Articles | Volume 16, issue 11
https://doi.org/10.5194/essd-16-5375-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-5375-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
26 Nov 2024
Data description article |
| 26 Nov 2024
| 26 Nov 2024
Mapping rangeland health indicators in eastern Africa from 2000 to 2022
Instituto de Estadística, Facultad de Ciencias Económicas y Administrativas, Universidad Austral de Chile, Valdivia, Chile
School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, NY, USA
Steven W. Wilcox
Department of Applied Economics, Utah State University, Logan, UT, USA
Patrick E. Clark
Northwest Watershed Research Center, USDA Agricultural Research Service, Boise, ID, USA
Francesco P. Fava
Department of Environmental Science and Policy, Università Degli Studi Di Milano, Milan, Italy
Nathaniel D. Jensen
The Global Academy of Agriculture and Food Systems, University of Edinburgh, Edinburgh, Scotland
Njoki Kahiu
Department of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, USA
Chuan Liao
Department of Global Development, Cornell University, Ithaca, NY, USA
Benjamin Porter
Forest Ecosystem Monitoring Cooperative, University of Vermont, Burlington, VT, USA
School of Integrative Plant Science, Soil and Crop Sciences Section, Cornell University, Ithaca, NY, USA
Christopher B. Barrett
Charles H. Dyson School of Applied Economics and Management, Cornell University, Ithaca, NY, USA
Jeb E. Brooks School of Public Policy, Cornell University, Ithaca, NY, USA
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Cited articles
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A., and Hegewisch, K. C.: TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015, Sci. Data, 5, 1–12, https://doi.org/10.1038/sdata.2017.191, 2018.
Adams, E. C., Parache, H. B., Cherrington, E., Ellenburg, W. L., Mishra, V., Lucey, R., and Nakalembe, C.: Limitations of remote sensing in assessing vegetation damage due to the 2019–2021 desert locust upsurge, Front. Climate, 3, 714273, https://doi.org/10.3389/fclim.2021.714273, 2021.
AghaKouchak, A., Farahmand, A., Melton, F. S., Teixeira, J., Anderson, M. C., Wardlow, B. D., and Hain, C. R.: Remote sensing of drought: Progress, challenges and opportunities, Rev. Geophys., 53, 452-480, https://doi.org/10.1002/2014RG000456, 2015.
Allred, B. W., Bestelmeyer, B. T., Boyd, C. S., Brown, C., Davies, K. W., Duniway, M. C., Ellsworth, L. M., Erickson, T. A., Fuhlendorf, S. D., Griffiths, T. V., Jansen, V., Jones, M. O., Karl, J., Knight, A., Maestas, J. D., Maynard, J. J., McCord, S. E., Naugle, D. E., Starns, H. D., Twidwell, D., and Uden, D. R.: Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty, Methods Ecol. Evol., 12, 841–849, https://doi.org/10.1111/2041-210X.13564, 2021.
Allred, B. W., Creutzburg, M. K., Carlson, J. C., Cole, C. J., Dovichin, C. M., Duniway, M. C., Jones, M. O., Maestas, J. D., Naugle, D. E., Nauman, T. W., Okin, G. S., Reeves, M. C., Rigge, M., Savage, S. L., Twidwell, D., Uden, D. R., and Zhou, B.: Guiding principles for using satellite-derived maps in rangeland management, Rangelands, 44, 78–86, https://doi.org/10.1016/j.rala.2021.09.004, 2022.
Angassa, A.: Effects of grazing intensity and bush encroachment on herbaceous species and rangeland condition in southern Ethiopia, Land. Degrad. Dev., 25, 438–451, https://doi.org/10.1002/ldr.2160, 2014.
Angassa, A. and Oba, G.: Herder perceptions on impacts of range enclosures, crop farming, fire ban and bush encroachment on the rangelands of Borana, Southern Ethiopia, Hum. Ecol., 36, 201–215, https://doi.org/10.1007/s10745-007-9156-z, 2008.
Barbier, E. B. and Hochard, J. P.: Land degradation and poverty, Nat. Sustain., 1, 623–631, https://doi.org/10.1038/s41893-018-0155-4, 2018.
