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General information¶

was produced as part of the project “Enabling the use of global data sources to assess and monitor land degradation at multiple scales”, funded by the Global Environment Facility.

Contacting the team¶

Contact the Trends.Earth team with any comments or suggestions. If you have specific bugs to report or improvements to the tool that you would like to suggest, you can also submit them in the issue tracker on Github for .

Authors¶

The Land Degradation Monitoring Project is a partnership of Conservation International, Lund University, and the National Aeronautics and Space Administration (NASA), and is funded by the Global Environment Facility (GEF).

Contributors to the documentation and to include Yengoh Genesis, Lennart Olsson, Mariano Gonzalez-Roglich, Monica Noon, Tristan Schnader, Anna Tengberg, and Alex Zvoleff.

uses Google Earth Engine to compute indicators in the cloud.

The Tools4LDN Project is a partnership of Conservation International, University of Bern, University of Colorado in partnership with USDA and USAID, University of California - Santa Barbara in partnership with University of North Carolina - Wilmington and Brown University and is funded by the Global Environment Facility (GEF).

Contributors to the documentation and to include Gabriel Daldegan, Mariano Gonzalez-Roglich, Monica Noon, and Alex Zvoleff in partnership with Jeff Herrick, Tatenda Lemann, Hanspeter Liniger, David Lopez-Carr, Kevin Mwenda, Jason Neff, George Peacock, Narcisa Pricope, Sanna Sokolow, and Ingrid Teich.

Acknowledgements¶

The feedback provided by early users of and by the participants in the webinars and workshops held by the GEF Land Degradation Monitoring Project have been critical to the development of the tool.

Neil Sims, Sasha Alexander, Renato Cumani, and Sara Minelli provided input on the implementation of the SDG 15.3 and LDN indicators in , on the structure of the tool, and on the UNCCD reporting process, and also provided early input and testing of the tool.

The project acknowledges the participants of the workshop held in Morogoro, Tanzania in October, 2017 for sharing their feedback and suggestions on the tool: Jones Agwata, Col. Papa Assane Ndiour, Lt. Fendama Baldé, Papa Nékhou Diagne, Abdoul Aziz Diouf, Richard Alphonce Giliba, Moses Isabirye, Vettes Kalema, Joseph Kihaule, Prof. D.N. Kimaro, James Lwasa, Paulo Mandela, Modou Moustapha Sarr, Joseph Mutyaba, Stephen Muwaya, Joseph Mwalugelo, Prof. Majaliwa Mwanjalolo, Edson Aspon Mwijage, Jerome Nchimbi, Elibariki Ngowi, Tabby Njunge, Daniel Nkondola, Blaise Okinyi, Joseph Opio, Rozalia Rwegasira, Ndeye Kany Sarr, Mamadou Adama Sarr, Edward Senyonjo, Olipa Simon, Samba Sow, Felly Mugizi Tusiime and John Wasige.

Citation¶

If you wish to cite , please use the following citation:

Trends.Earth. Conservation International. Available online at: http://trends.earth. 2022.

License¶

is free and open-source. It is licensed under the GNU General Public License, version 2.0 or later.

This site and the products of are made available under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0). The boundaries and names used, and the designations used, in do not imply official endorsement or acceptance by Conservation International Foundation, or its partner organizations and contributors.

Trademark¶

has a service mark trademark registered June 9, 2020 (reg. No. 6,074,442 and Int. Cl.: 9,35,42) by the United States Patent and Trademark Office.

Publications¶

Peer-reviewed Publications¶

The below peer-reviewed publications either use or relate to .

