Articles | Volume 13, issue 8
https://doi.org/10.5194/essd-13-3767-2021
© Author(s) 2021. 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-13-3767-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An update and beyond: key landscapes for conservation land cover and change monitoring, thematic and validation datasets for the African, Caribbean and Pacific regions
Zoltan Szantoi
CORRESPONDING AUTHOR
European Commission, Joint Research Centre, 21027 Ispra, Italy
Department of Geography and Environmental Studies, Stellenbosch
University, Stellenbosch 7602, South Africa
Andreas Brink
European Commission, Joint Research Centre, 21027 Ispra, Italy
Andrea Lupi
European Commission, Joint Research Centre, 21027 Ispra, Italy
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Cited articles
Brink, A., Eva, H., and Bodart, C.: Is Africa Losing Its Natural Vegetation?
Monitoring Trajectories of Land-Cover Change Using Landsat Imagery, in:
Remote Sensing of Land Use and Land Cover, Principles and Applications, vol.
20120991, edited by: Giri, C., CRC Press, Boca Raton, Florida, 369–376,
https://doi.org/10.1201/b11964-28, 2012.
Brink, A. B. and Eva, H. D.: Monitoring 25 years of land cover change
dynamics in Africa: A sample based remote sensing approach, 29, 501–512,
https://doi.org/10.1016/j.apgeog.2008.10.004, 2009.
Schulte to Büuhne, H., Wegmann, M., Durant, S. M., Ransom, C., de Ornellas, P., Grange, S., Beatty, H., and Pettorelli, N.: Protection status and national socio-economic context shape land conversion in and around a key
transboundary protected area complex in West Africa, 3,
https://doi.org/10.1002/rse2.47, 2017.
Di Gregorio, A.: Land cover classification system: classification concepts
and user manual: LCCS, Software version 2., Food and Agriculture
Organization of the United Nations, Rome, 190 pp., 2005.
Di Minin, E., Slotow, R., Hunter, L. T. B., Montesino Pouzols, F., Toivonen,
T., Verburg, P. H., Leader-Williams, N., Petracca, L., and Moilanen, A.:
Global priorities for national carnivore conservation under land use change, Sci. Rep., 6, 23814, https://doi.org/10.1038/srep23814, 2016.
European Commission: Science for the AU-EU Partnership building knowledge
for sustainable development, Joint Research Centre, 2018.
Foody, G. M.: Valuing map validation: The need for rigorous land cover map
accuracy assessment in economic valuations of ecosystem services, Ecol Econom., 111,
23–28, https://doi.org/10.1016/j.ecolecon.2015.01.003, 2015.
Fritz, S., See, L., Perger, C., McCallum, I., Schill, C., Schepaschenko, D.,
Duerauer, M., Karner, M., Dresel, C., Laso-Bayas, J.-C., Lesiv, M., Moorthy,
I., Salk, C. F., Danylo, O., Sturn, T., Albrecht, F., You, L., Kraxner, F.,
and Obersteiner, M.: A global dataset of crowdsourced land cover and land
use reference data, Sci. Data, 4, 1–8, https://doi.org/10.1038/sdata.2017.75, 2017.
Gallaun, H., Steinegger, M., Wack, R., Schardt, M., Kornberger, B., and
Schmitt, U.: Remote Sensing Based Two-Stage Sampling for Accuracy Assessment
and Area Estimation of Land Cover Changes, Remote Sens., 7, 11992–12008,
https://doi.org/10.3390/rs70911992, 2015.
Geist, H. J. and Lambin, E. F.: Proximate Causes and Underlying Driving
Forces of Tropical Deforestation, BioScience, 52, 143–150,
https://doi.org/10.1641/0006-3568(2002)052[0143:PCAUDF]2.0.CO;2, 2002.
Grafius, D. R., Corstanje, R., Warren, P. H., Evans, K. L., Hancock, S., and
Harris, J. A.: The impact of land use/land cover scale on modelling urban
ecosystem services, Landscape Ecol., 31, 1509–1522,
https://doi.org/10.1007/s10980-015-0337-7, 2016.
Güneralp, B., Lwasa, S., Masundire, H., Parnell, S., and Seto, K. C.:
Urbanization in Africa: challenges and opportunities for conservation,
Environ. Res. Lett., 13, 015002, https://doi.org/10.1088/1748-9326/aa94fe, 2017.
Haifeng, H., Jianrong, K., Xiaoke, Z., and Kaiyuan, D.: Atmospheric
correction of SPOT satellite images based on radiation transfer model,
International Conference on Computer Application and System Modeling (ICCASM
2010), https://doi.org/10.1109/ICCASM.2010.5619149, 2010.
Hugo, G.: Patterns and Trends of Urbanization and Urban Growth in Asia, in:
Internal Migration, Urbanization and Poverty in Asia: Dynamics and
Interrelationships, edited by: Jayanthakumaran, K., Verma, R., Wan, G., and
Wilson, E., Springer, Singapore, 13–45,
https://doi.org/10.1007/978-981-13-1537-4_2, 2019.
