Using Remote Sensing Imagery to Map Vegetation

Mapping vegetation rather than relying on ground sampling has the advantage o f providing data that can be used to ask specific questions about the location o f areas o f interest by those charged with the management o f these areas (Congalton et al. 2002; Foody & Cutler 2003).

The coverage o f specific areas o f interest and their location, as well as the associated condition o f these areas, are critical information that can assist in developing effective management practices. The cumulative effects o f different land use practices and management strategies can then be assessed over time as baseline maps can assist in the study o f environmental change in time and space (Levin 1992; Franklin 1995; Millington & Alexander 2000; Skidmore 2002; Reed et al. 1994). Nevertheless, it is still widely accepted that accurate land cover classification methods using remote sensing techniques still present scientific and technical challenges as a result o f the many spectral and spatial variables influencing surface reflectance, coupled with the constraints imposed by the spectral and spatial characteristics o f the remote sensing instrumentation (Zarco-Tejada & M iller 1999; Johnston & Barson 1993).

A erial photography

Aerial photography has been utilised extensively in the classification o f wetland areas (Ozesmi & Bauer 2002; Rutchey & Vilchek 1999; Doren et al. 1999; Welch et al. 1999) and has been an effective alternative to traditional methods that often involve high costs, subjectivity, and low spatial and temporal coverage. Schmidt et al. (2004), however, report that the process o f vegetation mapping in the salt marshes o f the Netherlands is a time consuming and expensive process with low classification accuracies (43%). Digital photography is being increasingly utilized in the environmental sciences and this may be a considerable growth area in the future (Gourmelon 2002; Murphy et al. 2004). Although aerial photography is generally preferred in place o f remote sensing imagery (Pitt et al. 1997;

Miyamoto et al. 2004), the use o f remote sensing imagery coupled with more detailed mapping o f vegetation and aerial photography can provide a greater source o f ecological

information to the end-users which are often those charged with the monitoring and management o f such sites (Pavri & Aber 2004; Ustin et al. 2004; Gamon et al. 2004; Phillips et al. 2005).

Airborne and satellite imagery f o r ecological applications

Some o f the benefits o f using airborne and satellite remote sensing imagery include the extensive coverage that they supply and the repeatability and objective nature o f the data collection (Haack 1996). A review o f literature on the remote sensing o f wetlands by Ozesmi and Bauer (2002) focuses on satellite platforms and concludes that classifications using satellite imagery, although difficult, is a promising and useful research area (Lunetta &

Balogh 1999; Jensen et al. 1984; Harvey & Hill 2001; Franklin et al. 1994). Congalton et al.

(2002) however, explored the use o f Landsat TM satellite imagery in the classification o f riparian vegetation and concluded that this type o f data was inadequate for use in policy decisions and the identification o f structural characteristics o f the vegetation. Dechka et al.

(2002) utilised IKONOS satellite imagery which represents fine spatial resolution coupled with coarse spectral resolution to classify a number o f wetland habitat classes and communities in the southern Saskatchewan in Canada; classification accuracies o f only 47%

were attained in broad wetland habitat classes. It is the coarse spectral and spatial resolutions o f much o f the satellite data currently available that make this type o f data unsuitable for vegetation classification at fine scales and, as such, has kept much o f the ecological community at bay.

Airborne remote sensing in wetland environments

Lee and Lunetta (1995) discuss wetland airborne remote sensing missions in their review on wetland monitoring using remote sensing. The studies reviewed, including some involving satellite data, reported high classification accuracies but involved the classification o f broad wetland communities (such as reedswamp or sedge meadows) and made use o f the broad spectral and spatial resolutions from instruments available at the time (May 1986; Jensen et al

1984; Jensen et al. 1986). Recent advances in technology have resulted in increased spectral and spatial resolutions whereby satellite imagery is now available at spatial resolutions similar to those obtained using airborne platforms thereby increasing the scope for remote sensing and ecological applications (Aplin 2005).

