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     Research Journal of Applied Sciences, Engineering and Technology


K-Nearest Neighbor Method for Classification of Forest Encroachment by Using Reflectance Processing of Remote Sensing Spectroradiometer Data

1Ahmed A. Mehdawi and 2Bahrin Bin Ahmad
1UTM, Skudi, Johor, Malaysia
2Faculty of Geoinformation and Real Estate, Institute of Geospatial Science and Technology (INSTeG), Universiti Teknologi, Malaysia
Research Journal of Applied Sciences, Engineering and Technology  2013  15:2881-2885
http://dx.doi.org/10.19026/rjaset.6.3799  |  © The Author(s) 2013
Received: April 06, 2013  |  Accepted: April 22, 2013  |  Published: August 20, 2013

Abstract

This study gives sophisticated result in the use of K-Nearest Neighbor Method classification of forest. The major focus is on the data and technique that can be used to identify the changes in forest features. This study will concentrate on identifying forest encroachment in tropical forests such as the forests of Malaysia. This technique study will establish a strong mechanism that can be used by different sectors such as forestry, local administration, surveying and agriculture. The main contribution of this study is that it utilizes of K-Nearest Neighbor Method with remote sensing data to detect forest encroachment. Hopefully, this study will serve as a reference for any future research on utilizes of K-Nearest classification as tools to identify of tropical forest encroachment.

Keywords:

K-nearest neighbor method, malaysia and encroachment, remote sensing, tropical forests,


References


Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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