# Chapter 6. Predicting Risk of Invasive Species Occurrence: Remote-sensing Strategies DRAFT April 23, 2007

Kendal E. Young and T. Scott Schrader, New Mexico Cooperative Fish and
Wildlife Research Unit, Department of Fishery and Wildlife Sciences, New
Mexico State University.

## Why Use Remotely Sensed Data?

![](media/image1.png){width="4.0625in"
height="3.1458333333333335in"}Knowledge of invasive species occurrence,
distribution, and potential invasion pathways is important in developing
appropriate long-term monitoring protocols. However, costs associated
with ground-based visits preclude the National Park Service from
inventorying all associated park lands to determine invasive species
presence. One potentially cost-effective approach in identifying
potential occurrences of invasive species is to predict their
distributions using remotely sensed data and knowledge of species
ecology and environmental tolerances (Everitt et al. 1996, Goslee et al.
2003, Osborn et al. 2002, Parker-Williams and Hunt 2004). Once potential
areas of invasive species occurrences are predicted, ground
reconnaissance can be more effectively used and applied to an early
detection context for monitoring, verification, and control.

Remote sensing is a means to describe characteristics of an area without
physically sampling the area. Remote sensors can be mounted on a
satellite, plane, or other airborne structure. Remotely sensed data
allow for landscape perspectives on management issues. Sensors measure
the electromagnetic energy reflected from an object or area on the
earth's surface (Figure 6.1). These sensors measure energy at
wavelengths which are beyond the range of human vision. The guiding
principal is that different objects (e.g., soils, plants, buildings,
water) reflect and absorb light differently at varying wavelengths.
Graphically plotting the amount of radiation reflected at a given
wavelength, provides a unique signature for an object, especially if
there is sufficient spectral resolution to distinguish its spectrum from
those of other objects (Figure 6.2). Reflectance of clear water is
typically low, with initial higher reflectance values in the blue end of
the spectrum which decreases as wavelength increases. Vegetation
reflectance is typically low in both the blue and red regions of the
spectrum, due to absorption by chlorophyll. Reflectance values peak at
the green region which is due to the green color of vegetation. In the
near infrared (NIR) region, reflectance is much higher than that in the
visible bands due to leaf cellular structure. Therefore, vegetation can
be identified by the ![](media/image2.png){width="4.184722222222222in"
height="3.0625in"}high NIR but generally low visible reflectance.
Spectral reflectance curves can be used to discriminate between
vegetation types or plant species.

Many Geographic Information System (GIS) datasets are created from
remotely sensed data. For example, digital elevation models (DEMs) are
derived from space or aircraft borne sensors. Most GIS software packages
can calculate slope and aspect from a DEM. Remotely sensed data can also
be used to augment GIS datasets, by allowing visual interpretation of
images for roads, waterways, fence lines, buildings, and other features.
These features can be readily digitized and placed into a GIS
environment.

Remote sensing and GIS technologies were initially developed for
different purposes. Traditionally, the two disciplines worked
independently developing new uses for spectral or spatial data (Atkinson
and Tate 1999). Recently, there has been a merging of these two
disciplines, as scientists learn the benefits of integrating remote
sensing with GIS (Hinton 1999). Current computer software and hardware
facilitate the easy integration of these data sources. Most GIS software
packages allow remotely sensed data to be analyzed, or at least viewed.
This ability allows the analyst to overlay remote sensing data layers
with other spatial data layers. Both spectral and spatial data can
provide information about the invasive species occurrences within
National Parks. As such, integrating remotely sensed data with GIS data
shows promise in modeling invasive plant habitats in National Parks
(Anderson et al. 1996).

Over the last decade, there has been a substantial increase in the
number of publications pertaining to modeling invasive species using
remotely sensed data (Chudamani et al. 2004). Information can be found
in a variety of journals, technical reports, symposia and workshops, and
on the internet. Journals that support remote sensing publications for
invasive species include: *Weed Technology*, *Weed Science*,
*Photogrammetric Engineering and Remote Sensing*, *Remote Sensing of
Environment*, *International Journal of Remote Sensing*, *Journal of
Ecology*, *Journal of Range Management*, and *Journal of Arid
Environments*.

We provide a review of the utility of using remotely sensed data in
modeling the potential distribution of invasive plant species. A
complete description of all remote sensing applications for invasive
species is beyond the scope of this document. Chudamani et al. (2004)
and Lass et al. (2005) also provide recent overviews on the utility of
using remotely sensed data for early detection of invasive plants. For
an application of remote sensing techniques to invasive plant early
detection in Big Bend National Park, see Chapter 13.

## When to Use Remotely Sensed Data 

Remotely sensed data may not always be the most effective approach for
early detection of invasive plants. There are still limitations
associated with the data resolution, processing, and costs.
Nevertheless, our ability to detect, map, and monitor invasive plant
populations or suitable habitats with remotely sensed data has greatly
increased over the last decade. Remotely sensed data could assist in
early detection protocols [if]{.underline}:

-   The area of consideration is large (park, network, region, or area
    too large for effective ground surveys).

-   Resource managers have access to GIS capabilities or contractors.

-   One or a few invasive plants of interest exhibit unique phenological
    or habitat associations.

-   There is a need to prioritize ground survey efforts. (Remote sensing
    can gather information over a wide geographic area in a short amount
    of time to assist ground surveys.)

-   There is a need to estimate the likelihood of invasive plant or
    suitable habitat presence in areas not easily accessible by ground.

-   There is a need to evaluate areas where an existing population may
    spread (early detection in new or adjacent areas). This may arise if
    an invasive plant occurs outside of a park, network, or other
    management unit and resource managers need to locate where the plant
    may start to occur inside the park.

-   There is a need to understand which land parcels are most at risk to
    plant invasion. Risk analyses using GIS and remotely sensed datasets
    allow for estimates that can cross jurisdictional boundaries.

-   There is a need to describe landscape trends prior to invasions or
    the initiation of monitoring programs. (Multiple years of imagery
    can be analyzed to create a multi-temporal dataset.)

Conversely, resource managers may wish to consider other modeling
approaches that assist in early detection (see Chapter 7)
[if]{.underline}:

-   Invasive plant populations are known to be sparse, small or diffuse
    patches, which may be the case for early detection programs. (Remote
    sensing techniques may not be cost effective.)

-   Complete census for invasive plants is feasible for the area of
    concern.

-   Degree or severity and location of the invasive population are
    already well known.

-   Invasive plant populations are obscured by the over story vegetation
    (e.g., canopy trees).

-   Invasive plant populations do not exhibit unique phenological
    differences from the surrounding landscape or unique habitat
    associations.

