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Multispectral and Texture Feature Application in Image-Object Analysis of Summer Vegetation in Eastern Tajikistan Pamirs

E. Salas,K. Boykin,Raul Valdez

Published 2016 in Remote Sensing

ABSTRACT

We tested the Moment Distance Index (MDI) in combination with texture features for the summer vegetation mapping in the eastern Pamir Mountains, Tajikistan using the 2014 Landsat OLI (Operational Land Imager) image. The five major classes identified were sparse vegetation, medium-dense vegetation, dense vegetation, barren land, and water bodies. By utilizing object features in a random forest (RF) classifier, the overall classification accuracy of the land cover maps were 92% using a set of variables including texture features and MDI, and 84% using a set of variables including texture but without MDI. A decrease of the Kappa statistics, from 0.89 to 0.79, was observed when MDI was removed from the set of predictor variables. McNemar’s test showed that the increase in the classification accuracy due to the addition of MDI was statistically significant (p < 0.05). The proposed method provides an effective way of discriminating sparse vegetation from barren land in an arid environment, such as the Pamir Mountains.

PUBLICATION RECORD

  • Publication year

    2016

  • Venue

    Remote Sensing

  • Publication date

    2016-01-21

  • Fields of study

    Geology, Computer Science, Environmental Science

  • Identifiers
    DOI 10.3390/rs8010078
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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