Desertification Forecasting in Semi-Arid Regions: Using Deep Learning to Probe the Observability Limits of Land Degradation Dynamics
We use a convolutional LSTM (ConvLSTM) trained on ten annual Earth Observation (EO) variables (2000-2023) over the semi-arid state of Ceará, Brazil, on a 256x206 grid at roughly 2.2 km resolution, to measure how much of the year-to-year evolution of a MapBiomas-derived degradation index (IDV) can be predicted from satellite data.
ProofPaper ↗
Key points
- Forecasting desertification has received far less attention than mapping it.
- The network predicts a bounded change on top of the last observed state, is trained in two stages (single-step, then recursive rollout) and is evaluated on a contiguous spatial and temporal holdout.
- An earlier formulation that redrew the whole field each year predicted changes unrelated to the observed ones (correlation -0.06) and produced a spurious decline when run recursively.
- Monte Carlo dropout and stochastic scenario maps convey the uncertainty that the expected-value forecast hides.
Sources (1)
- [1]Desertification Forecasting in Semi-Arid Regions: Using Deep Learning to Probe the Observability Limits of Land Degradation DynamicsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 11:20 AM
We use a convolutional LSTM (ConvLSTM) trained on ten annual Earth Observation (EO) variables (2000-2023) over the semi-arid state of Ceará, Brazil, on a 256x206 grid at roughly 2.2 km resolution, to measure how much of the year-to-year evolution of a MapBiomas-derived degradation index (IDV) can be predicted from satellite data.
Forecasting desertification has received far less attention than mapping it.
Extractive summary: sentences quoted from the sources.
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