INDUSTRY · Forestry

Aerial Forest Intelligence for Smarter, Safer and More Productive Decisions

Decision-ready forest data: tree-accurate, spatially consistent and model-aligned, for establishment quality, stocking control, inventory expansion, harvest design and environmental reporting.

Aerial view of production forest

Why drones

40–80%

MORE STAND COVERAGE PER DAY

  • Safer operations: reduced exposure to steep terrain, slash, unstable ground
  • Repeatable imagery and measurements for establishment, thinning and harvest assurance
  • Tree-level and grid-based information on stocking, spacing, canopy and gaps
  • LiDAR-derived terrain and canopy structure for inventory, access and harvest planning
  • Faster identification of understocked, overstocked, stressed or damaged areas
  • Targeted allocation of planting, thinning, pest-control and field-verification resources
  • Stronger evidence for contractor performance, valuation, environmental reporting and investment decisions

Drones don’t replace foresters. They amplify them.

Key use cases

Where drone forestry earns its place.

01

Stand count & stocking assessment

Drone imagery and LiDAR support virtual plots and stand-level counts, providing stems-per-hectare estimates and spatial variation that ground sampling alone cannot capture. Stocking grids clearly show understocked, target and high-density zones, strengthening silviculture decisions and contractor performance review.

TREE LOCATIONS · STOCKING GRIDS · PLOT SUMMARIES

02

Establishment and survival

High-resolution RGB capture reveals planted trees, natural regeneration, missed locations and early mortality across entire stands. Automated detections and survival maps highlight underperforming areas, enabling targeted blanking, field checks and early intervention, improving establishment quality and reducing costly rework.

TREE DETECTIONS · SURVIVAL MAPS · DENSITY HEATMAPS

03

Thinning assessment & prescription

Pre-thinning capture maps current stocking and identifies where removals are required to meet silvicultural targets. Repeat capture validates residual density, uniformity and compliance. Thinning maps and operational registers provide clear guidance for contractors and defensible evidence for quality assurance.

PRE/POST-THINNING MAPS · RESIDUAL-DENSITY MODELS · WORK REGISTERS

04

Tree-level inventory & yield modelling

LiDAR and calibrated field plots produce canopy-height models, structural metrics and spatial variation in forest attributes. When aligned with approved models, these surfaces support inventory imputation, yield analysis, harvest scheduling and valuation.

CHM · CANOPY METRICS · INVENTORY GRIDS · YIELD SURFACES

05

Harvest progress, cutover & residue assessment

Repeat aerial capture documents harvest boundaries, progress, completed cutover and remaining woody debris. Photogrammetry and LiDAR support residue assessment, stockpile measurement and compliance evidence, improving reporting accuracy and reducing disputes.

ORTHOMOSAICS · CUTOVER MAPS · RESIDUE DENSITY · STOCKPILE MEASUREMENTS

06

Terrain, access & harvest planning

LiDAR penetrates canopy to map ground terrain, slopes, drainage, roads and landings. These layers help planners understand access, machine suitability, extraction routes and safety constraints before mobilisation, reducing operational risk and improving harvest-block design.

DTM · SLOPE MAPS · ACCESS CONSTRAINTS · DRAINAGE LAYERS

07

Forest health, stress & pest monitoring

RGB, multispectral and thermal sensors reveal canopy discolouration, vegetation stress, pest activity and fire indicators. These layers help detect issues earlier, prioritise field verification and support pest-control planning: strengthening forest health monitoring across large, remote areas.

NDVI/NDRE · THERMAL IMAGERY · STRESS MAPS · PEST-RISK LAYERS

08

Carbon, biodiversity & environmental monitoring

Repeat remote sensing supports canopy-cover assessment, biomass-related metrics, riparian monitoring, erosion review and long-term change analysis. These layers strengthen environmental reporting, biodiversity assessment and carbon-relevant modelling across the estate.

CANOPY METRICS · CARBON SURFACES · HABITAT LAYERS · CHANGE DETECTION

09

Storm, fire & damage assessment

After wind, fire or slips, drones rapidly map affected areas without requiring immediate ground access. Georeferenced imagery and thermal data identify windthrow, blocked roads, unstable terrain and fire perimeters, supporting salvage decisions and recovery planning.

DAMAGE CLASSIFICATION · THERMAL LAYERS · RECOVERY PRIORITY MAPS

Deliverables

Drone data outputs.

A suite of forestry-ready deliverables that traditional field-only methods or satellite data cannot match for speed, resolution or completeness.

