Hyperspectral Remote Sensing of Invasive Species Inside

Cedar Point National Wildlife Refuge:

A Preliminary Report

By

Benjamin Thomas

With contributions from

Richard Beck, Chantal Ivenso, & Christopher Carr

Introduction

The goal of this project was to ascertain whether it was feasible to detect invasive plant species

inside Cedar Point National Wildlife Refuge located in Lucas County, Ohio. (Figure 1)

Figure 1. Cedar Point National Wildlife Refuge. Study area outlined in GPS tracks taken during our

reconnaissance of the study area

Mapping invasive species in a wetland such as Cedar Point National Wildlife Refuge with

traditional field survey methods is very difficult and potentially damaging to the vegetation inside the

refuge. In addition, wetlands are often too wet for walking and too dry for boating. Invasive species are

major concern to government agencies, such as the US Fish and Wildlife Service (FWS) and US

Department of Agriculture (USDA). Both FWS and USDA are tasked with figuring out ways of tracking

the spread of invasive species. To that end, FWS and USDA have created partnerships and grants

programs to assist interested organizations in combating invasive species. (For specifics see

http://www.fws.gov/grants/ and http://www.fws.gov/invasives/partnerships.html) (For a comprehensive

listing of grant programs to combat invasive species see

http://www.invasivespeciesinfo.gov/toolkit/grants.shtml). In this report, we examined the use of remote

sensing to map invasive species because of its non-destructive approach. In this study, we believe we

detected invasive species in Cedar Point National Wildlife Refuge using techniques described below.

Refuge History

Cedar Point National Wildlife Refuge is part of a series of three marshes which collectively form

Ottawa National Wildlife Refuge. The marsh regions around Lake Erie are remnants of the Great Black

Swamp (FWS Ottawa Refuge). Cedar Point consists of three shallow pools and grassy marshland which

cover approximately 2,445 acres (FWS Cedar Point Refuge).

Background

The three target species in question are the invasive plants Phragmites (Phragmites australis),

Purple Loosestrife (Lythrum salicaria), and Flowering Rush (Butomus umbellatus) (see appendix for

pictures). Phragmites is a tall, perennial grass believed to be native of Eurasia. It has the ability to

outcompete native vegetation and induces change in local marsh hydrology (National Park Service PCA).

Purple loosestrife is also a perennial grass and was brought to the United States from Europe for flower

gardens. This plant can crowd out native flowering plants which local wildlife relies on for food and

habitat (Univ of Minn Sea Grant). Purple loosestrife can reproduce very quickly creating a monoculture

environment. This led to FWS declaring purple loosestrife: “Public Enemy #1 on Federal Lands” (Liu et

al. 2006). Flowering rush is another perennial that is native of Eurasia and is thought to have also been

spread through the ballast of ships. It grows vigorously in waterways making it a possible hazard to boat

traffic (Indiana Department of Natural Resources).

Hyperspectral remote sensing is unique because of its ability to identify vegetation based on their

spectra. However, the spectral characteristics (colors) of plant species are often similar. Therefore it may

be difficult to map individual species with spectral methods depending on the species to be mapped and

its background (Bachman et al., 2002; Goodenough et al., 2002; Ustin et al., 2002; Lopez et al., 2004;

Pengra 2005). Success or failure will be based in part on location and ecosystem as well as time of year.

We began a feasibility study during the summer of 2010.

Data Collection and Processing

During the field season of July and August 2010, a group of students from the University of

Cincinnati began collecting reflectance spectra from these three species (Figure 2). Reflectance spectra

were collected for classification and mapping of our target species in conjunction with hyperspectral data

using techniques described below in the analysis section. To ensure an adequate representative sample

size was obtained, we collected 60 reflectance samples of the flowers of these species using the Analytical

Spectral Devices (ASD) field spectrometer which can detect spectra from the visible/near infrared to

shortwave infrared (350 – 2500nm). However, our attempt to collect reflectance spectra from phragmites

was hampered by technical errors and unfavorable atmospheric conditions which resulted in spectra that

were unusable. As a result, an unsupervised classification technique, described in greater detail below,

was used. Once the spectra were obtained, we averaged the middle 50 samples and created a spectral

library file (Figure 3).

