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1 April 2010 Monitoring aquatic weeds in a river system using SPOT 5 satellite imagery
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Aquatic weeds have caused significant problems in many lakes and river systems worldwide. Weed outbreaks of water hyacinth (Eichhornia crassipes) and para-grass (Urochloa mutica) are common in Australia and their ecological and recreational impacts mostly negative and costly. Remote sensing offers the ability to map and monitor the distribution of aquatic weeds and their early detection. The objective of this project was to develop an efficient method, using remote sensing techniques, to map and monitor the change of dense water weeds in a river system and to identify a suitable spatial scale for this process. Two SPOT (Satellite Pour l'Observation de la Terre) 5 images from May 2006 and May 2007 were used in combination with two mapping approaches on a) multispectral image data with 10 m spatial resolution and b) pan-sharpened multispectral image data with 2.5 m spatial resolution. A scale dependent validation resulted in case a) an overall producer's classification accuracy of 81%. Small outbreaks (~2 m2) alone were 71% accurate with increasing accuracies of >95% for outbreaks larger than 6.25m2 (2.5m x 2.5m pixel). Case b) generally had lower accuracies, with accuracies of >95% for outbreaks in the order of 100m2 (10m x 10m pixel) and larger. The results suggest that the river infestation by aquatic weeds in a test area of the mid-Brisbane River has increased by a factor of 2 to 3 during the 12-month period. The infested area is estimated to be between 13.6% and 15.9 % of the waterbody in 2007, while 6.2% to 6.8% in 2006. The method applied in this study included geometric and radiometric corrections, along with linear spectral unmixing and spectral angle mapper techniques. This method is applicable to other waterways worldwide and offers the potential for the early detection of infestations of aquatic surface weeds.
Michael Schmidt and Christian Witte "Monitoring aquatic weeds in a river system using SPOT 5 satellite imagery," Journal of Applied Remote Sensing 4(1), 043528 (1 April 2010).
Published: 1 April 2010

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