Difference between revisions of "ESC"

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Acronym: ESC
 
Acronym: ESC
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Web page: http://www.forestresearch.gov.uk/esc
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=== Brief overview ===
 
=== Brief overview ===
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[[Category:Ecological Classification|ESC ]]
 
[[Category:Ecological Classification|ESC ]]
 
[[Category:Species selection|ESC ]]
 
[[Category:Species selection|ESC ]]
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[[Category:Yield prediction]]
  
 
__TOC__
 
__TOC__
  
 
=== Scope of the system ===
 
=== Scope of the system ===
This tool encourages the decision maker on the election of a forest species according to their suitability to the soil properties and climatic data. It also provides the suitability of the species according to the expected evolution of the climate.
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This tool encourages the decision maker on the election of a forest species according to their suitability to the soil properties and climatic data. It also provides the suitability of the species according to the expected evolution of the climate, and predicts the potential yield in the form of a site index. The number of species presented is 26.
  
 
=== System origin ===
 
=== System origin ===
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=== Forest models ===
 
=== Forest models ===
  
Species suitability models based on accumulated temperature (AT5), continentality (CT), wind exposure (DAMS - Detailed Aspect Method of Scoring), moisture deficit (MD), soil moisture regime (SMR) and soil nutrient regime (SNR).
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Species suitability and yield prediction models are based on accumulated temperature (AT5), moisture deficit, continentality (CT), wind exposure (DAMS - Detailed Aspect Method of Scoring), moisture deficit (MD), soil moisture regime (SMR) and soil nutrient regime (SNR). From these six variables, climatic warmth (accumulated temperature) is assumed as the principal factor and from the remaining others only the most limiting factor may modify the site yield prediction <ref> REYNOLDS K.M., TWERY M., LEXER M.J., VACIK H., RAY D., SHAO G,. et BORGES J.G.: ''Decision Support Systems in Forest Management'' IN BURSTEIN F. et HOLSAPPLE C. W. (EDS.) (2008): ''Handbook on Decision Support Systems 2: Variations''. Springer Berlin Heidelberg. 800 pp. </ref>.
  
Models to predict SNR from indicator plants (via Hill Ellenberg/Wilson scores) and SMR from soil (AWC, rooting depth)/site (MD) properties.
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The link between biophysical factors and tree species suitability is defined by a set of knowledge based rules that represent an accord based on the combined discussion of a panel of forest scientists with knowledge and experience of the site-related growth potential of the 26 tree species presented. ESC site-yield estimates have been judged acceptable by many foresters and scientist at the ESC courses and demonstrations across a range of climatic zones and site types in the U.K. The model of Sitka spruce was also tested in the [http://en.wikipedia.org/wiki/Clashindarroch_Forest Clashindarroch Forest], in Aberdeenshire (Scotland), showing a slightly underestimated site-yield approach <ref> RAY et al 2001 </ref>.
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There are also the models that intend to predict SNR from indicator plants (via Hill Ellenberg/Wilson scores) and SMR from soil (AWC, rooting depth)/site (MD) properties.
  
  
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==References==
 
==References==
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===Cited references===
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<references/>
  
 
===External resources===
 
===External resources===
 
[http://www.eforestry.gov.uk/forestdss Forest Research Decision Support Portal] (note registration required)
 
[http://www.eforestry.gov.uk/forestdss Forest Research Decision Support Portal] (note registration required)

Revision as of 12:16, 7 September 2009

General System description

System name: Ecological Site Classification

Acronym: ESC

Web page: http://www.forestresearch.gov.uk/esc


Brief overview

Part of the GB Forestry Decision Support System, the system enables the appropriate choice of tree species or NVC woodland type on the basis of site climate and soil quality. Built into the tool are methods to assess soil quality from soil type and indicator plants.

Scope of the system

This tool encourages the decision maker on the election of a forest species according to their suitability to the soil properties and climatic data. It also provides the suitability of the species according to the expected evolution of the climate, and predicts the potential yield in the form of a site index. The number of species presented is 26.

System origin

  • The system was developed in the 1990s but the origins can be traced to a publication by Anderson in the 1950s and earlier works that identified relationships between site quality and vegetation.
  • Development led by Duncan Ray.
  • Currently free to use at stand scale via web.
  • GIS/Batch versions applied in consultancy work.
  • Some use in public/private sector and in education. Well known.

Support for specific issues

Species suitability and election, biodiversity and climate change effects on species suitability.

Support for specific thematic areas of a problem type

  • Silvicultural
  • Certification
  • Conservation
  • Restoration
  • Development choices / land use zoning
  • Policy/intervention alternatives
  • Sustainability impact assessment (SIA)

Capability to support decision making phases

  • Intelligence
gives user detailed site analysis (climate and soil parameters)
  • Design
provides site analysis in context of many themes
  • Choice
allows user to vary species choice, management options
  • Monitor
highlights risks which in theory could encourage monitoring

Related systems

Describe (and/or link to) other systems related

Data and data models

Typical spatial extent of application

Web based tool operates at stand scale, ca 1-5 hectares, batch GIS tool has generated regional and national scenarios.

