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We evaluate three approaches to mapping vegetation using images collected by an unmanned aerial vehicle (UAV) to monitor rehabilitation activities in the Five Islands Nature Reserve, Wollongong (Australia). Between April 2017 and July 2018, four aerial surveys of Big Island were undertaken to map changes to island vegetation following helicopter herbicide sprays to eradicate weeds, including the creeper Coastal Morning Glory (Ipomoea cairica) and Kikuyu Grass (Cenchrus clandestinus). The spraying was followed by a large scale planting campaign to introduce native plants, such as tussocks of Spiny-headed Mat-rush (Lomandra longifolia). Three approaches to mapping vegetation were evaluated, including: (i) a pixel-based image classification algorithm applied to the composite spectral wavebands of the images collected, (ii) manual digitisation of vegetation directly from images based on visual interpretation, and (iii) the application of a machine learning algorithm, LeNet, based on a deep learning convolutional neural network (CNN) for detecting planted Lomandra tussocks. The uncertainty of each approach was assessed via comparison against an independently collected field dataset. Each of the vegetation mapping approaches had a comparable accuracy; for a selected weed management and planting area, the overall accuracies were 82 %, 91 % and 85 % respectively for the pixel based image classification, the visual interpretation / digitisation and the CNN machine learning algorithm. At the scale of the whole island, statistically significant differences in the performance of the three approaches to mapping Lomandra plants were detected via ANOVA. The manual digitisation took a longer time to perform than others. The three approaches resulted in markedly different vegetation maps characterised by different digital data formats, which offered fundamentally different types of information on vegetation character. We draw attention to the need to consider how different digital m

Land managers are required to assess a range of environmental attributes prior to and after prescribed burning. Current environmental assessments vary depending on the organisation involved and the existing information about localised soil erosion. Auditing successful environmental assessments requires ongoing field monitoring to evaluate whether the magnitude and extent of predicted post-fire impacts are comparable. The impacts of post-fire erosion were assessed by the authors using the techniques of water sampling, sediment traps, erosion pins, laser scanning, photogrammetry and visual field assessment. Each data collecting method varies in its spatial and temporal reach in terms of monitoring landscape changes in a post-fire environment. The methods also vary in cost, time and technical complexity.This paper uses a case study of the Mount Lofty Ranges, South Australia to apply and assess post-fire erosion field techniques in relation to a wildfire at Mount Bold, a Holocene paleofire located at Cleland and ten prescribed burns conducted within the Mount Lofty Ranges. The techniques are assessed for their merits in the context of simplicity for land management staff to use and associated costs. They are further examined in light of their application to different timeframes, spatial scales, magnitude and frequency. Our investigation leads to the recommendation of a simple framework for quick and relatively easy assessment, which is cost effective and can be carried out by both researchers and land management agencies