Mapping drivers of tropical forest loss with satellite image time series and machine learning

Pišl, J., Rußwurm, M., Haydn Hughes, L., Lenczner, G., See, L. ORCID:, Dirk Wegner, J., & Tuia, D. (2024). Mapping drivers of tropical forest loss with satellite image time series and machine learning. Environmental Research Letters 19 (6) e064053. 10.1088/1748-9326/ad44b2.

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The rates of tropical deforestation remain high, resulting in carbon emissions, biodiversity loss, and impacts on local communities. To design effective policies to tackle this, it is necessary to know what the drivers behind deforestation are. Since drivers vary in space and time, producing accurate spatially explicit maps with regular temporal updates is essential. Drivers can be recognized from satellite imagery but the scale of tropical deforestation makes it unfeasible to do so manually. Machine learning opens up possibilities for automating and scaling up this process. In this study, we developed and trained a deep learning model to classify the drivers of any forest loss—including deforestation—from satellite image time series. Our model architecture allows understanding of how the input time series is used to make a prediction, showing the model learns different patterns for recognizing each driver and highlighting the need for temporal data. We used our model to classify over 588 ′ 000 sites to produce a map detailing the drivers behind tropical forest loss. The results confirm that the majority of it is driven by agriculture, but also show significant regional differences. Such data is a crucial source of information to enable targeting specific drivers locally and can be updated in the future using free satellite data.

Item Type: Article
Uncontrolled Keywords: deep learning; deforestation; earth observation; machine learning; remote sensing; time series; tropical forest
Research Programs: Advancing Systems Analysis (ASA)
Advancing Systems Analysis (ASA) > Novel Data Ecosystems for Sustainability (NODES)
Depositing User: Luke Kirwan
Date Deposited: 10 Jun 2024 07:30
Last Modified: 10 Jun 2024 07:30

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