Deep learning detects invasive plant species across complex landscapes using Worldview‐2 and Planetscope satellite imagery

Abstract

Abstract Effective management of invasive species requires rapid detection and dynamic monitoring. Remote sensing offers an efficient alternative to field surveys for invasive plants; however, distinguishing individual plant species can be challenging especially over geographic scales. Satellite imagery is the most practical source of data for developing predictive models over landscapes, but spatial resolution and spectral information can be limiting. We used two types of satellite imagery to detect the invasive plant, leafy spurge ( Euphorbia virgata ), across a heterogeneous landscape in Minnesota, USA. We developed convolutional neural networks (CNNs) with imagery from Worldview‐2 and Planetscope satellites. Worldview‐2 imagery has high spatial and spectral resolution, but images are not routinely taken in space or time. By contrast, Planetscope imagery has lower spatial and spectral resolution, but images are taken daily across Earth. The former had 96.1% accuracy in detecting leafy spurge, whereas the latter had 89.9% accuracy. Second, we modified the CNN for Planetscope with a long short‐term memory (LSTM) layer that leverages information on phenology from a time series of images. The detection accuracy of the Planetscope LSTM model was 96.3%, on par with the high resolution, Worldview‐2 model. Across models, most false‐positive errors occurred near true populations, indicating that these errors are not consequential for management. We identified that early and mid‐season phenological periods in the Planetscope time series were key to predicting leafy spurge. Additionally, green, red‐edge and near‐infrared spectral bands were important for differentiating leafy spurge from other vegetation. These findings suggest that deep learning models can accurately identify individual species over complex landscapes even with satellite imagery of modest spatial and spectral resolution if a temporal series of images is incorporated. Our results will help inform future management efforts using remote sensing to identify invasive plants, especially across large‐scale, remote and data‐sparse areas.

Plant phenotyping relevance

衛星画像とCNN/LSTMを用いて侵入植物個体群を直接検出する方法を開発・評価しており、植物状態の取得・抽出が研究の中心である。

Code and data availability

The article explicitly states that all analysis code for the leafy spurge deep learning models (WV-CNN, PS-CNN, PS-LSTM) is publicly available in the authors' GitHub repository. The 1-m land cover map used as ground truth is a cited prior dataset (Host et al., 2016), not a paper-specific asset, and no trained model or