, 

2005b). The Chinese tallow tree was detected successfully 

in bottomland hardwood, pine and cypress-tupelo forests, 

pine plantations, grassland fields, and topographic high 

spots in palustrine to estuarine marshes. Field validations 

of the Hyperion data indicated that Chinese tallow tree 

occurrences (per pixel) were detected correctly 78 percent of 

the time (fig. 5). 

Creation of Accurate Reflectance Images from 

Raw Hyperion Data 

Estimate of the Detection Limit for Percent 

Occurrence 

The degree of accuracy and precision required in the 

reflectance images to be derived from Hyperion data was 

discovered by comparing helicopter-based canopy reflectance 

spectra obtained from two pine stands of similar structure, one 

with a 17 percent occurrence of Chinese tallow trees within 

the area covered and one without any Chinese tallow trees (fig. 

6). The nearly coincident spectra attested to the high similarity 

of the stand structures (maturity, cover, background) wherein 

the small magnitude of deviation in the red wavelength 

region (600 to 700 nm) represented the presence of red-leaf 

Chinese tallow trees. The necessary accuracy of the canopy 

reflectance to detect the minor distinction between the two 

spectra (and thus to detect Chinese tallow tree occurrences) 

was calculated to be plus or minus 1 percent in the visible 

wavelengths (400 to 700 nm) and plus or minus 5 percent in 

the near-infrared wavelengths (here subset at 700 to 940 nm). 

Based on Hyperion reflectance images produced at these high 

Occurrence of target

plant = 10 percent

Hyperion IFOV (pixel)

Figure 3. An idealized representation of a mixed pixel 

composition The box represents an image pixel defined by the 

instantaneous field of view (IFOV) of the Earth Observing-1 

Hyperion sensor. The green color represents the typical top-ofcanopy (TOC) plant composition for the region, and the smaller 

red box represents the occurrence of the target plant having 

spectrally contrasting leaves reaching the TOC. The percent 

occurrence of the target plant is the ratio of the red box area to 

the IFOV.
Background 5

Figure 4. Color-infrared (CIR) photographs of a mixed hardwood 

and pine forest in southwestern Louisiana during fall senescence 

(Ramsey and others, 2002). Green foliage of hardwoods is 

depicted in red, and red foliage of Chinese tallow tree (Triadica 

sebifera), with a fairly high percent occurrence, is depicted 

in yellow. The instantaneous field of view (IFOV) of the CIR 

photography is 1 meter (m). The overlain grid simulates a sensor 

with an IFOV of 25 m (or a 25-m pixel). Although the 1-m CIR 

photography accurately detected the top-of-canopy positioned 

red-leaf Chinese tallow trees, it comprised only a portion of the 

25-m pixel (subpixel cover). 

Figure 5. Correspondence (by percent occurrence in the pixel) 

between field-observed and Hyperion-detected Chinese tallow 

trees (Triadica sebifera) occurring in the top of canopy at 34 field 

sites (Ramsey and others, 2005b). The field sites included all major 

land classes within the imaged area.

Figure 6. Reflectance spectra of two pine (Pinus L. spp) stands, 

one with and one without Chinese tallow trees (Triadica sebifera). 

The spectra were obtained by following methods described 

in Ramsey and others (2005b). Note the difference in spectral 

magnitude in the red wavelengths (600 to 700 nanometers [nm]) 

when Chinese tallow trees were present.
6 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

accuracies, red-leaf Chinese tallow trees were estimated to be 

detected 68 percent of the time when occupying 10 percent 

of the TOC area (or 30- by 30-m pixel) and 85 percent of the 

time when occupying 15 percent of the TOC area (Ramsey 

and others, 2005b). 

Derivation of Canopy Reflectance Image

from Hyperion Data

To achieve an acceptable level of precision and accuracy 

in the canopy reflectance values derived from raw Hyperion 

data, we used an atmospheric correction radiative transfer 

model driven by a nonlinear optimization procedure (Ramsey 

and Nelson, 2005) (fig. 7). The objective of the model 

was to transform the raw Hyperion spectra to reflectance 

spectra representing the intrinsic properties of the canopy 

composition. Results of the atmospheric correction and 

the subsequent normalization of the raw Hyperion data 

provided estimates of canopy reflectance with a 1 percent 

maximum reflectance error in the visible wavelengths 

and a 5 percent maximum reflectance error in the nearinfrared wavelengths (fig. 8). 

Creation of the Indicator Spectrum

for Chinese Tallow Tree 

Creation of an indicator spectrum for Chinese tallow 

tree, along with indicator spectra for all other dominant land 

classes, was accomplished by using the spectral analysis 

program suite, PolyVector Analysis (PVA) (Ramsey and 

others, 2005a). The objective of a PVA program is to identify 

those component spectra that can be linearly combined to 

reproduce all spectra within the input set, in this case, the 

Hyperion reflectance image. Although the physical process 

differs, separation of visible sunlight into its blue, green, and 

red components illustrates the similar objective of PVA (fig. 

9). PVA identifies the minimum and unique set of indicator 

spectra that will reconstruct the spectral variance of the input 

dataset. Once those unique spectra are defined, they can be 

used to calculate the proportion of each identified spectra in 

each unknown composite (for example, sunlight illumination 

spectra). In this study, each pixel in a Hyperion image 

defines an input composite spectrum. As in a previous study 

on detecting Chinese tallow tree in southwestern Louisiana 

(Ramsey and others, 2005a, b), the three component spectra—

red-leaf (senescing) Chinese tallow tree, green vegetation, 

and senescent vegetation (other than Chinese tallow tree)—

identified by PVA were used to determine the relative percent 

occurrences of each component in each Hyperion pixel (fig. 

10). Three maps, one representing the percent occurrence 

of red-leaf Chinese tallow tree, one representing green 

vegetation, and one representing senescent vegetation were 

produced (Ramsey and others, 2005a).

1,200

1,000

800

60

50

40

30

20

10

0

600

400

400 500 600 700 800 900 1,000 1,100

200

EXPLANATION

Downwelling irradiance

Upwelling hyperion canopy radiance

Upwelling helicopter radiance

Downwelling irradiance, W/m2 um

Upwelling radiance, W/m2 sr um

Wavelength, in nanometers

Atmosphere

Figure 7. Downwelling sunlight (irradiance) illuminating the 

forest canopy, upwelling spectra obtained from a raw Hyperion 

image, and canopy reflectance (used for calibration in the 

atmospheric correction of the Hyperion image data) obtained from 

a helicopter platform with a handheld radiometer. The groundbased downwelling irradiance and the helicopter-based upwelling 

recordings were done simultaneously. 

400 500 600 700 800 900 1,000 1,100

Wavelength, in nanometers

0.5

0.4

.03

0.2

0.1

0

Hardwood forest canopy reflectance, in percent

Figure 8. A canopy reflectance spectrum obtained with a handheld 

radiometer from a helicopter platform and a canopy reflectance 

spectrum derived from a raw Hyperion image by using the simultaneous 

downwelling sunlight recordings (fig. 7) and an atmospheric correction 

model. Correspondence between the helicopter-based calibrationvalidation and the atmospherically corrected and normalized Hyperion 

reflectance spectra is extremely close.
Background 7

Figure 9. The characteristic components (indicator spectra) 

of the composite spectrum of visible sunlight wherein the 

characteristic component spectra are the blue, green, and red 

wavelengths in each Hyperion pixel. The prism represents the 

spectral separator mechanism of the PolyVector Analysis (PVA), 

and the indicator spectra represent the senescent tallow, that 

is, red-leaf Chinese tallow trees (Triadica sebifera), along with 

live (green) and dead (senescent) vegetation that comprise the 

canopy within the pixel coverage.

Figure 10. An illustration of how the linear addition of each component or indicator spectrum (red, blue, and yellow) that best 

reproduces the composite spectrum (green) of the Hyperion pixel. The indicator spectra are related to the percent occurrence of

each vegetation class represented in the pixel by using ground observations of canopy compositions and helicopter-based calibration 

spectra. In this case, the combination of indicator spectra (spectral components) for the red-leaf Chinese tallow tree (Triadica sebifera), 

green (live) vegetation, and senescent vegetation define the proportions of each land class in the target pixel.
8 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

Methods

The Strategy for Collecting Hyperion Imagery of 

the Toledo Bend Reservoir Area 

The narrow 7.7-kilometer (km) east-to-west width of the 

Hyperion image swath required the formulation of a collection 

strategy (fig. 11) to accommodate the necessary coverage of 

the Louisiana and Texas sides of the Toledo Bend Reservoir 

(wider than a single-swath coverage) and to obtain the priority 

coverages requested by the Toledo Bend Authority. 

Simultaneously Collecting Hyperion and 

Advanced Land Imager Data

The Hyperion image data were transferred by NASA to 

the USGS as *.hrd and *.L1R files. The data contained 242 

image bands wherein each represented a specific wavelength. 

The Hyperion files were downloaded from a NASA Web 

site, reformatted, and input to the atmospheric correction 

and normalization software. The simultaneously collected 

Advanced Land Imager (ALI) data were downloaded from the 

same NASA site. ALI data files contained nine multispectral 

bands of which six replicated Landsat Thematic Mapper 

bands and three additional bands to enhance the mapping 

performance of the ALI sensor (table 1) (the ALI sensor also 

includes a panchromatic band). The 7.7-km wide swath of the 

Hyperion sensor was located on the most western portion of 

the 37-km wide swath of the ALI image (fig. 12). The more 

spatially extensive ALI swath produced a higher imaging 

frequency of the same location within the project area than did 

the Hyperion swath. 

Creating Hyperion Reflectance Images

The transformation of the raw Hyperion data into canopyreflectance estimates was to follow a previously developed 

successful strategy (Ramsey and Nelson, 2005). That strategy 

used the combined canopy-reflectance validation data derived 

from targeted helicopter-based upwelling and ground-based 

downwelling light measurements for validation of Hyperion 

spectra adjusted by using an atmospheric correction model. 

The result of this procedure was the transformation of raw 

Hyperion image data to reflectance estimates (for example, 

figs. 7 and 8). In this study, however, stabilization difficulties 

and timing issues during the helicopter survey resulted in 

only a limited number of validation-reflectance spectra being 

produced. That limited set of validation-reflectance spectra 

was inadequate for reproducing the previous transformation of 

Hyperion data based on Ramsey and Nelson (2005). In order 

to derive canopy-reflectance estimates from the raw Hyperion 

data, we purchased and applied (with adjustments, alterations, 

and adaptions) the proprietary atmospheric correction 

program, Atmospheric /Topographic Correction for Satellite 

Imagery (ATCOR) (Richter and Schläpfer, 2011).

The first step in producing Hyperion reflectance images 

was to choose an appropriate range of wavelengths from the 

full range of the available bands. Degradation of Hyperion 

spectral bands from 2002 to 2009 necessitated revision 

of spectral templates used in our previously developed 

atmospheric-correction programs (Ramsey and Nelson, 2005). 

In the initial inspections, the short-wavelength bandwidths 

used in 2003 (400 to 450 nm) were not useable. After testing, 

the workable wavelength range extended from 450 to 920 nm, 

which includes most of the visible and about half of the nearinfrared wavelengths (table 2). 

Next, the suite of ATCOR programs had to be adapted 

to the new wavelength ranges. This entailed building new 

wavelength files compatible with the source-code structure 

of ATCOR and implementing these via specialty programs 

into the database files used by ATCOR. Also during the 

initial implementation and calibration of ATCOR, user-input 

operational parameters that provided initial starting points 

were chosen by trial and error via application iterations.

In addition to tailoring the initiation of ATCOR 

via reformation of data inputs and optimization of user 

parameters, more intricate corrections and alterations 

were implemented through direct consultations with 

ATCOR program creators. A number of refinements were 

implemented to the ATCOR code, and subsequent revisions 

were implemented and tested. These corrections enhanced 

ATCOR performance specific to the Hyperion datasets used 

for the Toledo Bend Reservoir study area. The final ATCORbased canopy-reflectance estimates from the raw Hyperion 

data were greatly improved beyond the products created by 

the initial (nonrevised) ATCOR software version. Within 

that improvement process, we found that the Hyperion 

sensor had degraded and inherent signal noise of the sensor 

seemed increased since we previously conducted a Chinese 

Table 1. Spectral band centers of imagery acquired by the 

Advanced Land Imager sensor onboard the Earth Observing-1 

satellite. Imagery was used for mapping occurrences of Chinese 

tallow trees (Triadica sebifera) within the Toledo Bend Reservoir 

study area (Louisiana and Texas).

[ALI, Advanced Land Imager; TM, thematic mapper; nm, nanometers; IFOV, 

instantaneous field of view; m, meter; MS, multispectral]

ALI band Landsat TM band Wavelength (nm) IFOV (m)

Pan 480–690 10 x 10

1 MS-1' 433–453 30 x 30

2 MS - 1 450–515 30 x 30

3 MS - 2 525–605 30 x 30

4 MS - 3 630–690 30 x 30

5 MS - 4 775–805 30 x 30

6 MS - 4' 845–890 30 x 30

7 MS - 5' 1,200–1,300 30 x 30

8 MS - 5 1,550–1,750 30 x 30

9 MS - 7 2,080–2,350 30 x 30
Methods 9

7 7 7

3 5

4

6

1

2

7

7

EXPLANATION

2010 coverage

2009 coverage

Toledo Bend Reservoir study area

K J

I

H

G

F

E

D C B A

0 10 20 KILOMETERS

0 10 20 MILES

Figure 11. Illustration of the strategy for collecting Hyperion imagery for coverage of lands lying adjacent to the Toledo 

Bend Reservoir (Louisiana and Texas). Priority zones progressed from 1 as the highest priority to 7 as the lowest. The 

letters A–K represent the Hyperion image swaths. This graphic represents adjustments that were made to the initial 

coverage strategy because of timing difficulties and orientation misalignment of the sensor.
10 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

EXPLANATION

ALI footprint

Hyperion footprint

0 5 10 KILOMETERS

0 5 10 MILES

Figure 12. Nominal coverage pattern and relative locations of coverage provided by the Hyperion and Advanced Land 

Imager (ALI) sensors onboard the Earth Observing-1 satellite. (Example sensor footprints for October 17, 2009.)
Table 2. The set of wavelengths acquired by the Earth Observing-1 Hyperion sensor that was used to map occurrences of Chinese tallow trees (TriaToledo Bend Reservoir study area (Louisiana and Texas). The 46 bands highlighted in yellow are the final Hyperion spectral bands used in this study. 

Channel Wavelength Wavelength center Channel Wavelength Wavelength center Channel Wavelength Wavelength center Channel 

1 Band-8 0.4268 54 Band-61 0.9629 107 Band-114 1.5581 160 2 Band-9 0.437 55 Band-62 0.973 108 Band-115 1.5682 161 3 Band-10 0.4472 56 Band-63 0.9831 109 Band-116 1.5783 162 4 Band-11 0.4573 57 Band-64 0.9932 110 Band-117 1.5884 163 5 Band-12 0.4675 58 Band-65 1.0033 111 Band-118 1.5985 164 6 Band-13 0.4777 59 Band-66 1.0133 112 Band-119 1.6086 165 7 Band-14 0.4879 60 Band-67 1.0234 113 Band-120 1.6187 166 8 Band-15 0.498 61 Band-68 1.0335 114 Band-121 1.6288 167 9 Band-16 0.5082 62 Band-69 1.0436 115 Band-122 1.6388

10 Band-17 0.5184 63 Band-70 1.0537 116 Band-123 1.6489

11 Band-18 0.5286 64 Band-71 1.0638 117 Band-124 1.659

12 Band-19 0.5387 65 Band-72 1.0739 118 Band-125 1.669113 Band-20 0.5489 66 Band-73 1.084 119 Band-126 1.679214 Band-21 0.5591 67 Band-74 1.0941 120 Band-127 1.689315 Band-22 0.5693 68 Band-75 1.1042 121 Band-128 1.699416 Band-23 0.5795 69 Band-76 1.1142 122 Band-129 1.709517 Band-24 0.5896 70 Band-77 1.1243 123 Band-130 1.719618 Band-25 0.5998 71 Band-78 1.1344 124 Band-131 1.729719 Band-26 0.61 72 Band-79 1.1445 125 Band-132 1.739720 Band-27 0.6202 73 Band-80 1.1546 126 Band-133 1.749821 Band-28 0.6303 74 Band-81 1.1647 127 Band-134 1.759922 Band-29 0.6405 75 Band-82 1.1748 128 Band-135 1.7723 Band-30 0.6507 76 Band-83 1.1849 129 Band-136 1.780124 Band-31 0.6608 77 Band-84 1.195 130 Band-137 1.790225 Band-32 0.671 78 Band-85 1.2051 131 Band-138 1.800326 Band-33 0.6812 79 Band-86 1.2152 132 Band-139 1.981927 Band-34 0.6914 80 Band-87 1.2252 133 Band-140 1.99228 Band-35 0.7016 81 Band-88 1.2353 134 Band-141 2.002129 Band-36 0.7117 82 Band-89 1.2454 135 Band-142 2.032430 Band-37 0.7219 83 Band-90 1.2555 136 Band-143 2.042431 Band-38 0.7321 84 Band-91 1.2656 137 Band-144 2.0524
Channel Wavelength Wavelength center Channel Wavelength Wavelength center Channel Wavelength Wavelength center Channel 

32 Band-39 0.7423 85 Band-92 1.2757 138 Band-145 2.0626 33 Band-40 0.7524 86 Band-93 1.2858 139 Band-146 2.0726 34 Band-41 0.7626 87 Band-94 1.2959 140 Band-147 2.0828 35 Band-42 0.7728 88 Band-95 1.306 141 Band-148 2.0928 36 Band-43 0.7829 89 Band-96 1.3161 142 Band-149 2.1029 37 Band-44 0.7931 90 Band-97 1.3261 143 Band-150 2.113 38 Band-45 0.8033 91 Band-98 1.3362 144 Band-151 2.1231 39 Band-46 0.8135 92 Band-99 1.3463 145 Band-152 2.1332 40 Band-47 0.8237 93 Band-100 1.3563 146 Band-153 2.1433 41 Band-48 0.8338 94 Band-101 1.4269 147 Band-154 2.1533 42 Band-49 0.844 95 Band-102 1.437 148 Band-155 2.1634 43 Band-50 0.8542 96 Band-103 1.4471 149 Band-156 2.1735 44 Band-51 0.8644 97 Band-104 1.4572 150 Band-157 2.1836 45 Band-52 0.8745 98 Band-105 1.4673 151 Band-158 2.1937 46 Band-53 0.8847 99 Band-106 1.4774 152 Band-159 2.2038 47 Band-54 0.8949 100 Band-107 1.4875 153 Band-160 2.2139 48 Band-55 0.905 101 Band-108 1.4976 154 Band-161 2.224 49 Band-56 0.9152 102 Band-109 1.5077 155 Band-162 2.2341 50 Band-57 0.9254 103 Band-110 1.5178 156 Band-163 2.2442 51 Band-58 0.9356 104 Band-111 1.5279 157 Band-164 2.2542 52 Band-59 0.9427 105 Band-112 1.5379 158 Band-165 2.2643 53 Band-60 0.9528 106 Band-113 1.548 159 Band-166 2.2744 Table 2. The set of wavelengths acquired by the Earth Observing-1 Hyperion sensor that was used to map occurrences of Chinese tallow trees (Tria

Toledo Bend Reservoir study area (Louisiana and Texas). The 46 bands highlighted in yellow are the final Hyperion spectral bands used in this study. —
Methods 13

tallow tree mapping project in 2003 (Ramsey and Nelson, 

2005). Although we believe the Hyperion reflectance images 

provide the best obtainable canopy-reflectance estimates, any 

deviation in the derived reflectance products, particularly 

associated with the possible sensor degradations, could not be 

directly estimated.

