UAV-assisted seeding and monitoring of reforestation sites: a review

Abstract

Tree clearing in Australia has resulted in an immense loss of forest cover, attributed to agricultural industry growth, particularly cattle rearing, as well as an increase in bushfire prevalence and severity due to climate-change effects. The loss of native forests results in increased net emissions of carbon dioxide into the atmosphere, thus enhancing the greenhouse effect of global warming. Additionally, deforestation jeopardises biodiversity conservation and the complex ecological balance of native wildlife with its surrounding ecosystems. The use of uncrewed autonomous vehicles (UAVs) to deposit seed aggregates has been trialled globally as a means for regenerating tree cover in remote areas. Such use is purported to obtain high seed-deposition rates, impressive germination rates of seeds and decreased labour costs; moreover, UAVs equipped with appropriate sensors can be beneficial in the post-planting analysis of seed success, from the early germination stages to canopy development. Once site data are collected, artificial intelligence, particularly machine-learning algorithms, can be used to process captured images through the stages of data preparation, feature extraction, model training and predictions. The segmentation techniques commonly involve implementation of either watershed-related algorithms or convolutional neural networks. The use of machine learning in characterising tree parameters has been found to improve speed, accuracy and reliability compared with traditional imaging processing techniques. This review reports on the use of UAVs in forestry, particularly reforestation in remote landscapes in Australia. It summarises the use of UAV technology in the deployment of seeds through to the monitoring of reforestation sites and explores trends in these. It discusses the benefits and challenges of the UAV-assisted monitoring of reforestation sites and future trends in this area.