Predicting Future Forest Plantation Establishment Outcomes from UAV-Derived Pre-Planting Environmental Conditions

Predicting plantation establishment-failure prior to planting remains a major operational challenge due to the strong spatial variability in post-harvest environmental conditions. This study developed a spatially explicit modelling framework that integrated pre-planting unmanned aerial vehicle (UAV)-derived environmental, structural, terrain, and operational-treatment data to predict establishment risk across plantation landscapes. Environmental, terrain, vegetation, and structural predictors were derived from pre-planting multispectral UAV imagery, while plantation establishment outcomes were quantified approximately 21 months later using an automated tree-detection and assessment framework. The datasets were integrated within a ridge-regularised logistic regression model incorporating interaction terms, multi-scale predictors, operational treatment masks, and blocked spatial cross-validation. The model achieved strong predictive performance under within-site blocked spatial cross-validation, with moisture-related variables, vegetation condition, and structural metrics contributing most strongly to establishment-failure prediction. Predicted risk surfaces closely matched observed patterns of reduced stocking density and suppressed growth. Beyond predicting establishment-failure, the framework enables plantation managers to screen model-predicted outcomes under alternative treatment encodings before planting and to integrate the composite stocking-density and height response within a spatially explicit Establishment Index. The framework therefore demonstrates that future plantation establishment can be predicted from environmental conditions measured before planting and provides a scalable pathway for translating high-resolution UAV data into operational decision support.