Fig 1.
A false color picture of the QuickBird multispectral image (a composite of band 4, 3, 2 for red, green, and blue) showing the land covers over the study site.
Fig 2.
Generalized trends of the average monthly temperature (curve) and precipitation (bars) of the study site for the years 2006 to 2014.
Table 1.
Tree species used for spectral-spatial texture classification.
Fig 3.
Flowchart of image processing and analyses used in this study.
Table 2.
Calibration coefficients for absolute radiance conversion of 16-bit QuickBird images1).
Fig 4.
Relationship of classification accuracy using dataset 2 with various numbers of used VI images.
Table 3.
Full-bands transformed divergences of between-species in training samples for the original spectral signatures and the spectral-spatial texture signatures.
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Table 4.
Comparisons of the training-samples-based SCKC for the five HCA-species and the OKC for all 40 species in the classification using various data sets.
Fig 5.
An example of the species classification uncertainty caused by spectral signatures variations.
Fig 6.
A magnified view of the images (a) and classifications result of tree species (b).
Below each image, the prefixes “r-” and “e-” before the code of tree species denote training and test samples respectively. The white polygon in an image depicts the training samples or test samples of that specific species.
Fig 7.
A comparison of the training performance, test accuracy, and uncertainty among classifiers in variant classification schemes.