Articles | Volume 19, issue 5
https://doi.org/10.5194/bg-19-1377-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-19-1377-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Mar 2022
Research article |
| 07 Mar 2022
| 07 Mar 2022
MODIS Vegetation Continuous Fields tree cover needs calibrating in tropical savannas
Rahayu Adzhar
CORRESPONDING AUTHOR
UK Centre for Ecology & Hydrology, Wallingford, Oxfordshire,
UK
Department of Ecology, Evolution, and Environmental Biology, Miami
University, Oxford, Ohio, USA
Department of Life Sciences, Imperial College London, Berkshire,
UK
UK Centre for Ecology & Hydrology, Wallingford, Oxfordshire,
UK
Ning Dong
Department of Life Sciences, Imperial College London, Berkshire,
UK
Department of Biological Sciences, Macquarie University, North
Ryde, Australia
Charles George
UK Centre for Ecology & Hydrology, Wallingford, Oxfordshire,
UK
Mireia Torello Raventos
School of Earth and Environmental Science, James Cook University,
Cairns, Australia
Elmar Veenendaal
Plant Ecology and Nature Conservation, Wageningen University & Research,
Wageningen, the Netherlands
Ted R. Feldpausch
College of Life and Environmental Sciences, University of Exeter,
Exeter, UK
Oliver L. Phillips
School of Geography, University of Leeds, Leeds, UK
Simon L. Lewis
School of Geography, University of Leeds, Leeds, UK
Department of Geography, University College London, London, UK
Bonaventure Sonké
Plant Systematics and Ecology Laboratory, Department of Biology,
Higher Teachers' Training College, University of Yaoundé, Yaoundé,
Cameroon
Herman Taedoumg
Consultative Group on International Agricultural Research (CGIAR), Bioversity International, Yaoundé, Cameroon
Beatriz Schwantes Marimon
Departamento de Ciências Biológicas, Mato Grosso State University, Mato Grosso, Brazil
Tomas Domingues
Departamento de Biologia, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, University of
São Paulo, São Paulo, Brazil
Luzmila Arroyo
Departamento de Biología, Universidad Autónoma Gabriel René Moreno, Santa Cruz,
Bolivia
Gloria Djagbletey
Forest and Climate Change, Forestry Research Institute of
Ghana, Kumasi, Ghana
Gustavo Saiz
Facultad de Ciencias, Universidad Católica de la
Santísima Concepción, Concepción, Chile
France Gerard
UK Centre for Ecology & Hydrology, Wallingford, Oxfordshire,
UK
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Lee de Mora, Ranjini Swaminathan, Richard P. Allan, Jerry C. Blackford, Douglas I. Kelley, Phil Harris, Chris D. Jones, Colin G. Jones, Spencer Liddicoat, Robert J. Parker, Tristan Quaife, Jeremy Walton, and Andrew Yool
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Joao Carlos Martins Teixeira, Chantelle Burton, Douglas I. Kelly, Gerd A. Folberth, Fiona M. O'Connor, Richard A. Betts, and Apostolos Voulgarakis
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Revised manuscript not accepted
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Representing socio-economic impacts on fires is crucial to underpin the confidence in global fire models. Introducing these into INFERNO, reduces biases and improves the modelled burnt area (BA) trends when compared to observations. Including socio-economic factors in the representation of fires in Earth System Models is important for realistically simulating BA, quantifying trends in the recent past, and for understanding the main drivers of those at regional scales.
Camilla Mathison, Eleanor Burke, Andrew J. Hartley, Douglas I. Kelley, Chantelle Burton, Eddy Robertson, Nicola Gedney, Karina Williams, Andy Wiltshire, Richard J. Ellis, Alistair A. Sellar, and Chris D. Jones
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This paper describes and evaluates a new modelling methodology to quantify the impacts of climate change on water, biomes and the carbon cycle. We have created a new configuration and set-up for the JULES-ES land surface model, driven by bias-corrected historical and future climate model output provided by the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). This allows us to compare projections of the impacts of climate change across multiple impact models and multiple sectors.
Jane P. Mulcahy, Colin G. Jones, Steven T. Rumbold, Till Kuhlbrodt, Andrea J. Dittus, Edward W. Blockley, Andrew Yool, Jeremy Walton, Catherine Hardacre, Timothy Andrews, Alejandro Bodas-Salcedo, Marc Stringer, Lee de Mora, Phil Harris, Richard Hill, Doug Kelley, Eddy Robertson, and Yongming Tang
Geosci. Model Dev., 16, 1569–1600, https://doi.org/10.5194/gmd-16-1569-2023, https://doi.org/10.5194/gmd-16-1569-2023, 2023
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Recent global climate models simulate historical global mean surface temperatures which are too cold, possibly to due to excessive aerosol cooling. This raises questions about the models' ability to simulate important climate processes and reduces confidence in future climate predictions. We present a new version of the UK Earth System Model, which has an improved aerosols simulation and a historical temperature record. Interestingly, the long-term response to CO2 remains largely unchanged.