Ide, T., Palmer, L. R., and Barnett, J.: Environmental peacebuilding from
below: customary approaches in Timor-Leste, Int. Aff., 97,
103–117, https://doi.org/10.1093/ia/iiaa059, 2021.
Kebede, E., Kagochi, J., and Jolly, C. M.: Energy consumption and economic
development in Sub-Sahara Africa, Energ. Econom., 32, 532–537,
https://doi.org/10.1016/j.eneco.2010.02.003, 2010.
Lambin, E. F. and Meyfroidt, P.: Inaugural Article: Global land use change,
economic globalization, and the looming land scarcity, P. Natl. Acad. Sci., 108, 3465–3472, https://doi.org/10.1073/pnas.1100480108, 2011.
Lang, M. and Tychon, B.: Copernicus Global Land Component Product and
Service Detailed Technical Requirements Appendix 1 of Technical Annex,
https://land.copernicus.eu/global/documents/applicable (last access: 1 August 2021), 2015.
Lu, D., Mausel, P., Brondizio, E., and Moran, E.: Change detection
techniques, Int. J. Remote Sens., 25, 2365–2401, https://doi.org/10.1080/0143116031000139863, 2004.
MacKinnon, J., Aveling, C., Olivier, R., Murray, M., Paolini, C., European
Commission, and Directorate-General for International Cooperation and
Development: Larger than elephants: inputs for an EU strategic approach to
wildlife conservation in Africa: synthesis, European Commission, EU publication MN-02-15-558-EN-C, https://doi.org/10.2841/909032, 2015.
Marshall, M., Norton-Griffiths, M., Herr, H., Lamprey, R., Sheffield, J., Vagen, T., and Okotto-Okotto, J.: Continuous and consistent land use/cover change estimates using socio-ecological data, Earth Syst. Dynam., 8, 55–73, https://doi.org/10.5194/esd-8-55-2017, 2017.
Masek, J. G., Vermote, E. F., Saleous, N. E., Wolfe, R., Hall, F. G.,
Huemmrich, K. F., Gao, F., Kutler, J., and Lim, T.-K.: A Landsat Surface
Reflectance Dataset for North America, 1990–2000, IEEE Geoscience and Remote Sensing Letters, 3, 68–72,
https://doi.org/10.1109/LGRS.2005.857030, 2006.
Mora, B., Tsendbazar, N.-E., Herold, M., and Arino, O.: Global Land Cover
Mapping: Current Status and Future Trends, in: Land Use and Land Cover
Mapping in Europe, vol. 18, edited by: Manakos, I. and Braun, M., Springer
Netherlands, Dordrecht, 11–30,
https://doi.org/10.1007/978-94-007-7969-3_2, 2014.
Nathaniel, S. P., Nwulu, N., and Bekun, F.: Natural resource, globalization,
urbanization, human capital, and environmental degradation in Latin American
and Caribbean countries, Environ. Sci. Pollut. Res., 28, 6207–6221,
https://doi.org/10.1007/s11356-020-10850-9, 2021.
Nissan, H., Goddard, L., de Perez, E. C., Furlow, J., Baethgen, W., Thomson,
M. C., and Mason, S. J.: On the use and misuse of climate change projections
in international development, WIREs Clim. Change, 10, e579,
https://doi.org/10.1002/wcc.579, 2019.
Richter, R., Louis, J., and Müller-Wilm, U.: Sentinel-2 msi – level 2a
products algorithm theoretical basis document, Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), 72 pp., 2012.
Saah, D., Tenneson, K., Poortinga, A., Nguyen, Q., Chishtie, F., Aung, K.
S., Markert, K. N., Clinton, N., Anderson, E. R., Cutter, P., Goldstein, J.,
Housman, I. W., Bhandari, B., Potapov, P. V., Matin, M., Uddin, K., Pham, H.
N., Khanal, N., Maharjan, S., Ellenberg, W. L., Bajracharya, B., Bhargava,
R., Maus, P., Patterson, M., Flores-Anderson, A. I., Silverman, J., Sovann,
C., Do, P. M., Nguyen, G. V., Bounthabandit, S., Aryal, R. R., Myat, S. M.,
Sato, K., Lindquist, E., Kono, M., Broadhead, J., Towashiraporn, P., and
Ganz, D.: Primitives as building blocks for constructing land cover maps,
Int. J. Appl. Earth Observ. Geoinf., 85,
101979, https://doi.org/10.1016/j.jag.2019.101979, 2020.
Stehman, S. V.: Impact of sample size allocation when using stratified
random sampling to estimate accuracy and area of land-cover change, Remote
Sens. Lett., 3, 111–120, https://doi.org/10.1080/01431161.2010.541950, 2012.
Strobl, P., Baumann, P., Lewis, A., Szantoi, Z., Killough, B., Purss, M. B.
