Improved understorey bamboo cover mapping using a novel hybrid neural network and expert system

T. Wang, A.K. Skidmore, A.G. Toxopeus

Research output: Contribution to journalArticleAcademicpeer-review

23 Citations (Scopus)

Abstract

The giant panda is an obligate bamboo grazer. Therefore, the availability and abundance of understorey bamboo determines the quantity and quality of panda habitat. However, there is little or no information about the spatial distribution or abundance of bamboo underneath the forest canopy, due to the limitations of traditional remote sensing classification techniques. In this paper, a new method combines an artificial neural network and a GIS expert system in order to map understorey bamboo in the Qinling Mountains of south-western China. Results from leaf-off ASTER imagery, using a neural network and an expert system, were evaluated for their suitability to quantify understorey bamboo. Three density classes of understorey bamboo were mapped, first using a neural network (overall accuracy 64.7%, Kappa 0.45) and then using an expert system (overall accuracy 62.1%, Kappa 0.43). However, when using the results of the neural network classification as input into the expert system, a significantly improved mapping accuracy was achieved with an overall accuracy of 73.8% and Kappa of 0.60 (average z-value=53.35, p=0.001). Our study suggests that combining a neural network with an expert system makes it possible to successfully map the cover of understorey species such as bamboo in complex forested landscapes (e. g. coniferous-dominated and dense canopy forests), and with higher accuracy than when using either a neural network or an expert system.
Original languageEnglish
Pages (from-to)965-981
JournalInternational Journal of Remote Sensing
Volume30
Issue number4
DOIs
Publication statusPublished - 2009

Keywords

  • classification accuracy assessment
  • thematic mapper data
  • remotely-sensed data
  • satellite data
  • mixed pixels
  • giant pandas
  • forests
  • reserve
  • imagery
  • china

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