modelling challenges in improving the tools for better

© Luonnonvarakeskus © Luonnonvarakeskus

Jari Hynynen

Modelling challenges in

improving the tools for better

decision support in forest-based

bioeconomy

Modelling seminar 1.4.2015/ Jari Hynynen

© Luonnonvarakeskus

Contents

1. Models in decision support

2. Some examples of modelling challenges

– Models for supporting decision making in

Multifunctional and Sustainable Forest

Management

– Models for assessing risks

– Efficient utilization of Forest Big Data

3. Lessons learned (by trial and error)


© Luonnonvarakeskus Modelling seminar 1.4.2015/ Jari Hynynen

Requirements for the model structure

Capability to

predict the effects

of management

practices

Logical

behaviour in

extreme stand

conditions

Stand

description

compatible with

inventory data

Simple

description of

a tree and a

stand

Response

functions to

silvicultural

practices

Model design

based on

knowledge on

stand dynamics

Model properties

Evaluation of

alternative

management

schedules

Updating the

information of

forest resources

Scenarios for

timber

production

possibilities

Application of the models

Role of models in forest management


Silvicultural practices

regenation

precommercial thinning

fertilization

pruning

ditch maintainance

Cuttings

commercial thinnings

regeneration cuttings

Forest operations:

technology and logistics

forwarding

biomass recovery

secondary haulage

photos: Arto Rummukainen, Pentti Niemistö & Erkki Oksanen/Metla

Forest management and wood supply



Models for supporting decision

making in Multifunctional and

Sustainable Forest Management

Utilization of forest resources


MOTTI stand simulator Salminen et al. (2005), Hynynen et al. (2005, 2014)

• A stand-level analysis tool

for assessing the effects of alternative forest management on – development of stand and trees

– profitability of forest management

– biomass production and carbon sequestration

– forest biodiversity

• A workbench for modelers – a tool for model building and testing

• A support tool for stand-level decision making

Analysis

MOTTI- a stand-level decision system

User-defined input Stand simulation

Output of the results

Logging

parameters- applied machinery

- unit costs

Timber assortment

criteria

Management

options

Request for

reports

Regeneration - Growth - Mortality

Wood quality

Biodiversity

Carbon

- sequestration

- footprint

Forest economics

Yield

Stand inventory

data

Prediction of stand dynamics in varying

growth environment including the

- impact of environmental chances

- impact of forest management practices

- impact of genetic gain

Economical

parameters

MOTTI Stand Simulator

• a stand-level analysis tool for assessing the effects of alternative forest management practices on stand development and the profitability of forest management

• wide range of opportunities to define

• management practices or management regimes for the whole rotation

• timber assortments, unit stumpage prices, costs of management practices, discount rate, …

• prediction of stand dynamics

• growth and yield models applicable to Finnish condititions

• extensively tested models with the NFI data and data from permanent growth and yield experiments

• Compatible with the most common forest planning data

• Simulation of stand dynamics (in addition to growth and yield)

• Deadwood dynamics (mortality, decomposition of dead wood)

(Mäkinen et al 2006)

• Biomass by tree compartments (stem, foliage, stump, roots)

(Repola 2008, 2009)

• Yield impacts of energy wood recovery (whole-tree

harvesting) (Jacobsson et al. 2000)

• Yield Impacts of climate change (link to FinnFor-model )

(Matala et al. 2006)

• Yield impact of genetic gain of cultivated Scots pine stands (based on empirical results on see-orchard seedlots of Scots pine)

MOTTI An extended stand-level analysis tool for research purposes


• Simulation of management practices

• Energy-wood recovery

• flexible control of the type or combination of harvested raw material (energy wood pulp wood and saw timber) in thinnings and in final fellings

• Possibility to run several alternative user-defined management schedules with one command


• Forest economics

• Harvesting costs based on time consumption models and user-defined unit prices of operating machinery

• A wide range opportunities to control economical parameters

• prices can be obtained from official statistics or they may be defined by the user (stumpage or road-side prices)

