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Decision tree algorithm for detection of spatial processes in landscape transformation.

Jan Bogaert1, Reinhart Ceulemans, David Salvador-Van Eysenrode

  • 1Ecole Interfacultaire de Bioingénieurs, Universitié Libre de Bruxelles, 50 Av. F.D. Roosevelt, C.P. 165/05, B-1050 Brussels, Belgium, jan.bogaert@ulb.ac.be

Environmental Management
|January 7, 2004
PubMed
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Human activities transform landscapes, creating common spatial patterns. This study defines ten pattern change processes and introduces a decision tree algorithm to detect them using landscape metrics.

Area of Science:

  • Geosciences
  • Spatial Analysis
  • Ecology

Background:

  • Human activities significantly alter landscape spatial structure, leading to predictable patterns.
  • Understanding these patterns is crucial for landscape management and conservation.

Purpose of the Study:

  • To define and categorize landscape transformation processes based on pattern geometry.
  • To develop a decision tree algorithm for detecting these processes using landscape metrics.
  • To apply the algorithm to diverse land cover change scenarios.

Main Methods:

  • Defined ten landscape pattern change processes (e.g., fragmentation, aggregation, attrition) based on geometric properties.
  • Developed a decision tree algorithm using area, perimeter, and number of patches.
  • Applied the algorithm to case studies of woodland degradation, canopy gap formation, and forest regrowth.

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Main Results:

  • The decision tree algorithm successfully identified distinct landscape transformation processes in the studied scenarios.
  • Analysis revealed that long-term changes can be broken down into stages, each dominated by specific processes.
  • The importance of temporal resolution in capturing the dynamics of landscape change was highlighted.

Conclusions:

  • A standardized method for identifying landscape transformation processes has been established.
  • The decision tree algorithm provides a quantitative approach to understanding landscape change.
  • Recognizing distinct transformation processes aids in predicting and managing future landscape dynamics.