Alamanos, A. and Linnane, S., 2021. Estimating SDG Indicators in Data-Scarce Areas: The Transition to the Use of New Technologies and Multidisciplinary Studies. Earth, 2(3), pp.635-652.
Dong, J., Metternicht, G., Hostert, P., Fensholt, R., Chowdhury, R.R., 2019. Remote sensing and geospatial technologies in support of a normative land system science: status and prospects. Curr. Opin. Environ. Sustain. 38, 44–52. https://doi.org/10.1016/j.cosust.2019.05.003
Easdale, M.H., Fariña, C., Hara, S., Pérez León, N., Umaña, F., Tittonell, P., Bruzzone, O., 2019. Trend-cycles of vegetation dynamics as a tool for land degradation assessment and monitoring. Ecol. Indic. 107, 105545. https://doi.org/10.1016/j.ecolind.2019.105545
Giuliani, G., Chatenoux, B., Benvenuti, A., Lacroix, P., Santoro, M., Mazzetti, P., 2020a. Monitoring land degradation at national level using satellite Earth Observation time-series data to support SDG15 – exploring the potential of data cube. Big Earth Data 4, 3–22. https://doi.org/10.1080/20964471.2020.1711633
Giuliani, G., Mazzetti, P., Santoro, M., Nativi, S., Van Bemmelen, J., Colangeli, G., Lehmann, A., 2020b. Knowledge generation using satellite earth observations to support sustainable development goals (SDG): A use case on Land degradation. Int. J. Appl. Earth Obs. Geoinformation 88, 102068. https://doi.org/10.1016/j.jag.2020.102068
Gonzalez-Roglich, M., Zvoleff, A., Noon, M., Liniger, H., Fleiner, R., Harari, N., Garcia, C., 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. Environ. Sci. Policy 93, 34–42. https://doi.org/10.1016/j.envsci.2018.12.019
Jiang, L., Bao, A., Jiapaer, G., Liu, R., Yuan, Y. and Yu, T., 2022. Monitoring land degradation and assessing its drivers to support sustainable development goal 15.3 in Central Asia. Science of The Total Environment, 807, p.150868. https://doi.org/10.1016/j.scitotenv.2021.150868
Kadaverugu, A., Nageshwar Rao, C. and Viswanadh, G.K., 2021. Quantification of flood mitigation services by urban green spaces using InVEST model: a case study of Hyderabad city, India. Modeling Earth Systems and Environment, 7(1), pp.589-602. https://doi.org/10.1007/s40808-020-00937-0
Kust, G.S., Andreeva, O.V., Lobkovskiy, V.A., 2020. Land Degradation Neutrality: the Modern Approach to Research on Arid Regions at the National Level. Arid Ecosyst. 10, 87–92. https://doi.org/10.1134/S2079096120020092
Hu, Y., Wang, C., Yu, X. and Yin, S., 2021. Evaluating Trends of Land Productivity Change and Their Causes in the Han River Basin, China: In Support of SDG Indicator 15.3. 1. Sustainability, 13(24), p.13664. https://doi.org/10.3390/su132413664
Li, Z., Lun, F., Liu, M., Xiao, X., Wang, C., Wang, L., Xu, Y., Qi, W., Sun, D., 2021. Rapid diagnosis of agricultural soil health: A novel soil health index based on natural soil productivity and human management. J. Environ. Manage. 277, 111402. https://doi.org/10.1016/j.jenvman.2020.111402
Liniger, H., Harari, N., van Lynden, G., Fleiner, R., de Leeuw, J., Bai, Z., Critchley, W., 2019. Achieving land degradation neutrality: The role of SLM knowledge in evidence-based decision-making. Environ. Sci. Policy 94, 123–134. https://doi.org/10.1016/j.envsci.2019.01.001
Mariathasan, V., Bezuidenhoudt, E., Olympio, K.R., 2019. Evaluation of Earth Observation Solutions for Namibia’s SDG Monitoring System. Remote Sens. 11, 1612. https://doi.org/10.3390/rs11131612
Mazzetti, P., Nativi, S., Santoro, M., Giuliani, G., Rodila, D., Folino, A., Caruso, S., Aracri, G. and Lehmann, A., 2022. Knowledge formalization for Earth Science informed decision-making: The GEOEssential Knowledge Base. Environmental Science & Policy, 131, pp.93-104. https://doi.org/10.1016/j.envsci.2021.12.023
Meyer, D. & Riechert, M. Open source QGIS toolkit for the Advanced Research WRF modelling system. Environmental Modelling & Software 112, 166–178 (2019). https://doi.org/10.1016/j.envsoft.2018.10.018
