Sub-national scale mapping of individual olive trees integrating Earth observation and deep learning

Abstract

The olive tree holds great cultural, environmental, and economic significance in the Mediterranean region. In particular, Morocco has been making dedicated investments over $10 billion since 2008 to fuel the transition from cereal to olive production. Understanding the spatial extent of this large-scale land conversion is critical for a variety of socioeconomic purposes. In response to this demand, we conducted a study to map individual olive trees in northern Morocco using satellite imagery and deep learning techniques at a sub-national scale. This study utilized cloud-free, very-high-resolution DigitalGlobe imagery collected between 2018 and 2022 to identify each individual olive tree in six northern Morocco provinces. We compared various deep learning models, including both transformer-based and CNN-based models, to generate patch-level spatial constraints and pixel-level tree identification. We found that transformer-based models outperformed CNN-based models in both tasks. Additionally, spatially constraining the pixel-level results improved olive tree mapping accuracy to varying degrees, depending on the initial performance of the model. The evaluation of the olive map generated from this study shows high accuracy in both surveyed and unsampled regions. This research represents the first-of-its-kind individual olive tree mapping at the sub-national scale that can help monitor the large-scale land conversions such as about 110,000 ha of olive plantings in the six Moroccan provinces studies here. Meanwhile it demonstrates a cost-effective and efficient prototype approach that can be adapted to identify similar tree crop expansion occurring in other parts of the world.