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

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

A critical remote sensing approach illuminates the geopolitics of development within Myanmar and across its ethnic minority borderlands. By integrating nighttime light (NTL) data from 1992–2020, long-term ethnographic fieldwork, and a review of scholarly and gray literature, we analyzed how Myanmar’s economic geography defies official policy, attesting to persistent inequality and the complex relationships between state-sponsored and militia-led violence, resource extraction, and trade. While analysis of DMSP-OLS data (1992–2013) and VIIRS data (2013–2020) reveals that Myanmar brightened overall, especially since the 2010s in line with its now-halting liberalization, growth in lights was unequally distributed. Although ethnic minority states brightened more rapidly than urbanized ethnic majority lowland regions, in 2020, the latter still emitted 5.6-fold more radiance per km2. Moreover, between 2013 and 2020, Myanmar’s borderlands were on average just 13% as bright as those of its five neighboring countries. Hot spot analysis of radiance within a 50 km-wide area spanning both sides of the border confirmed that most significant clusters of light lay outside Myanmar. Among the few hot spots on Myanmar’s side, many were associated with official border crossings such as Muse, the formal hub for trade with China, and Tachileik and Myawaddy next to Thailand. Yet some of the most significant increases in illumination between 2013 and 2020 occurred in areas controlled by the Wa United State Party and its army, which are pursuing infrastructure development and mining along the Chinese border from Panghsang to the illicit trade hub of Mongla. Substantial brightening related to the “world’s largest refugee camp” was also detected in Bangladesh, where displaced Rohingya Muslims fled after Myanmar’s military launched a violent crackdown. However, no radiance nor change in radiance were discernible in areas within Myanmar where ethnic cleansing operations occurred, pointing to the limitations of NTL. The diverse drivers and implications of changes in light observed from space emphasize the need for political and economically situated remote sensing.

A Regression-Based Procedure for Markov Transition Probability Estimation in Land Change Modeling

Land change models commonly model the expected quantity of change as a Markov chain. Markov transition probabilities can be estimated by tabulating the relative frequency of change for all transitions between two dates. To estimate the appropriate transition probability matrix for any future date requires the determination of an annualized matrix through eigendecomposition followed by matrix powering. However, the technique yields multiple solutions, commonly with imaginary parts and negative transitions, and possibly with no non-negative real stochastic matrix solution. In addition, the computational burden of the procedure makes it infeasible for practical use with large problems. This paper describes a Regression-Based Markov (RBM) approximation technique based on quadratic regression of individual transitions that is shown to always yield stochastic matrices, with very low error characteristics. Using land cover data for the 48 conterminous US states, median errors in probability for the five states with the highest rates of transition were found to be less than 0.00001 and the maximum error of 0.006 was of the same order of magnitude experienced by the commonly used compromise of forcing small negative transitions estimated by eigendecomposition to 0. Additionally, the technique can solve land change modeling problems of any size with extremely high computational efficiency.