-   There is no access to GIS capabilities or contractors.

NASA Office of Earth Science and the U.S. Geological Survey are
developing a National Invasive Species Forecasting System. This system
is for early detection and management of invasive species, and includes
the use of satellite data for invasive species modeling
(<http://bp.gsfc.nasa.gov/>). Initiatives such as this will help develop
methodologies and models that will overcome existing challenges in using
remotely sensed data for invasive plant detection and management.

## Spatial, Spectral, and Temporal Scale Issues 

The efficacy of remote sensing data for detecting invasive plants or
associated habitat is a function of the sensors' spatial and spectral
(bandwidth) resolution, and the sensors\' repeat cycle. When planning
remotely sensed projects, these factors need to be considered with
respect to project objectives (Hobbs 1990). It is often a challenge to
balance the scale and resolution of the source data with the information
need.

### Spatial Considerations

**Spatial resolution** describes the amount of detail an image contains
across a given distance, typically a cell size. The ability to
"resolve," or describe small details or objects, is one way of
describing spatial resolution. As such, smaller objects are typically
better "resolved" or detected with high-resolution images. IKONOS and
QuickBird images have spatial resolutions less than 5 meters and thus
are considered high-resolution images. Conversely, Advanced Very High
Resolution Radiometer (AVHRR) is considered a low-resolution image with
a spatial resolution of 1.1 kilometers.

### Spectral Considerations

There are three generalized categories for sensors with different
bandwidths. *Panchromatic* sensors are sensitive to radiation within a
broad wavelength range. The physical quantity measured is the brightness
of the object. When the wavelength ranges coincide with the visible
range, the resulting image resembles a \"black-and-white\" photograph.
In this case, "color" information is lost. IKONOS, SPOT, and Landsat 7
Enhanced Thematic Mapper Plus (ETM +) each have a panchromatic
bandwidth. *Multispectral* sensors are sensitive to radiation within
several narrow wavelength bands. These sensors register reflectance in a
number of spectral bands throughout the visible, near- to far-infrared
portions of the electromagnetic spectrum. The result is a multilayer
image which contains both the brightness and spectral color information
of the landscape. These broad-band scanners have been successfully
applied to discriminate between broad land cover types. Multispectral
sensors include Landsat 7 Enhanced Thematic Mapper Plus (ETM +), SPOT,
IKONOS, and QuickBird, among others. *Hyperspectral* sensors acquire
data in more (10 to several 100) but narrower (from 10 to a few
nanometers wide) spectral bands than broad band sensors. This precise
spectral information allows for capturing finer spectral characteristics
(less variability per bandwidth) which yield better identification of
objects. NASA Jet Propulsion Laboratory Airborne Visible/Infrared
Imaging Spectrometer and the Probe-1 are examples of hyperspectral
systems.

Sensors that yield high spatial resolution data and have hyperspectral
capabilities have the highest likelihood of detecting microhabitats or
rare plants (Marcus 2002, Marcus et al. 2003, Lass et al. 2005, Lawrence
et al. 2006). However, these data also tend to be expensive and their
relatively small swath size (ground area of the image) requires
extensive computer processing time and storage for analyses of large
areas. As such, large parks will likely need to compromise bandwidth and
spatial resolution to model invasive species occurrences across large
landscapes.

### Temporal Considerations

Although remotely sensed data are often used for mapping vegetation and
general land cover types, mapping individual plant species imposes many
challenges. Similar spectral signatures between the target plant and the
surrounding environment, changes in soil color or moisture, and low
plant densities hinder discrimination efforts. However, seasonal
differences in plant phenology may help in detecting invasive plants.
Some invasive plants flower or green-up at a different time than the
surrounding vegetation. Multiple image dates allow for detecting these
phenological differences between target plants and the surrounding
landscape. For example, remote sensing data that coincided specifically
to flowering events aided in the detection of yellow starthistle
(*Centaurea solstitalis*) (Lass et al. 1996), and leafy spurge
(*Euphorbia esula*) (Anderson et al. 1996, Parker-Williams and Hunt
2002). Likewise, Peters et al. (1992) found that broom snakeweed
(*Gutierrezia sarothrae*) could be differentiated from grassland species
because of its distinct phenological characteristics. As such, the
repeat cycle of the sensor may be a key criterion to consider when
selecting imagery. Imagery dates should correspond to critical
recognition phases of the target plant (Hobbs 1990, McGowen et al. 2001,
Chudamani et al. 2004).

## Types of Remotely Sensed Data

There are a variety of remotely sensed data that may be used to detect
invasive plants or potential invasive plant habitats (Table 6.1). The
data chosen for individual projects or parks will depend on several
variables, including:

1)  fiscal considerations,

2)  goals and objectives,

3)  park size,

4)  distribution and patch sizes of current (known) invasive plants,

5)  invasive species (i.e., species phenologically different than the
    surrounding land cover),

6)  availability of computing resources,

7)  and availability of a Remote Sensing Analyst or consultant for image
    manipulations.

Figure 6.3 provides a general guide to selecting a potential group of
sensors to detect invasive species or model their habitats based on park
size, differences in plant phenology (target plants that exhibit unique
physical characteristics compared to surrounding vegetation), amount of
area infested by invasive plants, and the amount of canopy cover of
invasive plants. Other considerations may apply when selecting an
appropriate remote sensor.

Table 6.1. Spatial and spectral resolution of remote sensors that may be
useful for detecting potential invasive plants or modeling their
habitats. This list is not inclusive of all possible remote sensors
available as of December 2006.

  ------------------ -------------- -------------- -------------- ---------------- ----------- -----------
  **Sensor**         **Spatial      **Bands**      **Wavelength   **Color**        **Swath**   **Repeat
                     Resolution**                  (*u*m)**                                    Path**

  Advanced Very High                                                                           
  Resolution                                                                                   
  Radiometer (AVHRR)                                                                           

                     1.1 km         1              0.58 - 0.68    Red              2,399 km    2 per day

                     1.1 km         2              0.73 -1.10     Near Infrared    2,399 km    

                     1.1 km         3a             1.58 - 1.64    Mid Infrared     2,399 km    

                     1.1 km         3b             3.55 - 3.93    Mid Infrared     2,399 km    

                     1.1 km         4              10.30 - 11.30  Thermal Infrared 2,399 km    

                     1.1 km         5              11.50 - 12.50  Thermal Infrared 2,399 km    

                                                                                               

  Multispectral                                                                                
  Scanner (MSS)                                                                                

  []{.mark}          80 m           1              0.45 -- 0.52   Blue             185 x 170   16 days
                                                                                   km          