Beal, T., Gardner, C. D., Herrero, M., Iannotti, L. L., Merbold, L., Nordhagen, S., and Mottet, A.: Friend or foe? The role of animal-source foods in healthy and environmentally sustainable diets, J. Nutr., 153, 409–425, https://doi.org/10.1016/j.tjnut.2022.10.016, 2023.
Belgiu, M., and Drăguţ, L.: Random forest in remote sensing: A review of applications and future directions, ISPRS J. Photogramm., 114, 24–31, https://doi.org/10.1016/j.isprsjprs.2016.01.011, 2016.
Bestelmeyer, B. T., Ash, A., Brown, J. R., Densambuu, B., Fernández-Giménez, M., Johanson, J., Levi, M., Lopez, D., Peinetti, R., Rumpff, L., and Shaver, P.: State and transition models: theory, applications, and challenges. Rangeland systems: Processes, management and challenges, in: Rangeland Systems, Springer Series on Environmental Management, edited by: Briske, D., Springer, Cham, 303–345, https://doi.org/10.1007/978-3-319-46709-2_9, 2017.
Blackwell, P. J.: East Africa's Pastoralist Emergency: is climate change the straw that breaks the camel's back?, Third World Q., 31, 1321–1338, https://doi.org/10.1080/01436597.2010.541085, 2010.
Blanco, P. D., del Valle, H. F., Bouza, P. J., Metternicht, G. I., and Hardtke, L. A.: Ecological site classification of semiarid rangelands: Synergistic use of Landsat and Hyperion imagery, Int. J. Appl. Earth Obs. Geoinf., 29, 11–21, https://doi.org/10.1016/j.jag.2013.12.011, 2014.
Brandt, M., Tucker, C. J., Kariryaa, A., Rasmussen, K., Abel, C., Small, J., Chave, J., Rasmussen, L. V., Hiernaux, P., Diouf, A. A., Kergoat, L., Mertz, O., Igel, C., Gieseke, F., Schöning, J., Li, S., Melocik, K., Meyer, J., Sinno, S., Romero, E., Glennie, E., Montagu, A., Dendoncker, M., and Fensholt, R.: An unexpectedly large count of trees in the West African Sahara and Sahel, Nature, 587, 78–82, https://doi.org/10.1038/s41586-020-2824-5, 2020.
Browning, D. M., Maynard, J. J., Karl, J. W., and Peters, D. C.: Breaks in MODIS time series portend vegetation change: verification using long-term data in an arid grassland ecosystem, Ecol. Appl., 27, 1677–1693, https://doi.org/10.1002/eap.1561, 2017.
Browning, D. M., Snyder, K. A., and Herrick, J. E.: Plant phenology: Taking the pulse of rangelands, Rangelands, 41, 129–134, https://doi.org/10.1016/j.rala.2019.02.001, 2019.
Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N. E., Linlin, L., and Tarko, A.: Copernicus Global Land Service: Land Cover 100m: Version 3 Globe 2015–2019: Product User Manual, Geneve, Switzerland, 22 pp., Zenodo, https://doi.org/10.5281/zenodo.3938963, 2020.
Carlotto, M. J.: Effect of errors in ground truth on classification accuracy, Int. J. Remote Sens., 30, 4831–4849, https://doi.org/10.1080/01431160802672864, 2009.
Cheng, Y., Vrieling, A., Fava, F., Meroni, M., Marshall, M., and Gachoki, S.: Phenology of short vegetation cycles in a Kenyan rangeland from PlanetScope and Sentinel-2, Remote Sens. Environ., 248, 112004, https://doi.org/10.1016/j.rse.2020.112004, 2020.
Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger, J. C., Skakun, S. V., and Justice, C.: The Harmonized Landsat and Sentinel-2 surface reflectance data set, Remote Sens. Environ., 219, 145–161, https://doi.org/10.1016/j.rse.2018.09.002, 2018.
Coffer, M. M., Schaeffer, B. A., Zimmerman, R. C., Hill, V., Li, J., Islam, K. A., and Whitman, P. J.: Performance across WorldView-2 and RapidEye for reproducible seagrass mapping, Remote Sens. Environ., 250, 112036, https://doi.org/10.1016/j.rse.2020.112036, 2020.
Cooley, S. W., Smith, L. C., Stepan, L., and Mascaro, J.: Tracking dynamic northern surface water changes with high-frequency planet CubeSat imagery, Remote Sens., 9, 1306, https://doi.org/10.3390/rs9121306, 2017.
