Abstract

The understanding of forest growth is crucial for the preservation of forests in the future. Factors such as tree species, age, forest management and environmental conditions influence this growth.Tree height and age data can be combined to describe forest growth and infer known environmental effects. In this study, we constructed height-age growth curves for 14 monospecific and mixed-species stands using ground measurements and satellite data in northern France. A random forest height model was constructed using tree species and age, area of disturbance, and 125 environmental parameters (climate, altitude, soil composition, geology, stand ownership, and proximity to road and urban areas). Through feature elimination and SHAP analysis, six key features were identified that explain forest growth and their effect on height was investigated.The agreement between satellite and ground data justifies their simultaneous exploitation. Age and tree species emerged as the primary predictors of tree height, accounting for 49% and 10% of the variation, respectively. Post-disturbance growth is influenced by the disturbed patch area, which reveals the regeneration method, accounting for 19% of the variation. Soil pH, altitude, and summer climatic water budget have varying effects on tree height depending on age and tree species.The agreement between satellite and ground data justifies their simultaneous exploitation. Age and tree species are the primary predictors of tree height, accounting for 49% and 10% of the variation, respectively. Post-disturbance growth is influenced by the disturbed patch area, which reveals the regeneration method, accounting for 19% of the variation. Soil pH, altitude, and summer climatic water budget have varying effects on tree height depending on age and tree species. The integration of satellite and field data shows potential for analyzing future forest evolution.

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