  []{.mark}          80 m           2              0.52-0.60      Green            185 x 170   
                                                                                   km          

  []{.mark}          80 m           3              0.63-0.69      Red              185 x 170   
                                                                                   km          

  []{.mark}          80 m           4              0.76-0.90      Near Infrared    185 x 170   
                                                                                   km          

                                                                                   []{.mark}   []{.mark}

  Landsat 7 Enhanced                                                                           
  Thematic Mapper                                                                              
  Plus (ETM +)                                                                                 

  []{.mark}          15 m           Panchromatic   0.52 -- 0.90                    185 km      16 days

                     30 m           1              0.45-0.52      Blue             185 km      

                     30 m           2              0.53-0.61      Green            185 km      

                     30 m           3              0.63-0.69      Red              185 km      

                     30 m           4              0.75-0.90      Near Infrared    185 km      

                     30 m           5              1.55- 1.75     Shortwave        185 km      
                                                                  Infrared                     

                     60 m           6              10.40- 12.50   Thermal Infrared 185 km      

                     30 m           7              2.09 - 2.35    Shortwave        185 km      
                                                                  Infrared                     

  SPOT (5)                                                                                     

                     2.5 or 5 m     Panchromatic   0.48-0.71                       60 - 80 km  26 days

                     10 m           1              0.50-0.59      Green            60 - 80 km  

                     10 m           2              0.61-0.68      Red              60 - 80 km  

                     10 m           3              0.78-0.89      Near Infrared    60 - 80 km  

                     20 m           4              1.58-1.75      Mid Infrared     60 - 80 km  

  []{.mark}          []{.mark}      []{.mark}      []{.mark}      []{.mark}        []{.mark}   []{.mark}

  IKONOS                                                                                       

                     1 m            Panchromatic   0.53-0.93                       70.3 km     Various

                     4 m            1              0.45-0.52      Blue             23.9 km     

                     4 m            2              0.51-0.60      Green            23.9 km     

                     4 m            3              0.63-0.70      Red              23.9 km     

                     4 m            4              0.76-0.85      VNIR             23.9 km     

                                                                                               

  QuickBird                                                                                    

                     60 cm          Panchromatic   0.45-0.90                       16.5 km     3-7 days

                     2.4 m          1              0.45-0.52      Blue             16.5 km     

                     2.4 m          2              0.52-0.60      Green            16.5 km     

                     2.4 m          3              0.63-0.69      Red              16.5 km     

                     2.4 m          4              0.76-0.90      NIR              16.5 km     

                                                                                               

  NASA Jet                                                                                     
  Propulsion                                                                                   
  Laboratory                                                                                   
  Airborne                                                                                     
  Visible/Infrared                                                                             
  Imaging                                                                                      
  Spectrometer                                                                                 
  (AVIRIS)                                                                                     

                     20 m           224            0.40 - 2.45 in Blue -Shortwave  11 km       By Request
                                                                  Infrared                     

                                                                                               

  PROBE-1                                                                                      
  Hyperspectral                                                                                
  Instrument                                                                                   

                     1 -- 10 m      128            0.40 - 2.45 in Blue -Shortwave  \<1 - 6 km  By Request
                                                                  Infrared                     
  ------------------ -------------- -------------- -------------- ---------------- ----------- -----------

![](media/image3.png){width="6.379166666666666in"
height="5.565972222222222in"}

### Multispectral, Low Spatial Resolution Sensors

Multispectral, low spatial resolution sensors have limited ability to
detect individual invasive plants or small populations. The Advanced
Very High Resolution Radiometer (AVHRR) sensor is an example. This
sensor is a broad-band, 4- or 6-channel scanning radiometer, sensing in
the visible, near-infrared, and thermal infrared portions of the
electromagnetic spectrum. Ground resolution is 1.1 kilometers, which
prohibits its ability to detect small invasive plant populations.

The AVHRR sensor has useful temporal data, with fairly continuous global
coverage since 1979. Two acquisitions are available daily (morning and
afternoon). AVHRR data can be downloaded at no cost
(<http://edc.usgs.gov/products/satellite/avhrr.html>).

Although AVHRR data cannot detect small invasive plant populations,
these data may be useful for detecting landscape changes or evaluating
degraded landscapes (Eve et al. 1999). Bradley and Mustard (2005) used
AVHRR data, time series analyses, to estimate cheatgrass (*Bromus
tectorum*) area in the Great Basin. Further, large dense patches of
broom snakeweed were detected using this sensor (Peters et al. 1992).
AVHRR data are primarily used for investigation of clouds, land-water
boundaries, snow and ice extent, cloud distribution, and land and sea
surface temperatures. AVHRR data can also be used for studying and
monitoring vegetation conditions in ecosystems, including forests,
tundra, and grasslands, with applications that include land cover
mapping and production of large-area image maps.

### Multispectral, Medium Spatial Resolution Sensors

Multispectral, medium spatial resolution sensors such as Multispectral
Scanner (MSS), Landsat TM and ETM+, and SPOT have been used extensively
to model landscape vegetation types and conduct landscape change
analyses. MSS is a multispectral scanning radiometer that was carried
onboard Landsats 1 through 5. The instruments provided temporal coverage
from July 1972 to October 1992. MSS image data consist of four spectral
bands from within the visible green, red, and near-infrared wavelengths.
The resolution for all bands was 79 meters, and the approximate scene
size was 185 x 170 kilometers (115 x 106 miles). Due to the spatial
resolution, MSS data cannot detect small populations of invasive plants.
However, this dataset may be useful for detection of landscape changes
and attributes that promote invasive plant establishment (Pickup et al.
1993, Chavez and MacKinnon 1994, Lass et al. 2005). MSS data are
available as a digital product from:
<http://edc.usgs.gov/products/satellite/mss.html>.

The sensors onboard Landsat satellites have varied as technologies have
improved and certain types of data proved more useful than others. The
latest Landsat satellite is Landsat 7, which was launched in 1999. The
sensor on Landsat 7, the Enhanced Thematic Mapper Plus (ETM+),
replicates the capabilities of the Thematic Mapper (TM) instruments on
Landsats 4 and 5. Landsat 7 ETM+ has the same spectral bands as the
previous Landsat sensors, but the ETM+ also features a panchromatic band
with 15 meter spatial resolution and a thermal IR channel with 60 meter
spatial resolution. A mechanical failure of the scan line corrector
onboard Landsat 7 in May 2003, resulted in data gaps from this sensor.
Landsat TM and ETM+ data are still available and reasonably priced.