Drusch, M., Del Bello, U., Carlier, S., Colin, O., Fernandez, V., Gascon, F., and Bargellini, P.: Sentinel-2: ESA's optical high-resolution mission for GMES operational services, Remote Sens. Environ., 120, 25–36, 2012.
Dube, T., Shoko, C., Sibanda, M., Madileng, P., Maluleke, X. G., Mokwatedi, V. R., Tibane, L., and Tshebesebe, T.: Remote sensing of invasive Lantana camara (verbenaceae) in semiarid savanna rangeland ecosystems of south africa, Rangeland Ecol. Manag., 73, 411–419, https://doi.org/10.1016/j.rama.2020.01.003, 2020.
Ellis, E. C., Wang, H., Xiao, H. S., Peng, K., Liu, X. P., Li, S. C., Ouyang, H., Cheng, X., and Yang, L. Z.: Measuring long-term ecological changes in densely populated landscapes using current and historical high resolution imagery, Remote Sens. Environ., 100, 457–473, https://doi.org/10.1016/j.rse.2005.11.002, 2006.
Elmes, A., Alemohammad, H., Avery, R., Caylor, K., Eastman, J. R., Fishgold, L., Friedl, M. A., Jain, M., Kohli, D., Laso Bayas, J. C., Lunga, D., McCarty, J. L., Pontius, R. G., Reinmann, A. B., Rogan, J., Song, L., Stoynova, H., Ye, S., Yi, Z. F., and Estes, L.: Accounting for training data error in machine learning applied to Earth observations, Remote Sens., 12, 1034, https://doi.org/10.3390/rs12061034, 2020.
Fava, F. and Vrieling, A.: Earth observation for drought risk financing in pastoral systems of sub-Saharan Africa, Curr. Opin. Env. Sust., 48, 44–52, https://doi.org/10.1016/j.cosust.2020.09.006, 2021.
Foga, S., Scaramuzza, P. L., Guo, S., Zhu, Z., Dilley Jr, R. D., Beckmann, T., Schmidt, G. L., Dwyer, J. L., Hughes, M. J., and Laue, B.: Cloud detection algorithm comparison and validation for operational Landsat data products, Remote Sens. Environ., 194, 379–390, https://doi.org/10.1016/j.rse.2017.03.026, 2017.
Foody, G. M.: Status of land cover classification accuracy assessment, Remote Sens. Environ., 80, 185–201, https://doi.org/10.1016/S0034-4257(01)00295-4, 2002.
Foody, G. M.: The impact of imperfect ground reference data on the accuracy of land cover change estimation, Int. J. Remote Sens., 30, 3275–3281, https://doi.org/10.1080/01431160902755346, 2009.
Franks, S., Storey, J., and Rengarajan, R.: The new Landsat collection-2 digital elevation model, Remote Sens., 12, 3909, https://doi.org/10.3390/rs12233909, 2020.
Fuhlendorf, S. D., Engle, D. M., Elmore, R. D., Limb, R. F., and Bidwell, T. G.: Conservation of pattern and process: developing an alternative paradigm of rangeland management, Rangeland Ecol. Manag., 65, 579–589, https://doi.org/10.2111/REM-D-11-00109.1, 2012.
Gao, B. C.: NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space, Remote Sens. Environ., 58, 257–266, https://doi.org/10.1016/S0034-4257(96)00067-3, 1996.
Ghafari, S., Ghorbani, A., Moameri, M., Mostafazadeh, R., and Bidarlord, M.: Composition and structure of species along altitude gradient in Moghan-Sabalan rangelands, Iran, J. Mt. Sci., 15, 1209–1228, https://doi.org/10.1007/s11629-017-4820-2, 2018.
Giuliani, G., Mazzetti, P., Santoro, M., Nativi, S., Van Bemmelen, J., Colangeli, G., and Lehmann, A.: Knowledge generation using satellite earth observations to support sustainable development goals (SDG): A use case on Land degradation, Int. J. Appl. Earth Obs., 88, 102068, https://doi.org/10.1016/j.jag.2020.102068, 2020.
Gómez, C., White, J. C., and Wulder, M. A.: Optical remotely sensed time series data for land cover classification: A review, ISPRS J. Photogramm., 116, 55–72, https://doi.org/10.1016/j.isprsjprs.2016.03.008, 2016.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R.: Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031, 2017.