Producing Digital Orthographic Mosaics

of the Study Area

Digital orthophoto quarter quadrangle (DOQQ) 

mosaics were created to provide a geographic database for 

all Hyperion- and ALI-based image rectifications and for 

visual confirmation of landcover classes used in both the 

Chinese tallow tree mapping and ALI classifications. The 

mosaics were created for 2004, 2009, and 2010 to provide a 

preproject baseline and coverages congruent with Hyperion 

collections (fig. 13). Mosaic coverages were aligned with the 

core project area outlined in fig. 11. Mosaics were created 

with PCI Geomatica software (PCI Geomatics, 1998). The 

collection dates, numbers, and color-combination rendition of 

the photographic frames used to create the individual DOQQs 

are listed in table 3. All other pertinent details concerning the 

DOQQ sources, accuracy criteria, and mosaic construction are 

included in the associated metadata file.

Classifying Land Cover of the Study Area

Purpose of Classification Map

The purpose of creating a classification map of landcover 

was to better link detected occurrences of Chinese tallow tree 

to land classes (for example, pines versus hardwoods) and 

human activities (such as the silviculture cycle), as well as to 

provide a baseline classification of the project area pertinent 

to the time of mapping. In this case, successful detection of 

Chinese tallow tree required identifying a spectral reflectance 

signature uniquely associated with its occurrences. Restricting 

the spectral variability by applying the detection procedure 

per landcover class (versus allowing for the entire spectral 

variance of Chinese tallow tree among all landcover classes) 

would theoretically facilitate the identification of its unique 

spectral reflectance signature. Reduction of input spectral 

variability has been shown to enhance performance of spectral 

discrimination (Ramsey and Laine, 1997). 

Classification of Land Covers on

Advanced Land Imager Data

The ALI sensor offered near simultaneous coverage 

of the entire project area, thus minimizing classification 

nonconformities caused by changes in vegetation phenology. 

This minimization was particularly important because 

the primary collection timeframe for Hyperion data was 

within the transitional late fall to early winter seasons. The 

ALI sensor provided sufficient spectral information for 

discriminating the most prominent land classes. Importantly, 

ALI data were collected simultaneously with Hyperion data 

to ensure absolute conformity between vegetation conditions 

represented in the two types of imagery. 

Classification involved four general steps that followed 

procedures outlined in Ramsey and others (2001; additional 

pertinent classification details are included in the associated 

metadata file).The four steps are as follows: 

1. Classifications were performed separately on the northern 

(November 6, 2009) and southern (November 11, 2009) 

ALI coverages (see fig. 14). 

2. Classification was performed by using the PCI Geomatica 

program suite (PCI Geomatica, 1998). An Iterative Unsupervised Isodata routine was used to create 150 similar 

spectral clusters.

3. The spectral clusters were assigned class memberships 

based on comparisons with reconnaissance data and 

DOQQ mosaics. An enhanced spectral discriminator 

routine was employed to maximize classification performance (Ramsey and others, 2001).

4. The classification did not follow USGS map protocols 

or produce summary class accuracies because it was 

constructed to merely provide necessary information to 

advance the detection of Chinese tallow tree and associate the Chinese tallow tree with landcover classes 

and human activities.

Creating Rectified Advanced Land Imager Images for the 

Landcover Classification

Classifying the ALI images was not a straightforward 

process. Each ALI spectral band (of which there were nine) 

and spectral bank (of which there were four) (table 4) had to 

be separately rectified to the DOQQ mosaics. All bands within 

a bank were misaligned, and the shift between consecutive 

bands was not consistent, as shown in figure 15. A general 

description of the rectification steps is provided below 

(additional pertinent rectification details are entered into the 

associated metadata file):

1. Each band had to be separately rectified, resulting in 

there being 36 separate rectifications per ALI image.

2. After all 36 bands were rectified, the 9 bands common to 

each bank were regrouped. 

3. The four regrouped, orthorectified banks were mosaiced 

to reconstruct the single ALI image. 

4. Complete coverage of the Toledo Bend Reservoir study 

area required two ALI images. Seventy-two separate 

rectifications had to be completed and ALI mosaics 

reconstructed before the landcover classifications could 

be performed.
0 10 20 KILOMETERS

0 10 20 MILES

EXPLANATION

Toledo Bend Reservoir study areaToledo Bend Reservoir

2004 2009 2010

Figure 13. Extents of 2004, 2009, and 2010 digital orthophoto quarter quadrangle mosaics.
Methods 15

Table 3. Characteristics of the digital orthophoto quarter quadrangles spanning the Toledo Bend Reservoir study area (Louisiana and 

Texas) that were used to provide a geographic database for all Hyperion and ALI based image rectifications.

[DOQQ, digital orthophoto quarter quadrangle; m, meter; CIR, color-infrared; NAIP, National Agriculture Imagery Program; TNRIS, Texas Natural Resources 

Information System]

Year Resolution Acquisition Provided as Obtained 

from

Number of 

DOQQ tiles

Louisiana 2004 1 m winter CIR NAIP 76

2009 1 m January True color 76

2010 1 m May–August True color

Texas 2004 1 m December CIR TNRIS 82

2009 0.5 m January True color 93

2010 1 m Leaf-on (entire State observed April–September) True color

0 5 10 KILOMETERS

0 5 10 MILES

Figure 14. False-color composite 

mosaic, covering the Toledo Bend 

Reservoir study area, created by using 

bands 6, 4, and 3 of two Advanced Land 

Imager scenes.
16 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

Table 4. Orthorectification of banks and bands in Advanced Land 

Imager imagery.

[ALI, Advanced Land Imager]

ALI orthorectification process

Raw data Orthorectify Merge Mosaic

Bank 1 Band 1 oBank1 Orthorectified

ALI image

Oct. 17, 2009

Band 2

Band 3

Band 4

Band 5

Band 6

Band 7

Band 8

Band 9

Bank 2 Band 1 oBank2

Band 2

Band 3

Band 4

Band 5

Band 6

Band 7

Band 8

Band 9

Bank 3 Band 1 oBank3

Band 2

Band 3

Band 4

Band 5

Band 6

Band 7

Band 8

Band 9

Bank 4 Band 1 oBank4

Band 2

Band 3

Band 4

Band 5

Band 6

Band 7

Band 8

Band 9

Conducting Field Reconnaissance

The primary objective of the study was to locate Chinese 

tallow tree occurrences in the Toledo Bend Reservoir study 

area. In a previous mapping project of Chinese tallow tree 

in southwestern Louisiana, we found that occurrences were 

not uniformly distributed across the landscape but were 

differentially distributed across land classes and associated 

with agriculture and silviculture activities (Ramsey and others, 

2005b). Based on that previous experience, we conducted 

ground-based (November 5–21, 2009; October 28–31, 

2010; November 5–8, 2010; and December 20–22, 2010) 

and aerial helicopter (November 19, 2009) and fixed-wing 

airplane (November 4–5, 2010) (see table 5) reconnaissance 

of the study area for two purposes. First, the reconnaissance 

provided locations of Chinese tallow tree occurrences and an 

indication of its established spatial pattern and density across 

the landscape. Secondly, it provided familiarity of the land 

class coverages (compositions), their spatial distributions, and 

their structural variability, and it indicated whether Chinese 

tallow tree was selectively associated with certain land classes 

or human activities. In addition, during the reconnaissance 

we attempted to visit all observed Chinese tallow tree sites 

provided by the Toledo Bend Authority (fig. 16).

Follow-up ground-based reconnaissance surveys were 

conducted to add validation sites (based on local knowledge) 

of Chinese tallow tree occurrences and visual observations of 

associated land-class distributions. Aerial surveys were added 

to widen the breadth of ground-based observations and permit 

observations in inaccessible land tracts. The surveys occurred 

within the Hyperion collection time period but not necessarily 

concurrent with any one image collection date. A list of 

ground and aerial surveys by date is included in table 5.

Creating a Hyperlink for Interactive Query of 

Site-Based Reconnaissance Information

All site information compiled during the groundbased, helicopter, and fixed-wing reconnaissance activities 

was entered into a constructed interactive database that 

is available from the Web index page of this report. Each 

location was linked to the information contained in the sitesummary catalogue and is retrievable interactively through a 

hyperlinked map product. The hyperlinked product provides 

a visual presentation of the site picture(s), the geographic 

location, and the nature of the remotely sensed target. 
Band 1 Band 3 Band 5 Band 7 Band 9

FbbLOca 1 ocptbdfgi(cvbr
18 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

Table 5. List of field reconnaissance surveys conducted for 

mapping occurrences of Chinese tallow trees (Triadica sebifera) 

within the Toledo Bend Reservoir study area (Louisiana and 

Texas).

[No., number]

Date No. of 

people Field activities

November 5, 2009 2 Reconnaissance 

November 6, 2009 2 Reconnaissance 

November 7, 2009 2 Reconnaissance 

November 8, 2009 2 Reconnaissance 

November 17, 2009 4 Leaf samples, reconnaissance 

November 18, 2009 4 Leaf samples, reconnaissance 

November 19, 2009 2 Helicopter survey, Reconnaissance 

2 Hemispherical photography, 

downwelling sunlight 

recordings, reconnaissance 

November 20, 2009 4 Leaf samples, reconnaissance 

November 21, 2009 4 Leaf samples, reconnaissance 

October 28, 2010 2 Reconnaissance 

October 29, 2010 2 Reconnaissance 

October 30, 2010 2 Reconnaissance 

October 31, 2010 2 Reconnaissance 

November 4, 2010 3 Fixed-wing plane survey

 Morning

November 5, 2010

3 Fixed-wing plane survey

Afternoon

November 5, 2010

2 Reconnaissance 

November 6, 2010 2 Reconnaissance 

November 7, 2010 2 Reconnaissance 

November 8, 2010 2 Reconnaissance 

December 20, 2010 3 Calibration of oil platforms

December 21, 2010 3 Calibration of oil platforms

December 22, 2010 3 Calibration of oil platforms

Conducting the Helicopter Survey

The objective of the helicopter survey was to 

gather canopy-reflectance spectra for known vegetation 

compositions. In turn, these spectra were used for calibration 

of Hyperion-based spectral indicators of Chinese tallow tree 

and validation of mapping performance (Ramsey and others, 

2005a). The helicopter survey included validation sites 

selected in previously conducted ground surveys.

Collection of useable reflected sunlight (upwelling) 

spectra of selected canopies required an almost stationary 

helicopter platform located at a nearly constant aboveground 

or canopy level. Stability was required so that the handheld 

radiometer used to measure the reflected sunlight (upwelling) 

was kept at a near constant nadir orientation and altitude 

during the collection of up to six replicate spectra at each site. 

Simultaneous to the helicopter-based upwelling collections, 

sun illumination (downwelling) of the surface (or canopy) 

was collected with a fully diffuse and hemispherical stationary 

sensor positioned within the coverage area of the helicopter 

survey (for example, fig. 7). Unfortunately, stabilization 

difficulties during the helicopter survey severely limited the 

useable upwelling spectra, and a delay in the helicopter launch 

time restricted the downwelling collection period. Working 

within those limitations, spectra judged to be acceptable were 

processed following Ramsey and others (2005a). Canopy 

reflectance spectra were calculated from the upwelling to 

downwelling spectral ratio (fig. 8). In essence, the canopy 

reflectance is simply the amount of reflected sunlight from a 

canopy, divided by the amount of sunlight illuminating the 

canopy. By creating the ratio of these two measurements, 

an intrinsic reflectance estimate of the canopy is created. 

All passive sensor systems solely measure the reflected 

sunlight or upwelling light, thus providing only relative 

measures. Intrinsic properties will not vary with changes 

in sun illumination, view orientation (satellite and aircraft 

sensor and target geometry), or atmospheric transparency 

(for example, haze and clouds). An intrinsic property only 

changes if the target changes (for instance, a change in the 

canopy composition). 
Methods 19

0 5 10 MILES

EXPLANATION

Chinese tallow tree occurrences identified by TBA

Field sites visited in 2009

Field-site locations at TBA-identified Chinese tallow tree occurrences

Toledo Bend Reservoir study area

Toledo Bend Reservoir

0 5 10 KILOMETERS

Figure 16. Locations of Chinese tallow tree (Triadica sebifera) occurrences received from personnel of the Toledo Bend 

Authority (TBA) at the project initiation (black circles). 
20 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

Laboratory Measurements of

Leaf Spectral Properties

During the ground-based reconnaissance, we found 

that several tree species and one shrub species exhibited 

similar senescing foliage as did Chinese tallow tree. Because 

successful mapping of Chinese tallow tree occurrences 

depended upon the uniqueness of its red-leaf foliage in the 

landscape, spectral measurements of leaves were obtained 

in order to determine the spectral contrast between those of 

Chinese tallow tree and those of all other co-occurring plants 

within the project area (for example, fig. 2). Small branches 

were collected from Chinese tallow, red maple (Acer rubrum), 

and sweetgum (Liquidambar styraciflua) trees during the 

ground surveys, and these were analyzed within 24 hours. The 

leaf samples included green, yellow, and red leaves and leaves 

exhibiting progressive color changes between these dominant 

colors. Leaf-reflectance spectra were obtained by using a 

handheld radiometer fitted with a diffuse sample sphere. The 

various spectral measurements and replicates were entered 

into postprocessing algorithms that calculated the average 

diffuse reflectance for each leaf. Full details concerning the 

measurement methods and postprocessing analyses may be 

found in Ramsey and others (2005b). 

Nonvegetated Calibration and Validation Targets

Using Nonvegetated Targets for Calibration

Nonvegetated targets were included within the spectral 

calibration dataset in order to provide a more spatially uniform 

and spectrally stable surface than possible with vegetated 

canopies (fig. 17). This increased uniformity and stability 

enhanced the performance of the atmospheric correction 

process, and thereby, the accuracy of the produced canopyreflectance images. The nonvegetated targets consisted 

entirely of oil-well platforms that were predominantly located 

in the northern half of the project area; however, some exist 

in the southern region as well. The existence of oil-well 

platform sites was discovered during the 2010 fixed-wing 

reconnaissance flights (fig. 18). Permission for access to 

the oil-well platforms from Swift Energy Operating, LLC 

(Houston, Tex.) and Chesapeake Operating, Inc. (Oklahoma 

City, Okla.) was granted soon after the completion of the 

fixed-wing reconnaissance. 

Creating Calibration Spectra from

Nonvegetated Sources

We collected 10–20 ground-based upwelling and 

associated downwelling spectra sets at random locations 

within a 60- to 90-m area of each oil-well platform where the 

ground surface within the platform area exhibited fairly good 

composition and weathering uniformity. The ground-surface 

spectra were collected with a handheld radiometer (please 

refer to the “Conducting the Helicopter Survey” section on 

handheld radiometer collection and spectral definition) at a 

height of 1 m above the ground surface with a 15° aperture. 

At that height, the ground spatial area or field of view 

was 26 centimeters in length defining a maximum ground 

field-of-view of 0.068 square meters (m2). Next, four to six 

downwelling sunlight-illumination spectra were collected 

from an 18 percent grey card immediately after the completion 

of the upwelling spectra collection at each location in the 

selected platform area. The grey card was held horizontal to 

the platform ground surface (Ramsey and Jensen, 1995). 

The individual means for the upwelling and downwelling 

spectra at each location were calculated and the errors about 

the means calculated. The mean reflectance spectrum was 

calculated as the ratio of the upwelling and downwelling 

means for each location within the platform, and the error 

about mean was calculated by combining the mean errors for 

the upwelling and downwelling spectra. Finally, the mean and 

propagated error spectra (for each location) were combined 

to produce a calibration mean and error spectra for each 

calibration target site.

Deriving Indicator Spectra

for Chinese Tallow Tree 

Vegetated site locations within the Hyperion reflectance 

images that were to be used for calibration and validation 

of the produced Chinese tallow tree occurrence maps were 

primarily selected from the reconnaissance points. Site 

selections relied on observed locations of Chinese tallow trees 

within each Hyperion image and from land classes defined in 

the classified ALI images. For instance, sites associated with 

the “bottomland hardwood” class would contain both sites 

with observed Chinese tallow trees and sites without Chinese 

tallow trees. Selected reconnaissance sites were first located 

on the DOQQ mosaics and the ALI-based landcover map and 

then on the nonrectified Hyperion reflectance images. The 

nonrectified Hyperion images were used to avoid spectral 

averaging that occurs during the rectification processing. 

Extracting Hyperion Spectral Data

Extracting the composite reflectance spectrum from each 

Hyperion reflectance image was accomplished by using a PCI 

Geomatica procedure (PCI Geomatics, 1998). The number 

of pixels comprising each extracted mean spectrum varied, 

dependent on the vegetation makeup of the targeted site. 

Single-pixel extractions were normally associated with ground 

or helicopter locations. These Hyperion reflectance datasets 

were catalogued by land class and the presence or absence of 

Chinese tallow trees.

Initially all spectra extracted from the Hyperion 

reflectance image were combined in the dataset input to the 

PVA software suite (Ehrlich, 2000). Because of some swathto-swath variability in reflectance estimates, the datasets were 
Methods 21

EXPLANATION

Sites visited for use in calibration

Sites planned for use in calibration

0 5 10 KILOMETERS

0 5 10 MILES

Figure 17. Ground-based field sites visited or planned for use in calibrating atmospherically corrected Hyperion 

reflectance images (only a subset of the planned sites could be visited because of lacking access permissions).
Air_282 Air_566MAir_194 Air_564MA BC D

Figure 18. Oil-well platforms were used as nonvegetated calibration sites (see fig. 17 for platform locations). Oil platforms at different stages length of service. A, fresh gravel; B, in use for unknown period; C, under construction, dirt layer before gravel addition; D, in use for unknown period. The nAir_282, on the photograph indicates the site name, and more details of the sites can be obtained from the interactive query database availablpage of this report.
Results 23

constructed by swath and date. The reflectance dataset was 

entered into a formatted data file and processed with PVA 

software as described in Ramsey and others (2005b). If PVA 

failed to discriminate the Chinese tallow tree from the nonChinese tallow tree sites in the full dataset, input datasets were 

separated by land class or by selected combinations of land 

classes (for example, mixed pines and hardwood stands). 

