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Workshop: AI for Earth and Space Science

Monitoring a High-Arctic food web from space with machine learning

· Éliane Duchesne · Marie-Christine Cadieux · Gilles Gauthier · Joël Bêty · Pierre Legagneux · Audrey Durand


Long-term monitoring of northern ecosystems is necessary to address the challenges posed by climate change and evolving human needs and stressors. Such efforts to survey wildlife in hostile and remote areas can however be hindered by several logistical challenges. A notable example is the lockdown imposed by COVID-19 in 2020-2021, which has had tremendous impacts on research activities, especially for long-term monitoring projects. This was particularly the case in the Canadian Arctic, where Northern authorities drastically restricted travel within and outside each territory. Gaps in time series can limit our understanding of ecosystem functioning and jeopardize our ability to detect population trends. To cope with such exceptional situations and limit the loss of data, alternative monitoring strategies should be explored. We propose a machine learning pipeline to monitor from space a High-Arctic food web composed of the Arctic fox, the Greater Snow Goose, the Snowy Owl, the brown lemming, and the collared lemming. We first trained a Faster R-CNN neural network model to detect snow geese on WorldView-3 satellite images of Bylot Island (Nunavut), home to the world's largest Greater Snow Goose breeding colony. A mean F1-score of >90% is achieved on all of the six vegetation types found in Bylot, which suggests that our model is able to generalize well over the entire study area. Our pipeline then leverages the structuring role of the Greater Snow Goose within the food web to infer some key parameters on the remaining species. Notably, we are able to infer snowy owls nest density and lemming abundance. We successfully validate each individual step of our monitoring pipeline by comparing our estimates with historical field data. To our knowledge, our approach is one of the first of its kind that effectively combines ecological knowledge with machine learning algorithms in order to obtain a method that could represent a realistic alternative to Arctic field work.

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