| Company | Xgrids |
| Country | Hong Kong |
| Website | https://www.xgrids.com/industryDetails?page=geomatics&id=4 |
| Category | Planning & Inventory › Remote sensing & mapping |
Digital forest models are data-driven, computational representations of forest stands and landscapes that integrate information from multiple sources — including aerial LiDAR surveys, satellite imagery, ground-based sensor networks, and historical inventory records — to simulate and predict forest structure, growth, and dynamics over time. At their core, these models combine geographic information systems (GIS) with species-specific growth algorithms, biomass equations, and ecological parameters to produce spatially explicit outputs such as canopy height maps, stem density distributions, above-ground carbon stocks, and timber yield forecasts. In practice, a digital forest model can range from a relatively simple inventory database enriched with remote-sensing layers through to a fully dynamic simulation environment capable of modelling disturbance events such as wildfire, pest outbreak, and harvesting cycles across decades. In the forestry and logging automation context, digital forest models underpin harvest planning, road network optimisation, and silvicultural prescriptions by giving planners a precise picture of merchantable volumes, terrain constraints, and environmental sensitivities before any machinery enters the coupe. Australian forest managers — including state agencies such as VicForests (now wound down) and private plantation operators like Hancock Victorian Plantations and PF Olsen — have used LiDAR-derived digital forest models to refine area-control harvesting schedules and to identify retention zones for threatened species habitat. More recently, the integration of UAV-based photogrammetry has allowed near-real-time updates to plantation models, enabling managers to detect growth anomalies, wind-throw events, or disease pressure between formal inventory cycles. Advances in machine learning, particularly deep-learning segmentation of point-cloud data, are further improving individual-tree-level accuracy, supporting per-stem yield prediction that can feed directly into harvester onboard computers and timber supply chain logistics systems.
Provides high-value spatial insights that support safer, more risk-aware design and harvest planning.
| Field | Value |
|---|---|
| Company | Xgrids |
| Country | Hong Kong |
| Type | Company |
| FWPA RD&E | 7.3 |