Graetz, R. D., Carneggie, D. M., Hacker, R., Lendon, C., and Wilcox, D. G.: A quantitative evaluation of Landsat imagery of Australian rangelands, Rangel. J., 1, 53–59, https://doi.org/10.1071/RJ9760053, 1976.
Hansen, M. C., Townshend, J. R., DeFries, R. S., and Carroll, M.: Estimation of tree cover using MODIS data at global, continental and regional/local scales, Int. J. Remote Sens., 26, 4359–4380, https://doi.org/10.1080/01431160500113435, 2005.
Hill, M. J. and Guerschman, J. P.: The MODIS Global Vegetation Fractional Cover Product 2001–2018: Characteristics of Vegetation Fractional Cover in Grasslands and Savanna Woodlands, Remote Sens., 12, 406, https://doi.org/10.3390/rs12030406, 2020.
Hill, M. J. and Guerschman, J. P.: Global trends in vegetation fractional cover: Hotspots for change in bare soil and non-photosynthetic vegetation, Agr. Ecosyst. Environ., 324, 107719, https://doi.org/10.1016/j.agee.2021.107719, 2022.
Hoffman, T. and Vogel, C.: Climate change impacts on African rangelands, Rangelands, 30, 12–17, https://doi.org/10.2111/1551-501X(2008)30[12:CCIOAR]2.0.CO;2, 2008.
ILRI, IUCN, FAO, WWF, UNEP and ILC.: Rangelands Atlas. Nairobi Kenya: ILRI, ISBN 978-1-904722-67-0, 2021.
IPCC: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Pörtner, H.-O., Roberts, D. C., Tignor, M., Poloczanska, E. S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., Löschke, S., Möller, V., Okem, A., and Rama, B., Cambridge University Press, Cambridge University Press, Cambridge, UK and New York, NY, USA, 3056 pp., https://doi.org/10.1017/9781009325844, 2022.
Jarvis, A., Reuter, H. I., Nelson, A., and Guevara, E.: Hole-filled seamless SRTM data Version 4, available from the CGIAR-CSI SRTM 90m, International Centre for Tropical Agriculture CIAT, https://srtm.csi.cgiar.org (last access: 10 November 2023), 2008.
Jones, M. O., Allred, B. W., Naugle, D. E., Maestas, J. D., Donnelly, P., Metz, L. J., Karl, J., Smith, R., Bestelmeyer, B., Boyd, C., Kerby, J. D., and McIver, J. D.: Innovation in rangeland monitoring: annual, 30 m, plant functional type percent cover maps for US rangelands, 1984–2017, Ecosphere, 9, e02430, https://doi.org/10.1002/ecs2.2430, 2018.
Jones, M. O., Naugle, D. E., Twidwell, D., Uden, D. R., Maestas, J. D., and Allred, B. W.: Beyond inventories: Emergence of a new era in rangeland monitoring, Rangeland Ecol. Manag., 73, 577–583, https://doi.org/10.1016/j.rama.2020.06.009, 2020.
Li, G., Han, W., Dong, Y., Zhai, X., Huang, S., Ma, W., Cui, X., and Wang, Y.: Multi-Year Crop Type Mapping Using Sentinel-2 Imagery and Deep Semantic Segmentation Algorithm in the Hetao Irrigation District in China, Remote Sens., 15, 875, https://doi.org/10.3390/rs15040875, 2023.
Liao, C. and Clark, P. E.: Rangeland vegetation diversity and transition pathways under indigenous pastoralist management regimes in southern Ethiopia, Agr. Ecosyst. Environ., 252, 105–113, https://doi.org/10.1016/j.agee.2017.10.009, 2018.
Liao, C. and Fei, D.: Pastoralist Adaptation Practices under Non-Governmental Development Interventions in Southern Ethiopia, Rangeland J., 39, 189–200, https://doi.org/10.1071/RJ16015, 2017.
Liao, C., Clark, P. E., and DeGloria, S. D.: Bush encroachment dynamics and rangeland management implications in southern Ethiopia, Ecol. Evol., 8, 11694–11703, https://doi.org/10.1002/ece3.4621, 2018.