SPOT satellite imaging system was launched in 1986, and currently
collects data in green, red, near-infrared, and mid-infrared spectrum.
By combining imagery from other SPOT satellites, data are generated at
four levels of resolution (20, 10, 5, and 2.5 meters). This
multi-resolution approach offers users the geospatial information for
regional and local analyses. The SPOT imaging system can collect stereo
image pairs that contain topographic (3-D) information. SPOT satellites
can also be programmed to revisit a given geographic area at any
specific time. Imagery required specifically for a vegetation phenology
event may be obtained from this service.

Landsat TM and ETM+ have been extensively used to model broad vegetation
types. In some cases, these sensors can identify individual plant
species with unique spectral or temporal characteristics
(Parker-Williams and Hunt 2002). Dewey et al. (1991) compared dyers woad
(*Isatis tinctoria*) locations with 60 spectral classes created from
Landsat 5 TM data in northern Utah. The authors found strong
associations between 10 spectral classes and dyers woad locations. They
concluded that their remotely sensed predictive model provided resource
managers with a powerful tool for estimating potential dyers woad
distributions. Several authors have suggested that Landsat TM and ETM+
data are best used for detecting invasive plants that have patch sizes
around 0.5 hectares (1 acre) or larger (Anderson et al. 1993, Everitt
and Deloach 1990, Everitt et al. 1992). McGowen et al. (2001) utilized
Landsat 5 TM to map serrated tussock (*Nassella trichotoma*) and scotch
thistle (*Onopordum* spp.) in Australia. Detections were limited to
areas with infestations greater than 20% groundcover. Cheatgrass in the
Great Basin was modeled using Landsat TM and ETM+ data (Bradley and
Mustard 2005).

### Multispectral, High Spatial Resolution Sensors

Multispectral, high spatial resolution sensors such as IKONOS and
QuickBird have less than 5 meter spatial resolution. QuickBird's
panchromatic band has a spatial resolution of 60 centimeters. These high
spatial resolution sensors show promise for detecting individual species
and capturing plant phenological state (Asner and Warner 2003, Turner et
al. 2003, Wang et al. 2004), and mapping shallow aquatic habitats (Mumby
and Edwards 2002). Tasi et al. (2005) used QuickBird imagery to
accurately map the spatial extent of the invasive horse tamarind
(*Leucaena leucocephala*) in southern Taiwan. IKONOS and QuickBird are
commercial products by Space Imaging
(<http://www.spaceimaging.com/products/ikonos/index.htm>), and Digital
Globe (<http://www.digitalglobe.com/about/quickbird.html>),
respectively. Data from these sensors are more expensive than medium
spatial resolution sensors. IKONOS and QuickBird also have relatively
small swath sizes (around 20 kilometers). Thus, analyses of large areas
require extensive computer processing time and storage.

### Aerial Photography

Perhaps the oldest remote sensing method is aerial photography (Sabins
1987, Lillesand and Kiefer 1994). Historically, the use of aerial
photographs was limited to small areas, because of the high cost of data
acquisition (Lass et al. 2005). Advances in digital aerial photography
have improved both the spectral and spatial resolution. Digital cameras
can be attached to a variety of aircraft, providing greater flexibility
with resolution and timing. However, image pre-processing of raw digital
photography presents many challenges (Lass et al. 2005).

There are wide choices of photography with varying degrees of spectral
sensitivity (visible and infrared part of the spectrum). Color infrared
photography is often called \"false-color\" photography. Surface objects
that are normally red appear green; green objects (except vegetation)
appear blue; and \"infrared\" objects, which typically are not seen with
the human eye, appear red. A major use of color infrared photography is
for vegetation studies. Green vegetation with active photosynthesis is a
very strong reflector of infrared radiation and appears bright red on
color infrared photographs.

Digital Orthophoto Quadrangles (DOQs) are aerial images produced by the
United States Geological Survey (USGS). These computer-generated images
have been corrected for image displacement caused by terrain relief and
camera tilt. DOQs are either gray-scale, natural color, or
color-infrared images with 1-meter ground resolution. They cover an area
approximately 8-kilometers on each side, and have between 50 and 300
meter overlap with adjacent images. This overlap facilitates tonal
matching and mosaicking of adjacent images. DOQs have been used for
georegistering other imagery or GIS data, visual image interpretation,
and on-screen digitizing of landscape features (Coulter et al. 2000,
Lawrence et al. 2006). The National Agriculture Imagery Program (NAIP)
acquires imagery during the agricultural growing seasons, which enables
DOQ acquisition within the same year. NAIP imagery has a 1-meter spatial
resolution and is available at
[http://www.apfo.usda.gov](http://www.apfo.usda.gov/). There are also a
variety of commercial developers that specialize in obtaining aerial
photographs and videography (Lass et al. 2005). The cost of aerial
photographs and videography varies depending on the type and resolution
of the image, and the amount of area surveyed.

Aerial photographs or videography have been found to be useful in
detecting saltcedar (*Tamarix ramosissima*) (Everitt et al. 1992; 1996)
and leafy spurge (Everitt et al. 1995, Anderson et al. 1996). Further,
aerial photographs were used as a tool to estimate the rate of spread
for giant hogweed (*Heracleum mantegazzianum*) in the Czech Republic
(Mullerova et al. 2005) and for mimosa (*Mimosa pigra*) in northern
Australia (Lonsdale 1993). Color digital aerial photos flown in a
helicopter 100 meters above ground level were used to census Haleakala
silversword (*Argyroxiphium sandwicense*) within a remote volcano crater
in Hawaii (Landenberger et al. 2003). The authors found higher errors of
commission and omission with aerial photo census compared to ground
census. However, aerial photo analyses were favorable because it allowed
for a larger area to be surveyed with less time than ground based
surveys (Landenberger et al. 2003).

### Hyperspectral Sensors

Hyperspectral sensors are perhaps the most helpful group of remote
sensors for detecting small populations of invasive plants. These
sensors sample the electromagnetic spectrum in narrow continuous
increments which allows for improved identification of species. There
are many hyperspectral sensors available from both governmental and
commercial use. Sensors are airborne, but may be attached to a variety
of aircraft. NASA Jet Propulsion Laboratory Airborne Visible/Infrared
Imaging Spectrometer (AVIRIS) (<http://aviris.jpl.nasa.gov/>) has 224
spectral bands that measure light from the blue through the shortwave
infrared (0.4 to 2.5 µm with 0.01 µm increments), with a 20 meter
resolution. As such, each 20 meter pixel in the image has 224 spectral
attributes of data. Likewise, the Hyperion sensor
(<http://eo1.usgs.gov/hyperion.php>) has 220 spectral bands (0.4 to 2.5
µm), with a 30 meter resolution, and the Probe-1 sensor
(<http://www.earthsearch.com/index.php>) has 128 spectral bands ranging
from 0.4 to 2.5 µm. Spatial resolution varies between 1.0 and 10
kilometers depending on height of the aircraft.

