4.03.00 - Uncertainty analysis, computational ecology, and decision support



Call for submissions for special issue of Forests

Special issue of Forests:

Spatial Decision Support for Forest Management and Policy Formulation


Guest editors

Dr. Keith M Reynolds
USDA Forest Service Research
Pacific Northwest Research Station
Email: keith.reynolds2@usda.gov

Dr. Paul F Hessburg
USDA Forest Service Research
Pacific Northwest Research Station
Email: paul.hessburg@usda.gov

Prof. Jose Borges
University of Lisbon
Instituto Superior de Agronomia
Email: joseborges@isa.ulisboa.pt

Prof. Harald Vacik
University of Natural Resources and Life Sciences, Vienna
Institute of Silviculture
Email: harald.vacik@boku.ac.at 

Aims and scope

Decision support systems for forest management have been steadily evolving since about 1980 in response to growing demand from forest managers for sophisticated analytical systems that can address the complexities of contemporary forest management issues such as adaptive management in the context of concerns for managing for forest ecosystem sustainability, integrity, and resilience while ensuring the provision of ecosystem services. In this same time frame, there has also been a steady shift in emphasis from stand-level to landscape-level decision support systems, in part driven by improved ecological understanding of, and appreciation for, the need to account for patterns and processes in forest management and planning.

Accordingly, the Editors of Forests have commissioned a 2021 special issue on spatial decision support systems and their application to state-of-the-art landscape solutions for forest management and policy formulation. In this initial call, manuscript proposals are invited on original research and review articles addressing:

  • Contemporary, state-of-the art systems and their application, emphasizing spatial decision support for either forest management or policy formulation.
  • Forward-looking (and perhaps more speculative) articles on how to advance spatial decision support technologies for forest management and policy formulation beyond the current state of the art.

Spatial decision support technologies have evolved on numerous pathways, including

  • knowledge-based,
  • probabilitistic,
  • and linear programming systems,
  • as well as combinations of these and other technologies,

so articles addressing any of these areas are welcome.

Articles addressing complex spatial decision support topics, such as support for

  • adaptive forest management,
  • forest ecosystem sustainability,
  • forest ecosystem integrity,
  • forest ecosystem resilience,
  • managing for pattern and process, and
  • provision of ecosystem services

are especially encouraged.


This announcement is an initial call for proposed manuscripts to be included in the Special Issue. Please submit proposals to Dr. Reynolds (keith.reynolds2@usda.gov) , including

  • Proposed title of the manuscript
  • Initial (tentative) list of authors, including names, affiliations, and email addresses
  • A brief abstract if 100 to 200 words


30 October 2020

Receipt of initial manuscript proposals from this call by Dr. Reynolds.

1 January 2021

Expanded set of manuscript proposals managed by Forest Editors. This target date my be extended by the Forests Editors.

15 January 2021

Authors notified of final decisions by the guest editor on proposal acceptance. Note, though, that especially strong proposals will be accepted for publication on a rolling basis.

30 July 2021

Manuscripts submitted to Forests for refereed peer review.

27 August 2021

Initial release date of accepted manuscripts in the online journal.


This special issue is sponsored by the International Union of Forest Research Organizations, research group 4.03: Uncertainty Analysis, Computational Ecology, and Decision Support, and research group 4.04.04, Sustainable Forest Management Scheduling.

View all entries


Keith Reynolds, United States


Paul Hessburg, United States

Bogdan Strimbu, United States

Chonggang Xu, United States

About Unit

IUFRO Research Group 4.03 includes three Working Parties that cover:

  1. Characterization of uncertainty in forest modelling systems (4.03.01).
  2. Machine learning and computational ecology, especially as these methods apply to quantitative landscape ecology and forest modelling (4.03.02).
  3. Decision support systems and related information technologies for sustainable management of forest systems (4.03.03).

Each Working Party spells out its scope of work and the state of knowledge on its respective web page. For the 4.03 Research Group as a whole, though, our over-arching objective is to promote collaboration among the disciplines of the Research Group in ways that are not only mutually supportive but build upon the strengths of each discipline to produce synergistic advances in the science behind, and the management supporting, sustainable forestry.

We encourage visitors who visit the 4.03 web pages to join the Research Group's email list, https://www.iufro.org/science/divisions/division-4/40000/40300/mailing-list/, to stay informed on unfolding plans for the group and related meetings hosted or co-hosted by 4.03.

State of Knowledge

DSS frameworks are systems for building DSS applications, as opposed to the conventional notion of a DSS as a one-off application designed to support solutions for very a specific problem type. Frameworks are a higher order abstraction of the DSS concept, with the advantage that they are suitable for developing DSS solutions for a very broad range of problems.

Workflow engines and interfaces extend the capabilities of DSS frameworks with graphical programming tools that support data analysis, data processing (especially complex geoprocessing tasks), data transformation, scenario modelling, and system extensibility (by calling analytical engines external to the framework when needed).
Ontologies (or knowledge graphs) similarly are a way to enhance the power of DSS frameworks, in this case by invoking an ontology engine to query an ontology for relevant entities and relations formalized within it. For example, what are some alternative analytical sequences that be used to solve decision problems of type X? A bit out on the horizon, combining the capabilities of ontology engines and workflow engines in DSS frameworks opens up intriguing possibilities for querying for solution methods and then implementing them in workflows.

Parsimonious multiscale nonlinear models are difficult to develop given the computational challenges and hierarchical structure innate to forest ecosystems and landscapes. However, it is paramount to develop accurate description of the processes characterizing the status and dynamic of the forest.

Spatially explicit models for the development of tree components became the one of the central part of the current forest modelling, as the focuses shifted historically from stands to trees and now to branches and leaves.

Integration of remote sensing data into landscape modelling. Data acquired with remote sensing technologies became the source of almost unlimited amount of information that must be analysed. However, remote sensing brought a new challenge in modelling, namely wall to wall data that contains errors. Therefore, the models developed from remote sensing data must include now an assessment of error, or error propagation is now a part of the forest modelling process.

Machine learning modelling for the exploration and prediction of landscape disturbance patterns. The adaptability of machine learning to particular situation while considering the general trend in data made them suitable for solving a variety of forest problems, from landslides to growth and yield and inventory.