Lin, C., Jin, Z., Mulla, D., Ghosh, R., Guan, K., Kumar, V., and Cai, Y.: Toward Large-Scale Mapping of Tree Crops with High-Resolution Satellite Imagery and Deep Learning Algorithms: A Case Study of Olive Orchards in Morocco, Remote Sens., 13, 1740, https://doi.org/10.3390/rs13091740, 2021.
Liu, H. Q. and Huete, A.: A feedback based modification of the NDVI to minimize canopy background and atmospheric noise, IEEE T. Geosci. Remote., 33, 457–465, https://doi.org/10.1109/TGRS.1995.8746027, 1995.
Loveland, T. R. and Dwyer, J. L.: Landsat: Building a strong future, Remote Sens. Environ., 122, 22–29, https://doi.org/10.1016/j.rse.2011.09.022, 2012.
Magurran, A. E., Baillie, S. R., Buckland, S. T., Dick, J. M., Elston, D. A., Scott, E. M., Smith, R. I., Somerfield, P. J., and Watt, A. D.: Long-term datasets in biodiversity research and monitoring: assessing change in ecological communities through time, Trends Ecol. Evol., 25, 574–582, https://doi.org/10.1016/j.tree.2010.06.016, 2010.
Markham, B. L., Storey, J. C., Williams, D. L., and Irons, J. R. Landsat sensor performance: history and current status, IEEE T. Geosci. Remote Sens., 42, 2691–2694, https://doi.org/10.1109/TGRS.2004.840720, 2004.
Matongera, T. N., Mutanga, O., Sibanda, M., and Odindi, J.: Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges, Remote Sens., 13, 2060, https://doi.org/10.3390/rs13112060, 2021.
McFeeters, S. K.: The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features, Int. J. Remote Sens., 17, 1425–1432, https://doi.org/10.1080/01431169608948714, 1996.
Müller, M.: Information retrieval for music and motion, Springer, Berlin, 313 pp., ISBN 978-3-540-74048-3, https://doi.org/10.1007/978-3-540-74048-3, 2007.
Mundia, C. N. and Aniya, M.: Analysis of land use/cover changes and urban expansion of Nairobi city using remote sensing and GIS, Int. J. Remote Sens., 26, 2831–2849, https://doi.org/10.1080/01431160500117865, 2005.
Nandintsetseg, B., Chang, J., Sen, O. L., Reyer, C. P., Kong, K., Yetemen, O., Ciais, P., and Davaadalai, J.: Future drought risk and adaptation of pastoralism in Eurasian rangelands, npj Clim. Atmos. Sci., 7, 82, https://doi.org/10.1038/s41612-024-00624-2, 2024.
Oba, G., Weladji, R. B., Lusigi, W. J., and Stenseth, N. C.: Scale-dependent effects of grazing on rangeland degradation in northern Kenya: a test of equilibrium and non-equilibrium hypotheses, Land Degrad. Dev., 14, 83–94, https://doi.org/10.1002/ldr.524, 2003.
Okal, H. A., Ngetich, F. K., and Okeyo, J. M.: Spatio-temporal characterization of droughts using selected indices in Upper Tana River watershed, Kenya, Sci. African, 7, e00275, https://doi.org/10.1016/j.sciaf.2020.e00275, 2020.
Ott, J. E., Kilkenny, F. F., Summers, D. D., and Thompson, T. W.: Long-term vegetation recovery and invasive annual suppression in native and introduced postfire seeding treatments, Rangeland Ecol. Manag., 72, 640–653, https://doi.org/10.1016/j.rama.2019.02.001, 2019.
Padial-Iglesias, M., Serra, P., Ninyerola, M., and Pons, X.: A Framework of Filtering Rules over Ground Truth Samples to Achieve Higher Accuracy in Land Cover Maps, Remote Sens., 13, 2662, https://doi.org/10.3390/rs13142662, 2021.
Pellant, M., Shaver, P. L., Pyke, D. A., Herrick, J. E., Lepak, N., Riegel, G., Kachergis, E., Newingham, B. A., Toledo, D., and Busby, F. E.: Interpreting Indicators of Rangeland Health, Version 5, Tech Ref 1734-6, U.S. Department of the Interior, Bureau of Land Management, National Operations Center, Denver, CO, https://pubs.er.usgs.gov/publication/70215720 (last access: 10 November 2023), 2020.