Several invasive plants have been detected with the hyperspectral
sensors. DiPietro (2002) used AVIRIS data to discriminate riparian
vegetation from giant reed (*Arundo donax*) in California. Likewise,
AVIRIS data were used to estimate leafy spurge canopy cover and
distribution in Wyoming (Parker-Williams and Hunt 2002, 2004). Lawrence
et al. (2006) experienced high accuracy in detecting spotted knapweed
(*Centaurea maculosa*) and leafy spurge using the Probe-1 sensor. Other
hyperspectral sensors have been used to detect spotted knapweed (Lass et
al. 2002, 2005), yellow starthistle (Lass and Thill 2000), and
babysbreath (*Gypsophila paniculata*) (Lass et al. 2005). Small
infestations of leafy spurge were identified in southeastern Idaho using
HyMap ([http://www.intspec.com](http://www.intspec.com/)) hyperspectral
data (Glen et al. 2005).

## ![](media/image4.png){width="6.25in" height="2.7271653543307086in"}

### Fiscal and Technical Considerations

Our ability to detect invasive plants over large landscapes is greatly
improved by the use of remotely sensed data. The optimal remote sensing
data, or combination of data, would have characteristics of
hyperspectral sensors and high spatial resolution sensors. Although
hyperspectral data facilitates detection of individual plants,
hyperspectral data has approximately 75 times greater data volume than
an equivalent area using Landsat ETM+ (Thenkabail et al. 2004).
Likewise, multispectral, high spatial resolution sensors (e.g., IKONOS
or QuickBird) also show promise in detecting invasive plants with
spatial resolutions less than 5 meters. These sensors are also
encumbered by large data volumes over large areas. The new challenge is
to develop methods which integrate the required spectral resolution with
the ideal spatial resolution, and are efficient with the
high-dimensional datasets for large area analyses. Remote sensing
datasets also come with fiscal and technical expertise considerations.
Higher spectral and spatial resolution data are substantially more
expensive than multispectral, medium spatial resolution sensors, and
require greater technical expertise for image processing (Figure 6.4).

## Data Processing Considerations

## 

### Pre-Processing Considerations

One of the first considerations to evaluate when beginning a remote
sensing project is the availability of ample computation power and
storage capacity. Image processing is computationally intensive, in
terms of storage (hard drive space and RAM), and CPU usage. There are no
specific rules for the correct processor speed or amount of RAM needed
to process and analyze remotely sensed data. Generally, the computer
should have at least a 2.0 GHz processor (higher capacity CPUs are
better) and at least 1.0 GB of RAM. To allow for sufficient processing
space, the computer used for image analyses should have at least five
times the amount of hard drive storage space needed to store one copy of
the imagery needed for the area of interest. Computers that contain more
than one hard drive will optimize efficiency. One hard drive should be
used as the "read from" drive and another as the "write to" drive. If
possible, the operating system and virtual memory location should be on
a separate hard drive from the processing drives. This would require a
third hard drive but would increase processing efficiency. Further, the
amount of space on the virtual memory directory should be increased to
at least five times the amount of space as the physical memory (RAM).

No matter what remote sensing platform/imagery is chosen to meet
research needs, several processing steps may be needed to facilitate the
use of the imagery for invasive species analyses. These steps, commonly
referred to as "pre-processing" techniques, place imagery in a format
that facilitates analyses and reduces errors. Procedures used will vary
depending on imagery selected and software used for analyses and
processing. Most software packages have designed specific tools to
facilitate pre-processing satellite images. Therefore, we discuss
general pre-processing steps, instead of specific procedures for any one
software package. More information regarding the specific pre-processing
procedure can be found in the software manuals. Typically, imagery
"metadata" will describe processes that have been completed for each
dataset and therefore indicate steps that remain to be accomplished.
Explanations of pre-processing procedures can be found in Lillesand and
Kiefer (1994) and Jensen (2005). Aspinall et al. (2002) provide an
overview to pre-processing and processing considerations with
hyperspectral data.

### Acquire and Extract Imagery

Satellite imagery can be acquired from numerous sources in various file
formats. Common methods of distribution include CD-ROM, DVD, and FTP or
HTTP download. Imagery for a particular satellite sensor can be
available in several formats that are directly compatible with various
imagery manipulation software packages. Flat binary multi-band formats
are the most common form of storing remotely sensed data. This "generic
binary" format is generally accepted by all software packages. The
correct choice of format is dictated by the requirements and
capabilities of the software used for analyses. Multiple steps are often
required to place imagery in a format that is suitable for analyses.
Typically, the first step is to "import" or extract imagery from a
compressed transportation format into a format that the software can
understand and display. Some software packages may require an
intermediate format, requiring a second conversion from the storage
format, into a readable format, and then into an analysis format. For
example, imagery may arrive in a .bil format (binary, band interleaved
by line). This format can be imported, and saved (first conversion) in
an .img format (Erdas Imagine format). Depending on the software
capabilities, the .img format may need a second conversion to a grid
(ArcGIS format) for analyses. Although these steps are often
trivialized, a considerable amount of time can be expended to extract
imagery from its storage media or transportation format into a useable
form.

### Consolidate and Geo-Reference Imagery

Satellite images may be received in several discreet files, one file for
each band of data represented in a scene. For example, one file may
represent the red spectrum of an image, another file representing the
blue spectrum, and another file representing the green spectrum. Many
software packages allow users to combine or "layer stack" these discreet
bands of data into one file that contains all layers of information
simultaneously. For example, Landsat ETM+ data often come with each band
in a separate file. Once these files are combined, the resulting image
contains attributes for each band. This consolidation of several data
bands into one useable file allows users to efficiently manipulate
multi-spectral data in subsequent processing steps.

Geo-referencing is a generic term for the process of defining a spatial
coordinate system to each pixel of an image so that it is precisely
represented in the location where it is intended to be. The process of
defining coordinates to pixels will vary in complexity depending on the
precision required for analyses and the type and format of imagery used.
In some cases adequate precision can be achieved throughout the image by
defining coordinates to each of the corners of the image, such as in
Landsat ETM+ imagery. In other cases, such as in the use of some aerial
photography, several "ground control points" will need to be acquired to
achieve precision within the entire image. Often DOQs are used to
geo-reference other remotely sensed data. Defining a coordinate system
accurately is essential if several image dates or other GIS datasets are
to be analyzed simultaneously.

