Dataset of the mountain green cover index (SDG15.4.2) over the economic corridors of the Belt and Road Initiative for 2010-2019. Issue 1 (2nd January 2022)

• Record Type: Journal Article
• Authors: Bian, Jinhu; Li, Ainong; Nan, Xi; Lei, Guangbin; Zhang, Zhengjian
• Is Part Of: Big earth data. Volume 6:Issue 1(2022)
• Page Start: 77
• Page End: 89
• Publication Date: 2022-01-02
• Subjects: Sustainable development goals (SDGs) -- mountain green cover index (MGCI) -- dataset -- Belt and Road -- Landsat; Earth sciences -- Periodicals; Earth sciences -- Research -- Periodicals; Geographic information systems Periodicals; 550
• DOI: 10.1080/20964471.2021.1941571
• Languages: English
• ISSNs: 2096-4471

Abstract

ABSTRACT: Mountains are undergoing widespread changes caused by human activities and climate change. Given the importance of mountains, the protection and sustainable development of mountain ecosystems have been listed as the goals of the United Nations 2030 Sustainable Development Agenda. As one of the indicators, the Mountain Green Cover Index (MGCI) datasets can provide consistent and comparable status of green vegetation in mountainous areas, which can support the mapping of heterogeneous mountain ecosystem health and monitoring changes over time. The production of explicitly high-spatial-resolution MGCI datasets is therefore urgently needed to support the protection measures at subnational and multitemporal scales. In this paper, the MGCI datasets with 500-meter spatial resolutions, covering the economic corridors of the Belt and Road Initiative (BRI), were developed for 2010 to 2019 based on all available Landsat-8 data and the Google Earth Engine cloud computing platform. The validation of green vegetation cover with the ground-truth samples indicated that the datasets can achieve an overall accuracy of 94.06%, with well-detailed spatial and temporal variations. The archived datasets include the MGCI of each BRI economic corridor, matched to a geospatial layer denoting the economic corridor boundaries. The essential information of the datasets and their limitations, along with the production flow, were described in this paper. The published geospatial datasets are available at http://www.doi.org/10.11922/sciencedb.1005 .