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

References

• Angelopoulou, T., N. Tziolas, A. Balafoutis, G. Zalidis, and D. Bochtis. 2019. “Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review.” Remote Sensing 11 (6): 676. https://doi.org/10.3390/rs11060676.
   Web of Science ®Google Scholar
• Bardgett, R. D., J. M. Bullock, S. Lavorel, P. Manning, U. Schaffner, N. Ostle, M. Chomel, et al. 2021. “Combatting Global Grassland Degradation.” Nature Reviews Earth & Environment 2 (10): 720–735. https://doi.org/10.1038/s43017-021-00207-2.
   Google Scholar
• Brown, C. F., S. P. Brumby, B. Guzder-Williams, T. Birch, S. B. Hyde, J. Mazzariello, W. Czerwinski, et al. 2022. “Dynamic World, Near Real-Time Global 10 m Land use Land Cover Mapping.” Scientific Data 9 (1): 251. https://doi.org/10.1038/s41597-022-01307-4.
   Web of Science ®Google Scholar
• Campos, J. C., and J. C. Brito. 2018. “Mapping Underrepresented Land Cover Heterogeneity in Arid Regions: The Sahara-Sahel Example.” ISPRS Journal of Photogrammetry and Remote Sensing 146: 211–220. https://doi.org/10.1016/j.isprsjprs.2018.09.012.
   Web of Science ®Google Scholar
• Chen, Y., R. Cao, J. Chen, L. Liu, and B. Matsushita. 2021. “A Practical Approach to Reconstruct High-Quality Landsat NDVI Time-Series Data by Gap Filling and the Savitzky–Golay Filter.” ISPRS Journal of Photogrammetry and Remote Sensing 180: 174–190. https://doi.org/10.1016/j.isprsjprs.2021.08.015.
   Web of Science ®Google Scholar
• Cheng, W., L. Dan, X. Deng, J. Feng, Y. Wang, J. Peng, J. Tian, et al. 2022. “Global Monthly Gridded Atmospheric Carbon Dioxide Concentrations Under the Historical and Future Scenarios.” Scientific Data 9 (1): 83. https://doi.org/10.1038/s41597-022-01196-7.
   PubMed Web of Science ®Google Scholar
• Cowie, A. L., B. J. Orr, V. M. C. Sanchez, P. Chasek, N. D. Crossman, A. Erlewein, G. Louwagie, et al. 2018. “Land in Balance: The Scientific Conceptual Framework for Land Degradation Neutrality.” Environmental Science & Policy 79: 25–35. https://doi.org/10.1016/j.envsci.2017.10.011.
   Web of Science ®Google Scholar
• Cui, Y., and X. Li. 2022. “A new Global Land Productivity Dynamic Product Based on the Consistency of Various Vegetation Biophysical Indicators.” Big Earth Data 6 (1): 36–53. https://doi.org/10.1080/20964471.2021.2018789.
   Web of Science ®Google Scholar
• Cui, W., J. Liu, J. Jia, and P. Wang. 2021. “Terrestrial Ecological Restoration in China: Identifying Advances and Gaps.” Environmental Sciences Europe 33: 1–14. https://doi.org/10.1186/s12302-021-00563-2.
   Web of Science ®Google Scholar
• Cui, G., C. Xiao, J. Lei, X. Li, Y. Bao, and Q. LU. 2023. “China’s Governance: Strategy Choice and Future Vision for Combating Desertification.” Bulletin of Chinese Academy of Sciences (Chinese Version) 38 (7): 943–955. https://doi.org/10.16418/j.issn.1000-3045.20230706002.
   Google Scholar
• FAO (Food and Agriculture Organization of the United Nations) and ITPS (Intergovernmental Technical Panel on Soils). 2018. Global Soil Organic Carbon Map (GSOCmap) Technical Report. Rome, Italiana: FAO. 162 pp. https://www.fao.org/documents/card/en/c/I8891EN.
   Google Scholar
• Funk, C., P. Peterson, M. Landsfeld, D. Pedreros, J. Verdin, S. Shukla, G. Husak, et al. 2015. “The Climate Hazards Infrared Precipitation with Stations – a New Environmental Record for Monitoring Extremes.” Scientific Data 2 (1): 1–21. https://doi.org/10.1038/sdata.2015.66.
   Web of Science ®Google Scholar
• Gadzama, N. M. 2017. “Attenuation of the Effects of Desertification Through Sustainable Development of Great Green Wall in the Sahel of Africa.” World Journal of Science, Technology and Sustainable Development 14 (4): 279–289. https://doi.org/10.1108/WJSTSD-02-2016-0021.
   Google Scholar
• Gadzama, N. M., and H. K. Ayuba. 2016. “On Major Environmental Problem of Desertification in Northern Nigeria with Sustainable Efforts to Managing It.” World Journal of Science, Technology and Sustainable Development 13 (1): 18–30. https://doi.org/10.1108/WJSTSD-06-2015-0035.
