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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B1. Istanbul 2004 
characterised increases. Indeed, there are various examples of 
studies where fine spatial resolution imagery has been 
compared favourably to coarser spatial resolution imagery. For 
instance, IKONOS imagery has been found to be more accurate 
than SPOT HRV and Landsat Enhanced Thematic Mapper Plus 
(ETM+) imagery for monitoring forest storm damage (Schwarz 
et al. 2003) and mapping coral reefs (Capolsini et al. 2003). 
However, fine spatial resolution imagery is not always 
appropriate for ecological studies. Hochberg and Atkinson 
(2003) found IKONOS imagery less accurate than hyperspectral 
imagery for distinguishing coral, algae and sand, and Asner et 
al. (2002) concluded that IKONOS imagery was insufficient for 
accurate estimation of tree crown dimensions. Further, Sawaya 
et al. (2003) demonstrated IKONOS and QuickBird imagery to 
be useful for resource management, but suggest that these data 
are uneconomic for large area studies. 
It should be noted that airborne remote sensing may be a 
suitable alternative to fine spatial resolution spaceborne 
imagery, given that the relatively low altitude of airborne 
platforms enables the generation of very fine spatial resolution 
data. However, airborne remote sensing is limited in that data 
are acquired on a piecemeal basis (compared to continuous 
satellite sensor image acquisition), they may be expensive and 
they are particularly susceptible to geometric distortion (Goetz 
et al. 2003). Overall, the key factor determining successful 
applications in ecological remote sensing, and in remote sensing 
in general, is to match project goals to technical capabilities 
(Sawaya et al. 2003). Therefore, the level of detail required in 
any individual study will determine whether or not fine spatial 
resolution imagery is required. Sometimes, fine spatial 
resolution imagery may be useful only as a supporting data 
source, in combination with other resources (Quinton ef al. 
2003). For instance, Palandro ef al. (2003) describe the use of 
IKONOS imagery to assess the accuracy of Landsat TM and 
ETM+ image classification. 
4. AFRICAN APPLICATIONS 
Fine spatial resolution spaceborne imagery has been tested 
fairly extensively for a range of ecological analyses in North 
America, for tropical forest studies in South America and for 
coral reef projects at locations throughout the world. However, 
relatively little such work has been conducted throughout 
Africa. Thenkabail (2004) describes a major study conducted in 
Nigeria, Benin and Cameroon to compare the capabilities of 
IKONOS and Landsat ETM+ imagery for representing 
rainforest and savanna ecoregions. NDVI analysis was used to 
determine the vegetal component of a range of land cover 
classes and ecological units. Thenkabail (2004) concludes that 
IKONOS data provide a more detailed depiction of vegetation 
and related factors such as biomass than Landsat ETM+ data. 
This is due partly to the finer spatial resolution of IKONOS, 
and partly to the greater (11 bit) dynamic range of IKONOS 
data than (8 bit) Landsat ETM- data. In another project, 
Thenkabail et a/. (2004) conduct an exhaustive comparison 
between IKONOS imagery and various other sources of 
multispectral and  hyperspectral imagery for calculating 
rainforest biomass in Cameroon. In this case, IKONOS and the 
other multispectral data sources were markedly less accurate 
than hyperspectral Hyperion imagery. 
In a Zambian study, IKONOS imagery has been processed to 
define LAI and forest canopy roughness, used in a wider 
experiment to monitor energy fluxes between vegetation and 
327 
the atmosphere (Scanlon and Albertson 2003). Elsewhere in 
Zambia, Hansen ef al. (2002) describe the use of IKONOS 
imagery to generate an accurate tree crown cover map, used to 
validate a global percent tree cover data set, generated by the 
Moderate Resolution Imaging Spectroradiometer (MODIS). In 
fact, IKONOS imagery is conducted for MODIS validation 
elsewhere in Africa, including Botswana (Morisette et al. 
2003). 
4. HABITAT MONITORING IN SOUTHERN AFRICA 
Kruger National Park (KNP) represents a managed, semi- 
natural environment. The park operates as a preserve for 
endemic flora and fauna, and is a major visitor attraction. It is 
believed that management practices throughout the twentieth 
century have fundamentally altered vegetation distribution 
throughout the park (Van Wilgen er al, 1998). In particular, 
numerous artificial water resources were created to attract 
wildlife to specific locations for viewing by tourists. The 
increased water resources may have led to an overall increase in 
vegetation abundance, and may be contributing to structural 
homogenisation of the park's vegetation (Eckhardt et a/., 2000. 
Related processes include infestation of alien plant species and 
dramatic growth in certain animal populations, notably elephant 
and rhinoceros (Figure 1). 
  
N ® Black rhino 
0 km 50 : : 
White rhino 
& Elephant 
Figure 1. Recent growth in large herbivore distributions in 
Kruger National Park. 
There is a strong need for accurate ecological monitoring in 
KNP to inform management practices, thereby maintaining 
biodiversity. Remote sensing provides an excellent source of 
data for such ecological investigation and, in fact, has long been