Assessment of giant panda habitat based on integration of expert system and neural network

Xuehua Liu*, Andrew K. Skidmore, M. C. Bronsveld

*Corresponding author for this work

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

To conserve giant panda effectively, it is important to understand the spatial pattern and temporal change of its habitat. Mapping is an effective approach for wildlife habitat evaluation and monitoring. The application of recently developed artificial intelligence tools, including expert systems and neural networks, could integrate qualitative and quantitative information for modeling complex systems, and built the information into a GIS, which could be helpful for giant panda habitat mapping. This study built a mapping approach for giant panda habitat mapping, which integrated expert system and neural network classifiers (ESNNC), and used multi-type data within GIS. The giant panda habitat types and their suitability were mapped by ESNNC. The results showed that the habitat types and their suitability in Foping Nature Reserve were assessed with a higher accuracy (>80%) by ESNNC, compared with non-integrated classifiers, i. e., expert system, neural network, and maximum likelihood. Z-statistic test showed that ESNNC was significantly better than the other three non-integrated classifiers. It was recommended that the integrated approach could be widely applied into wildlife habitat assessment.

Original languageEnglish
Pages (from-to)438-443
Number of pages6
JournalChinese Journal of Applied Ecology
Volume17
Issue number3
Publication statusPublished - Mar 2006
Externally publishedYes

Keywords

  • expert system
  • Foping Nature Reserve
  • giant panda
  • GIS
  • habitat mapping
  • neural network
  • remote sensing
  • spatial analysis

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