Creating Indicator Spectra from the HelicopterBased Reflectance Dataset

In addition to calibration and validation spectra 

extracted directly from the Hyperion reflectance images, 

useable canopy-reflectance spectra obtained during the 

helicopter survey were entered into the PVA. The purpose of 

analyzing this dataset was to determine if a well-conditioned 

indicator spectrum for Chinese tallow tree reflectance could 

be identified from the available canopy-reflectance spectra. 

Indicator spectra from the reflectance dataset obtained by 

helicopter were combined with each Hyperion calibration 

and validation dataset. By including the indicator spectra 

derived from the helicopter dataset we hoped to enhance the 

PVA performance. Enhanced performance was judged on 

whether or not sites of known or suspected Chinese tallow 

tree occurrence, and conversely sites known or suspected to 

be without Chinese tallow tree occurrence, were properly 

identified within the PVA results.

Applying PolyVector Analysis-Derived Indicator 

Spectra to the Hyperion Reflectance Image

Promising indicator spectra identified by PVA were 

reformatted as standard ASCII II data files and entered into the 

PCI Geomatica SPUNMIX procedure. SPUNMIX operated 

individually on each pixel in the Hyperion reflectance 

image. Each canopy reflectance spectrum of each pixel was 

apportioned a weighted combination of input indicator spectra 

whose linear combination best replicated the composite 

reflectance spectra or the canopy reflectance spectrum of 

each Hyperion pixel. The resultant weighting of each input 

indicator spectrum (normalized to 100 percent) was mapped to 

the target pixel. At the end of the SPUNMIX application, each 

pixel in the Hyperion reflectance image was transformed to a 

continuous variable reflecting the percent occurrence of each 

indicator reflectance spectrum, and a real image was output 

for each indicator spectra. The real image files output by 

SPUNMIX were rectified to the appropriate DOQQ mosaic.

Overlaying Predicted Chinese Tallow Tree 

Occurrences onto the Landcover Map

The georectified map of Chinese tallow tree occurrences 

output by SPUNMIX was overlain on the ALI-based 

landcover map. The spatial distribution of Chinese tallow 

tree occurrences produced by the SPUNMIX program was 

examined for consistency with known and likely Chinese 

tallow tree occurrences.

Results

Hyperion and Advanced Land Imager Coverages

Hyperion Coverages

Hyperion images covering the project area were 

collected in 2009 (22 images), 2010 (19 images), and 2011 (10 

images) (table 6). The 2011 collections were in late winter 

(January and February). Of these collections, the useable 

Hyperion images are identified by color shading in figure 19. 

“Useable” refers to images that met the following criteria: 

(1) aligned with the swath-template pattern of orientation 

from northeast to southwestern and (2) free of cloud and 

cloud shadow contamination. Not listed in table 6 are those 

Hyperion collections that were scheduled but not collected 

because of scheduling difficulties and priority overrides. 

In total, there were 29 useable Hyperion images 

collected from 2009 to 2011. Stratified by coverage swath 

(fig. 19), the majority of useable Hyperion image collections 

were concentrated in the highest priority swaths identified 

by Toledo Bend Reservoir personnel. Because detection is 

based on the green-to-red color change of Chinese tallow 

tree senescing leaves, the Hyperion image collections that 

occurred from October through December were the most 

important with respect to detection. In total, there were 20 

useable Hyperion images collected within the time frame of 

senescence, with frequencies per swath ranging from 1 to 5. 

Hyperion images collected within the highest priority swaths 

during fall to early winter senescence were the first processed. 

Advanced Land Imager Coverages

Only useable ALI images were used in the calculation 

of coverage frequency per swath, with “useable” defined 

as it was in relation to the Hyperion imagery. The useable 

ALI images matched the dates of the useable Hyperion 

images (table 6). The highest frequency of ALI coverage 

was centered on the southern portion of the project area. 

Similarly, the highest Hyperion coverages were centered over 

the southern section; however, a single swath in the northern 

section of the Toledo Bend Reservoir also had high coverage. 

This slight geographic discrepancy in coverage frequency 

between the Hyperion and ALI sensors was because of the 

alignment of the Hyperion swath on the western edge of the 

ALI coverage as part of the simultaneous collections of the 

Hyperion and ALI sensors. 
24 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

Table 6. List of image collections by the Advanced Land Imager and Hyperion sensors aboard the Earth Observing-1 satellite that were 

used for mapping Chinese tallow tree (Triadica sebifera) in the Toledo Bend Reservoir study area (Louisiana and Texas). The light grey 

shades on the table show unusable images with high cloud cover; the dark grey shades show satellite data not acquired by the National 

Aeronautics and Space Administration (NASA).

[ID, identification; ALI, Advanced Land Imager; %, percent]

Month Day Image ID (ALI) Cloud Hyperion swath1 ALI2 Hyperion3

2009

October 14 EO10240382009287110 80–89% 1D Cloudy Cloudy

17 EO10240382009290110 0–9 % 1D Clear Clear

19 EO10250382009292110 0–9 % 3H Clear Clear

27 EO10240382009300110 90–100 % 2C Cloudy Cloudy

November 1 Cancelled by NASA 0–9 % 2C

4 EO10240382009308110 0–9 % 2C Clear Clear

6 EO10250382009310110 0–9 % 3H Clear Clear

11 EO10240382009315110 0–9 % 1D Clear Clear

14 EO10250382009318110 70–79% 4F Partly cloudy Partly cloudy

17 EO10250382009321110 60–69% 3H Cloudy Cloudy

19 EO10250382009323110 20–29% 5G Partly cloudy Partly cloudy

22 EO10250382009326110 90–100% 4F Cloudy Cloudy

24 EO10240382009328110 90–100% 1D Cloudy Cloudy

27 Cancelled by NASA 10–19% 1D

December 2 EO10240382009336110 90–100% 1D Cloudy Cloudy

5 EO10250382009339110 0–9 % 3H Clear Clear

7 EO10240382009341110 90–100% 1D Cloudy Cloudy

10 EO10250382009344110 50–59% 7I Partly cloudy Partly cloudy

12 EO10240382009346110 90–100% 1D Cloudy Cloudy

15 EO10240382009349110 90–100% 1D Cloudy Cloudy

18 EO10250382009352110 0–9 % 7I Clear Clear

20 EO10240382009354110 0–9 % 1D Clear Clear

23 EO10240382009357110 90–100% 2C Cloudy Cloudy

25 EO10250382009359110 0–9% 6E Clear Clear

2010

January 2 Cancelled by NASA 0–9% 2C

5 EO10250382010005110 0–9% 4F Clear Clear

7 EO10250382010007110 90–100% 5G Cloudy Cloudy

10 EO10240382010010110 0–9% 2C Clear Clear

12 EO10250382010012110 0–9% 5G Clear Clear

15 Cancelled by NASA 90–100% 1D Cloudy Cloudy

17 EO10240382010017110 60–69% 1D Cloudy Cloudy

23 EO10250382010023110 90–100% 3H Cloudy Cloudy

25 EO10240382010025110 0–9% 1D Clear Clear

October 8 EO10250382010281110 0–9% 6E Clear Clear

13 EO10240382010286110 0–9% 1D Clear Clear

15 EO10240382010288110 0–9% 2C Clear Clear

18 EO10250382010291110 20–29% 4F Partly cloudy Partly cloudy

23 Cancelled by NASA 5G
Methods 25

Month Day Image ID (ALI) Cloud Hyperion swath1 ALI2 Hyperion3

November 5 EO10240382010309110 0–9% 1D Clear Clear

10 EO10250382010314110 20–29% 3H Partly cloudy cloudy

18 EO10250382010322110 70–79% 3H Cloudy Cloudy

26 EO10240382010330110 90–100% 7B Cloudy Cloudy

28 EO10250382010332110 10–19% 3H Partly cloudy Partly cloudy

December 1 EO10240382010335110 0–9% 2C Clear Clear

19 EO10240382010353110 0–9% 7B Clear Clear

24 EO10250382010358110 40–49% 6E Partly cloudy Partly cloudy

2011

January 1 EO10250382011001110 90–100% 5G Cloudy Cloudy

6 EO10250382011006110 0–9% 5G Clear Clear

11 EO10250382011011110 0–9% 6E Clear Clear

19 EO10240382011019110 70–80% 1D Cloudy Cloudy

27 EO10240382011027110 0–9% 1D Clear Clear

February 6 EO10250382011037110KF 0–9% 4F Clear Clear

March 1 EO1A0250382011060110KF 0–9% 4F Clear Clear

9 EO1A0250382011068110KF 30–39% 4F Partly cloudy Partly cloudy

12 EO1A0250382011071110KF 80–89% 6E Cloudy Cloudy

27 EO1A0250382011086110 90–100% 6E Cloudy Cloudy

1

Hyperion swath location shown on figure 11.

2

Advanced Land Imager (ALI) provides image data from nine spectral bands and one panchromatic band (band designations). The instrument operates in a 

pushbroom fashion, with a spatial resolution of 30 meters for the multispectral bands and 10 meters for the panchromatic band. The standard scene width is 37 

kilometers. Standard scene length is 42 kilometers, with an optional increased scene length of 185 kilometers.

3

Hyperion collects 220 unique spectral channels ranging from 0.357 to 2.576 micrometers with a 10 nanometer bandwidth. The instrument operates in a 

pushbroom fashion, with a spatial resolution of 30 meters for all bands. The standard scene width is 7.7 kilometers. Standard scene length is 42 kilometers, with 

an optional increased scene length of 185 kilometers. U.S. Geological Survey web site http://edcsns17.cr.usgs.gov/eo1/lookAngles.php.

Table 6. List of image collections by the Advanced Land Imager and Hyperion sensors aboard the Earth Observing-1 satellite that were 

used for mapping Chinese tallow tree (Triadica sebifera) in the Toledo Bend Reservoir study area (Louisiana and Texas). The light grey 

shades on the table show unusable images with high cloud cover; the dark grey shades show satellite data not acquired by the National 

Aeronautics and Space Administration (NASA).—Continued

[ID, identification; ALI, Advanced Land Imager; %, percent]
26 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

0 10 20 KILOMETERS

0 10 20 MILES

EXPLANATION

One Hyperion and ALI useable image per swath within October–December 

Three Hyperion and ALI useable images per swath within October–December 

Five Hyperion and ALI useable images per swath within October–December 

Figure 19. The number of useable or nearly cloud-free Hyperion images collected per swath during the fall senescent 

period (October–December) in 2009 to 2010 (see fig. 11 for swath locations and naming conventions).
Results 27

Field Reconnaissance in Support of Chinese 

Tallow Tree Mapping

Ground-Based Field Reconnaissance

Ground-based field reconnaissance occurred during 5 time 

periods from 2009 through 2010 at 226 locations throughout 

the Toledo Bend Reservoir study area (table 5, fig. 20). 

Ground-based observations were restricted to accessible lands 

tracts. This restriction resulted in observations emphasizing 

edges mainly associated with public roadways, power lines, 

and areas of transition in forest-stand structure (primarily 

related to an abrupt transition from planted pine or scrubland 

to mature forest). There were excursions made into the forested 

areas; however, the overlapped timing of Chinese tallow tree 

senescence and hunting seasons severely limited these forays. 

Where agriculture lands (mostly grasslands for grazing), pine 

plantations, or scrublands occurred along the roadways, these 

were observed from the fence line. Observations of low-density 

development were also emphasized because of accessibility; 

however, single residences reachable by a private roadway 

typically were not visited. Public facilities were included in 

the survey (for example, campgrounds and boat launches). 

These public locations were where most shoreline observations 

of the Toledo Bend Reservoir were made. Even though the 

reconnaissance was not without significant limitations, the 

ground-based observations provided a sense of the magnitude 

and distribution of Chinese tallow tree occurrences in 

the study area.

Observations from Fixed-Wing Aircraft

During fixed-wing aircraft reconnaissance, which 

occurred on November 4 and 5, 2010, natural-color 

photographic data were collected at 547 vegetated locations 

throughout the project area (table 5, fig. 21). All photographs 

were oblique. In all but a few cases, the nominal direction 

of the target in the photography was recorded, as well as a 

general description of the target. To provide a more meaningful 

fixed-wing observational dataset, the recorded aircraft altitude 

and nadir position and estimated view angle were used to 

calculate the ground distance to the intended target feature in 

each photograph. In cases where the photographic data was 

judged to be most helpful in encapsulating the information 

derivable from the fixed-wing photography, the nadir positions 

of the aircraft were adjusted to more correctly locate the target 

feature in the photographs. In the photographic archive, an 

“m” was appended to photographs where target locations 

were repositioned in the coverage maps. Even though the 

fixed-wing reconnaissance allowed for more thorough 

coverage of the study area, it did not allow identification of 

herbaceous vegetation except into broad categories such as 

pine and hardwood and a nondescript shrub category. As was 

also the case in the leaf-reflectance results, differentiation 

between similarly colored leaves (red or green) of red maple, 

sweetgum, and Chinese tallow trees was unattainable. Poison 

sumac (Toxicodendron vernix), an herbaceous shrub, may have 

also been included in observations of red foliage. Because 

red leaves of the three species could not be distinguished, 

aerial observations were aggregated into a general 

“red trees” category.

At some sites where red trees were directly observed 

during the flight, the associated photography did not 

substantiate those direct observations. Where direct 

observations during the flight and (or) results (specific or 

extrapolated) of the previously conducted ground surveys 

indicated possible occurrences of Chinese tallow tree (within 

the “red tree” class), a category of “possible” occurrence 

was introduced into the mapped results of the fixed-wing 

aerial survey. 

In the fixed-wing aerial reconnaissance senescing 

hardwoods exhibited foliage tones that seemed, for the 

most part, visually distinguishable from those of red trees. 

Understory red trees in mature forests were at times observable, 

but these occurrences were mainly limited to forest gaps or 

instances when observations were made from a nearly vertical 

stance relative to the target (fig. 22A, B). Subcanopy red shrubs 

and trees were most often observed at edges of transition from 

pine plantation or scrubland to mature forest. For the most part, 

these edge-positioned subcanopy occurrences were associated 

with elongated and narrow hardwood stands surrounded 

by a pine plantation (fig. 23A, B, and C). Photographs were 

also collected of land classes that did not include red trees. 

These photographs were obtained to document the various 

land classes in the project area; however, photography was 

predominantly taken when red trees were observed. 

Helicopter-Based Observations

In total, vegetation compositions were documented 

at 12 sites during the helicopter flight on November 19, 

2009 (fig. 24). This was the only survey during which nearnadir photographs were collected. This helicopter-based 

aerial photography provided the only near nadir views of 

the compositions of various canopies, including Chinese 

tallow tree occurrences, thus supplementing ground-based 

observations. In addition, the near nadir sampling simulated 

the imaging geometries of the Hyperion and ALI sensors, 

albeit at a much higher spatial resolution (figs. 24 A, B). 

The helicopter sites included mature and planted pine 

forests, mixed hardwood stands, and open water. Sites 

with and without Chinese tallow tree were documented. 

Helicopter photographic and ground-based canopy 

composition observations were grouped within the ground 

observation category.

Spectral Properties of Leaves

Between November 17–21, 2009, 36 leaf samples 

were collected at 20 sites, including 15 sweetgum, 13 red 
28 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

EXPLANATION

Chinese tallow tree observed

Chinese tallow tree not observed

0 5 10 KILOMETERS

0 5 10 MILES

Figure 20. Locations of 226 field sites surveyed during ground reconnaissance for Chinese tallow trees (Triadica 

sebifera) in the Toledo Bend Reservoir study area (Louisiana and Texas). The sites indicated in yellow on the map are 

those where Chinese tallow tree was observed and the sites marked with green where Chinese tallow tree was not 

observed. 
Results 29

0 5 10 KILOMETERS

0 5 10 MILES

EXPLANATION

Field-site locations

Area where red trees were observed

Area where red trees were observed with

 possible occurances of Chinese tallow trees

Areas where red trees were not observed

Figure 21. Results of fixed-wing aerial reconnaissance for Chinese tallow trees (Triadica sebifera) in the Toledo Bend 

Reservoir study area (Louisiana and Texas). A general category of “red trees” is used because red foliage of multiple 

trees and shrubs were indistinguishable. Where direct observations during the flight and (or) results of a previously 

conducted ground survey indicated possible occurrences of Chinese tallow tree (within the red tree class), a category 

of “possible Chinese tallow tree occurrence” was introduced into the mapped results of the fixed-wing aerial survey.
Figantain fotretrethreD, trepiof naexsitththavof 

Air_247 2009_5092009_467 2009_469A

B

C

D
Results 31

Air_370

Air_244 Air_158

Air_378

Air_1712

F E

D C

A B

Air_378

Figure 23. Aerial view of red trees in hardwood forests. A, B, C, Red trees and shrubs occupying the understory of hardwood forests 

(hardwood fingers). D, Red trees in a canopy gap of a hardwood, or possibly a mixed hardwood forest and pine stand. E, Illustrates the 

high spectral mixing of the hardwood fingers in the surrounding pine, or mixed pine and hardwood forest. F, A clear depiction of the 

hardwood fingers intertwined in a young pine plantation and recently clear cut area.
32 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

HS5

HS7_M HS15_M

HS6_M

HS1_M HS2_M

HS3_M HS4_M

A

B

Figure 24. Photographs collected during the helicopter survey of the Toledo Bend Reservoir study area (Louisiana and Texas). A, 

Includes two young pine plantations with and without Chinese tallow trees, and a mixed pine and hardwood forest with and without 

Chinese tallow trees. B, Chinese tallow trees observed in canopy gaps and edge of a mixed pine and hardwood forest. The names on 

the photograph, for example, HS1_M, indicate the site name, and more details of the sites can be obtained from the interactive query 

database available at the Web index page of this report.
Results 33

maple, and 8 Chinese tallow tree. A mean transmittance and 

reflectance spectrum was obtained for each leaf sample. Figure 

25 shows an example of the green, yellow, and red leaves of 

Chinese tallow, red maple, and sweetgum trees. 