   Google Scholar
• Gonzalez-Roglich, M., A. Zvoleff, M. Noon, H. Liniger, R. Fleiner, N. Harari, and C. Garcia. 2019. “Synergizing Global Tools to Monitor Progress Towards Land Degradation Neutrality: Trends. Earth and the World Overview of Conservation Approaches and Technologies Sustainable Land Management Database.” Environmental Science & Policy 93: 34–42. https://doi.org/10.1016/j.envsci.2018.12.019.
   Web of Science ®Google Scholar
• Grömping, U. 2007. “Relative Importance for Linear Regression in R: The Package Relaimpo.” Journal of Statistical Software 17: 1–27. https://doi.org/10.18637/jss.v017.i01.
   Web of Science ®Google Scholar
• Guo, H., F. Chen, Y. Tang, Y. Ding, M. Chen, W. Zhou, M. Zhu, et al. 2023b. “Progress Toward the Sustainable Development of World Cultural Heritage Sites Facing Land-Cover Changes.” The Innovation 4 (5), https://doi.org/10.1016/j.xinn.2023.100496.
   Google Scholar
• Guo, H., C. Dou, H. Chen, J. Liu, B. Fu, X. Li, Z. Zou, and D. Liang. 2023a. “SDGSAT-1: The World’s First Scientific Satellite for Sustainable Development Goals.” Science Bulletin 68 (1): 34–38. https://doi.org/10.1016/j.scib.2022.12.014.
   PubMed Web of Science ®Google Scholar
• Li, X., Q. Lu, and X. Jia. 2021. “Harnessing big Earth Data to Facilitate Land Degradation Neutrality Goals—Practices and Prospects.” Bulletin of Chinese Academy of Sciences (Chinese Version) 36 (8): 896–903. https://doi.org/10.16418/j.issn.1000-3045.20210705002.
   Google Scholar
• Liu, J., W. Wang, and H. Zhong. 2020. “EarthDataMiner: A Cloud-Based big Earth Data Intelligence Analysis Platform.” In IOP Conference Series: Earth and Environmental Science 509 (1): 012032. IOP Publishing. https://doi.org/10.1088/1755-1315/509/1/012032.
   Google Scholar
• Liu, L., X. Zhang, and T. Zhao. 2023. “GLC_FCS30D: The First Global 30-m Land-Cover Dynamic Monitoring Product with Fine Classification System from 1985 to 2022” (Dataset). Zenodo. Accessed August 11, 2023. https://doi.org/10.5281/zenodo.8239305.
   Google Scholar
• Mirzabaev, A., M. Sacande, F. Motlagh, A. Shyrokaya, and A. Martucci. 2022. “Economic Efficiency and Targeting of the African Great Green Wall.” Nature Sustainability 5 (1): 17–25. https://doi.org/10.1038/s41893-021-00801-8.
   Web of Science ®Google Scholar
• Muñoz Sabater, J. 2019. “ERA5-Land Monthly Averaged Data from 1981 to Present.” (Dataset). Copernicus Climate Change Service (C3S) Climate Data Store (CDS). Accessed July 12, 2019. https://doi.org/10.24381/cds.68d2bb30.
   Google Scholar
• Rotllan-Puig, X., E. Ivits, and M. Cherlet. 2021. “LPDynR: A New Tool to Calculate the Land Productivity Dynamics Indicator.” Ecological Indicators 133: 108386. https://doi.org/10.1016/j.ecolind.2021.108386.
   Web of Science ®Google Scholar
• Salisbury, J. W., and D. M. D’Aria. 1992. “Infrared (8-14 μm) Remote Sensing of Soil Particle Size.” Remote Sensing of Environment 42 (2): 157–165. https://doi.org/10.1016/0034-4257(92)90099-6.
   Web of Science ®Google Scholar
• Samarinas, N., N. L. Tsakiridis, S. Kokkas, E. Kalopesa, and G. C. Zalidis. 2023. “Soil Data Cube and Artificial Intelligence Techniques for Generating National-Scale Topsoil Thematic Maps: A Case Study in Lithuanian Croplands.” Remote Sensing 15 (22): 5304. https://doi.org/10.3390/rs15225304.
   Web of Science ®Google Scholar
• Shen, T., X. Li, Y. Chen, Y. Cui, Q. Lu, X. J, and J. Chen. 2023. “HiLPD-GEE: High Spatial Resolution Land Productivity Dynamics Calculation Tool Using Landsat and MODIS Data.” International Journal of Digital Earth 16 (1): 671–690. https://doi.org/10.1080/17538947.2023.2179675.