Moussa, S., El Brirchi, E.H. and Alami, O.B., 2022. Monitoring Land Productivity Trends in Souss-Massa Region Using Landsat Time Series Data to Support SDG Target 15.3. In Geospatial Intelligence (pp. 119-129). Springer, Cham. https://doi.org/10.1007/978-3-030-80458-9_9
Ogorodnikov, S.S., 2021, March. Land Degradation Neutrality in the Tula region. In IOP Conference Series: Earth and Environmental Science (Vol. 723, No. 4, p. 042053). IOP Publishing. doi:10.1088/1755-1315/723/4/042053
Prakash, M., Ramage, S., Kavvada, A., Goodman, S., 2020. Open Earth Observations for Sustainable Urban Development. Remote Sens. 12, 1646. https://doi.org/10.3390/rs12101646
Philip, E., 2021. Coupling Sustainable Development Goal 11.3. 1 with current planning tools: city of Hamilton, Canada. Hydrological Sciences Journal, 66(7), pp.1124-1131. https://doi.org/10.1080/02626667.2021.1918340
Reith, J., Ghazaryan, G., Muthoni, F. and Dubovyk, O., 2021. Assessment of Land Degradation in Semiarid Tanzania—Using Multiscale Remote Sensing Datasets to Support Sustainable Development Goal 15.3. Remote Sensing, 13(9), p.1754. https://doi.org/10.3390/rs13091754
Rowe, H.I., Gruber, D. and Fastiggi, M., 2021. Where to start? A new citizen science, remote sensing approach to map recreational disturbance and other degraded areas for restoration planning. Restoration Ecology, 29(6), p.e13454. https://doi.org/10.1111/rec.13454
Schiavina, M., Melchiorri, M., Freire, S., Florio, P., Ehrlich, D., Tommasi, P., Pesaresi, M. and Kemper, T., 2022. Land use efficiency of functional urban areas: Global pattern and evolution of development trajectories. Habitat International, 123, p.102543. https://doi.org/10.1016/j.habitatint.2022.102543
Sims, N. C. et al. Developing good practice guidance for estimating land degradation in the context of the United Nations Sustainable Development Goals. Environmental Science & Policy 92, 349–355 (2019). https://doi.org/10.1016/j.envsci.2018.10.014
Teich, I., Gonzalez Roglich, M., Corso, M.L., García, C.L., 2019. Combining Earth Observations, Cloud Computing, and Expert Knowledge to Inform National Level Degradation Assessments in Support of the 2030 Development Agenda. Remote Sens. 11, 2918. https://doi.org/10.3390/rs11242918
Timm Hoffman, M., Skowno, A., Bell, W. & Mashele, S. Long-term changes in land use, land cover and vegetation in the Karoo drylands of South Africa: implications for degradation monitoring. African Journal of Range & Forage Science 35, 209–221 (2018). https://doi.org/10.2989/10220119.2018.1516237
Trifonova, T.A., Mishchenko, N.V., Shutov, P.S. et al. Estimation of the Dynamics of Production Processes in Landscapes of the South Taiga Subzone of the Eastern European Plain by Remote Sensing Data. Moscow Univ. Soil Sci. Bull. 76, 11–18 (2021). https://doi.org/10.3103/S0147687421010063
Venter, Z.S., Scott, S.L., Desmet, P.G., Hoffman, M.T., 2020. Application of Landsat-derived vegetation trends over South Africa: Potential for monitoring land degradation and restoration. Ecol. Indic. 113, 106206. https://doi.org/10.1016/j.ecolind.2020.106206
von Maltitz, G.P., Gambiza, J., Kellner, K., Rambau, T., Lindeque, L., Kgope, B., 2019. Experiences from the South African land degradation neutrality target setting process. Environ. Sci. Policy 101, 54–62. https://doi.org/10.1016/j.envsci.2019.07.003

Academic dissertations¶

Mahlaba, B., 2022. The assessment of degradation state in Ecological Infrastructure and prioritisation for rehabilitation and drought mitigation in the Tsitsa River Catchment (Masters dissertation, Rhodes University).
Owuor, G.O., 2021. Monitoring Land Degradation Neutrality using Geospatial Techniques in Support of Sustainable Land Management: A Case Study of Narok County (Doctoral dissertation, University of Nairobi).

Other resources¶

Print documentation from the Trends.Earth project (including fact sheets, reports, and other materials) is listed below.