Oil palm concessions in southern Myanmar consist mostly of unconverted forest

Nomura, Keiko; Mitchard, Edward T. A.; Bastide, Joan; Oswald, Patrick; Nwe, Thazin

doi:10.1038/s41598-019-48443-3

Cited by 29 publications

(20 citation statements)

References 34 publications

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“…Moreover, we developed a code to pre-process radar and optical datasets, as well as classify land-cover entirely in the Google Earth Engine. The improved accuracy for detecting oil palm plantations with the use of optical and radar data highlights the importance of multisensory and data fusion techniques, which integrate multiple data features to improve map detail and accuracy [60]. This is especially important in the tropics, where oil palm plantations expand most and where optical-based mapping is limited due to high extents of cloud cover.…”

Section: Discussionmentioning

confidence: 99%

Combining Radar and Optical Imagery to Map Oil Palm Plantations in Sumatra, Indonesia, Using the Google Earth Engine

et al. 2020

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Monitoring the expansion of commodity crops in the tropics is crucial to safeguard forests for biodiversity and ecosystem services. Oil palm (Elaeis guineensis) is one such crop that is a major driver of deforestation in Southeast Asia. We evaluated the use of a semi-automated approach with random forest as a classifier and combined optical and radar datasets to classify oil palm land-cover in 2015 in Sumatra, Indonesia, using Google Earth Engine. We compared our map with two existing remotely-sensed oil palm land-cover products that utilized visual and semi-automated approaches for the same year. We evaluated the accuracy of oil palm land-cover classification from optical (Landsat), radar (synthetic aperture radar (SAR)), and combined optical and radar satellite imagery (Combined). Combining Landsat and SAR data resulted in the highest overall classification accuracy (84%) and highest producer’s and user’s accuracy for oil palm classification (84% and 90%, respectively). The amount of oil palm land-cover in our Combined map was closer to official government statistics than the two existing land-cover products that used visual interpretation techniques. Our analysis of the extents of disagreement in oil palm land-cover indicated that our map had comparable accuracy to one of them and higher accuracy than the other. Our results demonstrate that a combination of optical and radar data outperforms the use of optical-only or radar-only datasets for oil palm classification and that our technique of preprocessing and classifying combined optical and radar data in the Google Earth Engine can be applied to accurately monitor oil-palm land-cover in Southeast Asia.

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Section: Discussionmentioning

confidence: 99%

Combining Radar and Optical Imagery to Map Oil Palm Plantations in Sumatra, Indonesia, Using the Google Earth Engine

et al. 2020

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“…In Borneo, for example, annual commercial plantation expansion has been found to be positively correlated with annual forest loss [ 73 ]. It is also evident that land use in some regions of Myanmar has shifted to rubber, betel nut, cashew and oil palm [ 35 ], [ 74 ], all of which needs conversion of abundant land areas [ 75 ].…”

Section: Discussionmentioning

confidence: 99%

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

2020

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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.

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“…As planted/cultivated area is subsequently rebounding during the same period, the afforestation pattern might be contributed by agricultural expansion. Commercial agriculture has been used as opportunities by Military to con scate abandoned lands and develop large-scale plantations of crops after the con ict (Lim et al 2017;Nomura et al 2019; Kevin M. Woods 2019). In this research, several new establishments assumed to be military campsite are shown on the maps.…”

Section: Discussionmentioning

confidence: 99%

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

Aung

2020

Preprint

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The impacts of armed conflict on the environment are extremely complex and difficult to investigate, given the impossibility of accessing the affected area and reliable data limitation. Very-high-resolution satellite imageries and highly reliable machine learning algorithms become very useful in studying direct and indirect impacts of war on the ecosystem, in addition to connected effects on human lives. The Rohingya conflict is described as one of the worst humanitarian crises and human-made disasters of the 21st Century. Quantification of damage due to the conflict and the suitability of human resettlement has been lacking despite the ongoing agreements to repatriate refugees and the importance of ecosystem services for the communities' survival. Here we report the investigation of environmental conditions pre-, during, and post-conflict in the conflict zone using satellite data. We implemented and experienced the Google Earth Engine (GEE) cloud-based computing platform with a widely applied algorithm, the Random Forest (RF) classifier. We found striking near-complete demolition of inhabited regions, dramatic and highly significant increase in burning areas, and substantial deforestation. We discuss the reasons behind such findings from the Rakhine case and debate some general conservation lessons applicable to other countries undergoing post-conflict transitions.

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Oil palm concessions in southern Myanmar consist mostly of unconverted forest

Cited by 29 publications

References 34 publications

Combining Radar and Optical Imagery to Map Oil Palm Plantations in Sumatra, Indonesia, Using the Google Earth Engine

Combining Radar and Optical Imagery to Map Oil Palm Plantations in Sumatra, Indonesia, Using the Google Earth Engine

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

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

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