Figure 2. Chantal Ivenso and Ben Thomas taking reflectance spectral samples of flowering rush

Figure 3. Reflectance spectral libraries of the three target species

After the field spectra were collected, we began to process the hyperspectral images that were

acquired of the entire refuge throughout the summer of 2010. The hyperspectral imager used was an

AISA Eagle imaging unit built by Specim Ltd., Oulu, Finland (Table 1). The instrument was installed in a

Cessna 172 Skyhawk airplane and flown at an altitude of 5,000 feet above ground level (AGL) which

resulted in an instrument spatial resolution of 1 meter. For our feasibility study, the hyperspectral image

used was taken on 5 August 2010 at approximately 12:00 pm EDT (Figure 4).

Table 1. AISA Eagle Hyperspectral Sensor Characteristics

Sensor type Pushbroom spectrometer

Positioning system Integrated GPS/INS navigation

Optical parameters Spectral range of 430 – 970 nm

128 spectral bands with selectable bandwidth

Ground swath 1024 meters

Figure 4. An unprocessed hyperspectral image displayed in true color of the northernmost point of Cedar

Point. Also visible is the SkySentry aerostat airship moored in the lake near the bottom left portion of the

refuge

We radiometrically and atmospherically corrected the images to filter out atmospheric and

instrumental influences using the Caligeo processing software. This software was developed by Specim

Ltd. for use in processing images taken from their camera products. The software runs a separate module

under ENVI-IDL remote sensing software. The resulting reflectance images were geo-registered to a 1

meter USDA National Agriculture Imagery Program (NAIP) true color image of the marsh taken in June

2010 to ensure their approximate location. After geo-registration, we performed a minimum noise fraction

(MNF) transformation on the hyperspectral images. The purpose of an MNF transformation is to reduce

and segregate noise and to reduce data dimensionality, which decreases processing time (Boardman and

Kruse, 1994).

During the month of October 2010, another group of students from the University of Cincinnati

returned to Cedar Point. One of the goals of this field operation was to ground truth the location of our

target invasive species to aid in accuracy assessment. This involved sampling along vegetation transects

spaced 1000 meters apart along the access road lining the marsh, which was only possible during this time

of the year as the marsh was at its driest point. Based on this spread, we performed 11 transects, which

was sufficient to cover all of Cedar Point. The vegetation transects started perpendicular to the side of the

road and continued until we could not walk any farther into the marsh. During the sampling, Ph.D

candidate Christopher Carr and I recorded the presence/absence of our target invasive species (see

appendix for data collection sheet).Ultimately, tall marsh vegetation stopped us from sampling farther on

some transects (Figure 5). We positively identified patches of phragmites and purple loosestrife on some

of the field sampling transects and marked their location using Garmin GPS units. We converted this data

into GIS files for later analysis (Figure 6).

Figure 5. Ben Thomas was stopped by tall marsh vegetation on transect 5

Figure 6. Because of very dry conditions, the northern section of Cedar Point allowed us to sample farther

than other transects. On GPS points 11, 12, and 14 we identified purple loosestrife. GPS point 15 is the

location of a large patch of phragmites

Analysis

The classification techniques include unsupervised classification and spectral angle mapper

(SAM), which is a form of supervised classification, using ENVI 4.7 image analysis software. The SAM

classification tool attempts to match image spectra to known spectra collected in the field or laboratory. It

does this by treating both the image and collected spectra as vectors and calculating the spectral angle

between the two in image space to determine a match (Dennison et al. 2004). (Figure 7)

Figure 7. The spectral angle is determined by user input (Adapted from ENVI help tutorial)

As a standard practice, I began the remote sensing analysis using an unsupervised classification to

see if it was possible to detect purple loosestrife or phragmites since both of these species are large

enough or occur in large patches to be seen by the hyperspectral imager. Flowering rush, unfortunately,

could not be identified during our field sampling in the marsh and appears to be too small to be detected

by the hyperspectral imager. This type of classification is used when the analyst knows little about the

characteristics of the study area. An unsupervised classification is a type of classification where the

software clusters pixels into similar groups in the dataset based on statistics alone without user knowledge

of the amount of variation within the image. In this case, the “statistics” are the spectral bands in the

image. In this way, one can see, for example, a cluster of trees separated by other trees based on their

spectra.

