Commercial forest species discrimination and mapping using image texture computed from WorldView-2 pan sharpened imagery in KwaZulu-Natal, South Africa.
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Date
2021
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Abstract
Forest species discrimination is vital for precise and dependable information, essential for
commercial forest management and monitoring. Recently, the adoption of remote sensing
approaches has become an important source of information in commercial forest management.
However, previous studies have utilized spectral data or vegetation indices to detect and map
commercial forest species, with less focus on the spatial elements. Therefore, this study using
image texture aims to discriminate commercial forest plantations (i.e. A. mearnsii, E. dunnii, E.
grandis and P. patula) computed from a 0.5m WorldView-2 pan-sharpened image in
KwaZuluNatal, South Africa. The first objective of the study was to discriminate commercial
forest species using image texture computed from a 0.5m WorldView-2 pan-sharpened image and
the Partial Least Squares Discriminate Analysis (PLS-DA) algorithm. The results indicated that
the image texture model (overall accuracy (OA) = 77%, kappa = 0.69) outperformed both the
vegetation indices model (OA = 69%, kappa = 0.59) and raw spectral bands model (OA = 64%,
kappa = 0.52). The most successful texture parameters selected by PLS-DA were mean,
correlation, and homogeneity, which were primarily computed from the red-edge, NIR1 and NIR2
bands. Lastly, the 7x7 moving window was commonly selected by the PLS-DA model when
compared to the 3x3 and 5x5 moving windows. The second objective of the study was to explore
the utility of texture combinations computed from a fused 0.5m WorldView-2 image in
discriminating commercial forest species in conjunction with the PLS-DA and Sparse Partial Least
Squares Discriminate Analysis (SPLS-DA) algorithm. The accuracies achieved using SPLS-DA
model, which performed variable selection and dimension reduction simultaneously yielded an
overall accuracy of 86%. In contrast, the PLS-DA and variable importance in the projection (VIP)
produced an overall classification accuracy of 81%. Generally, the finding of this study
demonstrated the ability of image texture to precisely provide adequate information that is
essential for tree species mapping and monitoring.
Description
Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.