Tree Detections: example drone data output

Tree Detections

Automated identification of individual trees or crowns from high-resolution RGB or LiDAR-derived canopy surfaces

Used for

  • Establishment review
  • survival assessment
  • stocking analysis
  • thinning validation
Orthomosaics: example drone data output

Orthomosaics

High-resolution, distortion-free georeferenced aerial maps built from RGB imagery

Used for

  • Stand mapping
  • boundary checks
  • cutover documentation
  • operational planning
Digital Terrain Models (DTM): example drone data output

Digital Terrain Models (DTM)

Bare-earth elevation models derived from LiDAR, with vegetation removed

Used for

  • Slope analysis
  • access planning
  • drainage review
  • harvest-block design
Digital Surface Models (DSM): example drone data output

Digital Surface Models (DSM)

Elevation of canopy, vegetation and visible surfaces captured from photogrammetry or LiDAR

Used for

  • Canopy structure analysis
  • height variation
  • gap identification
Canopy Height Models (CHM): example drone data output

Canopy Height Models (CHM)

Height of vegetation above ground, created by subtracting DTM from DSM

Used for

  • Tree-height estimation
  • inventory modelling
  • growth monitoring
LiDAR Point Clouds: example drone data output

LiDAR Point Clouds

Millions of georeferenced points representing ground, vegetation and canopy structure, classifiable into layers

Used for

  • Terrain modelling
  • canopy analysis
  • tree segmentation
  • inventory workflows
Tree Segmentation Layers: example drone data output

Tree Segmentation Layers

LiDAR-derived separation of individual trees or crowns into discrete objects

Used for

  • Tree-level metrics
  • stocking
  • thinning analysis
  • inventory calibration
Stocking & Density Grids: example drone data output

Stocking & Density Grids

Spatial summaries of stems-per-hectare across plots, grids or stands

Used for

  • Establishment quality
  • thinning design
  • resource allocation
Survival & Stress Maps: example drone data output

Survival & Stress Maps

Spatial layers showing survival rates, canopy discolouration or stress indicators

Used for

  • Blanking decisions
  • forest-health screening
  • targeted field checks
NDVI  NDRE Vegetation Indices: example drone data output

NDVI / NDRE Vegetation Indices

Multispectral layers showing vegetation vigour, stress and canopy condition

Used for

  • Health monitoring
  • pest-risk assessment
  • environmental reporting
Thermal Imagery: example drone data output

Thermal Imagery

Radiometric temperature layers captured from thermal sensors

Used for

  • Fire hotspots
  • pest activity
  • moisture detection
  • night operations
Cutover & Residue Layers: example drone data output

Cutover & Residue Layers

Georeferenced maps showing harvest boundaries, residue density and woody debris

Used for

  • Harvest progress tracking
  • residue assessment
  • compliance evidence
Change Detection Layers: example drone data output

Change Detection Layers

Time-series comparisons of imagery or LiDAR to reveal growth, damage or operational change

Used for

  • Seasonal monitoring
  • post-treatment verification
  • storm/fire assessment
Operational Registers: example drone data output

Operational Registers

Structured datasets linking stand/grid IDs to condition, targets, evidence and recommended action

Used for

  • Thinning prescriptions
  • contractor guidance
  • audit trails

Getting it right

Accuracy is everything.

With GNSS positioning and the right drone platform, your accuracy requirements are achievable.

  • RTK/PPK positioning
  • Ground Control Points (GCPs)
  • Checkpoints & QA workflows
  • Photogrammetry vs LiDAR selection
  • Clear communication of limitations
Aerial view over forest canopy

Fits the systems you already run.

Outputs arrive clean, structured and forestry-ready.

GIS

ArcGIS · QGIS · web-based forest mapping platforms

FOREST INFORMATION SYSTEMS

Estate · stand · compartment · inventory · activity-management systems

INVENTORY & YIELD

Plot-modelling environments · valuation tools · harvest-scheduling platforms

PHOTOGRAMMETRY

DJI Terra · Pix4D · RealityCapture · Agisoft Metashape

LIDAR

DJI Terra · TerraScan · CloudCompare

FIELD OPERATIONS

Mobile GIS · contractor work maps · silviculture verification forms

ENTERPRISE

SharePoint · secure cloud storage · dashboards · data lakes

Products

Systems & equipment for Forestry

Drones

Dock Systems

Accessories & Parts

Let’s talk

Ready to put tree-accurate data across the estate?