Using a Cellular Automata-Markov Model to Reconstruct Spatial Land-Use Patterns in Zhenlai County, Northeast China

Energies. 2015;8(5):3882-3902 DOI 10.3390/en8053882

 

Journal Homepage

Journal Title: Energies

ISSN: 1996-1073 (Print)

Publisher: MDPI AG

LCC Subject Category: Technology: Chemical technology

Country of publisher: Switzerland

Language of fulltext: English

Full-text formats available: PDF, HTML

 

AUTHORS

Yuanyuan Yang (College of Earth Science, Jilin University, 2199 Jianshe Street, Changchun 130061, China)
Shuwen Zhang (Northeast Institute of Geography and Agroecology, Chinese Academy Sciences, 4888 Shengbei Street, Changchun 130102, China)
Jiuchun Yang (Northeast Institute of Geography and Agroecology, Chinese Academy Sciences, 4888 Shengbei Street, Changchun 130102, China)
Xiaoshi Xing (Center for International Earth Science Information Network (CIESIN), Earth Institute, Columbia University, P.O. Box 1000 (61 Route 9W), Palisades, NY 10964, USA)
Dongyan Wang (College of Earth Science, Jilin University, 2199 Jianshe Street, Changchun 130061, China)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 11 weeks

 

Abstract | Full Text

Decadal to centennial land use and land cover change has been consistently singled out as a key element and an important driver of global environmental change, playing an essential role in balancing energy use. Understanding long-term human-environment interactions requires historical reconstruction of past land use and land cover changes. Most of the existing historical reconstructions have insufficient spatial and thematic detail and do not consider various land change types. In this context, this paper explored the possibility of using a cellular automata-Markov model in 90 m × 90 m spatial resolution to reconstruct historical land use in the 1930s in Zhenlai County, China. Then the three-map comparison methodology was employed to assess the predictive accuracy of the transition modeling. The model could produce backward projections by analyzing land use changes in recent decades, assuming that the present land use pattern is dynamically dependent on the historical one. The reconstruction results indicated that in the 1930s most of the study area was occupied by grasslands, followed by wetlands and arable land, while other land categories occupied relatively small areas. Analysis of the three-map comparison illustrated that the major differences among the three maps have less to do with the simulation model and more to do with the inconsistencies among the land categories during the study period. Different information provided by topographic maps and remote sensing images must be recognized.