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Abstract. Data and knowledge of surface water bodies (SWB), including large lakes andreservoirs (surface water areas > 1 km2), are critical forthe management and sustainability of water resources. However, the existingglobal or national dam datasets have large georeferenced coordinate offsetsfor many reservoirs, and some datasets have not reported reservoirs andlakes separately. In this study, we generated China's surface water bodies,Large Dams, Reservoirs, and Lakes (China-LDRL) dataset by analyzing allavailable Landsat imagery in 2019 (19 338 images) in Google Earth Engine andvery-high spatial resolution imagery in Google Earth Pro. There were∼ 3.52 × 106 yearlong SWB polygons in China for2019, only 0.01 × 106 of them (0.43 %) were of large size(> 1 km2). The areas of these large SWB polygons accountedfor 83.54 % of the total 214.92 × 103 km2 yearlongsurface water area (SWA) in China. We identified 2418 large dams, including624 off-stream dams and 1794 on-stream dams, 2194 large reservoirs (16.35 × 103 km2), and 3051 large lakes (73.38 × 103 km2). In general, most of the dams and reservoirs in Chinawere distributed in South China, East China, and Northeast China, whereasmost of lakes were located in West China, the lower Yangtze River basin, andNortheast China. The provision of the reliable, accurate China-LDRL dataseton large reservoirs/dams and lakes will enhance our understanding of waterresources management and water security in China. The China-LDRL dataset ispublicly available at https://doi.org/10.6084/m9.figshare.16964656.v3 (Wang et al., 2021b).
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Unexpected Expansion of Rare-Earth Element Mining Activities in the Myanmar–China Border Region
Mining for rare earth elements is rapidly increasing, driven by current and projected demands for information and energy technologies. Following China’s Central Government’s 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–China 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’s side and 327% on Myanmar’s 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.
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- Award ID(s):
- 2039857
- PAR ID:
- 10621679
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 15
- Issue:
- 18
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 4597
- Subject(s) / Keyword(s):
- mining extent rare earth elements Google Earth Engine Landsat change detection NDVI geopolitics remote sensing Kachin State Nujiang Nujiang Prefecture Yunnan Province
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/rs15184597