In my attempt at unsupervised classification, I used the Iterative Self Organizing Data (ISODATA)

unsupervised classification tool. The ISODATA tool calculates the means of each data value and then

iteratively clusters the remaining pixels. Each iteration attempts to classify pixel values with new means.

The iteration stops once the user supplied threshold is reached (Jensen 2005). The first attempt involved

using 10 spectral classes with a target threshold of 3%. Initial results proved favorable; however, it was

soon realized that our target species had spectral characteristics similar to grass and other marsh

vegetation that made it difficult to separate them spectrally. Next, I used 20 spectral classes with a target

threshold of 2% top see if any individual species could be classified in the image. The classification

resulted in better separation of different vegetation characteristics; yet, it also displayed anomalies which

could not be identified through photo interpretation. After this, I used 15 classes with a target threshold of

2%. This resulted in a classification that differentiates broad vegetation/eco zone classes within the marsh

without displaying too many anomalies. However, I was unable to differentiate phragmites or purple

loosestrife. (Figures 8 & 9)

Figure 8. Atmospherically corrected and georegistered hyperspectral true color image of the northern tip

of Cedar Point

Figure 9. Unsupervised ISODATA classification of the hyperspectral image with 15 classes

The SAM tool was subsequently used to see if it could detect purple loosestrife using our collected

ground reflectance spectra. In ENVI 4.7 software, the default radians angle is 0.10, which is a relatively

large angle. It is well documented in remote sensing literature that a lower threshold setting is necessary

for spectra matching as it allows for subtle differences in pixel values to be detected (Cho et al., 2009;

Kutser and Jupp, 2006). Using an iterative process, I changed the detection threshold of the SAM tool to

lower settings until the signature appeared where we found it and did not find it during our field sampling

(Figure 10). We confirmed its existence on one of our vegetation transects (Figure 11).

Figure 10. Spectral signature of pixels presumed to be purple loosestrife based on SAM classification.

Note that reflectance units are from 0 – 4000. Reflectance values are derived by dividing the reflectance

units by 10000

Figure 11. NAIP image with SAM classification of purple loosestrife signatures displayed in red. The

triangles represent our GPS transect points. In the circle above, we located a patch of purple loosestrife

Conclusion

Based on our results presented above, we believe our classification results using the SAM tool are

plausible. However, the SAM tool is based on subjective user input thus we need more ground truth data

to assess our accuracy. We also conclude that we are able to map broad wetland vegetation classes/eco

zones with unsupervised classification techniques. This is important as it allows us to see patterns and

relationships within the marsh.

For the next field season, it is advantageous to continue collecting spectra from our target invasive

species and ground truth data so that we can continue to refine our methods to achieve better accuracy.

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Appendix

Phragmites

Purple Loosestrife

Flowering Rush

Ben Thomas and Chris Carr

Cedar Point Vegetation Sampling

Date__________ Start Time_________ By__________

Transect number__________

GPS data Waypoint # of averages Est. accuracy GPS

owner/model

Start

End

Camera model ______________ Photo number ______________

Photo view direction (°M):__________________

Transect compass direction_______

Distance (m) Species Event

Species: Event:

Phragmites – PH Start

Purple Loosestrife – PLS Stop

Flowering Rush – FLR Overall length

Acknowledgements

Support for this remote sensing project was provided by SkySentry LLC and a grant from the

United States Army. In addition, I would like to recognize the help of Dr. Hongxing Liu, Haibin Su,

Chau-Sang Wu, Kevin Magee, and Andrew Rettig for data collection.