• Possibility to apply temporal trends for prices and costs

• Stand-level optimization

• Alternative optimization algorithms (e.g. Hooke-Jeeves, Picaia)


• Analysis of carbon and CO2-emissions

• Carbon in growing stock

• carbon sequestration to trees

• Litter production

• Soil carbon

• Motti is augmented with Yasso model

= > Carbon stock & dynamics in forest soilCO2

• CO2-emission of harvesting and management practices

• Based on time consumption of machines


Models for assessing risks

Environmental changes


Modeling the dynamics of Root Rot, European

Spruce Bark Beetle and wind damages in even-

aged Norway Spruce dominated forests

Juha Honkaniemi, Tuula Piri, Heli Peltola, Kari Heliövaara,

Risto Ojansuu & Risto Kasanen

Root rot by

Heterobasidion

annosum s.l.

Wind European Spruce

Bark Beetle (Ips

typographus)

• Aerial and vegetative

spread create a challenge

for control

• Decay in Norway spruce,

mortality in Scots pine

• Annual losses hard to

estimate • Europe: 790 million EUR

(Woodward et al. 1998)

• Wind fall and break trees

as well as weaken the

root systems

• Decay increases wind

damage risk

• Annual losses vary • Europe: 18.7 million

m3 of wood

(Schelhaas et al.

2003)

• Use weakened trees for

reproduction

• During outbreak can

infest and kill healthy,

living trees

• Annual losses vary

depending on the

outbreak status • Europe: 2.9 million m3

of wood (Schelhaas et

al. 2003)

Modelling seminar 1.4.2015/ Jari

Hynynen Juha Honkaniemi

Heterobasidion annosum dispersal to the stand

via spores


Hynynen

Juha Honkaniemi

H. annosum vegetative spread to subsequent

tree generation by mycelia via root systems


Hynynen

Juha Honkaniemi

H. annosum spread and decay development


Hynynen

Juha Honkaniemi

Wind velocity v at time t

Increasing wind load after a clearcut of

neighboring stand


Hynynen

Juha Honkaniemi

Bark beetle reproduction in the windthrown trees


Hynynen

Juha Honkaniemi

Bark beetle outbreak under favorable conditions


Hynynen

Juha Honkaniemi


Models utilizing Forest Big Data

Data acquisition and management



Information for localization

and updating of models

Input data and

definitions: goals,

constraints,

management

practices, assortment

rules, prices, costs,

etc.

Silviculture Wood

procurement

Roundwood trade

Measurements &

observations

Models predicting

forest structure and

dynamics

Decision-making and

planning

Decision

support

Guidelines and tutoring

Forest policymaker

Councelling

organization

Forest

owner

Forest

company

Harvesting or

silviculture

entrepreneur Tutoring systems for forest

operations

Forest management guidelines - thinning quidelines

- Un-even-aged management

- Etc.

Forest planning systems

Modelling data

& Input data


Airborne laser scanning data


Holopainen et al. 2014


Airborne and terrain laser scanning (ALS and TLS)

Modelling seminar 1.4.2015/ Jari Hynynen Holopainen et al. 2014


“New” data sources and modelling

Efficient utilization of multisource data from various sources

in modelling

• Laser scanning data in models

– Information of crown surface in growth models

– Applying pixel-information (16 * 16 m) in the

description of a stand characteristics in models =>

introdicing uneven spatial stand structure into models)

• Efficient use of data measured by forest machines in

modelling

• Efficient use of multisource data in forest terrain

trafficability monitoring and forecasting


Hynynen


Lessons learned by trial and error

Some desirable properties of decision suport tools

• Simplicity

– A tool has to be user-friendly – it is never too simple to use

– Try to find a balance between simplicity and versatility – it is not easy

• Reliability

– Report the assumptions underlying the simulations =>

– Define the range of applicability (where to use – where not to use) – be

realistic in your assessment

– Develop standard procedure for testing and validation routines

• Ease of system management

– Modular systems are easier to manage and tailor for different purposes

– Apply advanced version management system!

– Document promptly the source code and model architecture –a lot of

discipline is needed!


Hynynen

modelling challenges in improving the tools for better

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