Mapping canopy gaps in an indigenous subtropical coastal forest using high resolution WorldView-2 data

Abstract

Invasive species usually colonize canopy gaps in tropical and sub-tropical forests, which results in loss of native species. Therefore, an understanding of the location and distribution of canopy gaps will assist in predicting the occurrence of invasive species in such canopy gaps. We tested the utility of WorldView-2 with eight (8) spectral bands at 2 m spatial resolution to delineate forest canopy gaps in a subtropical Dukuduku coastal forest in South Africa. We compared the four (4) conventional visible-near infrared bands with the eight (8) band WorldView-2 image. The 8-band WorldView-2 image yielded higher overall accuracy of 86.90% (kappa = 0.82) than the resampled conventional 4 band image which yielded an overall accuracy of 74.64% (kappa = 0.63) in pixel-based classification. We further compared the vegetation indices which were derived from four conventional bands with those derived from WorldView-2 bands. The Enhanced Vegetation Index (EVI) yielded the highest overall accuracy in the category of conventional indices (85.59% at kappa = 0.79), while the modified Plant Senescence Reflectance Index (mPSRI) involving the red-edge band showed the highest overall accuracy (93.69%) in the category of indices derived from an eight band WorldView-2 imagery in object-based classification. Overall, the study shows that the unique high resolution WorldView-2 data can improve the delineation of canopy gaps as compared to the conventional multispectral bands.