Drone Reforestation Seed Drop Coverage Planner

Overview: what this planner helps you do

This calculator estimates how much ground a drone can cover per flight during aerial seeding missions and how many flights you will need to hit your reforestation target. It is designed for conservation teams, NGOs, startups, and landowners who are experimenting with drone-based reforestation or landscape restoration.

By entering your target area, desired seed density, drone payload capacity, release altitude, seed spread angle, flight speed, and battery life, the planner estimates:

• Coverage area per flight, limited by either payload or battery life.
• The number of sorties (flights) required to seed your entire target area.
• Approximate flight distance and time in the air per sortie.

This is a mission-planning tool rather than a full ecological model. It helps you translate drone and payload specifications into operational coverage, so you can compare hardware options, plan crew schedules, and spot obvious constraints before field deployment.

The calculator uses the metric system: distances in meters, areas in hectares (1 hectare = 10,000 m²), and seed density in seeds per square meter. Keeping units consistent is important for meaningful results.

Model and formulas used by the planner

The tool uses a simplified geometric and kinematic model to approximate coverage. It assumes that seeds disperse in a conical pattern underneath the drone and that the drone flies in straight, level passes over the area of interest.

Seed spread geometry

The seed spread is approximated as a cone with spread angle θ (in degrees) and release altitude h (in meters). The effective swath width w on the ground is:

where:

• h = release altitude (m)
• θ = seed spread angle (degrees), measured as the full angle of the cone

A larger spread angle or higher altitude increases the swath width, but in practice also increases exposure to wind drift and uneven distribution.

Flight distance and battery-limited area

Flight distance is based on horizontal speed and battery life. Let:

• v = flight speed (m/s)
• t = battery life (minutes)

Convert battery life to seconds: t_s = 60 × t. The maximum straight-line distance the drone could fly while seeding is:

d_f = v × t_s

Assuming continuous seeding over this distance and swath width w, the battery-limited coverage area is:

A_b = d_f × w (in m²)

Payload-limited area

Payload limits how many seeds can be released in a flight. Let:

• N_s = number of seeds or seed balls the drone can carry (payload capacity)
• ρ_s = desired seed density (seeds/m²)

If you aim for a uniform density ρ_s, the maximum area you can seed before running out of seeds is:

A_p = N_s / ρ_s (in m²)

This reflects the ecological target: higher desired density (more seeds per square meter) reduces payload-limited coverage per flight.

Coverage per flight and sorties required

The true coverage per flight is limited by whichever factor runs out first: battery or payload. The calculator takes the minimum of the two areas:

A_c = min(A_b, A_p)

Let your total target area be A_t. You enter this in hectares, so the planner converts it internally:

A_t,m² = A_t,ha × 10,000

The number of sorties (flights) needed is then:

sorties = ceil(A_t,m² / A_c)

where ceil means “round up to the next whole number,” because you cannot fly a fraction of a mission.

Worked example: wildfire reforestation mission

Consider a team reseeding land after a wildfire. Their mission parameters are:

• Target area: 15 hectares
• Desired seed density: 3 seeds/m²
• Drone payload capacity: 10,000 seed balls
• Release altitude: 50 m
• Seed spread angle: 60°
• Flight speed: 10 m/s
• Battery life: 15 minutes

Step 1: Compute swath width

Using h = 50 m and θ = 60°:

w = 2 × 50 × tan(60° / 2) = 100 × tan(30°) ≈ 100 × 0.5774 ≈ 57.74 m

Step 2: Compute battery-limited area

Battery life in seconds: t_s = 15 × 60 = 900 s.

Flight distance: d_f = 10 m/s × 900 s = 9,000 m.

Battery-limited coverage area:

A_b = d_f × w ≈ 9,000 × 57.74 ≈ 519,660 m²

In hectares, this would be roughly 51.97 ha if payload were unlimited.

Step 3: Compute payload-limited area

Payload capacity: N_s = 10,000 seed balls.

Desired density: ρ_s = 3 seeds/m².

Payload-limited coverage area:

A_p = N_s / ρ_s = 10,000 / 3 ≈ 3,333 m²

This equals 0.333 hectares per flight.

Step 4: Determine coverage per flight and sorties

The actual coverage per flight is the minimum of 519,660 m² and 3,333 m², which is 3,333 m². The team is clearly payload-limited, not battery-limited.

Target area in square meters: A_t,m² = 15 × 10,000 = 150,000 m².

Number of flights needed:

sorties = ceil(150,000 / 3,333) ≈ ceil(45) = 45

Each flight uses roughly 15 minutes of battery life, so total time in the air is about 675 minutes, or 11.25 hours. Real missions will also include time for takeoff, landing, battery swaps, loading seeds, and transit, so field time will be longer.