Green, red, and yellow Chinese tallow tree leaves 

spatially co-occurred within the project area. As seen in 

figure 26, most often the reflectance from red and yellow 

leaves contrast sharply with green Chinese tallow tree leaves. 

Reflectance spectra associated with red-leaf Chinese tallow 

tree, red-leaf sweetgum, and red-leaf red maple, however, 

were highly similar (fig.27). 

Reflectance Spectra Used for Calibration and 

Validation of Hyperion Reflectance Images

Non-Vegetated Reflectance Calibration

and Validation Targets

Mean reflectance spectra obtained from measurements at 

seven oil platforms exhibited similar shapes but high spectral 

variability (fig. 28). Not only did spectral magnitudes vary 

highly from site to site, but also the spectra of about half the 

calibration target sites exhibited fairly high internal variance, 

thus indicating that spectrally variable materials comprised 

the oil platform surface. Because of the significant variance 

from site to site, reflectance results from the oil platform sites 

Figure 25. Leaf samples from Chinese tallow (Triadica sebifera), 

red maple (Acer rubrum), and sweetgum (Liquidambar styraciflua)

trees in transition from live (green) to senescence.

Tallow leaf reflectance, in percent

0.5

0.4

0.3

0.2

0.1

0

400 500 600 700 800 900 1,000

Wavelength, in nanometers

EXPLANATION

Green-leaf Chinese tallow tree

Red-leaf Chinese tallow tree

Yellow-leaf Chinese tallow tree

Figure 26. Representative reflectance spectra of green, red, and 

yellow Chinese tallow tree (Triadica sebifera) leaves collected in 

the Toledo Bend Reservoir study area. Tallow leaf reflectance, in percent

0.5

0.4

0.3

0.2

0.1

0

400 500 600 700 800 900 1,000

Wavelength, in nanometers

EXPLANATION

Green-leaf Chinese tallow tree

Red-leaf Chinese tallow tree

Yellow-leaf Chinese tallow tree

Green-leaf red maple tree

Red-leaf red maple tree

Yellow-leaf red maple tree

Orange-leaf red maple tree

Green-leaf sweetgum tree

Red-leaf sweetgum tree

Yellow-leaf sweetgum tree

Figure 27. Representative reflectance spectra of green, red, and 

yellow leaves from Chinese tallow (Triadica sebifera) and sweetgum 

(Liquidambar styraciflua) trees and green, orange, red, and yellow 

leaves spectra from red maple (Acer rubrum) trees in the Toledo 

Bend Reservoir study area. Notice the high spectral overlap 

between leaves of the same color among the three species.
34 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

were not useful for calibration and validation of the calculated 

reflectance data obtained from the Hyperion sensor.

Vegetated Reflectance Calibration and Validation Targets

The helicopter-based collections of upwelling and 

simultaneous ground-based downwelling recordings for 

creation of canopy reflectance spectra of selected calibration 

and validation sites within the project area were undertaken on 

November 19, 2009. The vegetated sites consisted of young 

pine plantations, mature pine stands, grasslands, scrub-shrub 

stands, and hardwood and mixed forest stands each with and 

without Chinese tallow trees. Because of logistical problems, 

the helicopter carrying the radiometers to record the upwelling 

reflected sunlight from the target sites was launched late. 

The lateness of the launch and problems in platform stability 

severely limited the number and quality of canopy reflectance 

spectra obtained. Because of these limitations, the reflectance 

spectra calculated from the helicopter and simultaneous 

ground recordings were largely unusable for calibration of the 

atmospheric correction of the Hyperion data and validation of 

the Hyperion reflectance image products (following Ramsey 

and Nelson, 2005). Instead, ATCOR software was used to 

provide the necessary transformation of the Hyperion image 

data to canopy-reflectance estimates.

Field-Based Landcover Classes and

Features Associated with Chinese

Tallow Tree and Red Trees

One objective of the ground and aerial surveys was to 

provide a spatial context for describing the occurrences of 

Chinese tallow trees within the Toledo Bend Reservoir study 

area. The most conducive description included associations 

by land class (comprising structure) and land features. In 

the end, the land classes and features found to best describe 

Chinese tallow tree occurrence closely duplicated (but without 

marshes) those used in the previous study of mapping Chinese 

tallow tree occurrences in southwestern Louisiana (Ramsey 

and others, 2005a). The land classes and feature classes 

distinguishable in the study area included the following: (1) 

pine forest; (2) hardwood forest; (3) mixed pine and hardwood 

forest; (4) planted pine (not including mature planted pine); 

(5) cypress forest (including all observed swamps); (6) 

scrublands (from clear-cut regrowth to dense mixtures of 

shrubs and trees of different varieties); (7) grassland (grazing 

and fallow fields); (8) bare surfaces that included mudflats, 

development, and clear cuts (associated with logging and 

containing minimal vegetation); (9) water (along lakes, ponds, 

or stream shorelines); (10) fence line; and (11) edge (an abrupt 

change in land class). 

Observations of Chinese tallow trees during the ground 

surveys or red trees (including possible occurrences of 

Chinese tallow tree) during the fixed-wing aerial survey 

were associated with land classes and features that are 

described with accompanying photography (appendix 1). 

The organization of the photography emphasizes reoccurring 

classes and features in the landscape and how the Chinese 

tallow tree or red tree classes were associated with them. At 

most observation locations, multiple land classes and features 

were captured within the associated photography; however, 

the photograph titles define only the most meaningful single 

class or feature at each location. For instance, clear cuts, 

hardwoods, and planted pine classes may be all represented 

at a single location, but hardwood stands contained Chinese 

tallow trees or red trees. In that case, the class definition 

would be hardwood. For ease of reference, a separate map 

was created to show locations of all observations of Chinese 

tallow trees and red trees (including possible occurrences of 

Chinese tallow trees) during the fixed-wing aerial survey and 

ground-based reconnaissance (fig. 29). 

Land Features

Chinese Tallow Trees on Edges

Although edges of landcover transition are not included 

in any land class, occurrences of Chinese tallow trees 

(or red trees in fixed-wing aerial reconnaissance) were 

often observed along such edges. During the ground and 

aerial surveys, occurrences were commonly observed in 

Surface reflectance, in percent

0.3

0.25

0.2

0.15

0.1

0.05

0

400 500 600 700 800 900 1,000

Wavelength, in nanometers

Figure 28. Mean reflectance spectra of the ground surface 

within oil-platform sites (C100–C107) in the Toledo Bend Reservoir 

study area. (See fig. 17 for locations and fig. 18 for fixed-wing 

photography of oil platforms at different stages of development.) 

Although the spectral variability about each spectrum 

characterizing the mean site reflectance was acceptable, the 

spectral variance between sites (amplitude differences among the 

reflectance spectra) was high. 
Results 35

association with shrubs and trees in the subcanopy at the 

edge of forests adjacent to pine plantations, scrublands, 

roadways, clearings, and grasslands (fig. 30 A–H). As 

documented in the mapping of Chinese tallow tree 

occurrences in southwestern Louisiana, also observed during 

the current mapping project was that the abrupt edges were 

conducive to the establishment of Chinese tallow tree. Even 

though Chinese tallow or red tree sightings were more 

frequently observed on edges than other landscape features, 

sightings were still uncommon overall. Additionally, the 

Chinese tallow trees that were observed often retained a 

partial or full canopy of green leaves. 

Chinese Tallow Tree at Water Edges

Chinese tallow trees occurred sporadically along ponds, 

streams, and the Toledo Bend Reservoir shoreline. Although 

they were found in locations adjacent to water, they most 

often occurred along obstructions near water bodies, such 

as a roadway guard rails, fences, or shrub lines. Although 

uncommon, as depicted in fig. 30H, Chinese tallow trees 

were at times even found in standing water. 

Chinese Tallow Trees Along Fences

Chinese tallow trees and shrubs were most common 

along fence lines (fig. 31 A–D; but again, the qualification of 

“common” should be considered in the context of a generally 

uncommonly occurring feature).

Land Classes

Pine Plantations

In a previous project to map Chinese tallow tree, we 

found that shrub-pine plantations were susceptible to its 

establishment (Ramsey and others, 2005a). During the 

current mapping project, pine plantations were ubiquitous 

throughout the project area, as were clear-cut areas 

signifying ongoing silviculture (figs. 23F and 32A, B); 

however, in contrast to clear-cut areas, the pine plantation 

class varies widely in growth age and growth structure (or 

the patterns of canopy structure). It appeared that different 

methods were used to plant the pine seedlings, which 

resulted (with growth) in variable canopy structure patterns. 

During the ground-based survey, we found that only 

one of the eight young plantation sites visited contained any 

Chinese tallow trees. During the fixed-wing aerial survey, 

red trees (including but not limited to Chinese tallow trees) 

were observed on 17 of 56 pine plantations, and these 

occurrences were for the most part located within nearby 

hardwood fingers protruding into the plantation. In contrast, 

two seemingly poorly managed pine plantations contained a 

fair amount of red trees, similar to occurrences documented 

in our previous mapping project for southwestern 

Louisiana (fig. 33A, B).

Mature Pine Forest

Mature pine forests included both nonplanted and 

planted pine forests (fig. 22A–D). The canopy structure 

or openness of the nonplanted and planted pine canopies 

appeared to differ, and as surmised in a previous Chinese 

tallow tree mapping project (Ramsey and others, 2005a), 

canopy gaps provide opportunities for Chinese tallow tree 

establishment. Even though distinction of nonplanted and 

planted mature pine stands was desirable, the spectral 

contrast between naturally occurring and planted mature 

pines was not distinct. As a result of their non-spectral 

separability, the nonplanted and planted mature pine forests 

were both included in the mature pine class. Of the forest 

classes, mature pine stands were associated with the highest 

frequency of documented observations of Chinese tallow 

trees during the ground survey and were also associated 

with one of highest observation rates of red trees (including 

Chinese tallow trees) during the fixed-wing aerial survey. 

Observations of red trees during the fixed-wing aerial survey 

were most often of understory red-leafed trees or shrubs 

that were observed from a nearly vertical orientation in 

relation to the target. As figures 22A and B suggest, however, 

opportunistic establishment within the gaps and open canopy 

of the mature pine forests is not limited to Chinese tallow 

trees. In addition, in instances where a pine forest abruptly 

adjoined a scrubland or pine plantation, red trees were at 

times observed at the forest edge during the fixed-wing aerial 

reconnaissance (figs. 30A, B).

Hardwood Forests

Hardwoods and bottomland hardwoods were combined 

into the same land class because these forests often contain 

similar associations of tree species. The difference in 

class designation represents a difference in inundation 

frequency and duration, but spectral distinction of the upland 

(hardwood) and wetland (bottomland hardwood) classes is 

often not possible. 

Hardwood forests were associated with the second 

highest frequency (after pine forests) of Chinese tallow 

tree (ground survey) or red-tree (fixed-wing aerial survey) 

observations. As in pine forests, red trees in hardwood 

forests occurred most often in canopy gaps or where the 

canopy abutted a pine plantation, grassland, or scrubland (fig. 

23D), and often only one or two red trees in the subcanopy 

were observed at any given location. 

Hardwood Fingers

Although included in the hardwood forest land class, 

hardwood fingers were separately described because of 

their variable stand structure, particularly in relation to 

the length of exposed edges. During the fixed-wing aerial 

survey, red trees and shrubs were most often observed in the 

subcanopy of hardwood forests, particularly within narrow 

hardwood fingers. As illustrated in figure 23A, B, and C, 
36 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

EXPLANATION

Red trees (fixed-wing aerial survey)

Possible Chinese tallow trees (fixed-wing aerial survey)

Chinese tallow trees (ground survey)

0 5 10 KILOMETERS

0 5 10 MILES

Figure 29. All field sites where Chinese tallow trees and red trees were observed during ground-based and fixed-wing 

aerial reconnaissance in the Toledo Bend Reservoir study area (Louisiana and Texas). For results of the aerial survey, a 

general category of “red trees” is used because red foliage of multiple trees and shrubs were indistinguishable. Where 

direct observations during the flight and (or) results of the ground survey indicated possible occurrences of Chinese tallow 

trees (within the red tree class), a category of “possible Chinese tallow tree occurrence” was introduced into the mapped 

results of the fixed-wing aerial survey. 
Results 37

Air_112 A Air_250 B

2009_449

C

Oct10_341M

E

2009_434

G

Air_112 Air_250

2009_481

H

2009_T7M

D

2009_489

F

Figure 30. Chinese tallow trees (Triadica sebifera), or red trees and shrubs, observed during the ground survey, and red trees 

observed during the aerial survey in locations along forest edges. A, Red shrubs located along the edge of a young pine plantation. B, 

Scrubland in the understory of a mature pine forest. (Insets magnify the understory red shrubs.) A small patch of red shrubs located in 

the scrubland are pinpointed. C, D, Red- and green-leafed Chinese tallow trees along edges of fields. E, Green-leafed Chinese tallow 

tree shrubs along the edge of a power line clearing through a pine savannah. F, Red- and green-leafed Chinese tallow trees along a 

roadside. G, Chinese tallow tree shrubs along the edge of grasslands. H, Green-leafed Chinese tallow trees in a stream.
2009_410M 2009_471M

Oct10_352M 2009_T8M

A B

C D

Figure 31. Red- and green-leafed Chinese tallow trees and shrubs (Triadica sebifera) occurring along fences (A, B, C, D). The names on the phot2009_410M, indicate the site names, and more details of the sites can be obtained from the interactive query database available at the Web index 
Results 39

Air_133

Air_585M Air_211

Air_47 AirP_266

AirP_240

A B

Figure 32. Pine plantations (A, B) at various stages of growth and with variable structural patterns were observed during the 

reconnaissance surveys. The interest in pine plantations was due to proclivity for establishment of Chinese tallow trees (Triadica sebifera) 

within this land class. In the Toledo Bend Reservoir project area, only minor occurrences of Chinese tallow trees were observed, even in 

the nonreplanted clear cuts. In at least two cases, however, the establishment pattern of Chinese tallow trees imitated that observed in 

pine plantations of southwestern Louisiana (fig. 33 B). These occurrences appeared where maintenance of the planted pine appeared to 

be lacking. The names on the photographs, for example, Air_585M, indicate the site names, and more details of the sites can be obtained 

from the interactive query database available at the Web index page of this report.
40 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

HS15_M AirP_223M

A B

Figure 33. Red-leaf Chinese tallow trees (Triadica sebifera) occurring in pine plantations. A, Photograph taken during the helicopter 

aerial survey. B, Photograph taken during the fixed-wing aerial survey.

hardwood fingers penetrating into regrowing clear-cut areas 

or scrublands or into pine plantations provided an even 

higher percentage of forest to shrub edges in relation to stand 

area (fig. 30A). Although not a statistical survey, in our aerial 

observations, only scrublands were associated with a higher 

occurrence of red trees than were hardwood fingers. This 

same association was not prevalent where hardwood fingers 

were interlaced within mature pine forests (fig. 23E and F). 

In these cases, red trees were not often detected, possibly 

because they were hidden by the dense overstory canopy or 

because they had not become established because there was 

lack of abrupt structural forest edge. 

Pine and Hardwood Mixed Forests

 Pine and hardwood species were often mixed into 

a composite forest structure. The association of red trees 

(detected during fixed-wing aerial survey) within these 

mixed forests followed the same pattern as in the pine or 

hardwood forests. 

Grasslands

For the most part, the grassland class comprised 

agricultural fields used for grazing or fallow fields. Although 

fairly ubiquitous throughout the study area, grasslands did 

not normally dominate the landscape. In all fixed-wing aerial 

observations of grassland classes, there were no observations 

of red trees (including Chinese tallow trees) (fig. 34A, B). 

During the ground-based survey, however, grasslands 

containing clumps of trees and shrubs were some of the most 

likely places to find Chinese tallow trees (fig. 34C–H). By 

far, the highest density of shrub-sized Chinese tallow trees 

was observed in a field that looked as though it had been 

fallow for some time (fig. 34H). Even this relatively dense 

population was fairly scattered and unremarkable except 

when compared to the general lack of Chinese tallow trees 

elsewhere in the study area. In addition, as depicted in figs. 

34C, D, and E and common to a majority of Chinese tallow 

tree sightings, a good portion, if not the dominant portion, of 

Chinese tallow tree leaves had not turned red and remained 

mostly or totally green. Although relatively more common in 

the grassland class than all other land classes except possibly 

scrublands, Chinese tallow tree occurrences were uncommon 

in grasslands within the Toledo Bend Reservoir study area, 

and those occurrences tended to be a few shrubs or isolated 

trees that exhibited a high proportion of green leaves.

Scrublands

Scrublands were the most likely land class to contain 

Chinese tallow tree. As illustrated in the fixed-wing aerial 

survey (fig. 35A–D), however, red trees (including possible 

occurrences of Chinese tallow trees) in scrublands were still 

highly scattered and were not often observed. When Chinese 

tallow tree shrubs were observed during ground surveys, the 

leaves were as likely or more likely to be green instead of 

red (figs. 35E–H). Based on ground and aerial observations, 

however, scrubland was a minor land class and largely 
Results 41

Air_624

2009_T5M

2009_528

2009_433

Air_275

2009_T10

2009_495

2009_505

A B

C D

E F

G H

Figure 34. Photographs of grassland areas within the Toledo Bend Reservoir study area (Louisiana and Texas). A, B, Aerial 

photographs of large fields devoid of red-leaf Chinese tallow trees (Triadica sebifera). C, D, and E, Ground photographs of green- and 

red-leaf Chinese tallow trees and shrubs. F, G, and H, Ground photographs of red-leaf Chinese tallow tree shrubs. In F, the arrow 

differentiates the red-leaf Chinese tallow tree shrub from the red sumac shrub next to it in the foreground. In H, the most dense 

occurrence of red-leaf Chinese tallow trees and shrubs observed in all reconnaissance surveys is shown.
42 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

consisted of shrubs and grasses in clear-cut areas not yet 

replanted and in somewhat small, scattered patches. 

Cypress Forests

Although a minor land class in the project area, cypress 

forests occupy a unique position in the project landscape 

(fig. 36). Cypress forests are included in the broad wetland 

forest class, as are bottomland hardwood forests. Compared 

to upland forests, both bottomland hardwood and cypress 

forests undergo more frequent and longer inundations; 

however, bottomland hardwood inundations are infrequent 

compared to the permanent to semipermanent inundations of 

cypress forests. Even though flooding is nearly permanent in 

cypress forests, Chinese tallow trees can become established 

in these unique forests (Ramsey and others, 2005a). Spectral 

distinction and classification of cypress forests within the 

Toledo Bend Reservoir study area relied on relatively low 

stand density and the presence of saturated soils or standing 

water backgrounds. Chinese tallow trees were not observed 

in any cypress forests during the fixed-wing aerial survey. 