### Atmospheric Standardizations

Procedures to compensate for variations in atmospheric conditions across
multiple images and dates are referred to as atmospheric
standardization. There are many types of atmospheric standardization
routines available to users that, like geo-referencing, vary in
complexity depending on the precision desired and the type and format of
imagery used. Most of the effective procedures attempt to compensate for
differences in sun illumination intensity, atmospheric effects (e.g.,
absorptions and refraction), and instrument calibration. These
procedures typically normalize pixel values among adjacent image scenes
or across multiples dates. Atmospheric standardization procedures can
often improve the ability to conjoin adjacent imagery as well as enhance
comparisons of multiple images in the same location (time series).

### Mosaic or Reduce Imagery

Often, several satellite images will have to be joined because they were
acquired in spatial sizes that did not completely represent the desired
area for analyses. Several small adjacent scenes can be combined into
one large seamless mosaic image for analyses. The specific routines used
for this procedure vary by software package, but in general, this task
is accomplished by comparing overlapping pixels in adjacent images and
adjusting the values so that each will closely coincide when combined.
For this procedure to be effective, adjacent imagery should be acquired
within the same day or within the shortest possible time period. It is
also advisable to perform some form of image standardization prior to
this procedure for the best results.

When one satellite image represents more land area than is required for
analyses, it is often desirable to reduce the spatial extent of the
image through a "clipping" process. Smaller images will expedite
processing and analyses, and reduce storage requirements. Clipping can
be accomplished with GIS vector data, or other raster data. Other
related procedures, referred to as "masking", will also reduce required
file sizes and subsequent processing time for analysis. Masking is the
elimination of unwanted or un-needed pixels within an image. For
example, areas that are contaminated by excessive cloud cover or
cloud-shadowed areas, or areas that may corrupt analysis, such as snow
cover or sun glint on water bodies, can be removed through a masking
process. Areas that are removed to enhance image processing time and
analyses are dependent on specific goals and objectives of each project.

## Attributes for Predictive Models

Many of the examples discussed above used spectral characteristics as a
means to identify invasive plants. Some authors used straight forward
visual photo interpretation methods (Mullerova et al. 2005, Anderson et
al. 1996), while others used computer technology and various
classification algorithms to classify remotely sensed images. These
algorithms are based on spectral reflectance values. Because the
spectral reflectance of a given pixel is influenced by the mixture of
ground components in that pixel, remote sensing scientists have designed
a wide variety of analyses to discriminate spectrally distinct
vegetation types. Parker-Williams and Hunt (2002, 2004) applied the
Mixture Tuned Matched Filtering (MTMF) classification algorithm to
AVIRIS hyperspectral imagery to discriminate leafy spurge with as little
as 10% canopy cover. Likewise, Glen et al. (2005) used the MTMF
algorithm using HyMap hyperspectral imagery. Aspinall et al. (2002)
provides a summary of hyperspectral imagery classification algorithms
used for discriminating invasive plants.

Vegetation indices are classification algorithms commonly used to
discriminate plant populations. Most indices assess landscapes in terms
of vegetation greenness, soil reflectance, and soil moisture. Examples
of vegetation indices that may be useful in discriminating plant
populations or attributes that favor invasive plant establishment
include:

1)  the Normalized Difference Vegetation Index (NDVI) (Lauver and
    Whistler 1993),

2)  Soil Adjusted Vegetation Index (SAVI) (Huete 1988),

3)  Modified Soil Adjusted Vegetation Index (MSAVI) (Qi et al. 1994),

4)  tasseled cap transformation,

5)  simple band ratios, and

6)  the wide dynamic range index (Andries et al. 1994, Tanser and Palmer
    1999, Schmidt and Karnieli 2000, Gitelson 2004).

Unfortunately, there is not one index that works best for all vegetation
communities. Parks will need to evaluate multiple indices to determine
which is most useful for their area and objectives. Vegetation indices
can be derived from a variety of sensors, including AVHRR (Bradley and
Mustard 2005), Landsat TM (McGowen et al. 2001, Goward et al. 2003),
IKONOS and QuickBird (Asner and Warner 2003, Goward et al. 2003, Tsai et
al. 2005), and hyperspectral data (Thenkabail et al. 2004).

Recent technologies in discriminating landscape features include texture
analysis and object-oriented analysis. Image texture analysis
investigates the structural and statistical properties of spatial
patterns on images (Tsai et al. 2005). Object-oriented analyses classify
an image based on attributes of an image object, rather than attributes
of individual pixels (Benz et al. 2004). Homogeneous pixels are
aggregated into image objects using their spatial (size, shape,
location) or spectral characteristics (Laliberte et al. 2004). These
relatively new approaches show great promise for detecting invasive
plants, especially when combined with spectral data.

While remotely sensed data can detect plant species and populations,
some researchers have combined remotely sensed data with other spatial
datasets to enhance predictive models. Anderson et al. (1996) used
aerial photo interpretation, roads, trails, hydrographic data, slope,
and aspect to model leafy spurge in Theodore Roosevelt National Park.
The authors noted that using GIS and remote sensing data together proved
to be a powerful combination of tools. Yellow starthistle was predicted
using landscape variables such as elevation, slope, aspect, and Landsat
land use classifications (Shafii et al. 2003).

Remotely sensed data can also be used to assess attributes associated
with invasive plant presence. For example, many invasive plants are
associated with fragmented or degraded landscapes (Sakai et al. 2001,
With 2004). Tanser and Palmer (1999) used a measurement of landscape
heterogeneity to assess degradation. A moving standard deviation filter
was passed over Landsat TM imagery creating a moving standard deviation
index (MSDI). Degraded landscapes exhibited higher MSDI values than
undisturbed landscapes. Other environmental variables that are
associated with landscape heterogeneity and appear to have high
potential for remote sensing include total vegetation cover, relative
proportion of grass and shrub cover, and organic soil cover (Warren and
Hutchinson 1984, Schmidt and Karnieli 2000).

Species characteristics have been used to explain invasion patterns
(Rejmanek and Richardson 1996, Goodwin et al. 1999). For example,
vegetative propagation, leaf size, flowering period, and wind dispersal
were associated with invasive plant abundances on five Mediterranean
islands (Lloret et al. 2005). However, inclusion of species
characteristics into spatially explicit predictive models is hindered by
the lack of strong associations between species characteristics and
spatial data (see Chapter 7). Some species traits may be detected by
remotely sensed data. For example, invasive species that have flowers
and/or bracts, green-up or senesce at a different time than plants in
the surrounding environment, have a unique canopy architecture or growth
form, or have a unique coloration are good candidates for using species
related traits in predictive modeling.