Pratt, D. J., Greenway, P. J., and Gwynne, M. D.: A classification of East African rangeland, with an appendix on terminology, J. Appl. Ecol., 3, 369–382, https://doi.org/10.2307/2401259, 1966.
Pricope, N. G., Husak, G., Lopez-Carr, D., Funk, C., and Michaelsen, J.: The climate-population nexus in the East African Horn: Emerging degradation trends in rangeland and pastoral livelihood zones, Global Environ. Chang., 23, 1525–1541, https://doi.org/10.1016/j.gloenvcha.2013.10.002, 2013.
Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., and Sorooshian, S.: A modified soil adjusted vegetation index, Remote Sens. Environ., 48, 119–126, https://doi.org/10.1016/0034-4257(94)90134-1, 1994.
Quintano, C., Fernández-Manso, A., Shimabukuro, Y. E., and Pereira, G.: Spectral unmixing, Int. J. Remote Sens., 33, 5307–5340, https://doi.org/10.1080/01431161.2012.661095, 2012.
Račič, M., Oštir, K., Zupanc, A., and Čehovin Zajc, L.: Multi-Year Time Series Transfer Learning: Application of Early Crop Classification, Remote Sens., 16, 270, https://doi.org/10.3390/rs16020270, 2024.
Reeves, M. C. and Baggett, L. S.: A remote sensing protocol for identifying rangelands with degraded productive capacity, Ecol. Indic., 43, 172–182, https://doi.org/10.1016/j.ecolind.2014.02.009, 2014.
Reid, R. S., Galvin, K. A., and Kruska, R. S.: Global significance of extensive grazing lands and pastoral societies: an introduction, in: Fragmentation in semi-arid and arid landscapes, edited by: Galvin, K. A., Reid, R. S, Behnke Jr., R. H., and Hobbs, N. T., Springer, Dordrecht, 1–24, https://doi.org/10.1007/978-1-4020-4906-4, 2008.
Rigge, M., Shi, H., Homer, C., Danielson, P., and Granneman, B.: Long-term trajectories of fractional component change in the Northern Great Basin, USA, Ecosphere, 10, e02762, https://doi.org/10.1002/ecs2.2762, 2019.
Rigge, M., Homer, C., Cleeves, L., Meyer, D. K., Bunde, B., Shi, H., Xian, G., Schell, S., and Bobo, M.: Quantifying western US rangelands as fractional components with multi-resolution remote sensing and in situ data, Remote Sens., 12, 412, https://doi.org/10.3390/rs12030412, 2020.
Rigge, M., Homer, C., Shi, H., Meyer, D., Bunde, B., Granneman, B., Postma, K., Danielson, P., Case, A., and Xian, G.: Rangeland fractional components across the Western United States from 1985 to 2018, Remote Sens., 13, 813, https://doi.org/10.3390/rs13040813, 2021.
Rogan, J., Miller, J., Stow, D., Franklin, J., Levien, L., and Fischer, C.: Land-cover change monitoring with classification trees using Landsat TM and ancillary data, Photogramm. Eng. Rem. S., 69, 793–804, https://doi.org/10.14358/PERS.69.7.793, 2003.
Roques, K. G., O’Connor, T. G., and Watkinson, A. R.: Dynamics of shrub encroachment in an African savanna: relative influences of fire, herbivory, rainfall and density dependence, J. Appl. Ecol., 38, 268–280, https://doi.org/10.1046/j.1365-2664.2001.00567.x, 2001.
Roy, D. P., Kovalskyy, V., Zhang, H. K., Vermote, E. F., Yan, L., Kumar, S. S., and Egorov, A.: Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity, Remote Sens. Environ., 185, 57–70, https://doi.org/10.1016/j.rse.2015.12.024, 2016.
Safanelli, J. L., Poppiel, R. R., Ruiz, L. F. C., Bonfatti, B. R., Oliveira Mello, F. A. D., Rizzo, R., and Demattê, J. A.: Terrain Analysis in Google Earth Engine: A Method Adapted for High-Performance Global-Scale Analysis, ISPRS Int. Geo-Inf., 9, 400, https://doi.org/10.3390/ijgi9060400, 2020.
Sankey, T. T., Leonard, J. M., and Moore, M. M.: Unmanned aerial vehicle-based rangeland monitoring: examining a century of vegetation changes, Rangeland Ecol. Manag., 72, 858–863, https://doi.org/10.1016/j.rama.2019.04.002, 2019.
