dc.contributor.author |
Mafanya, Madodomzi
|
|
dc.contributor.author |
Tsele, Philemon
|
|
dc.contributor.author |
Botai, Joel Ongego
|
|
dc.contributor.author |
Manyama, Phetole
|
|
dc.contributor.author |
Swart, Barend
|
|
dc.contributor.author |
Monate, Thabang
|
|
dc.date.accessioned |
2018-01-16T06:17:36Z |
|
dc.date.issued |
2017-07 |
|
dc.description.abstract |
Invasive alien plants (IAPs) not only pose a serious threat to biodiversity and water resources but also have impacts on human and animal wellbeing. To support decision making in IAPs monitoring, semi-automated image classifiers which are capable of extracting valuable information in remotely sensed data are vital. This study evaluated the mapping accuracies of supervised and unsupervised image classifiers for mapping Harrisia pomanensis (a cactus plant commonly known as the Midnight Lady) using two interlinked evaluation strategies i.e. point and area based accuracy assessment. Results of the point-based accuracy assessment show that with reference to 219 ground control points, the supervised image classifiers (i.e. Maxver and Bhattacharya) mapped H. pomanensis better than the unsupervised image classifiers (i.e. K-mediuns, Euclidian Length and Isoseg). In this regard, user and producer accuracies were 82.4% and 84% respectively for the Maxver classifier. The user and producer accuracies for the Bhattacharya classifier were 90% and 95.7%, respectively. Though the Maxver produced a higher overall accuracy and Kappa estimate than the Bhattacharya classifier, the Maxver Kappa estimate of 0.8305 is not significantly (statistically) greater than the Bhattacharya Kappa estimate of 0.8088 at a 95% confidence interval. The area based accuracy assessment results show that the Bhattacharya classifier estimated the spatial extent of H. pomanensis with an average mapping accuracy of 86.1% whereas the Maxver classifier only gave an average mapping accuracy of 65.2%. Based on these results, the Bhattacharya classifier is therefore recommended for mapping H. pomanensis. These findings will aid in the algorithm choice making for the development of a semi-automated image classification system for mapping IAPs. |
en_ZA |
dc.description.department |
Geography, Geoinformatics and Meteorology |
en_ZA |
dc.description.embargo |
2018-07-30 |
|
dc.description.librarian |
hj2018 |
en_ZA |
dc.description.sponsorship |
The South African National Department of Environment Affairs through its funding of the South African National Biodiversity Institute Invasive Species Programme, project number P038. |
en_ZA |
dc.description.uri |
http://www.elsevier.com/ locate/ isprsjprs |
en_ZA |
dc.identifier.citation |
Mafanya, M., Tsele, P., Botai, J. et al. 2017, 'Evaluating pixel and object based image classification techniques for mapping plant invasions from UAV derived aerial imagery : Harrisia pomanensis as a case study', ISPRS Journal of Photogrammetry and Remote Sensing, vol. 129, pp. 1-11. |
en_ZA |
dc.identifier.issn |
0924-2716 (print) |
|
dc.identifier.issn |
1872-8235 (online) |
|
dc.identifier.other |
10.1016/j.isprsjprs.2017.04.009 |
|
dc.identifier.uri |
http://hdl.handle.net/2263/63558 |
|
dc.language.iso |
en |
en_ZA |
dc.publisher |
Elsevier |
en_ZA |
dc.rights |
© 2017 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in ISPRS Journal of Photogrammetry and Remote Sensing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published in ISPRS Journal of Photogrammetry and Remote Sensing, vol. 129, pp. 1-11, 2017. doi : 10.1016/j.isprsjprs.2017.04.009. |
en_ZA |
dc.subject |
Invasive alien plants (IAPs) |
en_ZA |
dc.subject |
Point- and area-based accuracy assessment |
en_ZA |
dc.subject |
Harrisia pomanensis |
en_ZA |
dc.subject |
Pixel- and object-based classification |
en_ZA |
dc.subject |
Unmanned aerial vehicle (UAV) |
en_ZA |
dc.title |
Evaluating pixel and object based image classification techniques for mapping plant invasions from UAV derived aerial imagery : Harrisia pomanensis as a case study |
en_ZA |
dc.type |
Postprint Article |
en_ZA |