{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T19:11:26Z","timestamp":1768677086176,"version":"3.49.0"},"reference-count":98,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T00:00:00Z","timestamp":1695081600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2039857"],"award-info":[{"award-number":["2039857"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mining for rare earth elements is rapidly increasing, driven by current and projected demands for information and energy technologies. Following China\u2019s Central Government\u2019s 2012 strategy to shift away from mining in favor of value-added processing, primary extraction has increased outside of China. Accordingly, changes in mineral exploitation in China and Myanmar have garnered considerable attention in the past decade. The prevailing assumption is that mining in China has decreased while mining in Myanmar has increased, but the dynamic in border regions is more complex. Our empirical study used Google Earth Engine (GEE) to characterize changes in mining surface footprints between 2005 and 2020 in two rare earth mines located on either side of the Myanmar\u2013China border, within Kachin State in northern Myanmar and Nujiang Prefecture in Yunnan Province in China. Our results show that the extent of the mining activities increased by 130% on China\u2019s side and 327% on Myanmar\u2019s side during the study period. We extracted surface reflectance images from 2005 and 2010 from Landsat 5 TM and 2015 and 2020 images from Landsat 8 OLI. The Normalized Vegetation Index (NDVI) was applied to dense time-series imagery to enhance landcover categories. Random Forest was used to categorize landcover into mine and non-mine classes with an overall accuracy of 98% and a Kappa Coefficient of 0.98, revealing an increase in mining extent of 2.56 km2, covering the spatial mining footprint from 1.22 km2 to 3.78 km2 in 2005 and 2020, respectively, within the study area. We found a continuous decrease in non-mine cover, including vegetation. Both mines are located in areas important to ethnic minority groups, agrarian livelihoods, biodiversity conservation, and regional watersheds. The finding that mining surface areas increased on both sides of the border is significant because it shows that national-level generalizations do not align with local realities, particularly in socially and environmentally sensitive border regions. The quantification of such changes over time can help researchers and policymakers to better understand the shifting geographies and geopolitics of rare earth mining, the environmental dynamics in mining areas, and the particularities of mineral extraction in border regions.<\/jats:p>","DOI":"10.3390\/rs15184597","type":"journal-article","created":{"date-parts":[[2023,9,19]],"date-time":"2023-09-19T02:47:52Z","timestamp":1695091672000},"page":"4597","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Unexpected Expansion of Rare-Earth Element Mining Activities in the Myanmar\u2013China Border Region"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8652-7633","authenticated-orcid":false,"given":"Emmanuel","family":"Chinkaka","sequence":"first","affiliation":[{"name":"Department of Geography and Spatial Sciences, University of Delaware, Newark, DE 19716, USA"},{"name":"Department of Earth Sciences, Malawi University of Science and Technology, Limbe P.O. Box 5196, Malawi"}]},{"given":"Julie Michelle","family":"Klinger","sequence":"additional","affiliation":[{"name":"Department of Geography and Spatial Sciences, University of Delaware, Newark, DE 19716, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4504-1407","authenticated-orcid":false,"given":"Kyle Frankel","family":"Davis","sequence":"additional","affiliation":[{"name":"Department of Geography and Spatial Sciences, University of Delaware, Newark, DE 19716, USA"},{"name":"Department of Plant and Soil Sciences, University of Delaware, Newark, DE 19716, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1953-8727","authenticated-orcid":false,"given":"Federica","family":"Bianco","sequence":"additional","affiliation":[{"name":"Department of Physics and Astronomy, University of Delaware, Newark, DE 19716, USA"},{"name":"Joseph R. Biden, Jr. School of Public Policy and Administration, University of Delaware, Newark, DE 19716, USA"},{"name":"Data Science Institute, University of Delaware, Newark, DE 19716, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"729","DOI":"10.14358\/PERS.82.9.729","article-title":"A Fully Automatic Method to Extract Rare Earth Mining Areas from Landsat Images","volume":"82","author":"Wu","year":"2016","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_2","first-page":"609","article-title":"Mapping of potential rare earth deposits in the Schiel alkaline complex using sentinel-2B multispectral sensor","volume":"24","author":"Muavhi","year":"2021","journal-title":"Egypt. J. Remote Sens. 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