Next Article in Journal
Field Spectroscopy in the VNIR-SWIR Region to Discriminate between Mediterranean Native Plants and Exotic-Invasive Shrubs Based on Leaf Tannin Content
Next Article in Special Issue
National Forest Aboveground Biomass Mapping from ICESat/GLAS Data and MODIS Imagery in China
Previous Article in Journal
Digital Mapping of Soil Properties Using Multivariate Statistical Analysis and ASTER Data in an Arid Region
Previous Article in Special Issue
Mapping Deciduous Rubber Plantation Areas and Stand Ages with PALSAR and Landsat Images
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(2), 1206-1224; doi:10.3390/rs70201206

Mapping Oil Palm Plantations in Cameroon Using PALSAR 50-m Orthorectified Mosaic Images

1
College of Information & Electrical Engineering, China Agricultural University, Beijing100083, China
2
Department of Microbiology and Plant Biology, Center for Spatial Analysis, University of Oklahoma, Norman, OK 73019, USA
3
Institute of Agricultural Research for Development (IRAD), Regional Center for Agricultural Research Nkolbisson (CRRANK), P.O. Box 2123, Yaoundé, Cameroon
*
Author to whom correspondence should be addressed.
Academic Editors: Nicolas Baghdadi and Prasad S. Thenkabail
Received: 13 September 2014 / Accepted: 14 January 2015 / Published: 23 January 2015
View Full-Text   |   Download PDF [10574 KB, uploaded 23 January 2015]   |  

Abstract

Oil palm plantations have expanded rapidly. Estimating either positive effects on the economy, or negative effects on the environment, requires accurate maps. In this paper, three classification algorithms (Support Vector Machine (SVM), Decision Tree and K-Means) were explored to map oil palm plantations in Cameroon, using PALSAR 50 m Orthorectified Mosaic images and differently sized training samples. SVM had the ideal performance with overall accuracy ranging from 86% to 92% and a Kappa coefficient from 0.76 to 0.85, depending upon the training sample size (ranging from 20 to 500 pixels per class). The advantage of SVM was more obvious when the training sample size was smaller. K-Means required the user’s intervention, and thus, the accuracy depended on the level of his/her expertise and experience. For large-scale mapping of oil palm plantations, the Decision Tree algorithm outperformed both SVM and K-Means in terms of speed and performance. In addition, the decision threshold values of Decision Tree for a large training sample size agrees with the results from previous studies, which implies the possible universality of the decision threshold. If it can be verified, the Decision Tree algorithm will be an easy and robust methodology for mapping oil palm plantations. View Full-Text
Keywords: unsupervised classification; K-Means; support vector machine; decision tree; PALSAR; oil palm unsupervised classification; K-Means; support vector machine; decision tree; PALSAR; oil palm
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

Li, L.; Dong, J.; Njeudeng Tenku, S.; Xiao, X. Mapping Oil Palm Plantations in Cameroon Using PALSAR 50-m Orthorectified Mosaic Images. Remote Sens. 2015, 7, 1206-1224.

Show more citation formats Show less citations formats

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