Sankey, T. T., Leonard, J., Moore, M. M., Sankey, J. B., and Belmonte, A.: Carbon and ecohydrological priorities in managing woody encroachment: UAV perspective 63 years after a control treatment, Environ. Res. Lett., 16, 124053, https://doi.org/10.1088/1748-9326/ac3796, 2021.
Sayre, N. F., McAllister, R. R., Bestelmeyer, B. T., Moritz, M., and Turner, M. D.: Earth stewardship of rangelands: coping with ecological, economic, and political marginality, Front. Ecol. Environ., 11, 348–354, https://doi.org/10.1890/120333, 2013.
Schmidt, G. L., Jenkerson, C., Masek, J. G., Vermote, E., and Gao, F.: Landsat ecosystem disturbance adaptive processing system (LEDAPS) algorithm description, 17 pp., https://pubs.usgs.gov/of/2013/1057/ (last access: 10 November 2023), 2013.
Seidel, M. and Hlawitschka, M.: An R-Based function for modeling of end member compositions, Math. Geosci., 47, 995–1007, https://doi.org/10.1007/s11004-015-9609-7, 2015.
Sexton, J. O., Song, X. P., Feng, M., Noojipady, P., Anand, A., Huang, C., Kim, D. H., Collins, K. M., Channan, S., DiMiceli, C., and Townshend, J. R.: Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error, Int. J. Digit. Earth, 6, 427–448, https://doi.org/10.1080/17538947.2013.786146, 2013.
Soto, G. E., Wilcox, S., Clark, P. E., Fava, F. P., Jensen, N. M., Kahiu, N., Liao, C., Porter, B., Sun, Y., and Barrett, C. B.: Mapping Rangeland Health Indicators in East Africa from 2000 to 2022, Zenodo [data set], https://doi.org/10.5281/zenodo.7106166, 2023.
Steele, C. M., Bestelmeyer, B. T., Burkett, L. M., Smith, P. L., and Yanoff, S.: Spatially explicit representation of state-and-transition models, Rangeland Ecol. Manage., 65, 213–222, https://doi.org/10.2111/REM-D-11-00047.1, 2012.
Sudmanns, M., Tiede, D., Lang, S., Bergstedt, H., Trost, G., Augustin, H., Baraldi, A., and Blaschke, T.: Big Earth data: disruptive changes in Earth observation data management and analysis?, Int. J. Digit. Earth, 13, 832–850, https://doi.org/10.1080/17538947.2019.1585976, 2020.
Thenkabail, P. S. (Ed.): Remotely sensed data characterization, classification, and accuracies, CRC press, https://doi.org/10.1201/b19294, 2015.
Treitz, P. and Rogan, J.: Remote sensing for mapping and monitoring land-cover and land-use change-an introduction, Prog. Plann., 61, 269–279, https://doi.org/10.1016/S0305-9006(03)00066-7, 2004.
U.S. Geological Survey (USGS): Landsat 8 Data Users Handbook, Sioux falls, S.D., 114 pp., https://www.usgs.gov/media/files/landsat-8-data-users-handbook (last access: 10 November 2023), 2019.
U.S. Geological Survey (USGS): Landsat Collection 2 (ver. 1.1, January 15, 2021): U.S. Geological Survey Fact Sheet 2021–3002, Sioux falls, S.D., 4 pp., https://doi.org/10.3133/fs20213002, 2021.
U.S. Geological Survey (USGS): Landsat 8-9 Collection 2 (C2) Level 2 Science Product (L2SP) Guide. Version 5.0., Sioux falls, S.D., 43 pp., https://www.usgs.gov/media/files/landsat-8-9-collection-2-level-2-science-product-guide (last access: 10 November 2023), 2022.
Vetter, S.: Rangelands at equilibrium and non-equilibrium: recent developments in the debate, J. Arid Environ., 62, 321–341, https://doi.org/10.1016/j.jaridenv.2004.11.015, 2005.
Weikmann, G., Paris, C., and Bruzzone, L.: Multi-year crop type mapping using pre-trained deep long-short term memory and Sentinel 2 image time series, in: Image and Signal Processing for Remote Sensing XXVII, Vol. 11862, SPIE, 171–181, https://doi.org/10.1117/12.2600559, 2021.
