Regression tree modeling of spatial pattern and process interactions

Trisalyn A. Nelson*, Wiebe Nijland, Mathieu L. Bourbonnais, Michael A. Wulder

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review


In forestry, many fundamental spatial processes cannot be measured directly and data on spatial patterns are used as a surrogate for studying processes. To characterize the outcomes of a dynamic process in terms of a spatial pattern, we often consider the probability of certain outcomes over a large area rather than on the scale of the particular process. In this chapter we demonstrate data mining approaches that leverage the growing availability of forestry-related spatial data sets for understanding spatial processes. We present classification and regression trees (CART) and associated methods, including boosted regression trees (BRT) and random forests (RT). We demonstrate how data mining or machine learning approaches are useful for relating spatial patterns and processes. Methods are applied to a wildfire data and covariate data are used to contextualize the quantified patterns. Results indicate that fire patterns are mostly related to processes influenced by people. Given the growing number of multi-temporal and large area datasets on forests and ecology machine learning and data mining approaches should be leveraged to quantify dynamic space-time relationships.

Original languageEnglish
Title of host publicationMapping Forest Landscape Patterns
Number of pages26
ISBN (Electronic)9781493973316
ISBN (Print)9781493973293
Publication statusPublished - 7 Sept 2017


  • Spatial pattern
  • Spatial process
  • Data mining
  • Classification and regression tree
  • Wildfire


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