Jing M. Chen, Rong Wang, Yihong Liu, Liming He, Holly Croft, Xiangzhong Luo, Han Wang, Nicholas G. Smith, Trevor F. Keenan, I. Colin Prentice, Yongguang Zhang, Weimin Ju, and Ning Dong
Earth Syst. Sci. Data, 14, 4077–4093, https://doi.org/10.5194/essd-14-4077-2022, https://doi.org/10.5194/essd-14-4077-2022, 2022
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Green leaves contain chlorophyll pigments that harvest light for photosynthesis and also emit chlorophyll fluorescence as a byproduct. Both chlorophyll pigments and fluorescence can be measured by Earth-orbiting satellite sensors. Here we demonstrate that leaf photosynthetic capacity can be reliably derived globally using these measurements. This new satellite-based information overcomes a bottleneck in global ecological research where such spatially explicit information is currently lacking.
Marina Corrêa Scalon, Imma Oliveras Menor, Renata Freitag, Karine S. Peixoto, Sami W. Rifai, Beatriz Schwantes Marimon, Ben Hur Marimon Junior, and Yadvinder Malhi
Biogeosciences, 19, 3649–3661, https://doi.org/10.5194/bg-19-3649-2022, https://doi.org/10.5194/bg-19-3649-2022, 2022
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We investigated dynamic nutrient flow and demand in a typical savanna and a transition forest to understand how similar soils and the same climate dominated by savanna vegetation can also support forest-like formations. Savanna relied on nutrient resorption from wood, and nutrient demand was equally partitioned between leaves, wood and fine roots. Transition forest relied on resorption from the canopy biomass and nutrient demand was predominantly driven by leaves.
Selena Georgiou, Edward T. A. Mitchard, Bart Crezee, Paul I. Palmer, Greta C. Dargie, Sofie Sjögersten, Corneille E. N. Ewango, Ovide B. Emba, Joseph T. Kanyama, Pierre Bola, Jean-Bosco N. Ndjango, Nicholas T. Girkin, Yannick E. Bocko, Suspense A. Ifo, and Simon L. Lewis
EGUsphere, https://doi.org/10.5194/egusphere-2022-580, https://doi.org/10.5194/egusphere-2022-580, 2022
Preprint archived
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Two major vegetation types, hardwood trees and palms, overlay the Central Congo Basin peatland complex, each dominant in different locations. We investigated the influence of terrain and climatological variables on their distribution, using a regression model, and found elevation and seasonal rainfall and temperature contribute significantly. There are indications of an optimal range of net water input for palm swamp to dominate, above and below which hardwood swamp dominates.
Gilvan Sampaio, Marília H. Shimizu, Carlos A. Guimarães-Júnior, Felipe Alexandre, Marcelo Guatura, Manoel Cardoso, Tomas F. Domingues, Anja Rammig, Celso von Randow, Luiz F. C. Rezende, and David M. Lapola
Biogeosciences, 18, 2511–2525, https://doi.org/10.5194/bg-18-2511-2021, https://doi.org/10.5194/bg-18-2511-2021, 2021
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The impact of large-scale deforestation and the physiological effects of elevated atmospheric CO2 on Amazon rainfall are systematically compared in this study. Our results are remarkable in showing that the two disturbances cause equivalent rainfall decrease, though through different causal mechanisms. These results highlight the importance of not only curbing regional deforestation but also reducing global CO2 emissions to avoid climatic changes in the Amazon.
Douglas I. Kelley, Chantelle Burton, Chris Huntingford, Megan A. J. Brown, Rhys Whitley, and Ning Dong
Biogeosciences, 18, 787–804, https://doi.org/10.5194/bg-18-787-2021, https://doi.org/10.5194/bg-18-787-2021, 2021
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Initial evidence suggests human ignitions or landscape changes caused most Amazon fires during August 2019. However, confirmation is needed that meteorological conditions did not have a substantial role. Assessing the influence of historical weather on burning in an uncertainty framework, we find that 2019 meteorological conditions alone should have resulted in much less fire than observed. We conclude socio-economic factors likely had a strong role in the high recorded 2019 fire activity.
Cited articles
Archer, C., Penny, A., Templeman, S., McKenzie, M., Hunt, E., Toral, D.,
Diakhite, M., Nhlapo, T., Mawoko, D., Vergnani, L., Chamdimba, C., Diop, H.,
Kalanzi, B., Touitha, Y., Jackson, A., Mchugh, J., Chang, O., Mohamad, A.,
Hunter, E., and Lopez, C.: State of the Tropics 2020 Report, James Cook University, ISBN 978-0-6486803-7-6, 2020.
