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Abstract
This study integrates machine learning (Random Forest) and open landscape data to predict the conservation value of forests in Sweden at a 1-hectare resolution. The model accurately predicts different levels of forest naturalness, validated using independent data. This wall-to-wall mapping addresses the need for assessing conservation targets, spatial planning, and forest landscape restoration in the face of intensive forestry.
Publisher
Communications Earth & Environment
Published On
Apr 11, 2024
Authors
Jakub W. Bubnicki, Per Angelstam, Grzegorz Mikusiński, Johan Svensson, Bengt Gunnar Jonsson
Tags
machine learning
conservation value
Random Forest
forests
Sweden
naturalness
spatial planning

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