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MAPPING ROBINIA PSEUDOACACIA FOREST HEALTH CONDITIONS BY USING COMBINED SPECTRAL, SPATIAL AND TEXTUREAL INFORMATION EXTRACTED FROM IKONOS IMAGERY

Hong Wang,Yijun Zhao,R. Pu,Zhizhang Zhang

Published 2016 in ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

ABSTRACT

Abstract. In this study grey-level co-occurrence matrix (GLCM) textures and a local statistical analysis Getis statistic (Gi), computed from IKONOS multispectral (MS) imagery acquired from the Yellow River Delta in China, along with a random forest (RF) classifier, were used to discriminate Robina pseudoacacia tree health levels. The different RF classification results of the three forest health conditions were created: (1) an overall accuracy (OA) of 79.5% produced using the four MS band reflectances only; (2) an OA of 97.1% created with the eight GLCM features calculated from IKONOS Band 4 with the optimal window size of 13 × 13 and direction 45°; (3) an OA of 94.0% created using the four Gi features calculated from the four IKONOS MS bands with the optimal distance value of 5 and Queen’s neighborhood rule; and (4) an OA of 96.9% created with the combined 16 spectral (four), spatial (four), and textural (eight) features. The experimental results demonstrate that (a) both textural and spatial information was more useful than spectral information in determining the Robina pseudoacacia forest health conditions; and (b) IKONOS NIR band was more powerful than visible bands in quantifying varying degree of forest crown dieback.

PUBLICATION RECORD

  • Publication year

    2016

  • Venue

    ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

  • Publication date

    2016-10-14

  • Fields of study

    Geography, Environmental Science

  • Identifiers
    DOI 10.5194/ISPRS-ARCHIVES-XLI-B8-1425-2016
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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