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GGW-BDF: an online tool for using earth observation and Chinese ecosystem restoration experiences in support of the Great Green Wall initiative

Authors :
Xiaosong Li
Tong Shen
Amos Tiereyangn Kabo-Bah
Jie Liu
Jianhui Li
Changyong Dou
Yingchao Piao
Xiaoxia Jia
Qi Lu
Huailin Wu
Ziyu Yang
Yubo Zhi
Licheng Zhao
Source :
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
Publication Year :
2024
Publisher :
Taylor & Francis Group, 2024.

Abstract

The United Nations Sustainable Development Goals (SDGs) establish a fresh global framework for evaluating the development agenda, emphasizing prosperity, growth, inclusivity, and transparency while safeguarding our planet Earth. Land Degradation Neutrality (LDN) represents a clear target for assessing the achievement of SDG 15.3. In response to the shortcomings of the African Great Green Wall initiative with respect to LDN monitoring, reporting, and intervention, we developed an online tool, the Great Green Wall Big Data Facilitator (GGW-BDF), based on earth observation data (e.g. SDGSAT-1), cutting-edge big data analysis technologies, and Chinese experience in combating desertification, to support the implementation of the African Great Green Wall initiative. The GGW-BDF comprises an LDN data portal, desertification control knowledge, and e-learning resources. This study highlights the functionality of this tool to support stakeholders across countries in monitoring LDN while providing the successful technoloiges to restore degraded or barren land. The contributions of the GGW-BDF are very useful in supporting the Pan-African Agency of the Great Green Wall (PAGGW), and will hopefully be continuously upgraded for being a part of the federated toolbox of the Group on Earth Observation (GEO) LDN Flagship.

Details

Language :
English
ISSN :
17538947 and 17538955
Volume :
17
Issue :
1
Database :
Directory of Open Access Journals
Journal :
International Journal of Digital Earth
Publication Type :
Academic Journal
Accession number :
edsdoj.9227b2107548cc83c657a946e2db03
Document Type :
article
Full Text :
https://doi.org/10.1080/17538947.2024.2364683