Hardware trade-offs and scenario comparison

Changing payload and battery capacity affects coverage differently depending on which constraint dominates. The table below shows simplified scenarios based on the example above, holding all other parameters constant.

When you are payload-limited, doubling payload nearly doubles coverage per flight and halves the required number of sorties. Doubling battery life alone does not help until payload and other factors are sufficient for battery to become the limiting factor. Use the planner to explore several configurations and identify the most impactful upgrades for your operations.

How to interpret the results

After you enter your inputs and run the calculation, you will typically see:

• Coverage per flight – the estimated area you can seed in a single sortie, in both m² and hectares.
• Number of flights (sorties) – how many sorties are required to cover the target area at the chosen seed density.
• Approximate flight distance/time – how far the drone will travel and how long it will remain in the air while seeding.

Use these values for high-level mission planning:

• Staffing and scheduling: Divide total flight hours by number of drones and available crews to estimate how many days are required.
• Battery logistics: Compare total flight time with the number of batteries and chargers you have to plan swap cycles.
• Risk and safety margins: Consider leaving a buffer (for example, plan for 10–20% more sorties than the ideal estimate) to account for wind, no-fly zones, and other inefficiencies.

Remember that the calculator estimates ground coverage, not germination rates or long-term forest establishment. For ecological success, you must also consider species selection, soil preparation, and post-planting monitoring.

How to choose realistic input values

Target area and units

• Target area (hectares): Use your GIS polygons, management plans, or burn perimeter maps to compute area. If you only know square meters, divide by 10,000 to convert to hectares.

Seed density

• Desired seed density (seeds/m²): Forestry and restoration guidelines often recommend anywhere from 0.5 to 10+ viable seeds per square meter, depending on species, mortality rates, and objectives (e.g., dense cover vs. scattered trees). Dryer, harsher sites or species with low germination often require higher densities.

Drone and payload parameters

• Drone payload capacity (seeds): Derive from your UAV’s maximum payload (kg) and the mass of each seed ball or capsule. Always leave a safety margin under the absolute maximum to avoid overstressing motors and batteries.
• Release altitude (m): Check local regulations and your drone’s capabilities. Lower altitudes (20–60 m) generally reduce drift and improve placement, but may narrow the swath and increase required flight lines.
• Seed spread angle (degrees): This depends on your dispenser design (spinner, pneumatic, gravity feed). If you are unsure, start with 45–60° as a rough estimate and adjust based on field tests.
• Flight speed (m/s): Typical mapping-style flights are in the range of 5–15 m/s. Slower speeds may improve seed placement; higher speeds cover ground faster but can stress the dispersal mechanism.
• Battery life (minutes): Use realistic mission endurance, not the best-case hover time from the spec sheet. Deduct time for takeoff, transit, and return-to-home when in doubt.

Assumptions and limitations

This planner is intentionally simplified. When using its outputs, keep these key assumptions and limitations in mind:

• Idealized seed spread: The model assumes a clean conical pattern with constant spread angle and swath width, which rarely holds perfectly in turbulent or complex wind conditions.
• No wind or drift: Wind speed, gusts, and thermals are ignored. In practice, wind can significantly shift and dilute seed patterns, especially at higher altitudes.
• Uniform terrain and vegetation: Slopes, cliffs, tree canopies, rocks, and obstacles are not modeled. The calculator assumes a flat, unobstructed surface.
• Constant speed and altitude: The drone is assumed to maintain constant flight speed and altitude while seeding, without turns, accelerations, or pauses.
• Even seed metering and viability: Seeds are assumed to be evenly distributed over the swath and fully viable. Real-world blockages, clumping, and variable germination reduce effective establishment.
• No regulatory or airspace constraints: The model does not account for no-fly zones, line-of-sight requirements, or other aviation rules that may alter your flight patterns.
• Coverage, not success probability: Outputs describe physical coverage only. They do not replace ecological assessment, field trials, or expert advice.

Because of these limitations, treat the planner as a strategic planning and comparison tool. Validate key parameters with small test flights and ground truthing before committing to large-scale deployments.

Using the planner responsibly

For responsible reforestation, combine this calculator with local ecological expertise and on-the-ground data. Before a mission, consult forestry agencies, landowners, and relevant authorities; verify that selected species are native and appropriate for the site; and plan for post-drop monitoring. The most effective drone reforestation programs use tools like this to frame logistics, then iterate based on field results and long-term ecosystem responses.