Next Article in Journal
Sensitivity Analysis of b-factor in Microwave Emission Model for Soil Moisture Retrieval: A Case Study for SMAP Mission
Next Article in Special Issue
Mapping Bush Encroaching Species by Seasonal Differences in Hyperspectral Imagery
Previous Article in Journal
Automatic Detection of Buildings and Changes in Buildings for Updating of Maps
Previous Article in Special Issue
Remote Sensing of Vegetation Structure Using Computer Vision
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2010, 2(5), 1249-1272;

Forecasting Areas Vulnerable to Forest Conversion in the Tam Dao National Park Region, Vietnam

Division of Spatial Information Science, Graduate School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan
Author to whom correspondence should be addressed.
Received: 24 February 2010 / Revised: 29 March 2010 / Accepted: 28 April 2010 / Published: 30 April 2010
(This article belongs to the Special Issue Ecological Status and Change by Remote Sensing)
Full-Text   |   PDF [1401 KB, uploaded 19 June 2014]   |  


Tam Dao National Park (TDNP) is a remaining primary forest that supports some of the highest levels of biodiversity in Vietnam. Forest conversion due to illegal logging and agricultural expansion is a major problem that is hampering biodiversity conservation efforts in the TDNP region. Yet, areas vulnerable to forest conversion are unknown. In this paper, we predicted areas vulnerable to forest changes in the TDNP region using multi-temporal remote sensing data and a multi-layer perceptron neural network (MLPNN) with a Markov chain model (MLPNN-M). The MLPNN-M model predicted increasing pressure in the remaining primary forest within the park as well as on the secondary forest in the surrounding areas. The primary forest is predicted to decrease from 18.03% in 2007 to 15.10% in 2014 and 12.66% in 2021. Our results can be used to prioritize locations for future biodiversity conservation and forest management efforts. The combined use of remote sensing and spatial modeling techniques provides an effective tool for monitoring the remaining forests in the TDNP region. View Full-Text
Keywords: multi-layer perceptron neural network; Markov chain; deforestation; Vietnam multi-layer perceptron neural network; Markov chain; deforestation; Vietnam

Figure 1

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

MDPI and ACS Style

Khoi, D.D.; Murayama, Y. Forecasting Areas Vulnerable to Forest Conversion in the Tam Dao National Park Region, Vietnam. Remote Sens. 2010, 2, 1249-1272.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



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