Adzhar, R., Kelley, D. I., Dong, N., George, C., Torello Raventos, M., Veenendaal, E., Feldpausch, T. R., Philips, O. L., Lewis, S. L., Sonké, B., Taedoumg, H., Schwantes Marimon, B., Domingues, T., Arroyo, L., Djagbletey, G., Saiz, G., and Gerard, F.: VCF_vs_sites, GitHub [code], https://github.com/douglask3/VCF_vs_sites, last access: 26 February 2022.
Bartholomé, E. and Belward, A. S.: GLC2000: a new approach to global
land cover mapping from Earth observation data, Int. J.
Remote Sens., 26, 1959–1977, https://doi.org/10.1080/01431160412331291297, 2005.
Bastin, J. F., Finegold, Y., Garcia, C., Mollicone, D., Rezende, M., Routh,
D., Zohner, C. M., and Crowther, T. W.: The global tree restoration
potential, Science, 365, 76–79, https://doi.org/10.1126/science.aax0848, 2019.
Becker, R. A., Minka, T. P., Wilks, A. R., Brownrigg, R., and Deckmyn, A.:
maps: Draw Geographical Maps,
http://CRAN.R-project.org/package=maps (last access: 1 July 2016), 2016.
Bicheron, P., Defourny, P., Brockmann, C., Schouten, L., Vancutsem, C., Huc,
M., Bontemps, S., Leroy, M., Frédéric, A., Herold, M., Ranera, F.
and Arino, O.: GLOBCOVER: products description and validation report, MEDIAS-France, JRC49240, 2008.
Boval, M. and Dixon, R. M.: The importance of grasslands for animal
production and other functions: a review on management and methodological
progress in the tropics, Animal, 6, 748–762,
https://doi.org/10.1017/S1751731112000304, 2012.
Brandt, M., Rasmussen, K., Penuelas, J., Tian, F., Schurgers, G., Verger,
A., Mertz, O., Palmer, J., and Fensholt, R.: Human population growth offsets
climate-driven increase in woody vegetation in sub-Saharan Africa, Nature
Ecology & Evolution, 1, 0081, https://doi.org/10.1038/s41559-017-0081, 2017.
Brandt, M., Tucker, C., Kariryaa, A., Rasmussen, K., Abel, C., Small, J.,
Chave, J., Rasmussen, L., Hiernaux, P., Diouf, A., Kergoat, L., Mertz, O.,
Igel, C., Gieseke, F., Schöning, J., Li, S., Melocik, K., Meyer, J.,
Sinno, S., 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.
Brovkin, V., Boysen, L., Raddatz, T., Gayler, V., Loew, A., and Claussen, M.:
Evaluation of vegetation cover and land-surface albedo in MPI-ESM CMIP5
simulations, J. Adv. Model. Earth Sy., 5, 48–57,
https://doi.org/10.1029/2012MS000169, 2013.
Brownrigg, R., Mcilroy, D., Minka, T. P., and Bivand, R.: mapproj: Map
Projections,
http://CRAN.R-project.org/package=mapproj (last access: 15 March 2018), 2017.
Burton, C., Betts, R., Cardoso, M., Feldpausch, T. R., Harper, A., Jones, C. D., Kelley, D. I., Robertson, E., and Wiltshire, A.: Representation of fire, land-use change and vegetation dynamics in the Joint UK Land Environment Simulator vn4.9 (JULES), Geosci. Model Dev., 12, 179–193, https://doi.org/10.5194/gmd-12-179-2019, 2019.
Burton, C., Kelley, D. I., Jones, C. D., Betts, R. A., Cardoso, M., and
Anderson, L.: South American fires and their impacts on ecosystems increase
with continued emissions, Clim. Resil. Sustain.,
https://doi.org/10.1002/cli2.8, online first, 2021.
DiMiceli, M. C.: MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3
Global 250m SIN Grid V006, MOD44Bv006 [data set], https://doi.org/10.5067/MODIS/MOD44B.006, 2017.
DiMiceli, C., Carroll, M., Sohlberg, R. A., Huang, C., Hansen, M. C., and
Townshend, J. R. G.: Annual global automated MODIS vegetation continuous
fields (MOD44B) at 250 m spatial resolution for data years beginning day 65,
2000–2014, collection 5 percent tree cover, version 6, University of
Maryland [data set], https://doi.org/10.5067/MODIS/MOD44B.006, 2017.
Fiala, A. C. S., Garman, S. L., and Gray, A. N.: Comparison of five canopy
cover estimation techniques in the western Oregon Cascades, Forest Ecol.
Manage., 232, 188–197, https://doi.org/10.1016/j.foreco.2006.05.069,
2006.