Bare/Developed

The most important landcover subclass included in 

the bare/developed class was clear-cut land (figs. 37–38). 

Widespread clear cutting that had occurred and was 

occurring within the forested areas dominated the more 

dramatic and frequent spatial changes in the vegetated 

landscape. The clear cuts existed in a variety of structural 

forms. These forms included replanted clear cuts ranging 

from newly planted pine seedlings (exhibiting wide, mostly 

bare spaces) to mature pine regrowth (exhibiting dense 

stands). Others were freshly cleared with residual woody 

material still covering the clearing, while older clearings 

had been cleared without replanting. Most often, these latter 

areas contained grasses and shrubs. 

Bare and un-replanted surfaces formed by clear cutting 

can form environments conducive for Chinese tallow tree 

establishment. Spectral distinction between bare ground, 

paved surfaces, and other largely nonvegetated surfaces was 

not directly possible, but distinction of these classes can 

be improved through the application of spatial association 

information (Ramsey and others, 2001). For this study, 

however, nonvegetated surfaces were not of immediate 

importance in detecting Chinese tallow trees. Further 

classification processing to distinctly identify clear-cut land 

classes was not performed. 

Interactive Query of

Reconnaissance Site Information

An interactive query system was implemented to combine 

the reconnaissance information contained in the site summary 

catalogue (appendix 1) with a map containing all aerial (fixedwing aircraft and helicopter) and ground-site locations (fig. 39 

and downloadable database from the Web index page of this 

report) overlaid on 2010 DOQQs of Louisiana and Texas. In 

addition to information displayed interactively, the database 

contains a landcover class variable and a notation stating the 

presence or absence of Chinese tallow trees or red trees at each 

site. The Chinese tallow tree variable is only associated with 

ground- or helicopter-based observations. The red tree variable 

is only associated with the fixed-wing aerial observations. 

At sites observed from the fixed-wing aircraft, the “red tree” 

class included red maple, sweetgum, and red-leaf Chinese 

tallow trees or shrubs; it also possibly included poison sumac. 

Different symbols were used to identify each site with the date 

and type of reconnaissance activity. 

Landcover Classification of

Advanced Land Imager Data

The northern and southern ALI images were classified 

into six land classes and five water classes (fig. 40, table 

7). The classification did not follow a particular national 

protocol but, rather, was based on visually distinct land classes 

observed during the ground and aerial surveys; they included 

planted pine, mature pine forests, hardwood forests, cypress 

forests, clear-cut scrub, grasslands, bare/developed, wetlands, 

and five water classes. The wetland class was defined based on 

the physical appearance of false-color composite photography 

and image data. The water classes mainly depict differences in 

water clarity or turbidity.

Pine Plantation and Mature Pine Forests

Collecting growth-stage information related to the 

canopy structure and age of pine plantations was an objective 

of the ALI landcover classification. Growth-stage information 

is important in identifying initial establishment of Chinese 

tallow tree; however, progressive stages in growth could 

not be captured because the differences amongst growth 

stages were not spectrally distinct (for example, Ramsey 

and others, 2005a and 2005b). To compensate for indistinct 

spectral boundaries, aggregation of the pine plantation 

classes into three growth stages—young pine plantation, 

shrub pine plantation (designating height), and mature pine 

plantation—was attempted. Even though mixing at the class 

boundaries was expected, the extensive confusion between 

the young and shrub pine plantation classes could not be 

overcome; therefore, they were combined into a single pine 

plantation class. Similarly, because of the high spectral 

confusion between mature pine plantations and “natural” pine 

forests, these two classes were combined as the mature pine 

forest class.

Hardwood Forests and Hardwood Fingers

The frequent observations during the ground and aerial 

surveys of hardwood fingers containing red trees and shrubs 
Results 43

2009_T7M 2009_437M

2009_485 2009_486

A B

C D

E F

G H

Air_372

Air_136 Air_58

Air_9

Figure 35. Photographs of scrubland areas within the Toledo Bend Reservoir study area (Louisiana and Texas). A, B, C, D, Regrowing 

clear-cut areas largely vacant of replanted pine. D includes a magnified inset depicting some red shrubs; however, the identity of the 

shrubs could not be determined from the photograph. E, F, G, H, Scattered red-leaf Chinese tallow trees (Triadica sebifera) in isolated 

and fairly small scrubland areas.
Air_299 Figure 36. Aerial photograph of a cypress (Cupressus L. spp.) forest within the Toledo Bend Reservoir study area (Louisiana and Texa

during the fixed-wing aerial survey.
Results 45

AirP_230

Air_165

A

Air_150

B

C

Figure 37. Aerial photographs of newly clear-cut areas (not yet 

replanted) within the Toledo Bend Reservoir study area (Louisiana 

and Texas). A–C, Clear-cut areas containing hardwood fingers 

without observed understory red trees. A includes a young pine 

plantation containing hardwood fingers. B includes the hardwood 

finger transitioning from the clear cut into the surrounding mature 

pine forest. C illustrates a recently cleared forest.

Figure 38. Photographs of developed areas containing red-leaf Chinese tallow tree (Triadica sebifera) within the Toledo Bend Reservoir 

study area (Louisiana and Texas). A, Service station. B, Private home.

2009_461 2009_459

A B
Figure 39. An example display of the interactive reconnaissance site database. The map user can select the identifier tool from the viewer main one of the site locations (in this case “Air_307”) to view attribute data, including field notes. For each site, there is also a regional view taken from resolution, 2010 digital orthophoto quarter quadrangle (DOQQ). Photography obtained during the fixed-wing aerial survey is also available when se(via the “lightning bolt” hyperlink tool). The downloadable database is available from the Web index page of this report.
Results 47

EXPLANATION

Planted pine

Mature pine forest

Hardwoods

Cypress forest

Clear cuts–Scrub

Grassland

Bare/Developed

Wetlands

Water 1

Water 2

Water 3

Water 4

Water 5

0 5 10 KILOMETERS

0 5 10 MILES

Figure 40. Landcover map of the Toledo Bend Reservoir study area (Louisiana and Texas) based on data acquired 

by the Earth Observing-1 Advanced Land Imager sensor.
48 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

(including Chinese tallow trees) led us to attempt ALI-based 

mapping of hardwood forests, especially hardwood fingers, in 

the study area. As illustrated in fig. 23E, however, hardwood 

fingers were often nearly spectrally hidden within mature 

pine forests. In other cases, scattered or poorly defined 

hardwood stands were intermixed with shrubs and grasses 

(figs. 23B and 30B). Hardwoods were at times mistakenly 

classified as planted and mature pine forests and scrublands, 

particularly when the scrub shrubs or pines and hardwoods 

were intermixed (fig. 41). Mixed pixels within the ALI 

classification, such as those pixels that contained both the 

dirt road and the adjacent vegetation, often were misclassified 

as hardwoods. Prominently, these types of mixed pixels 

were misclassified within the hardwood-bottomland 

hardwood class throughout the project area, thus creating an 

overexpansive hardwood class. 

Scrublands and Land Features

Scrublands were not distinguished in the landcover 

classification of the ALI imagery (fig. 35), so the amount of 

scrublands in the project area was not determined. In general, 

the scrubland class was mixed into the grassland, clear cut, 

and hardwood classes. The actual proportion of scrubland 

within each of the other land classes was not ascertained. 

Land features, fences, and edges were not directly 

distinguishable in the ALI imagery; therefore, these classes 

were incorporated into the surrounding land classes. For 

instance, fences were most often included in the surrounding 

grassland class, and edges were included in pine plantation, 

pine, hardwood, and mixed forest classes. 

Assessment of the Landcover Classification Map 

A formal assessment of the classification accuracy 

was not conducted; however, the integrity of the classes as 

representing a particular land class was assessed by way of 

ground-based observations (including photography), aerial 

documentations, and the 2009 and 2010 DOQQ mosaics. 

At times, validation of class membership by using the 

DOQQs was hindered by the season when the DOQQ base 

photography was acquired. Photography collected late in 

the winter (late December to February) exhibited leaf-off 

conditions for most hardwoods and shrubs. In these cases, 

it was difficult to differentiate between types of hardwood 

forests, shrubs, and recently cleared lands. In addition to the 

difficulties related to leaf-off timing of photography, spectral 

confusion between land classes in the ALI images produced 

varying degrees of misclassification. 

Based on the ALI landcover classification, the 

5,261.9 km2 landscape of the Toledo Bend Reservoir study 

area was dominated by pine and hardwood forests (covering 

around 35 percent) and pine plantations (covering 28 percent) 

(table 7). Grasslands (12 percent cover), primarily including 

grazing lands and fallow fields, existed, as did scrublands 

(4 percent cover) and cypress forests (5 percent cover) 

(table 7), but these land classes were relatively insignificant 

covers in the study area. Dominant landcover changes in the 

Table 7. Land classes identified within satellite imagery of the Toledo Bend Reservoir study area (Louisiana and Texas). Images were 

obtained by the Advanced Land Imager sensor aboard the Earth Observing-1 satellite.

[km2, square kilometers]

Land Class Total Area (km2) Percent cover per 

Land class Description

Planted Pine 1,471 28 Young planted pine

Planted scrub pine

Mature Pine Forests 543 10 Mature planted and non-planted pine

Hardwoods Forests 1,310 25 Hardwoods and Bottomland Hardwoods

Cypress Forests 279 5 Cypress forest includes all observed swamp areas

Clear cuts-Scrubland 201 4 Regrowing clear cuts and scrublands

Grasslands 606 12 Grasslands including grazing lands and fallow fields

Areas confused with scrub fields

Bare-Developed 147 3 Mudflats, developed (low intensity residential), roads and logged areas 

with minimal vegetation 

Wetlands 66 1 Woody and emergent herbaceous 

Water 293 12 Five different water types based on hue (most likely turbidity diffrences)

151

115

34

45
2010 DOQQ

2009_511 2009_510

AirP_210

AB

C

Figure 41. Ground and aerial photographs of planted pine (Pinus L. spp.) areas in the Toledo Bend Reservoir study area (Louisiana and Texas). The loare also illustrated on a 2010 digital orthophoto quarter quadrangle (DOQQ). Chinese tallow trees and shrubs are shown within a young pine plantationhardwood finger and the surrounding mature pine forest.
50 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

landscape were associated with forestry silviculture. In our 

previous study of southwestern Louisiana, Chinese tallow 

trees were found in cypress, hardwood, and pine forests and 

in pine and cypress plantations. Pine plantations, and some 

scrublands, contained some of the highest percent occurrences 

of Chinese tallow trees in the previous southwestern Louisiana 

study. Based on those previous findings, we expected similarly 

associated Chinese tallow tree occurrences. 

Helicopter Reflectance Data Spectral Dataset

The lack of quality in the obtained helicopter-based 

canopy reflectance spectra was mainly related to the inability 

to consistently image the same location on the ground. 

This targeting inconsistency led to the result of there being 

a variable composition mixture in the canopy target, thus 

compromising the interpretability of the resultant canopy 

reflectance spectra in relation to canopy composition. 

Although the direct use of the spectra for creating a spectral 

indicator for red-leaf Chinese tallow tree was diminished, 

the canopy-reflectance spectra provided estimates of the 

magnitudes and variation associated with spectra of mature 

forests: planted pine and shrub canopies containing or not 

containing Chinese tallow tree.

An examination of the obtained spectra illustrates the 

nature of the composite canopy spectra, the reproducibility 

of the spectra, and the possible contrasts associated with 

contribution of red-leaf Chinese tallow trees to the canopy 

composition. In the first example, canopy spectra obtained 

from adjacent planted pine fields (one with and one without 

Chinese tallow shrubs) (fig. 24A) are nearly identical in 

the visible (VIS) (400–700 nm) range and only moderately 

differ in the near-infrared (NIR) (700–912 nm) wavelength 

region (fig. 42). Differences in NIR magnitude mainly pertain 

to contrasting amounts of live material in the radiometer 

IFOV, whereas, pertinent to this mapping, the contribution 

of red-leaf Chinese tallow tree is most often exhibited as 

comparatively higher red reflectance magnitudes (600–700 

nm). The comparison of spectra from the adjacent pine 

plantations showed that the few red-leaf Chinese tallow tree 

shrubs amongst the planted pines were not reflected in the 

canopy spectra. 

In a second example, the canopy spectrum of a mature 

pine forest is compared to the spectra of two young pine 

plantations (fig. 42). Higher red magnitudes would tend 

to indicate the presence of red-leaf Chinese tallow tree (or 

another red-leafed plant) within the pine forest composition; 

however, based on helicopter observations there was no 

Chinese tallow tree present at this site. In this case, the much 

lower NIR and somewhat higher red magnitudes of the pine 

forest spectra most likely reflect the high proportions of 

dead understory (visible in fig. 43), which contributes to the 

canopy composition within the radiometer FOV. Ideally, the 

combination of low NIR and somewhat higher red magnitudes 

would provide enough evidence to exclude red-leaf Chinese 

tallow trees as the cause of the increase in red reflectance and, 

instead, predict a higher proportion of dead material. 

EXPLANATION

Mature pine (HS1_M)

Pine plantation (HS3_M)

Pine plantation (HS4_M)

0.2

0.16

0.12

0.08

0.04

0

400 600 800 1,000

Canopy reflectance, in percent

Wavelength, in nanometers

Figure 42. Canopy reflectance results for a mature pine (Pinus L. 

spp.) (HS1_M, also shown in fig. 43) and two young pine plantations 

within the Toledo Bend Reservoir study area (Louisiana and Texas 

[fig. 24A and fig. 41B]). The names, for example HS1_M, are the site 

names from where the spectra were collected.

Creating Indicator Spectra from the Helicopter 

Reflectance Spectral Dataset

PolyVector Analysis of Spectral Data

Even though uncertainty existed, the spectra were 

processed through the PVA suite of programs to ascertain 

whether or not a set of useable spectral indicators for 

Chinese tallow tree occurrences could be produced from 

helicopter-based spectra. By trial and error, mean-values of 

the helicopter-based canopy reflectance spectra obtained from 

the 12 calibration-validation sites were tested for consistency 

in results obtained via PVA. Five of the 12 canopy reflectance 

spectra were selected as the final PVA input dataset (fig. 44). 

The five helicopter-based spectra were associated with the 

following types of vegetation:

•	 One mature pine forest site,

•	 An isolated mixed pine and hardwood stand containing 

Chinese tallow tree,

•	 Two pine plantation sites (one containing a few Chinese 

tallow shrubs),

•	 One scrub-shrub site containing Chinese tallow tree, and

•	 Senescent vegetation spectra obtained from the 2002 Chinese tallow tree study in southwestern Louisiana (Ramsey 

and others 2005a). 
Results 51

HSM1_M

Figure 43. Aerial photograph of a mature pine (Pinus L. spp.) stand in the Toledo Bend Reservoir study area (Louisiana and Texas). As 

the illustration shows, the stand contained high proportions of senescent grass understory.

EXPLANATION

Mature pine forest (HS1_M)

Mixed pine and hardwoods (HS2_M)

Pine plantation (HS3_M)

Pine plantation (HS4_M)

Scrub shrub (HS13_M)

0.2

0.16

0.12

0.08

0.04

0

400 500 600 700 800 900

Canopy reflectance, in percent

Wavelength, in nanometers

Figure 44. Canopy reflectance spectra based 

on helicopter upwelling sunlight recording of 

four pine (Pinus L. spp.) forests and plantations 

and one scrub-shrub site within the Toledo 

Bend Reservoir study area (Louisiana and 

Texas). These spectra were selected for 

processing by PolyVector Analysis in order to 

determine indicator spectra for Chinese tallow 

tree (Triadica sebifera). The names, for example 

HS1_M, are the site names from where the 

spectra were collected. 
52 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

These indicator spectra did not define the components 

of a typical canopy as green, red, and brown (or nonliving) 

materials but, rather, as the integrated composite of the canopy 

materials, one of which contained some proportion of red-leaf 

Chinese tallow trees (fig. 45, table 8). Although we were not 

able to derive a unique indicator spectrum for red-leaf Chinese 

tallow trees from the helicopter-derived canopy reflectance 

spectra, the composition spectra provided at least an initial 

set of indicator spectra for possibly detecting vegetation 

represented within the Hyperion image that was associated 

with a higher relative likelihood of containing Chinese tallow 

trees or red trees. These indicator spectra were subsequently 

combined into canopy reflectance spectra datasets extracted 

from the Hyperion reflectance images in order to promote the 

detection of Chinese tallow trees or at least provide locations 

of higher likelihood of occurrences. 

Percent Occurrences of Chinese Tallow Tree 

Indicated by Hyperion Reflectance Images

The PVA and subsequent SPUNMIX transformation of 

the Hyperion reflectance data to percent-occurrence values 

involved numerous trial and error experiments. As previously 

discussed, technical difficulties related to validation and 

calibration data limited full implementation of the successful 

strategy applied in southwestern Louisiana. Although this 

limitation affected our ability to interpret products produced 

by PVA and SPUNMIX, it did not directly affect the strategy 

used for mapping red-leaf Chinese tallow tree occurrences. To 

identify a red-leaf Chinese tallow tree spectral detection tool, 

we aggregated our PVA and SPUNMIX applications according 

to the content of the input databases. Each section describing 

the different input databases and resultant PVA and SPUNMIX 

performances represents multiple trial and error efforts, and 

representative illustrations are included where warranted. 

PolyVector Analysis of Spectral Datasets

The first strategy we applied was successfully used 

in a previous project to map red-leaf Chinese tallow tree 

(by percent occurrence) in southwestern Louisiana. This 

strategy involved aggregating all major land classes into a 

single database for PVA processing. The constructed spectral 

datasets included average composite reflectance spectra 

extracted from pertinent Hyperion reflectance images. 

Locations for extraction of the average reflectance spectra 

0.2

0.16

0.12

0.08

0.04

0

400 500 600 700 800 900 1,000

Canopy reflectance, in percent

Wavelength, in nanometers

EXPLANATION

Live vegetation with

 Chinese tallow tree

Senescent vegetation

Live vegetation without

 Chinese tallow tree

Figure 45. Canopy reflectance of three indicator spectra used 

for detecting Chinese tallow tree (Triadica sebifera) occurrences 

within the Toledo Bend Reservoir study area (Louisiana and 

Texas). The variance of the five reflectance spectra were 

adequately described by three indicator spectra: the first 

represents live vegetation (pine) without Chinese tallow tree, 

the second represents senescent vegetation, and the last live 

vegetation (pine and mixed hardwoods) with Chinese tallow tree. 

The correlation strength among the three indicator spectra and 

the five input spectra is provided in table 8. The indicator spectra 

shown in figure 45 are identified by a row of ones and zeros in 

table 8. The indicator spectra were derived from a PolyVector 

Analysis of variance among five input spectra (fig. 44).

