Mapping the infestation of dendroctonus armandi in Shennongjia forested region using landsat and maxent model

Wang Ma, Lei Fang, Guo Fei Fang, Yue Yu, Xu Zhang, Jian Yang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Accurate spatial information on the location and extent of the forest pest infestation is important for the manager to take prompt and effective preventative measures. In 2014, Finns armandii in the Shennongjia forestry District was largely attacked by Dendroctonus armandi, a typical tree trunk-boring pest. In this study, we mapped the pest infestation based on the forest inventory data, Landsat images and DEM products. We proposed a novel method that employed a MaxEnt model and iteration threshold segmentation algorithm (MaxEnt-Segmentation) for this purpose. In order to evaluate reliability and accuracy of the proposed method, the traditional spectrum index analysis algorithm was also carried out and its performance was compared. The results showed that the MaxEnt model was capable of accurately mapping the infested area using spectral indices, elevation, slope, potential solar radiation, with the AUC as high as 0.938. MaxEnt-Segmentation algorithm had higher overall classification accuracy (73.68%) compared with the traditional spectral index algorithm (64.47%) when three classification classes (health, low- severity infestation, and high-severity infestation) were included. The results suggest that this proposed algorithm can improve the accuracy of pest detection and is suitable for mapping forest pest infestation in areas with mixed forest stands and variable terrains.

Original languageEnglish
Pages (from-to)2122-2131
Number of pages10
JournalChinese Journal of Ecology
Volume35
Issue number8
DOIs
StatePublished - Aug 10 2016

Bibliographical note

Publisher Copyright:
© 2016, Editorial Board of Chinese Journal of Ecology. All rights reserved.

Keywords

  • Dendroctonus armandi
  • Landsat
  • MaxEnt model
  • Threshold segmentation

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Ecology

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