Next Article in Journal
Estimating Ladder Fuels: A New Approach Combining Field Photography with LiDAR
Previous Article in Journal
Object-Based Change Detection in Urban Areas: The Effects of Segmentation Strategy, Scale, and Feature Space on Unsupervised Methods
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(9), 763; doi:10.3390/rs8090763

Seasonal Separation of African Savanna Components Using Worldview-2 Imagery: A Comparison of Pixel- and Object-Based Approaches and Selected Classification Algorithms

1
Institut de Gestion de l’Environnement et d’Aménagement de Territoire (IGEAT), Université Libre de Bruxelles, Brussels 1050, Belgium
2
School of Applied Environmental Sciences, Pietermaritzburg 3209, South Africa
3
Unit Remote Sensing and Earth Observation Processes, Flemish Institute for Technological Research (VITO), Mol 2400, Belgium
4
Council for Scientific and Industrial Research, Pretoria 0001, South Africa
5
Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria 0028, South Africa
*
Author to whom correspondence should be addressed.
Academic Editors: Giles M. Foody, Magaly Koch, Clement Atzberger and Prasad S. Thenkabail
Received: 15 May 2016 / Revised: 20 August 2016 / Accepted: 8 September 2016 / Published: 16 September 2016
View Full-Text   |   Download PDF [8763 KB, uploaded 16 September 2016]   |  

Abstract

Separation of savanna land cover components is challenging due to the high heterogeneity of this landscape and spectral similarity of compositionally different vegetation types. In this study, we tested the usability of very high spatial and spectral resolution WorldView-2 (WV-2) imagery to classify land cover components of African savanna in wet and dry season. We compared the performance of Object-Based Image Analysis (OBIA) and pixel-based approach with several algorithms: k-nearest neighbor (k-NN), maximum likelihood (ML), random forests (RF), classification and regression trees (CART) and support vector machines (SVM). Results showed that classifications of WV-2 imagery produce high accuracy results (>77%) regardless of the applied classification approach. However, OBIA had a significantly higher accuracy for almost every classifier with the highest overall accuracy score of 93%. Amongst tested classifiers, SVM and RF provided highest accuracies. Overall classifications of the wet season image provided better results with 93% for RF. However, considering woody leaf-off conditions, the dry season classification also performed well with overall accuracy of 83% (SVM) and high producer accuracy for the tree cover (91%). Our findings demonstrate the potential of imagery like WorldView-2 with OBIA and advanced supervised machine-learning algorithms in seasonal fine-scale land cover classification of African savanna. View Full-Text
Keywords: land cover; classifiers; random forest (RF); support vector machines (SVM); classification and regression trees (CART); maximum likelihood (ML); k-nearest neighbor (k-NN) land cover; classifiers; random forest (RF); support vector machines (SVM); classification and regression trees (CART); maximum likelihood (ML); k-nearest neighbor (k-NN)
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Kaszta, Ż.; Van De Kerchove, R.; Ramoelo, A.; Cho, M.A.; Madonsela, S.; Mathieu, R.; Wolff, E. Seasonal Separation of African Savanna Components Using Worldview-2 Imagery: A Comparison of Pixel- and Object-Based Approaches and Selected Classification Algorithms. Remote Sens. 2016, 8, 763.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top