ForestStudio

Try the future of a forest before it happens

FS-FORM team

2026-11-10

What is ForestStudio?

A forest manager decides today, but the forest answers over decades. ForestStudio simulates the forest tree by tree, so you can compare management strategies first.

Tree by tree

Decades of growth, mortality and regeneration, for every single tree in the landscape.

Your management

Thinning, species and timing, for one management unit or for all of them.

No programming

It runs on your laptop and is easy to use.

What is inside

Forest demography

  • Growth: species, size and competition
  • Mortality: background plus stress induced
  • Regeneration: seed trees, dispersal, establishment, climate, browsing and light

Management

  • Thinning from below or above, with limits
  • Species constraints: target diameter, large wood, habitat trees
  • Clear-cut and planting
  • CCF constraints possible

Assessment

  • Economic assessment: volume, timber, prices, costs, net revenue
  • Ecosystem services: carbon, biodiversity, recreation in development

App ForesStudio Edu

  • Create management scenarios
  • Run simulations
  • Explore forest metrics, economics and the data behind them

Input - Landscape definition

1 · Landscape split into management unitsThick lines: MU borders · each MU is managed as one stand2 · Zoom: hexagonal cells (~75 m²) and their trees2Tree > 7 cm DBH (size ∝ diameter):SpruceBeechOak

Landscape and management units

  • The landscape border is divided into small hexagonal cells (~75 m²)
  • Each cell belongs to one management unit (MU)

Terrain and soil, per cell

  • Digital elevation model (DEM): elevation, slope and aspect
  • Available water capacity (AWC): water the soil can hold
  • Both drive the soil water balance and drought stress

Forest inventory

Trees > 7 cm DBH with species, diameter and age, placed in their cell.

Climate and management

  • Climate: monthly temperature and precipitation, adjusted to cell elevation
  • Management: the strategy to apply, per MU and per species

Species parameterized (45)

Conifers (14)

  • Abies alba — Silver fir
  • Larix decidua — European larch
  • Picea abies — Norway spruce
  • Pinus mugo — Mountain pine
  • Pinus sylvestris — Scots pine
  • Taxus baccata — Yew
  • Also: Douglas fir, Sitka spruce, Japanese larch, western red cedar, eastern white, Monterey and lodgepole pine

Broadleaves (31)

  • Fagus sylvatica — European beech
  • Quercus robur, Q. petraea, Q. pubescens, Q. rubra, Q. ilex, Q. pyrenaica — Oaks
  • Carpinus betulus — Hornbeam
  • Fraxinus excelsior — Ash
  • Acer campestre, A. platanoides, A. pseudoplatanus — Maples
  • Other broadleaves: sweet chestnut, limes, elm, alders, birch, aspen, willows, cherries, wild apple and pear, rowan, whitebeam, wild service tree, hazel, holly, black locust

Spatial considerations

  • Hexagonal grid of 75 m²
  • Management Units (MUs)
  • Competition within and between hexagons (growth, mortality and regeneration)

Dots are trees. When competition is assessed, trees in closer cells count more.

Target cellRing 1 · 6 cellsRing 2 · 12 cells

One year in the model

Start of year n
\(N_{trees}\)

→

Growth

\(N_{trees}\)(ΔD)

→

Mortality
\(N_{trees}-N_{dead}\)

→

Regeneration
\(N_{trees}+N_{regen}\)

→

Harvest

\(N_{trees}-N_{harv}\)(thinning / clearcut)

→

Planting

(post-clearcut only)\(N_{plant}\)

→

Economic Assessment

\(N_{harv}\)

→

Snapshot: end of year n

Every tree goes through these steps once per simulated year. Climate and soil water are followed month by month behind each step.

Growth

Every year each adult tree adds to its diameter. How much depends on two questions.

What could this tree do?

Each species has a best-case growth rate. It holds while trees are young and mid-sized, then declines towards the largest diameter the species can reach.

How crowded is it?

The trees in the tree’s own cell and in two rings of surrounding cells are added up. Close neighbours count more. More crowding, less growth.

Mortality

Every year each tree faces a risk of dying, built from two sources. Then chance decides, tree by tree.

Background mortality

The everyday risk: species, size, how well the tree has been growing, how dense the stand is, and the local climate.

Drought stress new

  • Builds up over dry months and consecutive dry years
  • Triggered by an exceptionally dry year with dry soil
  • Judged against the local climate, and species differ in tolerance
  • Vitality carries the memory of past stress

Regeneration

Young trees join the population at 7 cm diameter.

1 · Seed production

Only trees past their species’ age of maturity.

2 · Dispersal

Most seeds land close, a few travel far.

3 · Climate check

Winters not too cold, growing season warm enough.

4 · Browsing

Can wipe out the seedlings of a cell in a year.

5 · Establishment new approach

More or less light on the ground, water and length of warm periods defines tree regeneration densities.

Management: the concept

Harvests are not scheduled. You define a strategy and its constraints, and an intervention happens only when the forest is ready for it.

Years →startBasal area of the MU (m²/ha)Target basal areaMinimum basal area (floor)Stopped by themax. harvest BAHI = 29 % removedStopped by theminimum BAHI = 31 % removedCycle emerges from forest dynamicsTrees are added to the harvest until the next one would break the floor or the cap.Harvest intensity HI = removed / BA must lie within [min, max], or nothing is cut.

Defined per management unit, refined per species. Besides thinning: clear-cut with planting, and Femelschlag in development. The harvesting cycle is not an input: it emerges from growth and from these limits.

