Red needle cast (RNC), mainly caused by Phytophthora pluvialis, is a very damaging disease of the widely grown species radiata pine within New Zealand. Using a combination of satellite imagery and weather data, a novel methodology was developed to pre-visually predict the incidence of RNC on radiata pine within the Gisborne region of New Zealand over a five-year period from 2019 to 2023. Sentinel-2 satellite imagery was used to classify areas within the region as being disease-free or showing RNC expression from the difference in the red/green index (R/G_diff) during a disease-free time of the year and the time of maximum disease expression in the upper canopy (early spring–September). Within these two classes, 1976 plots were extracted, and a classification model was used to predict disease incidence from mean monthly weather data for key variables during the 11 months prior to disease expression. The variables in the final random forest model included solar radiation, relative humidity, rainfall, and the maximum air temperature recorded during mid–late summer, which provided a pre-visual prediction of the disease 7–8 months before its peak expression. Using a hold-out test dataset, the final random forest model had an accuracy of 89% and an F1 score of 0.89. This approach can be used to mitigate the impact of RNC by focusing on early surveillance and treatment measures.

As the frequency of strong storms and cyclones increases, understanding wind risk in both existing and newly established plantation forests is becoming increasingly important. Recent advances in the quality and availability of remotely sensed data have significantly improved our capability to make large-scale wind risk predictions. This study models the loss of radiata pine (Pinus radiata D.Don) plantations following a severe cyclone within the Gisborne Region of New Zealand through leveraging repeat regional LiDAR acquisitions, optical imagery, and various surfaces describing key climatic, topographic, and storm-specific conditions. A random forest model was trained on 9713 plots classified as windthrow or no-windthrow. Model validation using 50 iterations of 80/20 train/test splits achieved robust accuracy (accuracy = 0.835; F1 score = 0.841; AUC = 0.913). In comparison to most European empirical models (AUC = 0.51–0.90), our framework demonstrated superior discrimination, underscoring its value for regions prone to cyclones. Among the 14 predictor variables, the most influential were mean windspeed during February, the wind exposition index, site drainage, and stand age. Model predictions closely aligned with the estimated 3705 hectares of cyclone-induced forest damage and indicated that 20.9% of unplanted areas in the region would be at risk of windthrow at age 30 if established in radiata pine. The resulting wind risk surface serves as a valuable decision-support tool for forest managers, helping to mitigate wind risk in existing forests and guide adaptive afforestation strategies. Although developed for radiata pine plantations in New Zealand, the approach and findings have broader relevance for forest management in cyclone-prone regions worldwide, particularly where plantation forestry is widely practised.

Aboveground biomass (AGB) mapping is fundamental to tropical forest carbon monitoring, yet national-scale estimation remains challenging because field plots are sparse and model performance is often sensitive to predictor choice and validation design. This study assessed whether geospatial embeddings improve national AGB mapping in Guyana when combined with environmental and topographic predictors. Predictor selection was undertaken using repeated grouped resampling at the plot-cluster level, and model performance was evaluated across 100 independent train–test repeats. Three final random forest models were compared. The environmental baseline model (Env + SRTM-derived elevation; 8 predictors) achieved a mean R² of 0.179, an RMSE of 148.5 Mg/ha and a relative RMSE of 36.1%. A retained 8-predictor model combining environmental variables with a selected embedding subset (Env + Emb*) improved performance slightly, with a mean R² of 0.189, an RMSE of 147.6 Mg/ha and a relative RMSE of 35.9%. The best performance was obtained with a 22-variable full-stack model combining environmental, topographic and embedding predictors, after all Sentinel-2 predictors had been eliminated during feature selection; this model achieved a mean R² of 0.203, an RMSE of 146.3 Mg/ha and a relative RMSE of 35.5%. Across models, isothermality, a measure of how day-to-night temperature variation compares to annual temperature variation, and precipitation of the coldest quarter were consistently the most influential predictors. Mean ensemble coefficient of variation, representing relative model disagreement, ranged from 0.336 to 0.361. These results indicate that geospatial embeddings provide useful complementary information, but predictive performance remained modest overall, with the best model explaining only about one-fifth of plot-level AGB variance. The resulting maps are therefore best interpreted as broad-scale decision-support products rather than high-precision local estimates of AGB.