Attributes chosen to predict invasive species distributions may be
direct, indirect, or "models of models" (see Chapter 7). In terms of
landscape studies, ecological parameters are generally sampled from GIS
or remotely sensed data. Close scrutiny of coarse resolution variables
may be warranted, as these variables may introduce spatial uncertainties
from interpolation errors, lack of sufficient ground data, and poor
associations with causal factors (Guisan and Zimmermann 2000). Variables
that have little to no direct physiological relevance for a species'
performance (slope, aspect, elevation, or topographic position) are
easily measured from field or spatial datasets and are often used
because of their good correlation to observed species patterns. Models
constructed with these resource variables are typically general, but are
applicable over larger areas. Given the complexity of natural
landscapes, spatially explicit predictive habitat models are generally a
compromise between precision and generality (Guisan and Zimmermann
2000). Chapter 7 provides greater discussion on attributes for species
distribution models and their interpretation limitations.

### Building Predictive Models

There are a variety of analytical methods that could be used to
construct predictive models for invasive species, ranging from simple
overlays to more statistically driven models. Simple models can be
developed directly within a GIS, using overlays of environmental
variables. Boolean approaches are modeling methods that employ overlay
rules (where individual layers are added, subtracted, or multiplied
together). These approaches have been widely used in wildlife habitat
relationship (WHR) models (e.g., GAP models). WHR models describe
resources and conditions present in areas where a species persists and
reproduces or otherwise occurs. These modeled relationships predict, and
spatially depict, areas of potentially suitable habitat. *While these
types of models are informative, they may lack the ability to make
statistical inferences.*

Statistical approaches to constructing species distribution models
(SDMs) will be discussed in Chapter 7. These approaches also apply to
remote sensing and GIS data, and can be more desirable than simple
overlay models. Statistical approaches to model building allow enhanced
accuracy and predictive power. Unfortunately, GIS software packages
still lack important statistical procedures for predictive purposes.
Some software packages have modules for classification and regression
trees (CART), clustering analyses, logistic regression, and various
other supervised classification procedures. However, statistical
analyses in the GIS environment may be limited by the current lack of
model selection procedures available in the software (e.g., stepwise
selection procedure for logistic regression) (Guisan and Zimmermann
2000). Many statistical software analyses can be easily implemented into
a GIS. For example, Generalized Linear Models (GLMs) and logistic
regression analyses can be placed in a GIS by multiplying each
regression coefficient with its related predictor variable layer. Most
GIS software packages allow users to write algorithms for image
manipulations. Alternatively, spatial datasets can be exported as ASCII
files, and analyzed in a variety of spatial programs. Guisan and
Zimmermann (2000) and Scott et al. (2002) provide additional insights
into predictive model approaches. Unfortunately, there is no one
analytical method that works for all scenarios. *The appropriate
analytical method for constructing predictive species models will be a
function of park goals and objectives, the type and structure of the
data, software availability, and expertise in statistical and GIS
modeling.*

The analytical method used to construct predictive models will dictate
the type of model produced. Predictive models may display:

1)  probabilities of occurrence (derived from logistic GLM analyses),

2)  the most probable abundance (derived from ordinal GLM analyses),

3)  predicted occurrence (based on non-probabilistic metrics), or

4)  the most probable entity (from hierarchical analyses) (Guisan and
    Zimmermann 2000).

Regardless of the type of model, assessing model performance is
essential in preparing adequate models. There are three forms of
assessment for spatial models:

1.  draft models that are verified,

2.  committed (final) models that are assessed for accuracy, and

3.  final models subjected to user validation.

Draft models are produced during an evolutionary and refinement process
which involves iterative collection and testing with verification data.
**Model verification** does not examine the accuracy of the model or the
usefulness of the model. Model verification examines only the model's
internal consistency (Conroy and Moore 2002). This is analogous to
"measures of model fit" discussed in Chapter 9. Subsequent to this
iterative process, models are committed to a final form, which is the
version that is subjected to **accuracy assessment**. No further
alteration of a committed model is permitted after accuracy assessment;
if further alterations are performed, then the model is a new version
that requires additional assessment to provide an applicable accuracy
statement. Final models, and associated accuracy statements, are
published for use by others. **Model validation** is performed by and
arises from judgments of intended users. Model validation depends
primarily on the goals of the users, rather than on statistics alone
(Guisan and Zimmermann 2000). See Chapter 9 for a greater discussion on
model validation.

Model performance can be evaluated by:

1.  using two independent sets of data for building and evaluation
    (often called "training" and "evaluation" data),

2.  cross-validation procedures where the data set is separated into two
    sets, a training set, and evaluation set,

3.  jack-knife procedures that resample the dataset based on deleting a
    portion of the original observations or input model variables in
    subsequent samples,

4.  bootstrap techniques that perform repeated random sampling with
    replacement from an original sample,

5.  randomization procedures where random samples are obtained by
    sampling without replacement,

6.  and re-substitution procedures where the same dataset is used for
    training and testing, with no partitioning of data.

Detailed explanations on these procedures, and others, are provided by
Efron and Tibshirani (1993), Fieldings and Bell (1997), and Guisan and
Zimmermann (2000). See also Chapter 9 for more information on model
evaluation and assessment.

From an applied perspective, there are two ways a habitat model can be
inaccurate:

1)  the model can [over predict]{.underline}, rating locations suitable
    although the location is unsuitable, or the species has not been
    detected in the predicted location (type I error),

2)  or the model can [fail to predict]{.underline} the presence of a
    species where it is indeed present (type II error) (Shrader,
    Frechetter, and McCoy 1993).

The first represents an error of commission. The second case represents
an error of omission. Both types of errors undermine the defensibility
of the model. However, errors of omission are less acceptable than
errors of commission if we wish to implement a precautionary principle
in biological conservation (Shrader, Frechetter, and McCoy 1993). For
example, if the predictive model and field assessment conclude an area
is suitable, and the species was not present, then the area would be a
good candidate to monitor for potential expansion of the species. Errors
of commission and omission are often summarized in confusion or error
matrices that cross-tabulate the observed and predicted presence/absence
patterns (see Chapter 9). These matrices can be summarized into percent
agreement and disagreement, or further analyzed to evaluate the
association between observed and predicted. For example, the correlation
coefficient is often used to measure the strength of a relationship
between two variables that are normally distributed. The kappa statistic
(Cohen 1960) provides a measurement of the proportion of chance or
expected agreement. The gamma coefficient, Spearman *R*, or Kendall
*tau* are nonparametric equivalents to the standard correlation (Goodman
and Kruskal 1979, Zar 1984). Other model performance assessments are
discussed in Chapter 9, including threshold-dependent and
threshold-independent measures.

