Exploring the utility of the additional WorldView-2 bands and support vector machines in mapping land use/land cover in a fragmented ecosystem, South Africa
Land use/land cover (LULC) classification is a key research field in environmental applications of remote sensing on the earthfs surface. The advent of new high resolution multispectral sensors with unique bands has provided an opportunity to map the spatial distribution of detailed LULC classes over a large fragmented area. The objectives of the present study were: (1) to map LULC classes using multispectral WorldView-2 (WV-2) data and SVM in a fragmented ecosystem; and (2) to compare the accuracy of three WV-2 spectral data sets in distinguishing amongst various LULC classes in a fragmented ecosystem. WV-2 image was spectrally resized to its four standard bands (SB: blue, green, red and near infrared-1) and four strategically located bands (AB: coastal blue, yellow, red edge and near infrared-2). WV-2 image (8bands: 8B) together with SB and AB subsets were used to classify LULC using support vector machines. Overall classification accuracies of 78.0% (total disagreement = 22.0%) for 8B, 51.0% (total disagreement = 49.0%) for SB, and 64.0% (total disagreement = 36.0%) for AB were achieved. There were significant differences between the performance of all WV-2 subset pair comparisons (8B versus SB, 8B versus AB and SB versus AB) as demonstrated by the results of McNemarfs test (Z score .1.96). This study concludes that WV-2 multispectral data and the SVM classifier have the potential to map LULC classes in a fragmented ecosystem. The study also offers relatively accurate information that is important for the indigenous forest managers in KwaZulu-Natal, South Africa for making informed decisions regarding conservation and management of LULC patterns.
Keywords: land use/cover classification, fragmented ecosystem, WorldView-2, support vector machines
Authors who submit papers to this journal agree to the following terms:
a) Authors retain copyright over their work, while allowing the journal to place this work on the journal website under a Creative Commons Attribution License, which allows others to freely access, use, and share the work, with an acknowledgment of the work's authorship and its initial publication in this journal.
b) Authors are able to waive the terms of the CC license and enter into separate, additional contractual arrangements for the non-exclusive distribution and subsequent publication of this work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
c) In addition, authors are encouraged to post and share their work online (e.g., in institutional repositories or on their website) at any point after publication on the journal website.