Next Article in Journal
Land Redistribution and Reutilization in the Context of Migration in Rural Nepal
Previous Article in Journal
Evolutionary Mismatch as a General Framework for Land Use Policy and Politics
Article Menu

Export Article

Open AccessArticle
Land 2014, 3(2), 524-540; doi:10.3390/land3020524

Mapping Woodland Cover in the Miombo Ecosystem: A Comparison of Machine Learning Classifiers

1
Asia Air Survey (AAS) Co., Ltd., Kanagawa 215-0004, Japan
2
TOPS Systems Corp., Tsukuba 305-0032, Japan
3
Department of Computer and Information Science, Faculty of Science and Technology, Seikei University, Tokyo 180-8633, Japan
*
Author to whom correspondence should be addressed.
Received: 21 April 2014 / Revised: 13 June 2014 / Accepted: 13 June 2014 / Published: 20 June 2014
View Full-Text   |   Download PDF [4372 KB, uploaded 20 June 2014]   |  

Abstract

Miombo woodlands in Southern Africa are experiencing accelerated changes due to natural and anthropogenic disturbances. In order to formulate sustainable woodland management strategies in the Miombo ecosystem, timely and up-to-date land cover information is required. Recent advances in remote sensing technology have improved land cover mapping in tropical evergreen ecosystems. However, woodland cover mapping remains a challenge in the Miombo ecosystem. The objective of the study was to evaluate the performance of decision trees (DT), random forests (RF), and support vector machines (SVM) in the context of improving woodland and non-woodland cover mapping in the Miombo ecosystem in Zimbabwe. We used Multidate Landsat 8 spectral and spatial dependence (Moran’s I) variables to map woodland and non-woodland cover. Results show that RF classifier outperformed the SVM and DT classifiers by 4% and 15%, respectively. The RF importance measures show that multidate Landsat 8 spectral and spatial variables had the greatest influence on class-separability in the study area. Therefore, the RF classifier has potential to improve woodland cover mapping in the Miombo ecosystem. View Full-Text
Keywords: Zimbabwe; Miombo woodlands; Landsat 8; decision trees; random forests; support vector machines Zimbabwe; Miombo woodlands; Landsat 8; decision trees; random forests; support vector machines
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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

Kamusoko, C.; Gamba, J.; Murakami, H. Mapping Woodland Cover in the Miombo Ecosystem: A Comparison of Machine Learning Classifiers. Land 2014, 3, 524-540.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Land EISSN 2073-445X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top