What output does the shelterbelt analyser produce?

For each photograph: a porosity percentage, a quality-of-capture score, and a zone breakdown (upper canopy / trunk / lower storey). For the batch: a filtered average porosity, a standard deviation across frames, a confidence label, a spatial heatmap of weak spots along the belt, and a wind-reduction estimate. Everything is exportable as a CSV suitable for client reports and grant applications.

Is the method reproducible and defensible for grant applications?

Yes. The algorithm follows the well-established photograph-based method (Kenney 1987; Heisler & DeWalle 1988; Cornelis & Gabriels 2005) used in peer-reviewed agroforestry research. Every measurement includes the capture metadata, per-photo evidence, and a repeatable filtered-average calculation. This is exactly the quantified structural evidence the UK Rural Payments Agency increasingly expects for boundary-item applications.

Shelterbelt Analyser — free online windbreak porosity tool

§ 01 — What it measures

Porosity, with evidence behind every figure.

§ 01

Per-photo porosity

Every photograph is measured individually — the fraction of the belt’s side-on silhouette that is sky-visible, to the nearest percent, with a confidence label reflecting capture quality.

Sky/not-sky segmentation — auditable binary mask per photograph
Ground-cutoff control — mask foreground, fences, and ground
Quality scoring — blur, exposure, framing, sky position

§ 02

Filtered batch average

Drop in a folder of photographs, low-confidence frames are excluded automatically, and a filtered mean porosity with standard deviation is produced for the belt as a whole.

Batch upload — dozens of frames per belt in one pass
Automatic filtering — poisoned frames don’t corrupt the mean
Consistency check — warns if frames don’t cluster

§ 03

Structural heatmap

See where along the belt the structural problems sit, not just an aggregate number. Red-green-blue heatmap surfaces weak sections by relative position.

Spatial heatmap — red dense, green optimal, blue open
Weak-spot detection — pinpoints sections by metres along belt
Three-zone breakdown — upper canopy vs trunk vs lower

§ 04

Defensible output

Everything designed to survive audit — client reports, Capital Grants boundary evidence, SFI agroforestry compliance, peer-reviewed research.

CSV export — every metric, every photo, batch summary
Capture metadata — GPS, timestamp, device preserved
Wind-reduction estimate — indicative range and shelter distance

§ 02 — Procedure

From folder to figure in under a minute.

Upload photographs
Drag a folder of side-on shelterbelt photos into the analyser. Phone, drone, or camera — JPG or PNG. No pre-processing needed.
Calibrate the cutoff
Drag the band on the preview to mask anything below the canopy — grass, fences, foreground crop. The algorithm measures only what you want measured.
Read the result
Per-photo porosity, filtered batch average, standard deviation, confidence label, structural heatmap. Export as CSV, archive, share.

§ 03 — Audience

Built for people who need the answer defensible.

Farmers & landowners Quantifiable evidence of windbreak condition for Capital Grants applications, SFI compliance, insurance claims, or stewardship audits.
Agroforestry consultants Defensible measurements for client reports, baseline surveys, and design recommendations. Branded CSV and report workflow.
Commercial drone pilots A new deliverable to add to existing aerial-survey services. Tree-line density and porosity assessment from drone imagery.
Researchers Rapid field assessment for studies of wind erosion, microclimate, biodiversity corridors, and carbon sequestration.

§ 04 — Background

Research method, web delivery.

The photograph-based porosity method has been the field standard for measuring windbreak structure since the foundational studies of the 1980s. Kenney (1987) and Heisler & DeWalle (1988) established the relationship between a belt’s optical silhouette and its aerodynamic behaviour. Cornelis & Gabriels (2005) validated the relationship at field scale. The consensus is that a belt’s optical porosity — the fraction of sky visible through the canopy — is a reliable predictor of wind shelter behaviour, and that the optimum for working farm windbreaks is roughly 40–50%.

ShelterMetrics takes that research method and makes it a web-based instrument. Same algorithm, same defensibility, delivered in a browser instead of a research lab. The guides section covers the method in depth:

How to measure shelterbelt porosity from a photograph — the step-by-step method
Why 40–50% is the target porosity — the research explainer
How to photograph a shelterbelt for analysis — capture technique
Optical vs. aerodynamic porosity — what the number tells you
2026 Capital Grants: proving shelterbelt condition — using the analyser for grant evidence

Frequently asked questions

What is a shelterbelt analyser?

A shelterbelt analyser measures the porosity of a windbreak — the fraction of the belt’s side-on silhouette that is sky-visible — from a photograph. Porosity is the primary structural indicator of how well a shelterbelt reduces wind speed and over what distance. ShelterMetrics is a web-based shelterbelt analyser: drop in side-on photos, get per-photo porosity, a filtered batch average, a confidence score, and a heatmap, with a CSV export.

Is the shelterbelt analyser free?

Yes, with a tiered plan. Two scans are free without signing up for quick evaluation. The Free tier (with a free account) allows 20 photographs per month. The Pro tier removes the cap and adds features, priced at £19/month or £190/year.

Do I need to install software?

No. ShelterMetrics runs entirely in your web browser. Upload photographs from a phone, drone, or camera — analysis runs server-side and results are delivered to the browser in seconds. No desktop application, no GIS software, no plug-ins.

What output does the analyser produce?

For each photograph: porosity percentage, quality score, zone breakdown (upper canopy / trunk / lower storey). For the batch: filtered average porosity, standard deviation, confidence label, spatial heatmap of weak spots, wind-reduction estimate. Everything is exportable as CSV for client reports and grant applications.

Is it defensible for grant applications?

Yes. The algorithm follows the well-established photograph-based method (Kenney 1987; Heisler & DeWalle 1988; Cornelis & Gabriels 2005) used in peer-reviewed agroforestry research. Every measurement includes capture metadata, per-photo evidence, and a repeatable calculation — the quantified structural evidence the UK Rural Payments Agency increasingly expects for boundary-item applications. See the Capital Grants guide.

Can I use drone photos?

Yes. Drone imagery is the standard input for tall belts (over about 5 metres) and for consistent framing across long belts. The analyser handles DJI, Autel, and generic drone JPEGs directly. See our drone-photography guide for flight planning and capture technique.