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.

Highlights

  • Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files

  • Land cover classification of the study area was determined, based on the existing Myanmar land-use maps developed by the United Nations Environmental Program (UNEP)

  • The aim of an accuracy assessment was to evaluate the ability of a model for detecting and delineating LULCC within a study area

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Summary

Introduction

Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files. The study region is located along the China-Myanmar Oil and Gas Pipelines in Ann township of Kyaukpyu District in Myanmar’s western-most state of Rakhine (See Fig 1) It has a tropical monsoon climate, featuring warm temperatures throughout the year and high annual rainfall with most of the rainfall from June to August. Land cover classification of the study area was determined, based on the existing Myanmar land-use maps developed by the United Nations Environmental Program (UNEP). To compare pre-operation, operation, and post-operation periods, images from two acquisition dates (t0 = 2010 November 17, t1 = 2012 October 17) with a spatial resolution of 0.5m (multispectral bands) were used for our main study area in Ann township These images were mainly used for tracking and classifying land cover changes in the area of immediate proximity to the pipelines, using the RF classification method. The estimated variances are them computed based on the sample size allocation

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