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Spatial Information Research

, Volume 27, Issue 2, pp 237–246 | Cite as

What makes the difference between memory and face of a landscape? A machine learning approach applied to the federal state Brandenburg, Germany

  • Ralf WielandEmail author
  • Monika Wulf
  • Kristin Meier
Article
  • 138 Downloads

Abstract

The paper introduces two types of models: the “memory of a landscape” and the “face of a landscape”. The memory of a landscape refers to the development of a landscape as a result of many small and some major events. It can be described by a multitude of features that are difficult to change by humans, such as the initial geological substrate and the availability of nutrients linked to it. The implementation of the “memory model” leads to a scientific modelling approach that models the influence of the basic factors on forest distribution. The face of a landscape on the other hand implements a Big Data approach. The face can be changed more easily, e.g. by clearing forest areas and converting them into arable land. Both types of models are used to conclude from today’s perspective on the development of historical forests around 1880. A machine learning algorithm is used to implement both model types and evaluate the importance of features. Both models show differences in accuracy and simulation, which are discussed in detail. The inherent evaluation of the importance of the model inputs can be used to critically review some doctrines. The combination of machine learning with the knowledge of experts who help to select and prepare the data can be used in the future to depict the memory of a landscape more comprehensively in a model than is possible with previous approaches.

Keywords

Feature selection Historical forest Machine learning Spatial modeling XGBoost 

Supplementary material

41324_2018_228_MOESM1_ESM.pdf (63 kb)
Supplementary material 1 (PDF 62 kb)
41324_2018_228_MOESM2_ESM.pdf (399 kb)
Supplementary material 2 (PDF 399 kb)

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Copyright information

© Korean Spatial Information Society 2018

Authors and Affiliations

  1. 1.Leibniz Centre for Agricultural Landscape Research (ZALF)MuenchebergGermany

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