In the heart of Southeast Asia, a battle for land is underway, and the stakes are high for the energy sector. The region’s tropical deforestation is largely driven by the expansion of oil palm plantations, a trend that has been challenging to track accurately with existing global and regional remote-sensing products. However, a recent study led by Yadanar Ye Myint from the Division of Forest and Biomaterials Sciences at Kyoto University, Japan, offers a promising solution to this pressing issue.
Published in the journal ‘Trees, Forests and People’ (which translates to ‘Trees, Forests and Humans’), the research demonstrates a straightforward method for “ground-truthing from the sky” using very high-resolution (VHR) photographic images to detect oil-palm cultivation and other drivers of deforestation across Southeast Asia. The study integrates data from the widely used MCD12Q1 land-cover product, which generates annual 500 m-pixel maps of 17 International Geosphere-Biosphere Programme (IGBP) classes, with VHR imagery from Google Earth.
The MCD12Q1 product, while practical for regional assessments, has a resolution too coarse to identify fine-scale, agriculture-driven conversions such as oil-palm expansion. To address this, the researchers randomly subsampled within each major land cover type to which forests had transitioned during two periods (2001–2010 and 2010–2018). Each of the 260 verification points in each post-transition land type was visually interpreted, generating correction factors to calibrate land-use change probabilities.
“The uncalibrated results showed that 12% and 9% of forested areas were converted to woody savanna and savanna,” explains Yadanar Ye Myint. “However, VHR images revealed that 32% to 46% of these areas were actually oil palm plantations.” This significant discrepancy highlights the importance of calibrating land-use change probabilities with VHR imagery.
After applying these correction factors, the researchers estimated that 40% and 48% of forest losses were due to oil palm expansion, with the remaining forest loss attributed to degradation to savannas and grasslands. Additionally, the study found that permanent wetlands classified by MCD12Q1 were predominantly mangroves (63%), aqua farms (20%), and oil palms (8%), rather than lakes, rivers, and marshes.
The implications of this research for the energy sector are substantial. Accurate detection of deforestation driven by agricultural activities is crucial for sustainable land management and the development of bioenergy crops. As Yadanar Ye Myint notes, “While MODIS remains a valuable source for analyzing land use changes across large areas, detection of deforestation driven by agricultural activities benefits from calibration with VHR imagery.”
This approach is not only straightforward but also requires minimal expertise, making it easily adoptable by local governments, NGOs, land managers, and other stakeholders. By providing a more accurate picture of land-use changes, this method can support better decision-making and policy development in the energy sector.
As the world grapples with the challenges of climate change and sustainable development, the need for precise and reliable data on land-use changes has never been greater. This research offers a valuable tool for meeting that need, paving the way for more informed and effective strategies in the fight against deforestation and for the sustainable development of bioenergy crops.