Forest data input

Location via OS GB six figure grid reference, e.g. NT090950. User also supplies an FC soil type (e.g. 1g, 4, 7bz), lithology/geology, vegetation (indicator plants), results from soil survey (soil texture, rooting depth, soil class (dry|wet) and humus form. More data increases the accuracy of the analysis.

Type of information input from user (via GUI)

User selects one or more tree species for detailed analysis from an intermediate screen.


Models

Forest models

Species suitability and yield prediction models are based on accumulated temperature (AT5), moisture deficit, continentality (CT), wind exposure (DAMS - Detailed Aspect Method of Scoring), moisture deficit (MD), soil moisture regime (SMR) and soil nutrient regime (SNR). From these six variables, climatic warmth (accumulated temperature) is assumed as the principal factor and from the remaining others only the most limiting factor may modify the site yield prediction [1].

The link between biophysical factors and tree species suitability is defined by a set of knowledge based rules that represent an accord based on the combined discussion of a panel of forest scientists with knowledge and experience of the site-related growth potential of the 26 tree species presented. ESC site-yield estimates have been judged acceptable by many foresters and scientist at the ESC courses and demonstrations across a range of climatic zones and site types in the U.K. The model of Sitka spruce was also tested in the Clashindarroch Forest, in Aberdeenshire (Scotland), showing a slightly underestimated site-yield approach [2].

There are also the models that intend to predict SNR from indicator plants (via Hill Ellenberg/Wilson scores) and SMR from soil (AWC, rooting depth)/site (MD) properties.


Decision Support

Definition of management interventions

Species or NVC woodland choice.

Prescription enumerating all selected possibilities at stand level, or thematic GIS layers (eg Oak or Sitka Spruce suitability in a defined area of interest).

Typical temporal scale of application

Has some support for future climate scenarios, baseline data is from 1961-1990.

Types of decisions supported

  • Management level
    • strategic decisions
    • administrative decisions
    • operating control decisions
  • Management function
  • planning decisions
    • organizing decisions
    • command decisions
    • control decisions
    • coordination decisions
  • decision making situation
    • unilateral
    • collegial
    • Bargaining / participative decision making

Decision-making processes and models

  • Logic modeling
  • Operations research modeling
    • Direct approaches
    • Heuristic manipulation of simulation models
  • Multiple criteria/ranking
  • Other


Output

Types of outputs

Stand version generates tables in HTML, thematic maps can be generated via a batch tool for visualisation in GIS, assuming suitable soil data is available.

Spatial analysis capabilities

  • integrated capabilities
  • GIS links via batch tool. Limitations in this context due to availability of digital soil maps.
  • provides standard data import/export formats
  • allows spatial analysis, batch tool generates thematic layers

Abilities to address interdisciplinary, multi-scaled, and political issues

Evaluate interactions between different basic information types (biophysical, economic, social). Produce coordinated results for decision makers operating at different spatial scales facilitate social negotiation and learning


System

System requirements

  • Java library deployed on Linux and Windows. UI available at stand level via web, or batch system via command line.
  • Utilises many open source Java libraries, GRASS GIS or ArcGIS required for batch stage. Data currently managed in Oracle database or as raster files.
  • Beta trial

Architecture and major DSS components

3 tier architecture ( UI, Models, Data)

Web based UI using JSP, HTML, CSS

Also desktop batch tool for GIS processing using Java.

Models are implemented in java. Three key models are species suitability, indicator plants and soil properties calculator.

Highly modular.

Some simple web map services with Google Maps (not intended for operational use).

Basic dataflow is location accesses site climate data, this and other user input data are then processed by the various models to generate outputs.

Usage

Used in education, public and private sector forestry and research.

Computational limitations

Longer runtime to compute national datasets.

User interface

Web UI requires some understanding of soil types, OS grid references, reference to geological maps. Interpretation of information can be challenging so support is being developed.

Documentation and support

Bulletin 124 describes the method, and version 1.7 is well documented.

Version 2.0 no formal training available to date. Support available via email.

Installation

  • Prerequisite knowledge: Requires web browser. Server installation requires specialised skills and tools. Batch mode requires some configuration on host machine.

Web browser for stand version. GIS batch tool requires Java 1.4, Grass and related ESC datasets. A spatial version also exists based on ArcView/Spatial Analyst.

Server installation requires J2EE server such as Tomcat or Oracle Application Server and an oracle database.


References

Cited references

  1. REYNOLDS K.M., TWERY M., LEXER M.J., VACIK H., RAY D., SHAO G,. et BORGES J.G.: Decision Support Systems in Forest Management IN BURSTEIN F. et HOLSAPPLE C. W. (EDS.) (2008): Handbook on Decision Support Systems 2: Variations. Springer Berlin Heidelberg. 800 pp.
  2. RAY et al 2001

External resources

Forest Research Decision Support Portal (note registration required)