Forest management planning aims at fulfilling the overall goals for the forest owner. The economic optimal scheduling of treatments in spatially discrete forest stands, the time dimension, has been thoroughly investigated in research. The spatial dimension is less investigated. Normally, spatially discrete stands are defined as treatment units. These are inventoried using subjective methods with unknown precision. As an alternative to this conventional way to describe the forest, the present investigation used kriging for estimating forest characteristics spatially continuously using georeferenced sample plots.Using stratification by age, several variables interesting for forest management planning displayed spatial autocorrelation, even though the estate was thoroughly managed. No hardwood variables displayed the autocorrelation necessary for using kriging.

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A pedagogic problem in forestry and landscape management is to visualize future landscape effects of forest growth and current management activities in the forest. This paper presents a method for forecasting digital image projections of forest landscape dynamics. Static nonlinear regression functions estimate the digital numbers in a Landsat Thematic Mapper image. Regressors used are forest stand variables. By estimating the future forest stand data, based on intermediate treatment and growth, future satellite digital images are created. In a case study example, the future landscape of a forest block in the province of V&sternorrland, Sweden, is projected to demonstrate the application of this visualization technique.

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