Optimizing the spatial resolution of WorldView-2 imagery for discriminating forest vegetation at subspecies level in KwaZulu-Natal, South Africa. Issue 8 (13th September 2016)

• Record Type: Journal Article
• Authors: Lottering, Romano; Mutanga, Onisimo
• Is Part Of: Geocarto international. Volume 31:Issue 8(2016)
• Page Start: 870
• Page End: 880
• Publication Date: 2016-09-13
• Subjects: Remote sensing -- optimal spatial resolution -- minimal variance -- PLS-LDA -- WorldView-2 imagery; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2015.1094519
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: The objective of this study was to identify an appropriate spatial resolution for discriminating forest vegetation at subspecies level. WorldView-2 imagery was progressively resampled to coarser spatial resolutions. At a compartment level, 30 × 30-m subsets were generated across forest compartments to represent the five forest subspecies investigated in this study. From the centre of each subset, the spatial resolution of the original WorldView-2 image was resampled from 6 to 34-m, with increments of 4-m. The variance was then calculated at every resampled spatial resolution using each of the eight WorldView-2 bands. Based on the sampling theorem, the 3-m spatial resolution provided an appropriate resolution for all subspecies investigated. The WorldView-2 image was subsequently classified using the partial least squares linear discriminant analysis algorithm and the appropriate spatial resolution. An overall classification accuracy of 90% was established with an allocation disagreement of 9 and a quantity disagreement of 1.