J., Craglia, M., Nativi, S., Held, A., and Dhu, T.: The six faces of the
data cube, in: Proc. of the 2017 conference on Big Data from Space
(BiDS'17), Big Data from Space (BiDS'17), Toulouse, France, 32–35,
https://doi.org/10.2760/383579, 2017.
Sylla, M. B., Pal, J. S., Wang, G. L., and Lawrence, P. J.: Impact of land
cover characterization on regional climate modeling over West Africa, Clim.
Dyn., 46, 637–650, https://doi.org/10.1007/s00382-015-2603-4, 2016.
Szantoi, Z., Escobedo, F., Abd-Elrahman, A., Smith, S., and Pearlstine, L.:
Analyzing fine-scale wetland composition using high resolution imagery and
texture features, Int. J. Appl. Earth Observ. Geoinf., 23, 204–212, https://doi.org/10.1016/j.jag.2013.01.003, 2013.
Szantoi, Z., Brink, A., Buchanan, G., Bastin, L., Lupi, A., Simonetti, D.,
Mayaux, P., Peedell, S., and Davy, J.: A simple remote sensing based
information system for monitoring sites of conservation importance, Remote
Sens. Ecol. Conserv., 2, 16–24, https://doi.org/10.1002/rse2.14, 2016.
Szantoi, Z., Geller, G. N., Tsendbazar, N.-E., See, L., Griffiths, P.,
Fritz, S., Gong, P., Herold, M., Mora, B., and Obregón, A.: Addressing
the need for improved land cover map products for policy support,
Environ. Sci. Pol., 112, 28–35,
https://doi.org/10.1016/j.envsci.2020.04.005, 2020a.
Szantoi, Z., Brink, A., Lupi, A., Mammone, C., and Jaffrain, G.: Key landscapes for conservation land cover and change monitoring, thematic and validation datasets for sub-Saharan Africa, Earth Syst. Sci. Data, 12, 3001–3019, https://doi.org/10.5194/essd-12-3001-2020, 2020b.
Szantoi, Z., Brink, A., and Lupi, A.: Land cover and change thematic and
validation datasets for selected African, Caribbean and Pacific areas, Pangaea,
https://doi.pangaea.de/10.1594/PANGAEA.931968, 2021a.
Szantoi, Z., Brink, A., and Lupi, A.: Land cover and change thematic and
validation datasets for selected African, Caribbean and Pacific areas [Data
set], Zenodo, https://doi.org/10.5281/ZENODO.4621375, 2021b.
Szantoi Z., Jaffrain, G., Gallaun, H., Bielski, C., Ruf, K., Lupi, A., Miletich, P., Giroux, A.C., Carlan, I., Croi, W., Augu, H., Kowalewski, C., and Brink, A.: Quality assurance and assessment framework for large area land cover maps validation in the Copernicus high resolution hot spot monitoring activity, Eur. J. Remote Sens., accepted, 2021c.
Tewkesbury, A. P., Comber, A. J., Tate, N. J., Lamb, A., and Fisher, P. F.:
A critical synthesis of remotely sensed optical image change detection
techniques, Remote Sens. Environ., 160, 1–14, https://doi.org/10.1016/j.rse.2015.01.006, 2015.
Tolessa, T., Senbeta, F., and Kidane, M.: The impact of land use/land cover
change on ecosystem services in the central highlands of Ethiopia, Ecosystem
Services, 23, 47–54, https://doi.org/10.1016/j.ecoser.2016.11.010, 2017.
Tsendbazar, N.-E., Herold, M., de Bruin, S., Lesiv, M., Fritz, S., Van De
Kerchove, R., Buchhorn, M., Duerauer, M., Szantoi, Z., and Pekel, J.-F.:
Developing and applying a multi-purpose land cover validation dataset for
Africa, Remote Sens. Environ., 219, 298–309,
https://doi.org/10.1016/j.rse.2018.10.025, 2018.
UNEP-WCMC and IUCN: Protected Planet: The World Database on Protected Areas
(WDPA) and World Database on Other Effective Area-based Conservation
Measures (WD-OECM), available at: https://www.protectedplanet.net/en, last access: March 2021.
van der Meer, E.: Carnivore conservation under land use change: the status
of Zimbabwe's cheetah population after land reform, Biodiv. Conserv., 27, 647–663,
https://doi.org/10.1007/s10531-017-1455-0, 2018.
Vondou, D. A. and Haensler, A.: Evaluation of simulations with the regional
climate model REMO over Central Africa and the effect of increased spatial
resolution, Int. J. Climatol, 37, 741–760, https://doi.org/10.1002/joc.5035, 2017.
Short summary
The ever-evolving landscapes in the African, Caribbean and Pacific regions should be monitored for land cover changes. The Global Land Monitoring Service of the Copernicus Programme, and in particular the Hot Spot Monitoring activity, developed a satellite-imagery-based workflow to monitor such areas. Here, we present a total of 852 025 km2 of areas mapped with up to 32 land cover classes. Thematic land cover and land cover change maps, as well as validation datasets, are presented.
The ever-evolving landscapes in the African, Caribbean and Pacific regions should be monitored...
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