Description
We combined regeneration density observations from the German NFI to map the forest regeneration across Germany and evaluate potential regeneration gaps using a three-step approach. First, we combined the NFI regeneration data with environmental data to construct species-specific regeneration models. Second, we evaluated the predictive performance of the regeneration models using 10-fold blocked cross-validation and used the validated models to predict regeneration densities for the forest area of Germany. Third, we mapped indicators of regeneration quantity and quality, demonstrating their potential application for Bavaria. Here this repository consists of:data.zip (input data)output.zip (output data)GermanRegenerationMaps2012_workflow.png (code workflow to generate output.zip from data.zip)Predictors.png (information on predictor variables)See additional information in related works:for full data references please look up our preprintfor related code see GitHub and Zenodoto view and explore the generated regeneration maps online please see Google Earth Engine
Citations (1)
- https://doi.org/10.32942/x2gs8xDataCite MDC
Cited on 31 May 2025
Weight: 1.00
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Publication Details
Subfield
Artificial Intelligence
Field
Computer Science
Domain
Physical Sciences
Confidence Score
37%
Source
Scholar Data Model