International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004
sensing reflectance imagery and found a significant relationship
between reflectance before and after Dreissena polymorpha
invasion. Hill et al. (1998) modeled the propagation of the
green alga Caulerpa taxifolia and predicted the local pattern of
expansion, increase of biomass and covered surfaces, and
invasive behaviour. Gross et al. (1988) estimated biomass of the
Spartina alterniflora using a hand-held fixed band radiometer
configured to collect data in Landsat TM. Welch et al. (1988)
related 13 invasive macrophytes distributions (including
Hydrilla verticillata, Potamogeton, Lemna perpusilla) to
environmental factors influencing aquatic plant growth using
bathymetry and herbicide applications maps and statistical data
on nutrients, dissolved oxygen, biological oxygen demand, and
turbidity into a PC-based GIS. A significant change was found
in the ratio of emergents to subemergents as well as the total
area of aquatic macrophyts.
2.3.2 Mixed canopy dominant species: Plant characteristics
such as life form, leaves, flowers etc determine reflectance. If a
species is dominant enough in the canopy and characteristics
can be distinguished from other species, than it is possible to
detect such individual species based on spectral reflectance. The
ability of high spectral and spatial resolution sensors to
discriminate between invasive and native species depends on
intra-specific variability in spectral reflectance. Everitt & Nixon
(1985c) demonstrated that a family of spectra can represent a
particular species, and invasive species are easily separated
using low altitude aerial photographs or field spectrographs.
They quantitatively distinguished Heterotheca subaxillaris from
other rangeland vegetation using spectroradiometric plant
canopy measurements. Everitt et al. (2001a) detected
Helianthus argophyllus, and Astragalus mollissimus var. earlei
using aerial photography. Menges et al. (1985) found colour IR
(CIR) aerial photography to be useful for detecting
Sarcostemma ~~ cyanchoides; — Parthenium hysterophorus;
Sorghum halepense; Sisymbrium irio and Amaranthus palmeri
in different crops. Young et al. (1976) detected growth timing
of Chrysothamnus viscidiflorus using colour photography.
Abdon et al. (1998) discriminated areas with predominance of
Salvinia auriculata and Scirpus cubensis using Landsat TM and
HRV-SPOT digital images. Feyaerts & van Gool (2001)
proposed an online system that distinguishes crop from weeds
based on multispectal reflectance gathered with an imaging
spectrograph.
2.3.3 Invaders influencing canopy dominant species:
Numerous investigators have worked on developing techniques
for using multispectral data in invasive species mapping and
detection (Eav et al., 1984; Zhang et al., 2002; Medlin et al.,
2000; Vrindts et al., 2002). Analysis of hyperspectral data has
produced encouraging results in the discrimination of healthy
and infected canopy dominant species infected by various
fungus such as Batrachochytrium dendrobatidis, Cryphonectria
parasitica, Ophiostoma ulmi, Phytophthora cinnamomi and
Pentalonia nigronervosa (Banana bunchy top virus). Using
habitat type, condition and soil type as the delineating
parameters, Bryceson (1991) located Chortoicetes terminifera
(Australian plague locust) by using Landsat-5 multispectral
scanner data. Kharuk et al. (2001) analysed large-scale outbreak
of the Dendrolimus sibiricus (Siberian moth) in the forests
using NOAA/AVHRR imagery and found that the imagery
could be used for detecting dying and dead trees. Rencz &
Nemeth (1985) detected the red stage of Dendroctonus
ponderosae (pine beetle) infestation using different ratio of
multispectral scanner bands. Epp et al. (1986) were able to
detect white spruce stands damaged by Choristoneura
fumiferana infestation using an airborne pushbroom scanner
and Thematic Mapper data. Using principal component and
cluster analyses Zhang et al. (2002) used spectral ratio analysis
based on principle component analysis and clustered analysis.
They observed that the sensitive spectral wavelengths and
reflectance values enabled them to discriminate Phytophthora
infestants infection on tomatoes. Fouche (1995) identified
rootrot-infested cashew nut trees, Phytophthora cinnamomi
infestation in avocado orchards and infected citrus trees. They
could be differentiated from their healthy neighbours, using
low-altitude aerial colour infrared (CIR) imagery. Gebhardt
(1986) used IR measurements of crop canopy temperature to
detect differences in water supply and nematode infestation.
Smirnov & Kotova (1994) monitored the infection by
Heterobasidion annosum in areas with pollution levels
exceeding 15 Ci/km? after the Chernobyl nuclear disaster in
Russia. Lee (1989) applied aerial photography to detect soil-
borne disease such as nematode Rorylenchulus reniformis on
cotton, — Phymatotrichum . of cotton, Phymatotrichum
omnivorum, and Phymatotrichopsis omnivora (root rot of
Lucerne), Armillaria tabescens (root rot of pecans), Radopholus
similis (burrowing nematode damage on citrus orchards) and
citrus tree root rot infestation.
Performing spatial correlations, GIS tools often does
identification of invaders influencing canopy dominant species.
For example, Kazmi & Usery (2001) monitored vector-borne
diseases, Bell (1995) detected grape phylloxera spread and
Terry & Edwards (1989) analysed the effect of insecticides and
parasites released for invasive species control.
2.3.4 Understory species: Few researchers have pointed out
the possibilities of application of remote sensing in studying
understory invasives. Plant species such as Chromolaena
odorata, Ulex europaeus, Clidemia hirta, Lantana camara,
Mimosa pigra, Psidium cattleianum, Rubus ellipticus, Schinus
terebinthifolius and most of the invasive animal species are
examples of this category. May et al. (2000) quantified remotely
sensed airborne data into physical and ecological variables,
obtaining an improved spatial and temporal representation of
the dynamics of native and exotic plant communities.
Most of the invasive animals, lower flora, herbs, shrubs and
fauna are found to be understory vegetation, making detection
using direct remote sensing techniques almost impossible.
Nevertheless a combination of remote sensing techniques, GIS
and expert knowledge still offer potential to detect understory
invasion through development of models and risk maps. These
can help predicting the probability of actual and potential sites
and areas where environmental conditions are susceptible to
infestation.
3. MONITORING AND PREDICTION OF INVASION
RISK
Predicting the probability of biological invasion and probable
invaders has been a long-standing goal of ecologists. A major
challenge of invasion biology lies in the development of pre and
post predictive models and understanding of the invasion
processes. Introduced species vary in their invasive behaviour
in different regions (Krueger er al., 1998). Prediction is more
difficult than finding an explanation. Predicting the ecological
behaviour of a species in a new environment may be effectively
impossible (Williamson, 1999). The consequence of a given
disturbance depends on the properties of the ecosystem Or
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