The Young Trees map was funded by DEFRA through the Natural Capital and Ecosystem Assessment (NCEA) programme. The young trees mapping project developed a machine learning methodology using remote sensing to identify restocked stands where saplings persist in healthy numbers. The approach uses an eight-year timeframe since planting, crucial for verifying government grant compliance. Automating this methodology ensures easy replication and model transferability across years by training on multi-year data, making it resilient to climatic variations.
Validation has confirmed the model’s accuracy, recommending high-confidence thresholds for restock classification. In the future, integration with the National Forest Inventory will enhance woodland mapping, accelerating updates and improving national indicators for forest extent and connectivity.
The aim of the young trees mapping project was to develop a machine learning methodology using remote sensing data, to identify stands where trees have been planted and saplings persist in healthy numbers. This was conducted within restock contexts across a specific timeframe, currently eight years since planting. This timeframe is significant because funding provided by government grants for planting can be reclaimed if it can be demonstrated that the funding has not been utilised by the landowner. Furthermore, the restock status of clearfell polygons has the potential to improve the accuracy of extent and connectivity environmental indicators developed as part of the Tree Health Resilience Strategy (THRS).
The aim of this part of the project was to automate the methodology in such a way that it can be easily replicated, and to make the model transferable across years. Specifically, to train the model using multiple years of data, which makes the model agnostic to variable annual climactic conditions. The model is both robust and accurate, as demonstrated by the validation. It is recommended that only polygons with over 95% and under 5% confidence are treated as restocked or not restocked with any certainty. Outside of these limits confidence scores are only indicative of the restock status.
In the future, the model is likely to be implemented as part of the National Forest Inventory (NFI) woodland map creation procedure. This will result in accelerated turnover of polygon labels from clearfell to young trees, where appropriate and will provide an important improvement to a national indicator for woodland extent and connectivity.
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