Weltje, G. J.: End-member modeling of compositional data: Numerical-statistical algorithms for solving the explicit mixing problem, Math. Geol., 29, 503–549, https://doi.org/10.1007/BF02775085, 1997.
Weng, Q.: Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling, J. Environ. Manag., 64, 273–284, https://doi.org/10.1006/jema.2001.0509, 2002.
Williams, D. L., Goward, S., and Arvidson, T.: Landsat: Yesterday, today and tomorrow, Photogramm. Eng. Rem. S., 72, 1171–1178, https://doi.org/10.14358/PERS.72.10.1171, 2006.
Wulder, M. A., Masek, J. G., Cohen, W. B., Loveland, T. R., and Woodcock, C. E.: Opening the archive: How free data has enabled the science and monitoring promise of Landsat, Remote Sens. Environ., 122, 2–10, https://doi.org/10.1016/j.rse.2012.01.010, 2012.
Wulder, M. A., Loveland, T. R., Roy, D. P., Crawford, C. J., Masek, J. G., Woodcock, C. E., Alle, R. G., Anderson, M. C., Belward, A. S., Cohen, W. B., Dwyer, J., Erb, A., Gao, F., Griffiths, P., Helder, D., Hermosilla, T., Hipple, J. D., Hostert, P., Hughes, M. J., Huntington, J., Johnson, D. M., Kennedy, R., Kilic, A., Li, Z., Lymburner, L., McCorkel, J., Pahlevan, N., Scambos, T. A., Schaaf, C., Schott, J. R., Sheng, Y., Storey, J., Vermotev, E., Vogelmann, JJ., White, J. C., Wynne, R. H., and Zhu, Z.: Current status of Landsat program, science, and applications, Remote Sens. Environ., 225, 127–147, https://doi.org/10.1016/j.rse.2019.02.015, 2019.
Wulder, M. A., Roy, D. P., Radeloff, V. C., Loveland, T. R., Anderson, M. C., Johnson, D. M., Healey, S., Zhu, Z., Scambos, T. A., Pahlevan, N., Hansen, M., Gorelick, N., Crawford, C. J., Masek, J. G., Hermosilla, T., White, J. C., Belward, A. S., Schaaf, C., Woodcock, C. E., Huntington, J. L., Lymburner, L., Hostert, P., Gao, F., Lyapustin, A., Pekel, J. F., Strobl, P., and Cook, B. D.: Fifty years of Landsat science and impacts, Remote Sens. Environ., 280, 113195, https://doi.org/10.1016/j.rse.2022.113195, 2022.
Wynants, M., Kelly, C., Mtei, K., Munishi, L., Patrick, A., Rabinovich, A., Nasseri, M., Gilvear, D., Roberts, N., Boeckx, P., Wilson, G., Blake, W. H., and Ndakidemi, P.: Drivers of increased soil erosion in East Africa's agro-pastoral systems: changing interactions between the social, economic and natural domains, Reg. Environ. Change, 19, 1909–1921, https://doi.org/10.1007/s10113-019-01520-9, 2019.
Yang, X. and Lo, C. P.: Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area, Int. J. Remote Sens., 23, 1775–1798, https://doi.org/10.1080/01431160110075802, 2002.
Zabel, F., Delzeit, R., Schneider, J. M., Seppelt, R., Mauser, W., and Václavík, T.: Global impacts of future cropland expansion and intensification on agricultural markets and biodiversity, Nat. Comm., 10, 1–10, https://doi.org/10.1038/s41467-019-10775-z, 2019.
Zhou, B., Okin, G. S., and Zhang, J.: Leveraging Google Earth Engine (GEE) and machine learning algorithms to incorporate in situ measurement from different times for rangelands monitoring, Remote Sens. Environ., 236, 111521, https://doi.org/10.1016/j.rse.2019.111521, 2020.
Short summary
This paper uses machine learning and linear unmixing to produce rangeland health indicators: Landsat time series of land cover classes and vegetation fractional cover of photosynthetic vegetation, non-photosynthetic vegetation, and bare ground in arid and semi-arid Kenya, Ethiopia, and Somalia. This represents the first multi-decadal Landsat-resolution dataset specifically designed for mapping and monitoring rangeland health in the arid and semi-arid rangelands of this portion of eastern Africa.
This paper uses machine learning and linear unmixing to produce rangeland health indicators:...
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