Nature is heterogeneous and often difficult to predict accurately from a
single, although complex, model. As such, model assessments over a wider
range of situations (in space and time) will provide a better definition
of the range of applications for which the model predictions are
suitable (Guisan and Zimmermann 2000).

## 

## Landscape Risk Assessments to Prioritize Areas for Conservation Efforts

Understanding where to concentrate survey efforts to find new species or
expanding populations of existing species is paramount to early
detection protocols. Most natural resource managers prioritize survey
efforts based on management considerations and documented predictors of
invasiveness. However, given the variability associated with species
traits, climatic events, and landscape characteristics, there is no set
of conditions or traits that can be universally applied to accurately
characterize all successful invasions (Alpert et al. 2000). Risk
assessment procedures can assist natural resource managers in
prioritizing areas for conservation efforts. **Risk assessment** for
invasive species is the process of obtaining quantitative or qualitative
measures of risk levels, by incorporating a broad array of information
describing factors that may influence the distribution of invasive
species (Allen et al. 2006). There are several approaches to modeling
risk of invasions. For example, neutral landscape models, which evaluate
flows through spatially heterogeneous landscapes, were used to assess
the risk of invasions in fragmented landscapes (With 2004). Landis
(2004) describes a relative risk model that incorporates a system of
numerical ranks and weighting of factors that may influence the
distribution of invasive species. The Landis (2004) risk model takes
into account the spatial relationships of the locations of species
introductions, migration paths, and the habitat structure or
suitability. As such, modeling potentially suitable habitat and
migration paths (potential vectors and pathways) for the introduction or
spread of invasive species are important components in conducting risk
assessments in parks and other natural areas. Vectors refer to the
mechanism of plant introduction, while pathways refer to the route
taken. Examples of vectors include wind, water, and animals (Sakai et
al. 2001). Examples of pathways include roads, trails, and waterways.
Discussions on invasive plant vectors and pathways are presented in
Chapter 2. Remote sensing and GIS datasets can model potential habitats,
vectors and pathways to allow for landscape scale risk assessments.

Few communities are impenetrable to invasion by nonnative species, but
communities differ in their susceptibility to invasion (Sakai et al.
2001). Although it is difficult to generalize about invasive species
dispersal across landscapes (Tackenberg 2003), repeated introductions
increase the chances of establishment (Sakai et al. 2001, Perrings et
al. 2002). In many parks and other natural areas, roads and waterways
are perhaps the pathways of most concern. These pathways enhance species
invasions by acting as dispersal corridors, providing suitable habitat,
and containing reservoirs of propagules (Parendes and Jones 2000).
Disturbance along roads by vehicle traffic and maintenance activity
(e.g., road grading, ditch clearing, and trimming of overhanging
vegetation), is often the source of repeated introductions. Waterway
disturbances occur from floods and associated transport of sediment.

GIS datasets for invasive species pathways are readily accessible. Many
parks and natural areas already have road, trail, and waterway GIS
layers. However, road and hydrologic layers are available from a variety
of GIS data clearinghouses, federal agencies, and private companies.
Digital line graphs (DLGs) (digital vector data derived from USGS maps),
Digital Raster Graphics (DRGs) (scanned digital images of USGS
topographic quadrangles), and National Hydrography Datasets (NHD) can be
downloaded from the USGS website,
[http://edc.usgs.gov](http://edc.usgs.gov/).

Spatial datasets on potential invasive species vectors are not as
readily accessible, and would likely have to be created specifically for
the target species and the area of interest. Many authors have
analytically modeled potential vectors, especially seed dispersal by
wind. Schurr et al. (2005) created a mechanistic model for secondary
seed dispersal by wind (the wind-driven movement of seeds along the
ground surface). The authors found a relationship between seed dispersal
and seed size, but noted that the model tended to underestimate
dispersal rates. Tackenberg (2003) also modeled seed dispersal by wind
and found that long-distance dispersal was primarily influenced by
weather conditions that yielded thermal turbulence and convective
updrafts. Tackenberg (2003) noted that the inclusion of topography in
estimating dispersal rates is important, even in landscapes that exhibit
only small differences in elevation and slight slopes. In addition,
Campbell et al. (2002) simulated landscape scale invasions of plants
that use rivers to transport propagules.

Few studies have created spatial models of vectors or pathways.
Favorable predictive spatial models incorporate:

1)  invasive species distribution data,

2)  population rates,

3)  factors influencing the number of propagules,

4)  dispersal modes,

5)  landscape structure,

6)  ecological processes, and

7)  statistically explained patterns (Moody and Mack 1988, Higgins et
    al. 1996, Higgins and Richardson 1999, Wadsworth et al. 2000, Sakai
    et al. 2001).

## Summary

Remotely sensed images have a number of features which make them ideal
for predicting invasive species in parks and other natural areas. Remote
sensing technologies look beyond the human view, in the electromagnetic
spectrum, which allows better detection of vegetation. Remote sensors
allow for regional analyses that would be cost-prohibitive using
ground-based visits. Regional analyses allow for prioritizing ground
reconnaissance visits to survey, control, or eradicate potential
invasive plant populations. Further, the ease of securing temporal data
allows for repeat analyses and change detections.

Our ability to detect invasive plants using remotely sensed data has
increased with improved sensors, computer technology, and classification
techniques (Lass et al. 2005). Although integrating remotely sensed data
with other spatial datasets enhances our abilities to model invasive
plants, detecting small or sparse plant populations is still hampered by
spatial and spectral resolution, and our ability to analyze large
datasets. Datasets that have the highest likelihood of detecting
invasive plants come with high fiscal and technical considerations.
Overall, the use of remotely sensed data will be most appropriate for
species whose biological characteristics, habitat composition and
structure, and landscape context combine to offer a data quality and
logistical advantage to ground based methods (Landenberger et al. 2003).

Parks and other natural areas need predictive models that can help in
setting priorities for control of invasive species and predict the
potential of future invasions. Remotely sensed data can aid in the
development of spatially explicit predictive habitat models and
estimates of distributional vectors and pathways (see Chapter 13 for an
example). This information will provide land managers with early
detection tools, a means to evaluate current and future control needs,
and a means to prioritize conservation efforts. Early detection methods
increase our ability to eradicate invasive plants and reduce costs of
control (Rejmanek and Pitcairn 2002).

## Recommended Readings

-   Introductory Digital Image Processing: A Remote Sensing Perspective
    (Jensen 2005).

-   Introduction to Remote Sensing (Campbell 2002).

-   Remote sensing and image interpretation (Lillesand and Kiefer 1994).