Advances in Remote Sensing, 2015, 4, 73-82 Published Online March 2015 in SciRes. http://www.scirp.org/journal/ars http://dx.doi.org/10.4236/ars.2015.41007 How to cite this paper: Phompila, C., Lewis, M., Clarke, K. and Ostendorf, B. (2015) Applying the Global Disturbance Index for Detecting Vegetation Changes in Lao Tropical Forests. Advances in Remote Sensing, 4, 73-82. http://dx.doi.org/10.4236/ars.2015.41007 Applying the Global Disturbance Index for Detecting Vegetation Changes in Lao Tropical Forests Chittana Phompila 1,2 , Megan Lewis 2 , Kenneth Clarke 2 , Bertram Ostendorf 2 1 Faculty of Forestry, The National University of Laos, Vientiane, Lao People’s Democratic Republic 2 School of Biological Sciences, The University of Adelaide, Adelaide, Australia Email: chittana.phompila@adelaide.edu.au , megan.lewis@adelaide.edu.au , Kenneth.clarke@adelaide.edu.au , bertram.ostendorf@adelaide.edu.au Received 2 March 2015; accepted 16 March 2015; published 23 March 2015 Copyright © 2015 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract Land cover change is a major challenge for many developing countries. Spatiotemporal informa- tion on this change is essential for monitoring global terrestrial ecosystem carbon, climate and biosphere exchange, and land use management. A combination of LST and the EVI indices in the global disturbance index (DI) has been proven to be useful for detecting and monitoring of changes in land covers at continental scales. However, this model has not been adequately applied or assessed in tropical regions. We aimed to demonstrate and evaluate the DI algorithm used to detect spatial change in land covers in Lao tropical forests. We used the land surface temperature and enhanced vegetation index of the Moderate Resolution Imaging Spectroradiometer time-se- ries products from 2006-2012. We used two dates Google Earth TM images in 2006 and 2012 as ground truth data for accuracy assessment of the model. This research demonstrated that the DI was capable of detecting vegetation changes during seven-year periods with high overall accuracy; however, it showed low accuracy in detecting vegetation decrease. Keywords Tropical Vegetation Change, Disturbance Index, Land Surface Temperature (LST), Enhanced Vegetation Index (EVI), Lao PDR 1. Introduction Global measures of land cover change are important for global terrestrial ecosystem carbon schemes, climate