Land use and land cover changes along the China-Myanmar Oil and Gas pipelines – Monitoring infrastructure development in remote conflict-prone regions

Abstract

Energy infrastructures can have negative impacts on the environment. In remote and / or sparsely populated as well as in conflict-prone regions, these can be difficult to assess, in particular when they are of a large scale. Analyzing land use and land cover changes can be an important initial step towards establishing the quantity and quality of impacts. Drawing from very-high-resolution-multi-temporal-satellite-imagery, this paper reports on a study which employed the Random Forest Classifier and Land Change Modeler to derive detailed information of the spatial patterns and temporal variations of land-use and land-cover changes resulting from the China-Myanmar Oil and Gas Pipelines in Ann township in Myanmar's Rakhine State of Myanmar. Deforestation and afforestation conversion processes during pre-and post-construction periods (2010 to 2012) are compared. Whilst substantial forest areas were lost along the pipelines, this is only part of the story, as afforestation has also happened in parallel. However, afforestation areas can be of a lower value, and in order to be able to take quality of forests into account, it is of crucial importance to accompany satellite-imagery based techniques with field observation. Findings have important implications for future infrastructure development projects in conflict-affected regions in Myanmar and elsewhere.

Satellite analysis of the environmental impacts of armed-conflict in Rakhine, Myanmar

“…These results con rm that armed-con icts and civil war aggravate Myanmar's already declining forest trend and degrading forest quality. Myanmar loses 0.87% of its forest cover annually (Aung et al 2020;Leimgruber et al 2005;Yang et al 2019). Myanmar is also the current mangrove deforestation hotspot globally, and more than 90% of mangrove forests are located in Rakhine and Tanintharyi (De Alban et al 2020;Richards and Friess 2016).…”

“…Before classi cation, I conducted RF hyperparameter tuning to update bands and select the best hyperparameter values. As it is necessary to have the speci cation of several parameters to execute the RF model, each RF tree was established by training each tree in the forest (ntree) with the number of input predictor-variables (mtry), randomly chosen at each split from the training dataset (Aung, Fischer, and Buchanan 2020). In the last section of RF classi cation in GEE, I set hyperparameters values after hyperparameter tuning, trained the classi er, and exported classi ed raster images and accuracy assessment results to the google drive folder.…”

Satellite Analysis of the Environmental Impacts of Armed-Conflict in Rakhine, Myanmar

“…These results con rm that armed-con icts and civil war aggravate Myanmar's already declining forest trend and degrading forest quality. Myanmar loses 0.87% of its forest cover annually (Aung et al 2020;Leimgruber et al 2005;Yang et al 2019). Myanmar is also the current mangrove deforestation hotspot globally, and more than 90% of mangrove forests are located in Rakhine and Tanintharyi (De Alban et al 2020;Richards and Friess 2016).…”

Uneven Frontiers: Exposing the Geopolitics of Myanmar’s Borderlands with Critical Remote Sensing

“…Researchers have also tracked natural resource extraction within the country's borderlands, from mining in Shan State bordering China [61] to oil palm and rubber plantations in the southeast [62,63], and along the Chinese and Laotian borders [64]. Particularly relevant to our inquiry is Aung et al's (2020) analysis combining satellite imagery and field observations to measure LULC change spurred by the construction of the China-Myanmar oil and gas pipelines [65]. This research into the environmental impacts of energy infrastructure also demonstrates the ability to combine observations from above and below in order to connect locally situated insights with national and international dynamics.…”