Regression tree modeling of spatial pattern and process interactions

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

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Scopus citations


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 (US)
Title of host publicationMapping Forest Landscape Patterns
PublisherSpringer New York
Number of pages26
ISBN (Electronic)9781493973316
ISBN (Print)9781493973293
StatePublished - Sep 7 2017

ASJC Scopus subject areas

  • General Agricultural and Biological Sciences
  • General Environmental Science
  • General Engineering


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