Table 8. Results of PolyVector Analysis of spectra obtained 

during helicopter reconnaissance for Chinese tallow tree (Triadica 

sebifera) in the Toledo Bend Reservoir study area (Louisiana 

and Texas). Analysis of the five input spectra resulted in three 

indicator spectra representing live vegetation (pine) without 

Chinese tallow, senescent vegetation, and live vegetation 

(pine and hardwoods) with Chinese tallow (red background), 

respectively. The entry in each cell provides the correlation 

magnitude between each vegetation type (row) in the input 

database and each indicator spectrum (column). The indicator 

spectra are identified by a column of ones and zeros. 

Type of

vegetation Site Fieldsite

Indicator

1 2 3

Senescent

vegetation

GR Previous 

study

0 1 0

Mature pine 

forest

P14 HS1_M 0.95 0.19 0

Mixed pine and 

hardwoods

P25 HS2_M 0.69 0 0.48

Pine plantation P35 HS3_M 0.78 0 0.37

Pine plantation P4P HS4_M 1 0 0

Scrub-shrub P13 HS13_M 0 0 1
Results 53

were selected based on ground and air reconnaissance data 

pinpointing observations of Chinese tallow trees (during 

ground or helicopter surveys) or red trees (during the fixedwing aerial survey). To these selected spectra, we added 

spectra for locations at which the presence or absence of 

Chinese tallow trees (or red trees) was unknown (lacking 

ground or aircraft observations). The database input to the 

PVA solely included composite spectra extracted directly 

from the Hyperion reflectance images. 

The PVA resulted in suites of indicator spectra that 

faithfully represented the internal variance of the dataset, 

but no indicator spectra clearly reproduced the distribution 

of red-leaf Chinese tallow tree (or red tree) occurrences 

detected during the reconnaissance surveys. Even though 

noncongruence was high, PVA produced indicator spectra 

showing some promise for mapping Chinese tallow tree 

(or red tree) occurrences were reformatted (from the PVA 

output) and entered into the PCI SPUNMIX mapping 

procedure. Unfortunately, none of the occurrence images 

produced by the SPUNMIX procedure aligned with the 

observed and possible Chinese tallow tree occurrences 

documented during the ground and aerial (helicopter and 

fixed-wing aircraft) surveys. In contrast to reconnaissance 

observations, the Chinese tallow tree occurrences predicted 

by the SPUNMIX procedure were widely distributed and 

included many contiguous and extensive occurrences. 

Seeding the PolyVector Analysis (PVA)

In an attempt to focus the PVA input database to a more 

desirable outcome, we seeded it with reflectance spectra 

known to be associated with red-leaf Chinese tallow tree. 

The known reflectance spectra were obtained from the 

following sources:

•	 Red-leaf Chinese tallow tree leaves (figs. 25 and 26), 

•	 Red-leaf Chinese tallow tree indicator spectrum identified during a previous project to map Chinese tallow tree 

occurrences in southwestern Louisiana (Ramsey and 

others, 2005b), 

•	 Adapted spectra of the Chinese tallow tree (red) leaf and 

of previously collected Chinese Tallow tree (red) canopy, 

and 

•	 Indicator spectra produced from the helicopter-based 

canopy reflectance data (table 8, figs. 44 and 45). 

Although seeding the PVA input database with 

known reflectance spectra somewhat improved on the 

results we obtained by solely using Hyperion reflectance 

spectra, the results still did not provide the detection 

capability we desired.

Seeding PolyVector Analysis (PVA) combined 

with Land Class Information

Our next step in trying to identify a well-conditioned 

Chinese tallow tree indicator spectrum by using PVA 

processing was to organize the PVA input database by land 

class. To develop a unique spectral identifier, we adapted 

and combined leaf and canopy spectra of red-leaf Chinese 

tallow trees. These indicator spectra were then seeded to 

the PVA input database and entered into the SPUNMIX 

procedure to produce occurrence maps. Along with the 

leaf-canopy indicator spectra, input to the SPUNMIX 

procedure also included spectra extracted from Hyperion 

imagery obtained at locations where red-leaf Chinese tallow 

trees (or red trees) were observed or not observed during 

ground and aerial surveys (table 9). The Hyperion imagery 

used was limited to the hardwood-bottomland hardwood 

land class within the 1D swath coverage on October 17 and 

November 11, 2009 (matching the ALI image data used in the 

landcover classification).

Mapping Chinese Tallow Tree Occurrences

in the Hardwoods Class

PVA was applied to the (November 11, 2009) Hyperion 

reflectance spectra extracted from 12 unknown and 6 known 

sites (observed red trees) (see table 9) within the hardwood 

land class contained in the 1D swath (fig. 11). As was most 

often the case in our other analyses, one of the seed spectra 

contained in the dataset input into PVA was selected as the 

red-leaf Chinese tallow tree indicator spectrum (created 

Chinese tallow tree) (table 10, fig. 46). Correlations with the 

indicator spectrum exhibited proper magnitudes (1–5 percent), 

but four of the six sites where red-leaf Chinese tallow trees (or 

red trees) were observed during aerial reconnaissance had zero 

or negative associations (row entries under indicator 1) (table 

10). The remaining two spectra, one selected by PVA from 

the unknown and the other from the observed sites, exhibited 

mixed results; however, this particular PVA (input of the leafcanopy adapted spectrum and landcover spectra) produced 

the overall best results within these land-class associated 

spectral datasets, including seeded spectra. Based on the best 

possible criteria, this set of PVA-derived indicator spectra was 

entered into the SPUNMIX procedure, from which the red-leaf 

Chinese tallow tree percent-occurrence map for the hardwood 

landcover class was produced. 

Limited to the Hyperion 1D swath coverage and the 

hardwood landcover class, SPUNMIX output percent 

occurrences that were associated with the red-leaf Chinese 

tallow tree indicator spectra (similar to those produced with 

PVA and included in table 10). Although SPUNMIX outputs 0 

to 100 percent occurrences per pixel, for graphical clarity, the 

percent occurrences were transformed to a binomial variable 

that represents the predicted presence (1) or absence (0) of 

red-leaf Chinese tallow trees or red trees; no information is 

conveyed related to percent-occurrence magnitudes.
54 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

Table 9. Database input to the PolyVector Analysis of preliminary indicator spectra created from leaf and canopy spectra and 

Hyperion reflectance spectra. The analysis was performed in an attempt to identify a unique set of spectral indicators for Chinese tallow 

tree (Triadica sebifera) in the Toledo Bend Reservoir study area (Louisiana and Texas).

[UTM15, Universal Transverse Mercator, zone 15; Air_*, fixed-wing plane survey field site]

Site descriptor Source Locations

1 Created Chinese tallow tree indicator Ground and helicopter

2 hardwoodunknown1 Hyperion 434310E, 3510870N UTM15

3 hardwoodunknown2 Hyperion 432000E, 3503490N UTM15

4 hardwoodunknown3 Hyperion 436710E, 3500910N UTM15

5 hardwoodunknown4 Hyperion 431400E, 3498630N UTM15

6 hardwoodunknown5 Hyperion 435720E, 3492780N UTM15

7 hardwoodunknown6 Hyperion 434160E, 3483300N UTM15

8 hardwoodunknown7 Hyperion 428220E, 3474900N UTM15

9 hardwoodunknown8 Hyperion 429480E, 3463800N UTM15

10 hardwoodunknown9 Hyperion 427770E, 3457710N UTM15

11 hardwoodunknown10 Hyperion 427080E, 3452100N UTM15

12 hardwoodunknown11 Hyperion 418560E, 3440310N UTM15

13 hardwood-redtrees1 Hyperion Air_208

14 hardwood-redtrees2 Hyperion Air_225

15 hardwood-redtrees3 Hyperion Air_225

16 hardwood-redtrees4 Hyperion Air_227

17 hardwood-redtrees5 Hyperion Air_384

18 hardwood-redtrees6 Hyperion Air_234

0.4

0.3

0.2

0.1

0

400 500 600 700 800 900 1,000

Indicator spectra, in percent

Wavelength, in nanometers

EXPLANATION

Hardwood forest with red trees

Created red-leaf Chinese tallow tree

Hardwood forest without red trees

Figure 46. Spectral results of a PolyVector 

Analysis of an input indicator spectrum for 

red-leaf Chinese tallow tree (Triadica sebifera) 

and Hyperion spectra from selected locations 

within the Toledo Bend Reservoir study area 

(Louisiana and Texas) (see table 9 input spectra). 

The resultant indicator spectra included the seed 

indicator spectrum for red-leaf Chinese tallow 

tree, a hardwood forest stand without red trees 

spectrum, and a hardwood forest stand with red 

trees spectrum.
Results 55

The binomial overlay results were highlighted on two regions 

within the project area (fig. 47), one on the Louisiana side (fig. 

48A) and the other on the Texas side (fig. 48B).

The overlay indicating expected Chinese tallow tree or 

red tree occurrences portrays a widely scattered distribution 

of Chinese tallow trees in the hardwood forests. As illustrated 

in figures 48A and 48B, fixed-wing observations of red 

trees do concur with the predicted occurrences; however, 

our reconnaissance data does not support the widespread 

establishment of Chinese tallow tree or red trees. For 

example, figure 48A contains insets showing the overlay 

and fixed-wing photography of the same area. In each of the 

three insets, although red trees are visible in the collocated 

photography, the predicted occurrences associated with 

senescing hardwoods are more extensive than were observed 

in ground and aerial reconnaissance. Of particular interest 

are the predicted occurrences shown in inset 3 of figure 48A

associated similarly with senescent hardwoods; however, 

these hardwoods form hardwood fingers protruding into a 

young pine plantation. In this latter case, ground and aerial 

observations have confirmed relatively higher establishments 

of Chinese tallow trees or red trees.

Similar associations between predicted Chinese tallow 

tree or red tree occurrences and fixed-wing observations 

are shown in figure 48B. In inset 1 of figure 48B, scattered 

occurrences predicted within the hardwood forest and along 

the edges of isolated pine stands were not widely observed 

in the fixed-wing reconnaissance. Lack of confirmation, 

however, could be due to the difference in the times of the 

reconnaissance and image collection. In addition, Chinese 

tallow trees were observed in ground and aerial observations 

along edges, included along mature pine forests abutting pine 

plantations or shrubs. Similarly, inset 2 of figure 48B shows 

predicted occurrences within an area containing hardwood 

fingers and a young pine plantation, where yellow trees 

were observed and concentrated along edge, and within the 

hardwood fingers, where higher occurrences of Chinese tallow 

trees and red trees have been observed in ground and aerial 

reconnaissance. In this illustration, however, the predicted 

occurrences extend beyond the hardwoods, incorporating 

roadway and planted pines. This overextended prediction 

of Chinese tallow trees or red trees not associated with 

hardwoods is caused by the misclassification of hardwoods in 

the ALI imagery. Inset 3 of figure 48B exhibits the predicted 

Table 10. PolyVector Analysis output indicator spectra. Correlations of the three indicator spectra representing hardwood (with red 

trees), seeded indicator, and hardwood with all input spectra extracted from the Hyperion reflectance image within the hardwood 

landcover. The entry in each cell provides the correlation magnitude between each site descriptor (row) in the input database and each 

indicator spectra (column). The indicator spectra are identified by a column of ones and zeros.

[Air_*, fixed-wing plane survey field site; UTM15, Universal Transverse Mercator, zone 15. Red font signifies seed indicator spectrum, and green fonts denote 

indicator spectra associated with ground and aerial sites]

Site descriptor1 Locations

Indicator

1 2 3

1 Created Chinese tallow tree indicator Seed indicator 0.0000 1.0000 0.0000

2 hardwoodunknown1 434310E, 3510870N UTM15 0.3750 -0.0372 0.6621

3 hardwoodunknown2 432000E, 3503490N UTM15 0.5838 -0.0273 0.4435

4 hardwoodunknown3 436710E, 3500910N UTM15 0.4256 0.0263 0.5481

5 hardwoodunknown4 431400E, 3498630N UTM15 0.5546 0.0195 0.4259

6 hardwoodunknown5 435720E, 3492780N UTM15 0.7834 0.0261 0.1906

7 hardwoodunknown6 434160E, 3483300N UTM15 0.5793 -0.0501 0.4709

8 hardwoodunknown7 428220E, 3474900N UTM15 0.1602 0.0183 0.8215

9 hardwoodunknown8 429480E, 3463800N UTM15 0.5918 -0.0532 0.4614

10 hardwoodunknown9 427770E, 3457710N UTM15 0.7619 0.0128 0.2253

11 hardwoodunknown10 427080E, 3452100N UTM15 0.0000 0.0000 1.0000

12 hardwoodunknown11 418560E, 3440310N UTM15 0.5611 0.0104 0.4285

13 hardwood-redtrees1 Air_208 0.5902 0.0387 0.3711

14 hardwood-redtrees2 Air_225 0.7960 -0.0564 0.2604

15 hardwood-redtrees3 Air_225 0.5311 -0.0464 0.5153

16 hardwood-redtrees4 Air_227 0.6607 -0.0364 0.3757

17 hardwood-redtrees5 Air_384 1.0000 0.0000 0.0000

18 hardwood-redtrees6 Air_234 0.9336 0.0590 0.0075

1

2–18 spectra extracted from Hyperion reflectance image.
56 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

EXPLANATION

2010 coverage swath

0 5 10 KILOMETERS

0 5 10 MILES

±

a

b

1D

Figure 47. Mapped occurrences of red-leaf trees. Indicator spectra output from PVA (fig. 46) were input into the SPUNMIX 

procedure that produced the occurrences of red-leaf Chinese tallow trees (Triadica sebifera) and red trees in association 

with hardwood forest stands and fingers within the Hyperion 1D swath coverage of the Toledo Bend Reservoir study area 

(Louisiana and Texas). The binomial representation of the SPUNMIX output is highlighted in figure 48 for insets “a” and “b.” 
Results 57

A

1

2 3

0 1 2 KILOMETERS

0 1 2 MILES EXPLANATION

Planted pine

Mature pine forest

Hardwoods

Cypress forest

Clearcuts–Scrub

Grassland

Bare–Developed

Wetlands

Water 1

Water 2

Water 3

Water 4

Water 5

0 0.25 0.5 KILOMETERS

0 0.25 0.5 MILES

Air384 ±

Air174 Air393

1 2

3

Figure 48A. A binomial representation of SPUNMIX-predicted occurrences (yellow mask overlay) of red-leaf Chinese tallow trees 

and red trees in association with hardwood forests and fingers in the Toledo Bend Reservoir study area (Louisiana and Texas). Areas of 

predicted occurrences are overlain on the mosaicked Advanced Land Imager coverages that were used in the landcover classification 

of the study area, and fixed-wing photos of the same areas are provided. A, Area corresponding to inset “a” on figure 47 (located on the 

eastern [Louisiana] side of the reservoir). Insets 1 and 2 illustrate occurrences within hardwood stands in pine forests, whereas inset 3 

exhibits occurrences associated with hardwood fingers. The names on the photographs, for example Air49, indicate the site names.
58 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

B

0 1 2 KILOMETERS

0 1 2 MILES

EXPLANATION

Planted pine

Mature pine forest

Hardwoods

Cypress forest

Clearcuts–Scrub

Grassland

Bare–Developed

Wetlands

Water 1

Water 2

Water 3 ± Water 4

±

0 0.5 1 KILOMETER

0 0.5 1 MILE

Air49 Air1 Air46 AirP209M

Water 5

1 2 3

1

2

3

Figure 48B. A binomial representation of SPUNMIX-predicted occurrences (yellow mask overlay) of red-leaf Chinese tallow trees and red 

trees in association with hardwood forests and fingers in the Toledo Bend Reservoir study area (Louisiana and Texas). Areas of predicted 

occurrences are overlain on the mosaicked Advanced Land Imager coverages that were used in the landcover classification of the study 

area, and fixed-wing photos of the same areas are provided. Area corresponding to inset “b” on figure 47 (located on the western [Texas] side 

of the reservoir). Inset 1 illustrates occurrences in a hardwood forest, inset 2 in an area of pine plantations, and inset 3 in hardwood stands in 

a pine forest. The names on the photographs, for example Air49, indicate the site names.
Results 59

occurrences associated with senescing hardwoods. During 

fixed-wing aerial reconnaissance, red trees were observed at 

this location; however, as in all illustrations, the extensiveness 

of the predicted occurrences was not confirmed in the 

reconnaissance data. 

Overall, the association of predicted distribution of 

Chinese tallow trees or red trees seems overemphasized. 

Although the predicted occurrences include observed red 

trees, they also extend into senescing hardwood-bottomland 

hardwood stands where red trees were not directly observed. 

In addition, erroneously predicted occurrences were 

associated with land classes other than hardwoods, but 

those artifacts were becuase of errors in the ALI landcover 

classification. Even accounting for prediction errors because 

of hardwood misclassifications, red-leaf Chinese tallow 

tree or red tree predicted occurrences seemed overextended 

into senescing hardwoods than reasonable. While observed 

Chinese tallow trees and red trees in hardwoods were 

correctly predicted by using the PVA seeded with the 

input indicator spectra shown in fig. 46, high errors of 

commission (inclusion of non-Chinese tallow trees and red 

trees) in the prediction seemed likely. In order to more fully 

assess this set of seed indicator spectra, we extended this 

occurrence mapping to other landcovers based on the same 

seed indicator spectra. 

Extending the Seeded and PolyVector Analysis (PVA) to

Multiple Landcover Classes

As in the PVA analyses of seeded spectral input data 

associated with the hardwood landcover class, PVA was 

applied to planted and mature pine, grassland, and hardwood 

associated spectral datasets extracted from October 17 and 

November 11, 2009, Hyperion reflectance images (1D swath). 

Spectral databases incorporating various seed combinations 

were input into the PVA analyses and results tracked and a 

summary of these attempts made by using various datasets 

and combinations of seed spectra reported (table 11). Where 

the indicator associations (for example, numeric entries in 

table 11) indicated at least partial alignment with expectations 

based on the input known spectra, the resultant output 

indicator spectra were input into SPUNMIX and those results 

again tracked and noted in the last column of table 11. 

No spectral seed combination, land cover class, or 

collection date produced a visually consistent landcover 

spatial distribution of Chinese tallow tree or red tree 

occurrences. As in the hardwood-associated mapping 

illustrated in figures 47, 48A, and 48B, errors in commission 

in the predicted occurrences often were judged too high. 

That failure lead to a simplified and generalized strategy 

focused on mapping higher likelihoods than directly detecting 

Chinese tallow tree occurrences. The likelihood mapping 

strategy entailed limiting the input seed spectra to those 

identified from the helicopter canopy reflectance dataset while 

retaining land class selectivity. 

