A PROCEDURE FOR IDENTIFYING INVASIVE WILD PARSNIP PLANTS BASED ON VISIBLE BANDS FROM UAV IMAGES
- Department of Geography and Planning, Queens University, Kingston, Canada
Keywords: Wild parsnip, UAV images, pixel-based, object-based, Support Vector Machine (SVM)
Abstract. Wild parsnip is an invasive plant that has serious health risks to humans due to the toxin in its sap. Monitoring its presence has been a challenging task for conservation authorities due to its small size and irregular shape. Unmanned Aerial Vehicles (UAV) can obtain ultra-high resolution (UHR) imagery and have been used for vegetation monitoring in recent years. In this study, UAV images captured at Lemoine Point Conservation Area in Kingston, Ontario, are used to test a methodology for distinguishing wild parsnip. The objective of this study is to develop an efficient invasive wild parsnip classification workflow based on UHR digital UAV imagery. Image pre-processing flow includes image orientation, digital elevation model (DEM) and digital surface model (DSM) extractions, and orthomosaicking using Simactive’s software. Three vegetation indices and three texture features are calculated and added to the mosaicked images as additional bands. Image analysis frameworks namely pixel- and object-based method and three classifiers are tested and the object-based Support Vector Machine (SVM) is selected to distinguish wild parsnip from other vegetation types. The optimal image resolutions are undertaken by comparing accuracy assessments. The results provide an executable workflow to distinguish wild parsnip and show that UAV images, with a simple digital camera, are an appropriate and economic resource for small and irregular vegetation detection. This method yields reliable and valid outcomes in detecting wild parsnip plants and demonstrates excellent performance in mapping small vegetation.