At present, airborne imagery is a suitable compromise in terms o f spatial and spectral resolutions with multispectral and hyperspectral remote sensing instruments and fine spatial resolution at scales comparable to most ecological studies (<10 m2). Airborne imagery has been used successfully to map vegetation in terms o f ecological condition (Jago et al. 1999) and work in a coastal wetland in southern California by Shuman and Ambrose (2003) identified the use o f low altitude, high resolution colour and NIR photographs as an accurate and efficient means o f sampling vegetation cover (Phinn et al. 1999) and classifying simple habitats, although individual species could not be identified. This may be attributable to the low spectral resolution dataset that was used as Underwood et al. (2003) successfully applied hyperspectral airborne imagery to predict the spatial pattern o f certain invasive coastal wetland species in California. Although, Schmidt et al. (2004) achieved accuracies o f only 40% when classifying coastal vegetation in the Netherlands using hyperspectral airborne remote sensing; variation in the terrain was accounted for using an expert classification system which then resulted in an improvement in accuracy up to 66%.

Classification methods: Supervised and Unsupervised

Ejmaes et al. (2004) describe the use o f supervised methods for classifying grassland vegetation and conclude that supervised methods deserve more attention in vegetation science. Their direct connection with ecologically meaningful information is conceptually attractive and methods such as Maximum Likelihood Classification (MLC) (Lewis 1998;

Munyati 2000; Lee et al. 1992; Gould 2000) involve the production o f probability maps which can have direct ecological application in, for example, vegetation prediction models and ecological modelling (Guisan & Zimmermann 2000; Franklin 1995; Foody et al. 1992;

Doren et al. 1999). There have been mixed results in studies classifying wetland vegetation

types involving low classification accuracies (Dechka et al. (2002) with successful studies applying only broad vegetation classifications (Munyati 2000) or utilising coarse resolution datasets (Gould 2000).

Unsupervised classification techniques are a way o f assessing the spectral clusters within a dataset and determining specific areas that may overlap spectrally with others (Bachmann et al. 2002). These methods o f classification have been applied to accurately represent land cover types but this is largely in areas o f broad land cover types (Mackey 1990; Wulder et al.

2004b) and at spatial scales that are o f limited value for habitat scale management and ecological applications. Gourmelon (2002), however, has demonstrated that the use o f digitized infrared and colour aerial photographs coupled with a Digital Elevation Model (DEM) can be successfully applied to the large-scale mapping o f terrestrial plants using unsupervised classification methods in a coastal environment averaging accuracies o f 70%.

These results are promising although it is likely that topography has a greater influence on vegetation patterns in a coastal environment than inland freshwater wetlands (Schmidt et al.

2004); this method o f classification has not been adequately discussed in literature concerning the remote sensing o f wetlands.

Spectral information has been found to correlate with various plant pigments, as well as biochemical and biophysical components (Penuelas et al. 1993; Matson et al. 1994; Johnson et al. 1994) and so, airborne imagery may then be applied in indirect ecological interpretations (Aplin 2005; Svoray & Shoshany 2003). Sampson et al (2003) used CASI imagery to remotely detect vegetation stress using chlorophyll content as an environmental proxy (Coops et al. 2003). It was concluded that this capability could be readily applied to classifying forest condition based on chlorophyll content and the usefulness o f this technique in change analysis studies was also acknowledged. In a similar study, Zarco-Tejada and Miller (1999) obtained high classification accuracies using red edge parameters as a feasible and robust method which successfully outperformed other classification methods. They noted the potential use o f systematic differences in canopy pigment or chemistry by cover type as a

basis for land cover classification. This approach has yet to be explored in a wetland environment in relation to habitat types.

Temporal datasets o f NDVI have been used to classify large scale land cover types at the continental and global level using unsupervised classification (Defries & Townshend 1994;

Moody & Strahler 1994). Many studies have explored the application o f NDVI imagery and unsupervised classification to landscape studies that may contribute to conservation management (Van Wagtendonk & Root 2003; Chust et al 1999). Other work utilises NDVI maps in the understanding o f the spatial distribution o f species richness and fauna and the potential o f this within wetland environments is explored here (Seto et al. 2004; Mittelbach et al. 2001; Engstrom et al 2002). In particular, work by Gould et al. (2000) establishes a strong relationship between imagery derived NDVI vales and ground based measures o f species richness in the Arctic but, this is at at the landscape level and, there is little work published that assesses the relationship between NDVI and species richness at the local level.