Lao People's Democratic Republic (PDR) has been experiencing significant forest depletion since the 1980s, but there is little evidence to demonstrate the major causes and underlying drivers for the forest cover changes. In this study, we investigated the relationship between forest cover decrease and increase in the south of Lao PDR between 2006 and 2012 and selected physical and socio-economic factors. We used a map of forest cover changes derived from analysis of Landsat ETM+ imagery in 2006 and 2012, together with socio-economic and physical environmental data from the national authorities. The study area has experienced noticeable forest cover changes: both forest decreases and increases were unevenly distributed throughout the region. Logistic regression models were used to test relationships between forest cover decrease or increase and selected physical and socio-economic factors. Forest clearance was associated strongly with elevation, distance to main roads and shifting cultivation practices. Meanwhile, forest cover increase was more likely to correlate with rubber plantations. Native forest and shifting cultivation lands were vulnerable to being converted into rubber plantations. This research provides much-needed information on which to base forestry policy and decision making to minimize and prevent current deforestation, as well as manage potential risks in the future.

MODIS enhanced vegetation index (EVI) and land surface temperature (LST) are key indicators for monitoring vegetation cover changes in broad ecosystems. However, there has been little evaluation of these indices for detecting changes in a range of land covers in tropical regions. In this study, we investigated the characteristics and seasonal responses of LST and EVI for four different land covers in Lao tropical forests: native forest, rubber plantation, mixed wooded/cleared areas and agriculture. We calculated long-term averages of MODIS LST and EVI 16-day time series and compared their monthly transitions over the seven-year period from 2006 to 2012. We also tested whether these indices can be used to classify these four land covers. The findings demonstrate the complex interrelationship of LST and EVI and their monthly transitions for different land covers: they each showed distinctly different intra-annual LST and EVI variations. Native forests have the highest EVI, and the lowest LST throughout the year. In contrast, agricultural areas with little or no vegetation cover have the highest LST. The transition of LST/EVI for the land covers other than native forests showed marked seasonality. Linear discriminant analysis (LDA) showed that there was high overall accuracy of separation of land covers by these indices (86%). The encouraging results indicate that the combined use of MODIS LST and EVI holds promise for improving monitoring of changes in a Lao tropical forest. OPEN ACCESSRemote Sens. 2015, 7 6027

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