Detection of Aquatic Alligator Weed (Alternanthera philoxeroides) from Aerial Imagery Using Random Forest Classification

Alligator weed (Alternanthera philoxeroides (Mart.) Griseb) forms dense infestations in aquatic environments and is the focus of intensive management programs in many jurisdictions within Australia, including Victoria. A critical component of weed biosecurity programs is surveillance to find the location and extent of the target weed so that control strategies can be implemented. Current approaches within Victoria rely heavily on ground surveys and community reporting. However, these methods do not provide a systematic approach to surveillance across landscapes, resulting in undiscovered infestations. The aim of this study was to detect alligator weed from aerial photography and demonstrate the potential use of remote sensing data to support existing ground surveys and monitoring programs. Two random forest algorithms were trained based on data from 2010 and 2016. Both classifiers had high levels of accuracy, with an overall pixel-based classification accuracy of 96.8% in 2010 and 98.2% in 2016. The trained classifiers were then applied to imagery acquired annually between 2010 and 2016. The classification outputs were combined with class probability and water proximity data to produce a weighted, normalised alligator weed likelihood data layer. These datasets were evaluated by assessing alligator weed patch detection rates, using manually delineated areas of weed for each year. The patch detection rates for each year ranged from 76.5% to 100%. The results also demonstrate the use of this approach for monitoring alligator weed infestations at a site over time. The key outcome of the study is an approach to support existing biosecurity monitoring and surveillance efforts at a landscape scale and at known infested localised sites.

Automated summary

This study used aerial photography and remote sensing data to detect the distribution of alligator weed and demonstrated the potential of remote sensing data in supporting existing ground surveys and monitoring programs. Trained algorithms and image data were used to generate a weighted alligator weed likelihood data layer, which was evaluated by assessing patch detection rates.