English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Journal Article

A matrix clustering method to explore patterns of land-cover transitions in satellite-derived maps of the Brazilian Amazon

Authors
/persons/resource/mhansen

Müller-Hansen,  Finn
Potsdam Institute for Climate Impact Research;

Cardoso,  M. F.
External Organizations;

Dalla-Nora,  E. L.
External Organizations;

/persons/resource/Donges

Donges,  Jonathan Friedemann
Potsdam Institute for Climate Impact Research;

/persons/resource/heitzig

Heitzig,  Jobst
Potsdam Institute for Climate Impact Research;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

/persons/resource/Kirsten.Thonicke

Thonicke,  Kirsten
Potsdam Institute for Climate Impact Research;

External Ressource
No external resources are shared
Fulltext (public)

7316oa.pdf
(Publisher version), 5MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Müller-Hansen, F., Cardoso, M. F., Dalla-Nora, E. L., Donges, J. F., Heitzig, J., Kurths, J., Thonicke, K. (2017): A matrix clustering method to explore patterns of land-cover transitions in satellite-derived maps of the Brazilian Amazon. - Nonlinear Processes in Geophysics, 24, 1, 113-123.
https://doi.org/10.5194/npg-24-113-2017


Cite as: https://publications.pik-potsdam.de/pubman/item/item_21122
Abstract
Changes in land-use systems in tropical regions, including deforestation, are a key challenge for global sustainability because of their huge impacts on green-house gas emissions, local climate and biodiversity. However, the dynamics of land-use and land-cover change in regions of frontier expansion such as the Brazilian Amazon are not yet well understood because of the complex interplay of ecological and socioeconomic drivers. In this paper, we combine Markov chain analysis and complex network methods to identify regimes of land-cover dynamics from land-cover maps (TerraClass) derived from high-resolution (30 m) satellite imagery. We estimate regional transition probabilities between different land-cover types and use clustering analysis and community detection algorithms on similarity networks to explore patterns of dominant land-cover transitions. We find that land-cover transition probabilities in the Brazilian Amazon are heterogeneous in space, and adjacent subregions tend to be assigned to the same clusters. When focusing on transitions from single land-cover types, we uncover patterns that reflect major regional differences in land-cover dynamics. Our method is able to summarize regional patterns and thus complements studies performed at the local scale.