When can a harvest happen?

When the management unit is dense enough: its basal area has reached the target basal area.

Which trees are removed?

Each tree gets a removal probability from its diameter: thinning from below or from above. Species rules can force removal (target diameter) or protect trees (large wood, habitat trees).

What must be respected?

A minimum basal area must remain, a maximum can be removed, and the harvest intensity must fall inside a window, otherwise nothing is cut that year.

Which trees are removed? (cont.)

00.510102030405060708090Tree diameter, DBH (cm)Probability of removallarge_wood_diametertarget_diameterMU thinning curve (a, b): all speciesSpruce ≥ target diameter → 1Oak large wood (share below min) → 0Habitat trees (largest beech) → 0Example: thinning from above, a = −5, b = 0.10 (50 % chance at 50 cm DBH)

MU level · thinning curve

  • a (intercept): overall level · b (slope): b < 0 from below, b > 0 from above
  • Applies to every tree in the MU; species rules then override it

Species · target diameter → 1

  • target_diameter (cm): trees at or above it are always candidates

Species · large wood → 0

  • large_wood (0–1): minimum share of MU basal area in large trees of the species
  • large_wood_diameter (cm, default 50): from which DBH a tree is large. Below the share, they are protected

Species · habitat trees → 0

  • habitat_trees_per_ha, habitat_tree_species, habitat_tree_min_dbh
  • The largest qualifying trees are kept for good; a dead one is replaced the next year

Assessment

Economic assessment available

  • What was harvested: volume by species, management unit and year
  • What it is worth: species-specific prices that rise with log size
  • What it costs: terrain, harvesting method and species mix
  • What is left: net revenue

Ecosystem services

  • Timber production: harvested volume and income available
  • Carbon storage: living trees and deadwood in development
  • Biodiversity: structure, deadwood, species diversity, habitat trees in development
  • Recreation: tall trees, variety of species, natural deadwood in development

Prices and costs per m³

05010015020025010203040506070Mean size of harvested trees, QMD (cm)CHF per m³energy wood 50 CHF/m³OakBeechSpruceSpruce, QMD 35 cm:98 − 29 = 69 CHF/m³small trees:cost > pricePrice (by species)Cost: Harvester + forwarder, >90 % conifersCost: Motor-manual + skidder, ≤10 % conifers

Price: species × tree size

  • Rises with tree size (QMD): from energy wood (50 CHF/m³) to sawlogs
  • Species-specific: oak, maple, larch gain most from size
  • Your prices: one reference sawlog price per species rescales its curve

Cost: method × conifer share × tree size

  • You choose the method per MU (harvest_manual): harvester + forwarder or motor-manual + skidder
  • The conifer share picks the curve, which falls with tree size
  • Your costs: a scaling factor per method (1.2 = +20 %)

Net revenue

Volume × (price − cost), per species, MU and year. 120 m³ of spruce at QMD 35 cm ≈ 120 × 69 ≈ 8’300 CHF.

Case Study Feuerthalen

Business as usual: continuous cover forestry with thinning from above, defined per management unit from the 2014–2024 management plan.

Feuerthalen Forest

The public forest of Feuerthalen (Canton of Zurich) is the landscape used in ForestStudio Edu and in the Landscape-Level Forest Management course at ETH Zurich.

68,453

individual trees at the start

8

tree species

150

management units with trees

46%

of the trees are European beech

Broadleaf-dominated: beech, ash and Norway spruce make up most of the forest, with oak, silver fir, sycamore maple, Scots pine and larch.

How the starting forest was built: no tree-level inventory was available, so the individual trees were derived from aggregated MU-level information on species proportions and development stages. Minor species may therefore be missing from the simulated forest.

Climate scenarios new

The same management can be simulated under different climates. For Feuerthalen, three futures are available, compared here with 1970–1999.

−0.1 °C

No climate change
the climate of the reference period continues

+2.1 °C

SSP2-4.5
middle-of-the-road emissions

+4.2 °C

SSP5-8.5
high emissions

Change in mean annual temperature, 2071–2100 against 1970–1999 (climate reference period). Annual precipitation changes little; summers become much hotter.

BAU scenario (all MUs)

Species Large wood share Large wood diameter Target diameter
Silver fir (aalb) 0.10 50 cm 45 cm
Sycamore maple (apse) – 50 cm 60 cm
Ash (fexc) – 50 cm 60 cm
Beech (fsyl) 0.10 50 cm 60 cm
Larch (ldec) 0.10 50 cm 60 cm1
Norway spruce (pabi) 0.40 50 cm –
Scots pine (psyl) 0.15 50 cm 55 cm1
Pedunculate oak (qrob) 0.20 50 cm 70 cm

Large wood share: minimum share of the MU’s basal area in large trees of the species; below it, those trees are protected (P = 0). Large wood diameter: the DBH from which a tree counts as large wood. Target diameter: trees at or above it get P = 1. 1 45 cm for sawlog production, to discuss.

00.510102030405060708090DBH (cm)P(removal)50 % at 50 cmThinning from abovea = −5, b = 0.1: large trees most likely to go

MU constraints (all MUs)

  • Target BA: 30 m²/ha · Minimum BA: 15 m²/ha
  • Max. harvest BA: 25 m²/ha
  • Harvest intensity: 0 – 1 (no limit)

Try it

Install ForestStudio Edu, run your first simulation, and build your own management scenario.

Install · The model · Default landscape