   Web of Science ®Google Scholar
• Sims, N. C., J. R. England, G. J. Newnham, S. Alexander, C. Green, S. Minelli, and A. Held. 2019. “Developing Good Practice Guidance for Estimating Land Degradation in the Context of the United Nations Sustainable Development Goals.” Environmental Science & Policy 92: 349–355. https://doi.org/10.1016/j.envsci.2018.10.014.
   Web of Science ®Google Scholar
• Sims, N. C., G. J. Newnham, J. R. England, J. Guerschman, S. J. D. Cox, S. H. Roxburgh, R. A. Viscarra Rossel, et al. 2021. Good Practice Guidance. SDG Indicator 15.3.1, Proportion of Land That Is Degraded Over Total Land Area. Version 2.0. Bonn, Germany: United Nations Convention to Combat Desertification. https://www.unccd.int/resources/manuals-and-guides/good-practice-guidance-sdg-indicator-1531-proportion-land-degraded.
   Google Scholar
• Song, X., Z. Liu, and Y. Zhao. 2004. “Cloud Detection and Analysis of MODIS Image.” In IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium 4: 2764–2767. https://doi.org/10.1109/igarss.2004.1369875.
   Google Scholar
• Thomas, A., L. Bentley, C. Feeney, S. Lofts, C. Robb, E. C. Rowe, A. Thomson, E. Warren-Thomas, and B. Emmett. 2023. “Land Degradation Neutrality: Testing the Indicator in a Temperate Agricultural Landscape.” Journal of Environmental Management 346: 118884. https://doi.org/10.1016/j.jenvman.2023.118884.
   PubMedGoogle Scholar
• UNCCD (United Nations Convention to Combat Desertification). 2015. Report of the Conference of the Parties on its Twelfth Session, Held in Ankara from 12 to 23 October 2015. Part Two: Action Taken. Bonn, Germany: UNCCD. https://www.unccd.int/official-documents/cop-12-ankara-2015.
   Google Scholar
• WOCAT. 2021. “WOCAT, the world overview of conservation approaches and technologies [online]”. Accessed 3 March 2021. https://www.wocat.net/en/.
   Google Scholar
• Yang, J., X. Li, B. Wu, J. Wu, B. Sun, C. Yan, and Z. Gao. 2021. “High Spatial Resolution Topsoil Organic Matter Content Mapping Across Desertified Land in Northern China.” Frontiers in Environmental Science 9: 668912. https://doi.org/10.3389/fenvs.2021.668912.
   Web of Science ®Google Scholar
• Zeng, N. 2003. “Drought in the Sahel.” Science 302 (5647): 999–1000. https://doi.org/10.1126/science.1090849.
   PubMed Web of Science ®Google Scholar
• Zhang, X., L. Liu, X. Chen, Y. Gao, S. Xie, and J. Mi. 2021. “GLC_FCS30: Global Land-Cover Product with Fine Classification System at 30 m Using Time-Series Landsat Imagery.” Earth System Science Data 13 (6): 2753–2776. https://doi.org/10.5194/essd-13-2753-2021.
   Web of Science ®Google Scholar
• Zhang, X., L. Liu, T. Zhao, X. Chen, S. Lin, J. Wang, J. Mi, and W. Liu. 2023. “GWL_FCS30: A Global 30 m Wetland map with a Fine Classification System Using Multi-Sourced and Time-Series Remote Sensing Imagery in 2020.” Earth System Science Data Discussions 15: 265–293. https://doi.org/10.5194/essd-15-265-2023.
   Google Scholar
• Zhang, X., L. Liu, T. Zhao, Y. Gao, X. Chen, and J. Mi. 2022. “GISD30: Global 30 m Impervious-Surface Dynamic Dataset from 1985 to 2020 Using Time-Series Landsat Imagery on the Google Earth Engine Platform.” Earth System Science Data 14 (4): 1831–1856. https://doi.org/10.5194/essd-14-1831-2022.
   Web of Science ®Google Scholar
• Zhang, X., T. Zhao, H. Xu, W. Liu, J. Wang, X. Chen, and L. Liu. 2024. “GLC_FCS30D: The First Global 30-m Land-Cover Dynamic Monitoring Product with a Fine Classification System from 1985 to 2022 Using Dense Time-Series Landsat Imagery and Continuous Change-Detection Method.” Earth System Science Data Discussions 16: 1353–1381. https://doi.org/10.5194/essd-16-1353-2024.
   Google Scholar
• Zucca, C., Q. B. Le, P. Karampiperis, T. Lemann, R. Thomas, B. A. Thiombiano, T. Hermassi, et al. 2024. “Toward an Operational Tool to Integrate Land Degradation Neutrality into Land use Planning: LUP4LDN.” Land Degradation & Development 35 (7): 2489–2507. https://doi.org/10.1002/ldr.5075.
   Web of Science ®Google Scholar