Despite the importance of tropical forest ecosystems, they continue to be transformed at
an alarming rate. In Southeast Asia, the historical deforestation narrative of a growing
population gradually encroaching upon forest land is being replaced by the dominating
influence of large-scale plantations. In particular, the global demand for natural rubber
(Hevea brasiliensis) has been reported as the cause of widespread forest conversion. A
critical component of forest conservation strategies, such as Reduced Emission from
Deforestation and forest Degradation (REDD+), relies upon the monitoring of forest cover
using satellite remote sensing technology. Recently, there has been a shift in data protection
policy where rich archives of satellite imagery are now freely available. This has
spurred a new era in satellite-based forest monitoring leading to advancements in optical
time series processing and open source analysis methods. This thesis explores the utility
of optical time series to monitor forest loss in Southeast Asia, with a specific focus on
Cambodia. Methodological objectives focus on testing two distinct forest monitoring
approaches using 1) annual Landsat time series and LandTrendr, and 2) intra-annual
MODIS time series and Breaks for Additive Season and Trend (BFAST). Aspects of data
quality and the influence of forest type on time series analysis are explored, as well as
working towards a methodological framework for integrating Landsat and MODIS time
series for enhanced forest monitoring systems. Thematic objectives of the research
focussed on estimating forest loss in Cambodia in the post-2000 era, determining how
much of this loss was caused by conversions to natural rubber tree cover, and analysing if
there is a link between forest-to-rubber conversion rates and global rubber markets.
At the Landsat 30-m resolution, annual time series coupled with linear segmentation using
LandTrendr was found to be an effective approach for monitoring forest disturbance, with
moderate to high accuracies, depending on forest type. At the MODIS 250-m resolution,
intra-annual time series and BFAST decomposition was found to be a useful approach for
large area mapping, with accuracies again depending on forest type, but also the scale of
the disturbance. Generally, forest disturbance accuracies in both Landsat and MODIS time
series analysis tended to be higher for evergreen forest and lower for deciduous forest
types, suggesting a link between accuracy and a gradient of forest deciduousness. The
MODIS based research also suggests that it may be better to make separate sub-models for
distinctive forest types as oppose to using a common generalised forest approach. Cloud
and aerosol masking remain a challenge for optical time series analysis and the research
highlights a systematic bias in the MODIS aerosol quality flag.
Cambodia’s forest cover was found to be experiencing rapid change. Forest clearance rates
tripled after 2010 compared to the early 2000s, suggesting the country is at the initial
phase of the forest transition curve. Forest-to-rubber conversions were estimated to be
responsible for 20% of total forest clearances, and were more prevalent in the later years.
Annual forest-to-rubber conversion rates were found to be highly correlated to global
rubber prices at local and national scales. Although global rubber markets can be linked to
forest cover change, the effects of land policy in Cambodia, and beyond, have also had a
major influence. It remains to be seen if intervention initiatives such as REDD+ can
materialise over the coming years to make a meaningful contribution to tropical forest
conservation.