Seeding PolyVector Analysis (PVA) with 

Helicopter-Based Indicator Spectra in

Hardwood Landcovers

The Hyperion-based reflectance spectra represented 

locations of observed (ground and aerial) and unknown 

occurrences of red-leaf Chinese tallow trees and red trees 

(fixed-wing aerial survey) in hardwood forests (table 12). The 

three helicopter-based seed spectra (see table 8, fig. 45) were 

included in the PVA input dataset (table 12). As described 

in the “Creating Indicator Spectra from the Helicopter 

Reflectance Spectral Dataset” section, new indicator spectra 

were calculated by PVA to represent the variance of the full 

input helicopter-based reflectance database. In this case, three 

indicator spectra that best represented the spectral variance 

of the input database were selected by PVA (fig. 49). The 

three indicator spectra included a Hyperion-based spectrum, 

“Indicator 1—live vegetation,” representing an aerially 

observed location, and two helicopter-based PVA spectra, 

“Indicator 2—senescent vegetation” and “Indicator 3—live 

vegetation with red-leaf Chinese tallow tree.” 

Of the two helicopter-based indicator spectra, only 

Indicator 3 included a spectral response due to the occurrence 

of red-leaf Chinese tallow trees mixed into the live green 

vegetation canopy. The inability to discover a unique red-leaf 

Chinese tallow tree indicator spectrum as accomplished in 

Ramsey and others (2005a) meant that instead of mapping 

Chinese tallow trees directly, the likelihood of a canopy 

(within each pixel) to contain Chinese tallow trees or red trees 

was mapped. More specifically, the predicted occurrences 

referenced canopies that exhibited a spectral reflectance 

component with features similar to the mixed live vegetation 

and red-leaf Chinese tallow canopy indicator spectrum, thus 

indicating a higher likelihood of red-leaf Chinese tallow tree 

or red tree occurrence. The associations of these indicator 

spectra and all other input spectra (referring to locations in the 

Hyperion image) are provided in table 13. 

The alignment of the input landcover spectra, the 

Hyperion-calculated indicator spectrum, and the two PVAcalculated indicactor spectra suggested that the ability to 

detect red trees or Chinese tallow trees within hardwood 

stands was intercorrelated with the performance of the seed 

live vegetation indicator spectrum. The helicopter-based 

seed live vegetation indicator spectrum (row 3 and column 

3 of table 13) exhibited a high correlation (77 percent, 

column 3) with the helicopter-based seed live vegetation 

with Chinese tallow indicator spectrum. Indicator 3 reflecting 

correlations with the live vegetation with Chinese tallow tree 

seed-indicator was also associated highly (9 and 18 percent) 

with a locations containing higher observed concentrations 

of observed Chinese tallow trees along a fence line and 

along a forest edge (rows 7 and 11 of table 13). Remaining 

correlations with Indicator 3 within hardwood stands 

containing observed red trees or Chinese tallow trees were 

varied and low. Magnitudes of correlation between Indicator 3 
Table 11. Summary of mapping results achieved by using various datasets and combinations of seed spectra aimed at producing a unique set of indred-leaf Chinese tallow tree (Triadica sebifera) occurrences in the Toledo Bend Reservoir study area (Louisiana and Texas). [No., number; PVA, PolyVector Analysis; em, endmember indicator; TBRT, tallow leaf bright; TC, tallow canopy; TCMED, tallow canopy and leaf medium; TDRK, tallow leaftallow leaf medium] Class stratified seeded Land class Hyperion image swath and date No. of training spectra from Hyperion image for PVA Seeding with ground, canopy and cre- ated indicator spectra PVA-output em Analysis–Results PVA 

spe

Pine planted

1D -11Nov 2009

10 2002 tallow canopy and helicopter '09 

(green vegetation)

none no good indicator for tallow 

discrimnation 

10 i)2002 TC and non-live material none no good indicator for tallow 

discrimnation ii) TBRT

iii) TMEDiv) TDRKv) TLF

13 two helicopter '09 spectra (green vegetation and green with red) 

 i) TLF i) none i)no good indicator for tallowii) TC ii) 3em fuzzy ii)no good indicator for tallowiii) created TCMED iii) 3em fuzzy weighted 

more on slightly on helicopter seeds

iii) maybe tallow indicator 

currpres

iv) created TCMED2 iv) 3em fuzzy weighted obse

more on slightly on the helicopter seeds 

iv) maybe tallow indicator
Class stratified seeded 

Land class

Hyperion 

image

swath and 

date

No. of training 

spectra from 

Hyperion 

image

for PVA

Seeding with ground, canopy and created indicator spectra PVA-output em Analysis–Results

PVA 

spe

Hardwoods

1D -11Nov 2009

11 i)Air_208 i) 3em weighted more on 

on Air_208 

i) maybe red tree indicator i) ga

trew

 ii)Air_208 and tallow canopy ii) 3em ii) no red tree discrimnator

11 Air_208, Air_225, Air_234, 2009_384 

and 

 i) created TCMED2 i) 3em weighted more 

on on ground and air points showing red trees 

i) maybe a red tree indicator i) ga

trew

 ii) adapted TCMED3 ii) 3 em ii) no good red tree indicatoriii) TLF iii) 3 em iii) no good red tree indicator

19 1) Air_208, Air_225, Air_234, 2009_384 1) none 1) no red tree indicator 

 2) Air_208, Air_225, Air_234, 2009_384 

and

i) created TCMED i) none i) no red tree indicator  ii) created TCMED2 2ii) em3 is weighted 

more on Air point with red trees

 2ii) maybe a red tree indicator 2ii)d

re

 iii) created TCMED3 2iii to 2 vi - none 2iii) to 2vi) no red tree 

indicator

iv) TBRT v) TDRK vi) tallow canopy 

Table 11. Summary of mapping results achieved by using various datasets and combinations of seed spectra aimed at producing a unique set of indred-leaf Chinese tallow tree (Triadica sebifera) occurrences in the Toledo Bend Reservoir study area (Louisiana and Texas).—Continued[No., number; PVA, PolyVector Analysis; em, endmember indicator; TBRT, tallow leaf bright; TC, tallow canopy; TCMED, tallow canopy and leaf medium; TDRK, tallow leaftallow leaf medium]
Class stratified seeded Land class Hyperion image swath and date No. of training spectra from Hyperion image for PVA Seeding with ground, canopy and cre- ated indicator spectra PVA-output em Analysis–Results PVA 

spe

Grasslands

1D -11Nov 2009

14 1)2009_410,2009_415,2009_421,2009_5

04,2009_505 and 

i) tallow leaf i) 3em i) 3em gives some solution i) ga

trew

ii) tallow canopy ii) 3em ii) 3em gives a better solution for red tree indicator ii) ga

rerepo

iii) tallow leaf bright red iii) none iii) has no solutioniv) tallow leaf medium iv) 3em iv) 3em maybe a red tree 

indicator

iv) d

re

18 1)2009_410,2009_415,2009_421,2009_5

04,2009_505, Air_578 and 

i) to 

mreob

i) created tallow indicator3 i) 3em i) maybe a red tree indicator ii) created tallow indicator1 ii) 4em ii)maybe a red tree indicator iii) tallow leaf bright iii) 4em iii) some indicationiv) tallow canopy iv) 4em iv) maybe a talllow indicator iv) th

haw

Mature Pine 25 i) tallow canopy i) none no good indicator for tallow 

discrimnation ii) tallow leaf and green helicopter 

spectra

ii) none

iii) tallow leaf medium iii) none

32 i) tallow leaf bright i) none

ii) tallow leaf medium ii) noneiii) tallow canopy iii) 6em iii) may give a tallow indicator iii) d

re

iv)created tallow canopy and leaf medium (TCMED) iv) none

Table 11. Summary of mapping results achieved by using various datasets and combinations of seed spectra aimed at producing a unique set of indred-leaf Chinese tallow tree (Triadica sebifera) occurrences in the Toledo Bend Reservoir study area (Louisiana and Texas).—Continued[No., number; PVA, PolyVector Analysis; em, endmember indicator; TBRT, tallow leaf bright; TC, tallow canopy; TCMED, tallow canopy and leaf medium; TDRK, tallow leaftallow leaf medium]
Class stratified seeded 

Land class

Hyperion 

image

swath and 

date

No. of training 

spectra from 

Hyperion 

image

for PVA

Seeding with ground, canopy and created indicator spectra PVA-output em Analysis–Results

PVA 

spe

Hardwoods

1D -17 Oct 2009

8 Air_393, Air_384, Air_173, 2009_483, 

2009_482 and

i) tallow leaf bright none no indicator for tallow discrimnation ii) tallow leaf darkiii) tallow leaf mediumiv) created tallow canopy and leaf 

medium(TCMED)

Grasslands 9 Air_229, 2009_415M, 2009_410M, 

2009_413, 2009_421

3em may give a tallow indicator 

map 

Air_229, 2009_415M, 2009_410M, 

2009_413, 2009_421 and

none no indicator for tallow discrimnation 

i) tallow leaf brightii) tallow leaf darkiii) created tallow canopy and leaf 

medium(TCMED)

Table 11. Summary of mapping results achieved by using various datasets and combinations of seed spectra aimed at producing a unique set of indred-leaf Chinese tallow tree (Triadica sebifera) occurrences in the Toledo Bend Reservoir study area (Louisiana and Texas).—Continued[No., number; PVA, PolyVector Analysis; em, endmember indicator; TBRT, tallow leaf bright; TC, tallow canopy; TCMED, tallow canopy and leaf medium; TDRK, tallow leaftallow leaf medium]
64 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

Table 12. PolyVector Analysis input database—helicopter and hyperion spectra.

[UTM15, Universal Transverse Mercator, zone 15; Air_*, fixed-wing plane survey field site]

Site names Source Site location

Heli-LiveVegetationwithChineseTallowtree Helicopter Seed-Indicator spectra

Heli-NonLiveVegetation Helicopter Seed-Indicator spectra

Heli-LiveVegetation Helicopter Seed-Indicator spectra

Hyp-ChineseTallowtreesinHardwoods-Airpoint1 Hyperion Air_584

Hyp-ChineseTallowtreesinHardwoods-Airpoint2 Hyperion Air_587

Hyp-RedtreesHardwoods-Airpoint3 Hyperion Air_208

Hyp-RedtreesHardwoods-Airpoint4 Hyperion Air_209

Hyp-RedGreenChineseTallowtreeedge-groundpoint5 Hyperion 2009_514

Hyp-ChineseTallowtreemoregreen-groundpoint6 Hyperion 2009_521

Hyp-GreenYellowChineseTallowtree-ground7 Hyperion 2009_480

Hyp-RowfenceChineseTallowtree-groundpoint8 Hyperion 2009_415M

Hyp-RedYellowGreenChineseTallowtree-groundpoint9 Hyperion 2009_505

Hyp-spectra1 Hyperion 434010E, 3510870N UTM15 

Hyp-spectra2 Hyperion 432000E, 3503490N UTM15

Hyp-spectra3 Hyperion 440070E, 3503070N UTM15

EXPLANATION

Live vegetation without red tallow

Senescent vegetation

Live vegetation with red tallow

0.4

0.3

0.2

0.1

0

400 500 600 700 800 900 1,000

Canopy reflectance, in percent

Wavelength, in nanometers

Figure 49. Spectral indicators resulting from a 

PolyVector Analysis of helicopter-based (previously 

derived) indicator spectra and Hyperion-based 

spectra (table 13). Spectral indicators were sought 

for remotely sensed identification of red-leaf Chinese 

tallow tree (Triadica sebifera) within the Toledo Bend 

Reservoir study area (Louisiana and Texas). 
Results 65

Table 13. PolyVector Analysis output indicator spectra correlations. Correlations of the three indicator spectra representing 

helicopter-based live vegetation (with Chinese tallow tree [Triadica sebifera]) and non-live vegetation, seeded indicators, and hardwood 

with all input spectra extracted from the Hyperion reflectance image within the hardwood landcover. The entry in each cell provides 

the correlation magnitude between each spectra or location (row) in the input database and each indicator spectrum (column). The 

indicator spectra are identified by a column of ones and zeros.

[UTM15, Universal Transverse Mercator, zone 15; Air_*, fixed-wing plane survey field site. Red font signifies seed indicator spectrum, and green font denotes 

indicator spectra associated with ground and aerial sites]

Site names1 Site location

Indicator

1 2 3

LiveVegetationwithChineseTallowtree Seed-Indicator Spectrum 0.0000 0.0000 1.0000

NonLiveVegetation Seed-Indicator Spectrum 0.0000 1.0000 0.0000

LiveVegetation Seed-Indicator Spectrum 0.0603 0.1719 0.7678

ChineseTallowtreeinHardwoods-Airpoint1 Air_584 0.9707 0.0503 -0.0210

ChineseTallowtreeinHardwoods-Airpoint2 Air_587 1.0000 0.0000 0.0000

RedtreesHardwoods-Airpoint3 Air_208 0.8612 0.1342 0.0046

RedtreesHardwoods-Airpoint4 Air_209 0.6967 0.2130 0.0903

RedGreenChineseTallowtreeedge-groundpoint5 2009_514 0.7194 0.2563 0.0243

ChineseTallowtreemoregreen-groundpoint6 2009_521 0.7644 0.2385 -0.0029

GreenYellowChineseTallowtree-groundpoint7 2009_480 0.7803 0.1478 0.0719

RowfenceChineseTallowtree-groundpoint8 2009_415M 0.6551 0.1610 0.1840

RedYellowGreenChineseTallowtree-groundpoint9 2009_505 0.5405 0.3709 0.0887

Hyperion Spectrum1 434010E, 3510870N UTM15 0.9044 0.1027 -0.0071

Hyperion Spectrum2 432000E, 3503490N UTM15 0.8297 0.1642 0.0061

Hyperion Spectrum3 440070E, 3503070N UTM15 0.9323 0.1288 -0.0611

1

Seed-indicator spectra were created from helicopter recordings while the remaining spectra were extracted from the Hyperion reflectance image.

and spectra representing unknown (lacking ground or air 

observations) hardwood locations were near zero. 

Results of the PVA processing of helicopter-based 

indicator spectra did not uniquely represent one canopy 

component, such as red-leaf Chinese tallow trees, but 

represented canopies likely or unlikely to contain some redleaf Chinese tallow trees. The correlation magnitudes were 

related, but not necessarily directly related, to the percent 

occurrence of red trees or red-leaf Chinese tallow trees. The 

correlation magnitudes indicated greater or lesser degrees of 

similarity between the indicator spectrum and each spectrum 

in the input reflectance dataset (table 13) extracted from the 

Hyperion reflectance image. Correlation with Indicator 3 

could simply represent similarity in overall spectrum shape, 

as reflected by the high correlation calculated with the other 

helicopter-based live vegetation spectra. The fact that this 

latter spectrum was associated with a location absent of 

Chinese tallow tree increased caution in direct interpretation 

of Chinese tallow tree or red tree occurrence predictions 

based on Indicator 3. Nevertheless, the preliminary 

validation based on ground and aerial observations provided 

a reasonable expectation that the set of three seed indicator 

spectra could provide mapped occurrences of the canopies 

most likely to contain some proportion of Chinese tallow 

trees or red trees. Based on that reasoning, the three 

indicator spectra were reformatted and entered into the PCI 

SPUNMIX procedure. 

Subsequent applications of these PVA indicator spectra 

to the 1D swath Hyperion reflectance image by using the 

SPUNMIX procedure demonstrate a possibility that it could 

provide relative likelihoods of red-leaf Chinese tallow tree (or 

red tree) occurrences within senescing or live vegetation (figs. 

50A–C). Limiting the occurrence mapping to the hardwood 

class and accounting for the misclassification associated 

with this land class (fig. 51), the predicted spatial distribution 

associated with each of the three indicator spectrum within 

the hardwood class were reasonable, particularly when 

associated with the live vegetation with Chinese tallow 

tree indicator spectrum. Although the likely occurrences of 

Chinese tallow trees or red trees, as represented by the live 

vegetation with Chinese tallow tree occurrence map, were 

spatially extensive, the associated magnitudes or percent 

occurrences associated with this spectral indicator were 

typically low (figs. 50 and 51). 
66 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

0 2.5 5 KILOMETERS

0 2.5 5 MILES

Live vegetation Senescent

vegetation

Live vegetation 

with red-leaf

Chinese tallow trees

A B C

Figure 50. Maps representing percent occurrence of A, live vegetation, B, senescent vegetation, and C, live vegetation with red-leaf 

Chinese tallow trees (Triadica sebifera). The percent occurrence is represented by the grey intensity; a lighter grey reflects a higher 

percent occurrence, whereas a darker grey reflects a lower percent occurrence (or in this case likely occurrence) within the 30- by 

30-meter Hyperion pixels. The coverages are within the Hyperion 1D swath.
X

Y

X

Y

0 2.5 5 KILOMETERS

0 2.5 5 MILES

0 2.5 5 KILOMETERS0 2.5 5 MILES

i ii

i ii

EXPLANATION

Extent of the swathExtent of the insetsX and YExtent of i and iiBitmap

A B C

X

Y

Figure 51. Imagery showing the likelihood of Chinese tallow trees (Triadica sebifera) or red trees occurring within hardwood forest stands in the Tolearea (Louisiana and Texas). A, Map showing percent predicted likelihood of Chinese tallow trees or red trees occurring within hardwood stands (recrebinomial representation of A with yellow highlighting of pixels in the Hyperion image (1D swath) to indicate all areas with a likelihood greater than zero or red tree occurrences within hardwood stands (binomial mask is overlain on the Advanced Land Imager [ALI] false-color image). C, Insets “X” and “predicted occurrences as depicted on the ALI image. Insets “i” and “ii” recreated in figure 52 illustrate the association of predicted occurrences with 
68 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

As illustrated on the insets (fig. 51C X), likely 

occurrences of vegetation containing red Chinese tallow tree 

are associated with senescing hardwood-bottomland stands. 

As observed in the fixed-wing survey, actual occurrences 

were fewer than exhibited in the percent occurrence map. The 

overestimation is related in part to occurrences associated 

with non-hardwood classes that had been misidentified as 

hardwoods in the ALI classification. In contrast, on the 

opposite shore (fig. 51C Y), relatively few likely occurrences 

of red-leaf Chinese tallow tree are contained in the large 

hardwood stand in the southwestern portion of the Hyperion 

1D swath. In addition, ground observations (photographs 

corresponding to fig. 52 i) tend to support the association 

of the predicted likelihoods of the hardwood containing 

red-leaf Chinese tallow tree or red trees as observed at the 

location captured in the aerial photograph corresponding to 

figure 52 ii. Overall, the predictions of red tree or red-leaf 

Chinese tallow tree occurrences were promising. Even though 

promising, they seemed more related to the likelihood of live 

vegetation containing some percentage of red tree or red-leaf 

Chinese tallow tree. In that context, the procedure performed 

as expected.

