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.

Storm damage poses an increasing risk to radiata pine (Pinus radiata D. Don) plantations in New Zealand as extreme wind events intensify under climate change. This study quantified wind damage following ex-tropical Cyclone Gabrielle in a seven-year-old genetics trial comprising 12 genotypes grown under four stand configurations defined by contrasting stocking (833 and 1282 stems/ha) and cultivation (with and without cultivation) treatments. The genotypes comprised a Pinus attenuata × P. radiata var. cedrosensis hybrid, ten anonymised radiata pine clones and an industry-standard radiata pine seedlot. Field assessments and unmanned aerial vehicle UAV laser scanning were used to classify damage into stem breakage and overturning and to derive structural metrics, including tree diameter, height, slenderness, volume, crown width and crown volume. Overall, 16.7% of trees were damaged, with stem breakage (10.2%) occurring more frequently than overturning (6.5%). Averaged across the four treatments, total damage significantly ranged from 10.4% in the high stocking cultivated treatment to 23.5% in the low stocking no cultivation treatment. Variation between the 12 genotypes was highly significant, with breakage, overturning and total damage ranging from 3.3%–25.4%, 1.4%–15.0% and 6.6%–29.5%, respectively, between the 12 genotypes. Two radiata pine clones with high growth rates and low to moderate wind damage had the highest post-storm total stem volume per hectare, which greatly exceeded that of the hybrid or the widely planted radiata pine seedlot. These findings highlight the potential of clones that combine high growth rates and resistance to wind damage to maintain high productivity under a changing climate with a greater frequency of extreme weather events.

Under climate change, extreme wind events are predicted to become both more common and more severe, increasing the vulnerability of plantation forests. In February 2023, ex-tropical Cyclone Gabrielle caused widespread wind damage to radiata pine (Pinus radiata D. Don) forests across the North Island of New Zealand, providing a rare opportunity to evaluate mechanistic wind-risk modelling under extreme storm conditions. This study assessed the performance of the ForestGALES model in predicting wind damage within the Rangipo genetic accelerator trial and examined the influence of stocking and cultivation on wind vulnerability. Using detailed pre-cyclone field measurements and high-resolution unmanned aerial vehicle light detection and ranging (ULS) data, ForestGALES was parameterised for the Rangipo trial and applied at both individual-tree and stand scales. Model predictions were compared with observed post-cyclone damage using balanced area under the receiver operating characteristic curve (AUC), accounting for strong class imbalance. Wind damage was observed in 16.7% of trees, of which 10.2% showed stem breakage and 6.5% overturning. Across both spatial scales, overturning was more accurately predicted than stem breakage. At the individual-tree scale, ForestGALES showed moderate predictive skill, with balanced AUC values of 0.650 ± 0.014 for overturning, 0.595 ± 0.011 for breakage, and 0.621 ± 0.008 for total damage. Model performance was stronger at the stand scale, where discrimination was highest for overturning (AUC 0.811 ± 0.122), followed by breakage (0.693 ± 0.116) and total damage (0.623 ± 0.083). Silvicultural treatments significantly influenced predicted critical wind speeds (CWS). High-stocking treatments exhibited consistently higher CWS values and therefore greater wind firmness than low-stocking treatments, while cultivation effects were smaller but significant. Simulated reductions in stocking further demonstrated increased wind vulnerability as stocking declined, highlighting thinning as a primary determinant of wind risk. These findings demonstrate that ForestGALES can reliably discriminate wind damage at operational stand scales under extreme cyclone conditions and highlight the importance of stand structure in improving plantation resilience under increasingly storm-prone climates.

Windthrow is a major disturbance risk for radiata pine (Pinus radiata D. Don) plantations, but operational susceptibility models must transfer across regions and storm events. We developed a multi-regional framework combining airborne laser scanning (ALS), aerial imagery, mapped stand and site variables, climate, soils, and event-period weather. These data were used to detect storm damage and model windthrow susceptibility following major storm events in Gisborne, Hawke’s Bay and Tasman, New Zealand. Windthrow was mapped from repeat ALS canopy-height differencing in Gisborne and Hawke’s Bay, and from post-storm aerial imagery in Tasman, producing 29,244 balanced windthrow and no-windthrow plot observations. Random-forest models were evaluated using stand-grouped, spatially blocked and leave-one-region-out validation. Stand structure provided the strongest predictive signal, with windthrow concentrated in older, taller and higher-volume stands. Adding long-term climate produced the largest improvement beyond the Base stand/site formulation, giving a pooled ROC–AUC of 0.901 ± 0.013. Spatially blocked ROC–AUC for the selected model ranged across the three regions from 0.780 to 0.856, while leave-one-region-out ROC–AUC ranged from 0.649 to 0.802, demonstrating useful but region-dependent transfer. Adding soil and event-period weather did not consistently improve transferability. Prevalence-calibrated conditional scenario estimates increased with stand development under the mapped regional prevalence and the conditions represented by the reference events. These estimates provide a scalable basis for comparative windthrow-risk screening but should not be interpreted as independently validated absolute or annual windthrow probabilities.