Article, Other literature type English OPEN
H. Wang ; Y. Zhao ; R. Pu ; Z. Zhang (2016)
  • Publisher: Copernicus Publications
  • Journal: The International Archives of the Photogrammetry (issn: 1682-1750, eissn: 2194-9034)
  • Related identifiers: doi: 10.5194/isprs-archives-XLI-B8-1425-2016
  • Subject: TA1-2040 | T | TA1501-1820 | Applied optics. Photonics | Engineering (General). Civil engineering (General) | Technology

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 <i>Robina pseudoacacia</i> 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.
  • References (33)
    33 references, page 1 of 4

    Abdel-Rahman, E.M.; Mutanga, O.; Adam, E.; Ismail, R., 2014.

    Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers. Journal of Photogrammetry and Remote Sensing, 88, pp.48-59.

    Breiman, L., 2001. Random forests. Machine Learning, 45, pp.5-32.

    Congalton, R.G.; Mead, R.A., 1983. A quantitative method to test for consistency and correctness in photointerpretation.

    Photogrammetric Engineering and Remote Sensing, 49, pp. 69- 74.

    Coops, N.C.; Johnson, M.; Wulder, M.A.; White, J.C., 2006.

    Remote Sensing of Environment, 103, pp.67-80.

    Dye, M.; Mutanga, O.; Ismail, R., 2012. Combining spectral and textural remote sensing variables using random forests: predicting the age of Pinus patula forests in KwaZulu-Natal, South Africa. Journal of Spatial Science, 57, pp.193-211.

    Franklin, S.; Wulder, M.; Lavigne, M., 1996. Automated derivation of geographic window sizes for use in remote sensing digital image texture analysis. Computer and Geosciences, 22, pp.665-673.

    Getis, A.; Ord, J.K., 1992. The analysis of spatial association by use of distance statistics. Geographical Analysis, 24, pp.189- 206.

  • Similar Research Results (1)
  • Metrics
    No metrics available