Mapping and Analyzing Forest Landscapes with Drones

Selected theme: Mapping and Analyzing Forest Landscapes with Drones. Step into the canopy from above with stories, tools, and field-proven methods that turn aerial data into meaningful forest insights. Subscribe, comment with your questions, and help shape our next flight plan.

Speed, safety, and precision where boots can’t go

Steep slopes, dense understory, and wildlife habitats often slow traditional surveys. Drones cover hectares in minutes, reducing exposure to hazards while collecting consistent, repeatable data. Tell us which terrain slows your team most, and we’ll suggest flight patterns that cut hours to minutes.

Resolution that reveals forest structure and subtle change

Flying lower than satellites, drones capture centimeter-level detail that exposes canopy gaps, skid trails, windthrow edges, and regeneration patches. This clarity helps prioritize restoration, track fuel loads, and document fine-scale changes. Comment if you need help matching resolution to specific management goals.

Accessible tech for small teams and big landscapes

From foldable airframes to open-source processing, modern tools fit in a backpack and small budget. You can pilot, process, and publish maps in a single day. Subscribe for gear checklists, sample datasets, and training modules designed for lean teams working far from labs.

Planning the Perfect Forest Mission

Know the rules before you launch: FAA Part 107 or your regional equivalent, airspace permissions, visual line of sight, and observer roles. Forests hide people and infrastructure; plan safe altitudes, emergency landing spots, and clear communication. Post your country, and we’ll link specific guidance.

Planning the Perfect Forest Mission

For photogrammetry, aim for 75–85% front overlap and 65–75% side overlap. Use terrain-following to keep ground sampling consistent over ridges and ravines. Avoid high winds, strong thermals, and shifting shadows. What conditions dominate your site? We’ll suggest resilient patterns and settings.

Sensors That See the Forest Differently

High-quality RGB sensors create detailed orthomosaics and 3D models using structure-from-motion. They are affordable, rugged, and ideal for mapping roads, canopy gaps, and storm damage. Share your camera model below, and we’ll advise on shutter settings to minimize motion blur in dark forests.

Sensors That See the Forest Differently

Narrowband sensors capture red edge and near-infrared, enabling vegetation indices that indicate stress, vigor, and phenology. Track drought impacts, disease hotspots, and recovery after thinning. Tell us your target species, and we’ll propose index combinations and calibration routines that fit your season.

Sensors That See the Forest Differently

LiDAR penetrates canopy to model ground and height profiles, crucial for fuel models, biomass estimates, and habitat structure. Thermal maps reveal cold-air drainage and heat stress. Considering LiDAR? Ask about point density, scan angles, and flight lines tailored to dense conifers or mixed hardwoods.

From Pixels to Maps: A Robust Processing Pipeline

Import images, set coordinate systems, align photos, optimize camera parameters, and build dense clouds. Generate DSMs, DTMs, and orthomosaics with confidence intervals. Stuck on misalignments? Share a screenshot and metadata; we’ll help diagnose overlap gaps, motion blur, or poor tie points.

Ecology in Focus: Turning Maps into Forest Insight

Combine NDVI with texture metrics to flag stress patches, then ground-truth with leaf samples and pest surveys. Repeat flights during key phenological windows to track recovery. Tell us your sampling constraints, and we’ll design a plot strategy aligned with your flight cadence.

Ecology in Focus: Turning Maps into Forest Insight

Derive canopy height models, gap sizes, and edge density to understand species niches. Map least-cost corridors between refugia using land-cover resistance. Post your focal species, and we’ll suggest corridor parameters calibrated to movement behavior and seasonal resource needs.

Detecting Change: Fire Scars, Storms, and Illegal Logging

Establish a high-quality baseline map, then fly the same grid post-disturbance to compute difference layers in canopy height and vegetation indices. Standardize sun angle and altitude to avoid false positives. Need a template? We’ll share a revisit and naming convention that scales.

Detecting Change: Fire Scars, Storms, and Illegal Logging

Thermal anomalies and red-edge shifts can precede visible dieback in bark beetle outbreaks or leaf pathogens. Flag suspicious pixels, then verify on the ground. Comment with recent symptoms you observed, and we’ll suggest spectral thresholds tailored to your forest type.