Co-ordinating Editor: J.L. Ohmann
Landscape-scale detection and mapping of invasive African Olive (Olea europaea L. ssp. cuspidata Wall ex G. Don Ciferri) in SW Sydney, Australia using satellite remote sensing
Article first published online: 9 MAR 2009
© 2009 International Association for Vegetation Science
Applied Vegetation Science
Volume 12, Issue 2, pages 145–154, April 2009
How to Cite
Cuneo, P., Jacobson, C.R. and Leishman, M.R. (2009), Landscape-scale detection and mapping of invasive African Olive (Olea europaea L. ssp. cuspidata Wall ex G. Don Ciferri) in SW Sydney, Australia using satellite remote sensing. Applied Vegetation Science, 12: 145–154. doi: 10.1111/j.1654-109X.2009.01010.x
- Issue published online: 16 MAR 2009
- Article first published online: 9 MAR 2009
- Received 4 December 2007;Accepted 28 July 2008.
- Invasive species mapping;
- Remote sensing;
- Satellite imagery;
- Woody weed
Question: Is satellite imagery an effective tool for mapping and examining the distribution of the invasive species Olea europaea L. ssp. cuspidata at a regional landscape scale?
Location: Southwest Sydney, Australia.
Methods: Remote sensing software was used to classify pixels of Olea europaea L. ssp. cuspidata (African Olive) and major vegetation types from satellite imagery, using a “supervised classification” technique across a 721 km2 study area in the Cumberland Plain region of western Sydney. A map of African Olive distribution was produced from the image analysis and checked for accuracy at 337 random locations using ground observation and comparison with existing vegetation maps. The African Olive distribution data were then used in a GIS analysis with additional spatial datasets to investigate the relationship between the distribution of African Olive and environmental factors, and to quantify the conservation threat to endangered native vegetation.
Results: A total area of 1907 ha of dense African Olive infestation was identified, with an omission error of 7.5% and a commission error of 5.4%. African Olive was found to occur on the steepest slopes (mean slope 14.3°) of the vegetation classes examined, with aspect analysis identifying a high prevalence on south- and southwest-facing slopes. The analysis also quantified the level of African Olive infestation in endangered ecological communities, with Western Sydney Dry Rainforest (25% affected) and Moist Shale Woodland (28% affected) identified as most vulnerable to African Olive invasion.
Conclusion: The distribution of African Olive can be efficiently mapped at a landscape scale. This technique, used in association with additional spatial datasets, identified African Olive as a significant environmental weed in SW Sydney, occupying a greater area than previously recognised and threatening several endangered native vegetation communities.