Abstract
Invasive plants are a major agent threatening biodiversity conservation and directly affecting our living environment. This study aims to evaluate the potential of deep learning, one of the fastest-growing trends in machine learning, to detect plant invasion in urban parks using high-resolution (0.1 m) aerial image time series. Capitalizing on a state-of-the-art, popular architecture residual neural network (ResNet), we examined key challenges applying deep learning to detect plant invasion: relatively limited training sample size (invasion often confirmed in the field) and high forest contextual variation in space (from one invaded park to another) and over time (caused by varying stages of invasion and the difference in illumination condition). To do so, our evaluations focused on a widespread exotic plant, autumn olive (Elaeagnus umbellate), that has invaded 20 urban parks across Mecklenburg County (1410 km2) in North Carolina, USA. The results demonstrate a promising spatial and temporal generalization capacity of deep learning to detect urban invasive plants. In particular, the performance of ResNet was consistently over 96.2% using training samples from 8 (out of 20) or more parks. The model trained by samples from only four parks still achieved an accuracy of 77.4%. ResNet was further found tolerant of high contextual variation caused by autumn olive’s progressive invasion and the difference in illumination condition over the years. Our findings shed light on prioritized mitigation actions for effectively managing urban invasive plants.
Highlights
Biological invasions have become a major non-climatic driver of global environmental change [1]
The model residual neural network (ResNet)-18 was able to achieve an accuracy of 77.4%
With extra training samples, model performance was noticeably improved and consistently better than 96.2% (Figure 3)
Summary
Biological invasions have become a major non-climatic driver of global environmental change [1]. Such invasions have detrimental effects on both local and global ecology and economy [2]. Among those invasive agents, exotic plant species continuously threaten biodiversity conservation and natural resource management, and negatively affect human and environmental health [3]. The propagation of invasive plant species has been expedited by the ability to survive and grow over a wide range of habitats such as disturbed areas, abandoned farmlands, and forests, whether in soil or aquatic ecosystems [5]. With limited resources available for monitoring invasive species distributions (traditional efforts are mostly field survey-based), developing alternative approaches for an accurate and timely detection of the presence and spread of invasive species is crucial for successful implementation of mitigation actions [6]
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