The limitation of the product was in the inability to 

directly validate the prediction accuracy or to directly 

apply this information to detecting red-leaf Chinese tallow 

trees. Based on the PVA association results (table 13) and 

the occurrence distribution (fig. 51A and C inset Y and 

fig. 52 ii), the spectral indicator seemed to discriminate 

between senescing hardwood stands likely to contain redleaf Chinese tallow trees and senescing hardwoods stands 

not likely to contain red-leaf Chinese tallow trees. Selection 

was not broadcast over all senescing hardwoods but only 

certain hardwood stands. The implication was that the 

predicted percent-occurrence magnitudes were related to an 

increased likelihood of red tree or red-leaf Chinese tallow 

tree occurrence; however, the accuracy of such likelihood 

predictions could not be determined based on ancillary or 

collected information. Nevertheless, because supporting 

evidence bolstered those likelihood predictions, this analysis 

and mapping method was applied to the other landcover 

classes and multiple Hyperion swath coverages. 

Extending PolyVector Analysis (PVA) Mapping with 

Helicopter-Based Indicator Spectra to Multiple Landcover 

Clases and Hyperion Swaths

In the extended mapping analyses, spectral databases 

were produced for all the major classes identified in the ALI 

classification: hardwoods, grasslands, planted pine, and mature 

pine; each of the priority Hyperion swaths (1D, 2C, 6E, 7B, 

and 4F); and for up to three dates for each swath-landcover 

class. An independent PVA input database was constructed for 

each variation in landcover class, swath, and date, and for each 

of these combinations, two different seed indicator sets were 

applied: one containing and one without a red-leaf Chinese 

tallow tree reflectance spectra. The format of each constructed 

input database and the PVA or SPUNMIX operational 

procedures followed the hardwood landcover class 1D swath 

example as described in the “Seeding with Helicopter-Based 

Indicator Spectra” section. Although confining each input 

database by land class decreased the variance input into 

PVA, it multiplied the number of databases that had to be 

constructed. Results of these applications are summarized and 

reported in table 14 and in the interactive version of that table 

available from the Web index page of this report. 

Landcover is the top-level division in table 14. Landcover 

is subdivided into Hyperion swaths (see fig. 11), and each 

swath is subdivided subsequently by collection date. Each 

collection date is associated with a hotlink to a datasheet 

containing the spectral database input into the PVA. Clicking 

on the hotlink retrieves the actual input datasheet used. PVA 

was applied to each input datasheet after including seed 

indicator spectra. Two indicator spectra sets were entered. 

The first contained only the “Helicopter-Vegetation with 

Tallow” and the “Nonlive Material” spectra (see fig. 45). The 

second additionally contained the “Known Red Tallow Leaf” 

spectrum (see fig. 26). The last column contains hotlinks 

to datasheets containing the PVA results based on the two 

indicator spectra sets (with and without the “Known Red 

Tallow” spectrum). In the PVA results datasheets, the seed 

indicator spectra correlations are highlighted in grey and 

indicator spectra associated with nonseed indicator spectra are 

highlighted in brown. Next, the PVA output indicator spectra 

correlations (results listed in the last column of table 14) were 

then used in the SPUNMIX spectral unmixing algorithm. 

The transformation of Hyperion image data to percentoccurrence image per landcover class did not consistently 

correspond with the observed and possible Chinese tallow tree 

occurrences as recorded during ground and aerial surveys. 

The predicted percent-occurrence maps did not show any 

consistent pattern per swath, date, or land cover class that 

could be associated with likely occurrence of Chinese tallow 

trees (or red trees). 

Discussion and Conclusion

Unprecedented and coordinated field and EO-1 NASA 

Hyperion and ALI data collections of the Toledo Bend 

Reservoir area were carried out between 2009 and early 

2011. In total, 24 clear-sky Hyperion and ALI image pairs 

were collected and 801 field ground and aerial field points 

were documented during the fall to early winter seasons of 

Chinese tallow tree senescence. The coordinated collections 

emphasized detection of senescing red-leaf Chinese tallow 

tree by linking observed occurrences to changes in the 

Hyperion data transformed to canopy reflectance. Linkage 

was performed by deriving Chinese tallow tree indicator 

spectra from the Hyperion canopy reflectance, helicopterbased canopy reflectance, and leaf-reflectance datasets. 

Segmentation by an ALI-based landcover class enhanced the 

subpixel detection performance. 
0 0.2 0.4 KILOMETER0 0.2 0.4 MILE

i

ii

2009_4992009_500

2009_502

AirP_209

Figure 52. Illustrations detailing possible associations between spectrally indicated predictions of likely occurrences of red-leaf Chinese tallow treeobserved occurrences documented (by photography) during aerial and ground reconnaissance within the Toledo Bend Reservoir study area (Louisiancorresponding to inset i of figure 51B is labeled “i,” and the area corresponding to inset ii of figure 51B is labeled “ii.” The names on the photographs, indicate the site names.
70 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

Table 14. Mapping vegetation likely associated with red-leaf Chinese tallow tree (Triadica sebifera) occurrences.

Land class

Hyperion image

Number of input 

Hyperion reflectance spectra

Seeding with Indicator spectra

PolyVector Analysis 

output -indicator spectra correlations Swath Date

Helicopter- vegetation

with Chinese tallow tree

Non-live 

material

Known red 

tallow leaf

Hardwoods 1D 11-Nov-09 23 X X Results-1

X X X Results-2

20-Dec-09 16 X X Results-1

X X X Results-2

5-Nov-10 17 X X Results-1

X X X Results-2

2C 4-Nov-09 25 X X Results-1

X X X Results-2

1-Dec-10 25 X X Results-1

X X X Results-2

6E 25-Dec-09 22 X X Results-1

X X X Results-2

24-Dec-10 16 X X Results-1

X X X Results-2

7B 19-Dec-10 25 X X Results-1

X X X Results-2

4F 18-Oct-10 8 X X Results-1

X X X Results-2

Grasslands 1D 11-Nov-09 12 X X Results-1

X X X Results-2

20-Dec-09 23 X X Results-1

X X X Results-2

5-Nov-10 23 X X Results-1

X X X Results-2

2C 4-Nov-09 12 X X Results-1

X X X Results-2

1-Dec-10 11 X X Results-1

X X X Results-2

6E 25-Dec-09 16 X X Results-1

X X X Results-2

24-Dec-10 6 X X Results-1

X X X Results-2

7B 19-Dec-10 11 X X Results-1

X X X Results-2

4F 18-Oct-10 5 X X Results-1

X X X Results-2

Planted Pine 1D 11-Nov-09 16 X X Results-1

X X X Results-2

20-Dec-09 14 X X Results-1

X X X Results-2

5-Nov-10 14 X X Results-1

X X X Results-2
Discussion and Conclusion 71

Land class

Hyperion image

Number of input 

Hyperion reflectance spectra

Seeding with Indicator spectra

PolyVector Analysis 

output -indicator spectra correlations Swath Date

Helicopter- vegetation

with Chinese tallow tree

Non-live 

material

Known red 

tallow leaf

2C 4-Nov-09 19 X X Results-1

X X X Results-2

1-Dec-10 19 X X Results-1

X X X Results-2

6E 25-Dec-09 15 X X Results-1

X X X Results-2

24-Dec-10 10 X X Results-1

X X X Results-2

7B 19-Dec-10 13 X X Results-1

X X X Results-2

4F 18-Oct-10 13 X X Results-1

X X X Results-2

Mature Pine 1D 11-Nov-09 13 X X Results-1

X X X Results-2

20-Dec-09 13 X X Results-1

X X X Results-2

5-Nov-10 13 X X Results-1

X X X Results-2

2C 4-Nov-09 18 X X Results-1

X X X Results-2

1-Dec-10 18 X X Results-1

X X X Results-2

6E 25-Dec-09 14 X X Results-1

X X X Results-2

24-Dec-10 10 X X Results-1

X X X Results-2

7B 19-Dec-10 12 X X Results-1

X X X Results-2

4F 18-Oct-10 4 X X Results-1

X X X Results-2

Table 14. Mapping vegetation likely associated with red-leaf Chinese tallow tree (Triadica sebifera) occurrences.—Continued
72 Remote Sensing Survey of Chinese Tallow Trees in the Toledo Bend Reservoir Area, Louisiana and Texas

Field Reconnaissance

Field reconnaissance provided critical information about 

the distribution of Chinese tallow tree and located sites of 

known occurrences that were used to calibrate and validate 

the performance of the detection process. Ground and aerial 

surveys suggest that Chinese tallow tree occurrences are 

infrequent and scattered without any observed pattern relative 

to the Toledo Bend Reservoir. The only pattern that did exist 

was the sporadic but repetitive occurrence of Chinese tallow 

tree along forest edges, water edges, and fence lines, probably 

most in line with seed dispersal by birds. Even though 

densities were low, Chinese tallow trees were observed to 

be more densely dispersed within some scrublands and 

grasslands than within pine, hardwood, and mixed forests. 

Excluding a couple of seemingly nonmanaged planted pine 

fields, we did not observe any notable establishment of 

Chinese tallow trees in the ubiquitous pine plantations. If 

the understory red trees observed in the hardwood fingers 

are Chinese tallow trees, the highest potential threat from 

this invasive species is most likely related to the close spatial 

association of pine plantations and hardwood fingers.

Problems in identifying Chinese tallow tree in aerial, 

and even on the ground surveys in 2009 and, especially, 

in 2010, were the high proportion of the foliage was green 

instead of red. Another problem in detection was related to 

spectral confusion. Spectral analyses of tree leaves collected 

throughout the study area found that red maple and sweetgum 

trees could contain senescing red leaves that were spectrally 

similar to Chinese tallow tree senescing red leaves. That 

confusion caused ambiguity in the identification of red-leafed 

trees as Chinese tallow trees in the aerial surveys. In order 

to acknowledge that identification confusion, the term “red 

trees” was applied to all observations of red-leafed trees 

during aerial surveys. 

Chinese tallow tree, or for that matter red maple and 

sweetgum trees containing senescing red leaves, were 

uncommon at the top of canopy (TOC) position within mature 

pine or hardwood canopies and within pine plantations, 

scrublands, and grassland fields. Unless these understory 

red trees were observable through gaps in the canopy, it was 

unlikely they could be detected in the canopy-reflectance 

spectra whether derived from aerial or the Hyperion image 

data. Furthermore, where red-leaf Chinese tallow trees 

did occur, the percent occurrences within multiple and 

contiguous 30- by 30-m patches (size of area determined to 

simulate the Hyperion IFOV as represented as image pixels) 

were extremely low. The highest frequency of observed 

concentrations of red-leaf Chinese tallow trees and red trees 

were found in isolated small patches containing scattered 

shrubs or one to two canopy trees (figs. 24 (HS15), 33A, and 

35E–H). As an estimate, the two TOC-positioned red-leaf 

Chinese tallow trees (figs. 38A and B) could encompass an 

area of 9–36 m2 that, if perfectly contained in a single 30-m2

patch, would account for a 1–4 percent occurrence in the 

Hyperion pixel. 

Creation of Workable Calibration Spectra

Previous work discussed in the “Background” section 

found that 10 percent occurrence rates of red-leaf Chinese 

tallow trees were accurately detected in a Hyperion pixel 68 

percent of the time and 15 percent Chinese tallow trees 85 

percent of the time (fig. 5; Ramsey and others, 2005b). Three 

to four TOC-positioned mature Chinese tallow trees fully 

foliated with red leaves could represent upwards of 10 percent 

coverage within a single Hyperion pixel. Similarly, tens of 

Chinese tallow tree shrubs fully foliated with red leaves in an 

open area could comprise 10 percent of a pixel. As confirmed 

in the same studies, however, red-leaf Chinese tallow trees 

making up much less than 10 percent of the Hyperion pixel 

were often detected. Although the detection accuracy at a 10 

percent occurrence threshold is 68 percent, detection of redleaf Chinese tallow tree at lower percentages of occurrence 

was not excluded. The accuracy of detection at increasingly 

lower occurrence rates became progressively lower. 

 In this project, few observed occurrences of red-leaf 

Chinese tallow trees reached 1 to 4 percent coverage within 

the 30- by 30-m Hyperion pixel. Given the detection-accuracy 

threshold of 68 percent associated with vegetation coverage of 

10 percent of the pixel (much higher than what we observed), 

it was unknown whether or not we could identify a workable 

set of calibration spectra from the canopy reflectance data. 

For Ramsey and others (2005b), we had created a successful 

detection-calibration spectrum for red-leaf Chinese tallow 

trees by using canopy-reflection spectra extracted from 

Hyperion reflectance images. For that study, we had used 

helicopter-based spectra to cross-validate the set of spectra 

derived from the Hyperion images. In effect, procedures in 

the current study largely followed those previously successful 

methods, with the most important difference being the lack of 

red-leaf Chinese tallow trees occurring at or above 10 percent 

coverage within the Toledo Bend Reservoir study area. 

Attempting the Detection of Chinese Tallow Tree 

with Calibration Spectra

Even though the scarcity of Chinese tallow trees caused 

a challenge in building a workable spectral calibration dataset 

for Hyperion subpixel detection, calibration datasets were 

constructed. The initial calibration datasets included canopyreflectance spectra of selected locations where ground and 

aerial reconnaissance provided known presence or absence 

of red-leaf Chinese tallow trees (or red trees) and of locations 

where presence or absence of Chinese tallow tree occurrences 

were unknown were extracted from Hyperion reflectance 

images. In these initial attempts to identify calibration spectra, 

all land classes were combined within a single dataset, but 

this procedure failed to identify a set of calibration spectra 

capable of distinctly identifying the targeted vegetation. The 

failure of the first procedure led to separating the Hyperion 

reflectance datasets by landcover classes (based on the ALI 

classification map). Separating the data by class tended to 
References Cited 73

reduce the variance of the input dataset, thereby promoting 

spectral discrimination and raising the performance of the 

spectral analysis. Added to these class-based reflectance 

calibration datasets were spectra with known associations 

with red-leaf Chinese tallow trees. These known spectra 

included results of previous Chinese tallow tree mapping 

projects (Ramsey and others, 2005b), leaf spectra collected 

during the current study, and modified spectra from the 

canopy and leaf data. The addition of these seed spectra was 

intended to guide the program used to identify the calibration 

spectra. Unfortunately, none of these attempts resulted in an 

unambiguous set of calibration spectra with reliable capability 

to detect red-leaf Chinese tallow tree occurrences within the 

Toledo Bend Reservoir study area (table 14). The scarcity 

of red-leaf Chinese tallow tree occurrences in the study area 

prevented identifying a set of calibration spectra that would 

specifically detect it. 

Even though specificity in detecting Chinese tallow 

tree was lacking, based on indirect evidence, primarily 

aerial photography that spatially coincided with predicted 

occurrences, a pattern emerged that indicated the more 

spatially expansive predicted distributions were more likely 

to incorporate observed locations of red-leaf Chinese tallow 

tree than not. The associated pattern, however, changed 

from one produced map to the next. No one set of spectral 

indicators produced more consistent results or clear patterns of 

association than did another. Although the likely association 

was tenuous, we examined the helicopter-based spectral 

indicator set with the idea in mind of mapping vegetation 

associations within a class that had higher likelihoods of 

containing red-leaf Chinese tallow trees, instead of directly 

detecting and mapping specific occurrences.

Indicator Spectra Derived from Helicopter-Based 

Reflectance Spectra

The helicopter-based spectral indicator set was chosen 

because it offered a single indicator set and was directly 

identified from canopy-reflectance spectra representing 

observed red-leaf Chinese tallow tree occurrences and 

vegetation classes. The drawbacks were related to the limited 

number of landcover classes represented, inherent noise, and 

lack of a specific Chinese tallow tree indicator spectrum. Even 

with these drawbacks, we believed the canopy spectra derived 

from helicopter and simultaneous ground measurements 

provided the best opportunity to construct likelihood of 

occurrence maps that could be interpreted with respect to redleaf Chinese tallow trees. 

The helicopter-based spectral indicator set was integrated 

into spectral calibration datasets representing each major 

landcover class in each of the five Hyperion swaths that make 

up the priority coverage of the dominant landcover classes 

in the study area. Predicted occurrences produced from 

these calibration datasets illustrated a possibility of creating 

occurrence maps that would indicate vegetation associations 

most likely to contain Chinese tallow trees, instead of specific 

occurrences of the species itself. Inspection of output datasets 

showed mixed performance in the differential alignment of 

each indicator spectrum with vegetations likely or unlikely to 

contain red-leaf Chinese tallow trees (or red trees). Similarly, 

predicted occurrence maps exhibited no overall consistent 

pattern per landcover class or per Hyperion swath that 

could be solely associated with likely Chinese tallow tree 

occurrences. At times, the predicted distribution appeared 

promising; however, at many other times the predicted 

distribution of red-leaf Chinese tallow trees (or red trees) 

co-occurred with nearly the entire land class distribution. 

Ultimately, the inability to fully evaluate the predicted 

relationship of Chinese tallow trees with particular vegetation 

associations within each land class prevented further analyses 

of this possible relationship.

Implications for Future Mapping Efforts

As observed in the ground and aerial surveys, even 

though Chinese tallow tree occurrences are scattered 

and its numbers are low, the distribution is widespread 

throughout the study area. These scattered occurrences, 

combined with landscape dynamics and the bird-dispersal 

mechanism, indicate the potential for further spread in the 

study area unless contained by natural forces or management 

strategies. Future efforts to document and control the spread 

of this invasive species in the Toledo Bend Reservoir area 

can benefit from the current study. We suggest that a high 

spectral resolution sensor similar to the Hyperion but with 

an increased signal-to-noise level and, most importantly, a 

higher ground-level spatial resolution would better detect 

Chinese tallow tree at the present level of occurrences. A 

10- by 10-m ground-level spatial resolution would increase 

the detectable range of Chinese tallow tree occurrences from 

less than 5 percent to 45 percent of the ground pixel, which 

would be well above the maximum range associated with the 

successful mapping of the species in southwestern Louisiana. 

Aircraft and sensor systems are available to conduct high 

spectral mapping at this ground-level spatial resolution and 

much higher. Built on the knowledge and baseline databases 

and maps developed in this study, the probable success rate of 

mapping with a higher ground-level spatial resolution and a 

higher spectral resolution is high.

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Ramsey and others—Remote Sensing Survey of Chinese Tallow Tree in the Toledo Bend Reservoir Area, Louisiana and Texas—Open-File Report 2012–1215