Rain‐triggered landslides cause significant socioeconomic loss and long‐term ecological impact, disrupting topsoil and seed banks and creating unfavourable conditions for vegetation establishment. Although hazard management effectively reduces landslide risk, most management plans primarily focus on the societal impacts (loss of property and human lives), while environmental impacts related to landslide occurrence and recurrence are often overlooked. The role of different vegetation types in mitigation strategies remains uncertain due to the time and spatial constraints of traditional field study methods. Therefore, the role of vegetation in landslide restoration should be reconsidered. In this study, we investigated post‐landslide vegetation recovery patterns, analysed the duration of vegetation recovery, identified factors influencing these patterns, examined the role of forests in occurrence and recurrence probability and explored the implications of these findings for each element within an integrated management framework for landslide restoration. We utilised long‐term landslide inventory data (1924–2018) covering the entire city of Hong Kong (~1100 km²) and combined it with structural data derived from airborne LiDAR scanning in 2020 to capture landslide recovery. Our findings revealed significant differences in recovery trajectories between the main bodies and foot areas of scars, with the former taking approximately 46 years to recover and the latter recovering in about 38 years. Scar age, average wind speed, elevation and proximity to the forest were identified as key factors influencing structural recovery on scars, while the proximity to forest also regulated recovery rate of scars. We observed decreased landslide occurrence in forested areas, with recurrent landslides primarily appearing on older, unrecovered scars on barren hillsides. Synthesis and applications: Our research highlights the dynamic patterns, recovery time and drivers of vegetation recovery on landslide scars; and the higher probability of landslide occurrence in non‐forest areas and increased recurrence on bare, low vegetated scars. This information provides valuable insights for informing restoration efforts and managing landslide‐prone areas. Such knowledge is crucial for developing effective mitigation strategies and enhancing the resilience of vulnerable ecosystems facing climate change and increased frequency of extreme weather events.

Forests are invaluable natural resources that provide essential ecosystem services, and their carbon storage capacity is critical for climate mitigation efforts. Quantifying this capacity would require accurate estimation of forest structural attributes for deriving their aboveground biomass (AGB). Traditional field measurements, while precise, are labor-intensive and often spatially limited. Handheld Mobile Laser Scanning (HMLS) offers a rapid alternative for building forest inventories; however, its effectiveness and accuracy in diverse subtropical forests with complex canopy structure remain under-investigated. In this study, we employed both HMLS and traditional surveys within structurally complex subtropical forest plots, including old-growth forests (Fung Shui Woods) and secondary forests. These forests are characterized by dense understories with abundant shrubs and lianas, as well as high stem density, which pose challenges in Light Detection and Ranging (LiDAR) point cloud data processing. We assessed tree detection rates and extracted tree attributes, including diameter at breast height (DBH) and canopy height. Additionally, we compared tree-level and plot-level AGB estimates using allometric equations. Our findings indicate that HMLS successfully detected over 90% of trees in both forest types and precisely measured DBH (R² > 0.96), although tree height detection exhibited relatively higher uncertainty (R² > 0.35). The AGB estimates derived from HMLS were comparable to those obtained from traditional field measurements. By producing highly accurate estimates of tree attributes, HMLS demonstrates its potential as an effective and non-destructive method for rapid forest inventory and AGB estimation in subtropical forests, making it a competitive option for aiding carbon storage estimations in complex forest environments.

Summary: Tropical cyclones (TCs) sporadically cause extensive damage to forests. However, little is known about how TCs affect forest dynamics in mountainous terrain, due to difficulties in modelling wind flows and quantifying structural changes. Typhoon Mangkhut (2018) was the strongest TC to strike Hong Kong in over 40 yr, with gusts > 250 km h⁻¹. Remarkably, the event was captured by a dense anemometer network and repeated LiDAR surveys across natural forests and plantations. We mapped long‐term mean and extreme wind speeds using CFD models and analysed corresponding changes in canopy height, which uncovered TC‐forest dynamics at unprecedented scales (> 400 000 pixels, 1108 km²). Forest height was more strongly limited by wind exposure than by background topography, a limitation attributable to a dynamic equilibrium between growth and disproportionate TC damage to taller forests. Counterintuitively, wind‐sheltered forests also suffered heavy damage. As a result, canopies of wind‐sheltered forests were more rugged, which contrasted with flat‐topped forests at wind‐exposed sites. Plantations were more susceptible to TCs compared to natural rainforests of similar stature (canopy height change −0.86 m vs −0.39 m). Our findings highlight TCs as important, often overlooked factor that fundamentally shapes forest structure and dynamics.

Succession is the process and pattern of changes in communities following a disturbance over time. This process can be disrupted by recurrent disturbances or arrested at an early successional stage even if disturbances cease. Delayed succession can hinder the natural regeneration of vegetation, weakening ecosystem development, functions and services and in severe cases, increase the risk of fires. Despite its ecological significance, delayed succession is typically analysed in a site‐ or species‐specific context, lacking the broad spatial quantification needed for effective restoration planning. Using the tropical vegetated landscapes of Hong Kong as a model system, we identified the delayed succession phenomenon using a multi‐source remote sensing approach that integrates satellite‐based time‐series imagery and airborne‐based canopy height data. We then investigated the influence of biotic, abiotic and disturbance factors on the occurrence and spatial variability of delayed succession using XGBoost. Finally, we analysed the drivers of natural regeneration and predicted the recovery trajectories of disturbed grasslands following the cessation of disturbances. Our findings indicate that delayed succession affects 4.2% (30.78 km²) of Hong Kong's vegetated area, predominantly on ridges and mid‐slopes. Biotic factors were the primary drivers, with vegetation density (<0.2 m) being the most significant. Fire frequency emerged as a critical disturbance factor; the probability of delayed succession increased sharply after more than three fire instances during the study period. Notably, vegetation density (<0.2 m) shifted from mitigating to promoting delayed succession as fire frequency increased. Upon cessation of post‐fire disturbances, natural recovery was more dependent on the proximity of natural woodland remnants than on plantations. Synthesis and applications. Our study challenges the view that fire prevention alone is sufficient for restoring degraded landscapes. We show successful natural regeneration hinges on nearby remnant woodlands acting as crucial seed sources. This research provides a remote sensing framework to quantify the risk of delayed succession where ecosystems fail to recover within a short term. This enables managers to strategically prioritize resources, focusing fire prevention on high‐risk sites while directing active restoration like planting to areas with low natural recovery potential. This approach facilitates cost‐effective, climate‐resilient restoration.