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4.03.01 - Uncertainty analysis of forest models


Annika Kangas, Finland


Minna Räty, Finland

About Unit

Mathematical models are widely used for assessing sustainability.  They are used to predict the future state of forest systems and for testing hypotheses. Models are now frequently used to answer many questions. Is a desired forested landscape sustainable today and in the foreseeable future? What is the influence of climate on sustainability of tree species in a mountainous environment? What is the current size and health of forest resources of a nation or nations? What is the size and long term viability of certain wildlife populations that reside in forests fragmented by urban sprawl? Models are being used everywhere and by everyone on the Globe.

Needless to say, for real world systems, projections made with the simplest to the most complicated model have statistical errors and uncertainties.  For many ecological and environmental models, there can be hundreds of sources of uncertainties due to measurements, sampling, knowledge gaps, parameter estimates, multiple temporal and spatial scales, stochasticity, etc. If one doesn't account for the uncertainties, the outcomes from models have little or no value. Assessing consequences of the propagation of uncertainties becomes particularly complex as scientists make spatially explicit projections forward in time, or when they test complex hypotheses based on models.

The Working Party goals have been to develop a comprehensive framework to statistically identify and manage error and uncertainty for both non-spatial and geospatial large scale natural resource monitoring and projection systems and models. Emphasis has been given to geospatial systems, where both ground and remote sensed monitoring systems have been used for inputs of large scale landscape natural resource modeling systems.

State of Knowledge

Technological development has made possible to model and project natural resources for large areas using remote sensing and geospatial methods. Development in modelling methods allows for more efficient use of data. Identifying the sources of uncertainties, modeling their accumulation and propagation, quantifying them and assessing the value of the information has been important focus of the research. The application area of the models covers a wide variety of research fields from ecology to economics and from estimation of state to change estimation etc.