Friedl, M. A., McIver, D. K., Hodges, J. C. F., Zhang, X. Y., Muchoney, D.,
Strahler, A. H., Woodcock, C. E., Gopal, S., Schneider, A., Cooper, A.,
Baccini, A., Gao, F., and Schaaf, C.: Global land cover mapping from MODIS:
algorithms and early results, Remote Sens. Environ., 83,
287–302, https://doi.org/10.1016/S0034-4257(02)00078-0, 2002.
Gao, Y., Mas, J. F., Paneque-Gálvez, J., Skutsch, M., Ghilardi, A.,
Pacheco, J. A. N., and Paniagua, I.: Validation of MODIS vegetation
continuous fields in two areas in Mexico, in: 2014 Third International
Workshop on Earth Observation and Remote Sensing Applications (EORSA),
14–18, https://doi.org/10.1109/EORSA.2014.6927840, 2014.
Gao, Y., Ghilardi, A., Paneque-Gálvez, J., Skutsch, M. M., and Mas, J.:
Validation of MODIS Vegetation Continuous Fields for monitoring
deforestation and forest degradation: two cases in Mexico, Geocarto
Int., 31, 1019–1031, https://doi.org/10.1080/10106049.2015.1110205, 2015.
Gaughan, A., Holdo, R., and Anderson, T.: Using short-term MODIS time-series
to quantify tree cover in a highly heterogeneous African savanna,
Int. J. Remote Sens., 34, 6865–6882,
https://doi.org/10.1080/01431161.2013.810352, 2013.
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and
Rubin, D. B.: Bayesian Data Analysis, 3rd Edn., CRC Press, https://doi.org/10.1201/9780429258411, 2013.
Gerard, F., Hooftman, D., Langevelde, F., Veenendaal, E., White, S., and
Lloyd, J.: MODIS VCF should not be used to detect discontinuities in tree
cover due to binning bias. A comment on Hanan et al. (2014) and Staver and
Hansen (2015), Global Ecol. Biogeogr., 26, 854–859, https://doi.org/10.1111/geb.12592,
2017.
Giriraj, A., Babar, S., and Murthy, M.: Evaluating MODIS-vegetation
continuous field products to assess tree cover change and forest
fragmentation in India – A multi-scale satellite remote sensing approach,
Egyptian Journal of Remote Sensing and Space Science, 20, 157–168,
https://doi.org/10.1016/j.ejrs.2017.05.004, 2017.
Gross, D., Achard, F., Dubois, G., Brink, A., and Prins, H. H. T.:
Uncertainties in tree cover maps of Sub-Saharan Africa and their
implications for measuring progress towards CBD Aichi Targets, Remote
Sensing in Ecology and Conservation, 4, 94–112, https://doi.org/10.1002/rse2.52,
2018.
Hanan, N., Tredennick, A., Prihodko, L., Bucini, G., and Dohn, J.: Analysis
of stable states in global savannas: Is the CART pulling the horse?, Global
Ecol. Biogeogr., 23, 259–263, https://doi.org/10.1111/geb.12122, 2013.
Hansen, M. C., Defries, R., Townshend, J., Marufu, L., and Sohlberg, R.:
Development of a MODIS tree cover validation data set for Western Province,
Zambia, Remote Sens. Environ., 83, 320–335, https://doi.org/10.1016/S0034-4257(02)00080-9, 2002.
Hansen, M. C., DeFries, R. S., Townshend, J. R. G., Carroll, M., Dimiceli, C.,
and Sohlberg, R.: Global Percent Tree Cover at a Spatial Resolution of 500
Meters: First Results of the MODIS Vegetation Continuous Fields Algorithm,
Earth Interact., 7, 1–15, 2003.
Hansen, M. C., Townshend, J., DeFries, R., 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.
Harris, N. L., Brown, S., Hagen, S. C., Saatchi, S. S., Petrova, S., Salas,
W., Hansen, M. C., Potapov, P. V., and Lotsch, A.: Baseline map of carbon
emissions from deforestation in tropical regions, Science, 336,
1573–1576, https://doi.org/10.1126/science.1217962, 2012.
Herold, M., Mayaux, P., Woodcock, C., Baccini, A., and Schmullius, C.: Some
challenges in global land cover mapping: An assessment of agreement and
accuracy in existing 1 km datasets, Remote Sens. Environ., 112,
2538–2556, https://doi.org/10.1016/j.rse.2007.11.013, 2008.
Hijmans, R. J.: raster: Geographic Data Analysis and Modeling, http://CRAN.R-project.org/package=raster (last access: 15
March 2018), 2017.
Huang, S. and Siegert, F.: Land cover classification optimised to detect
areas at risk of desertification in North China based on SPOT VEGETATION
imagery, J. Arid Environ., 67, 308–327,
https://doi.org/10.1016/j.jaridenv.2006.02.016, 2006.
Huete, A. R., Liu, H., and van Leeuwen, W. J. D.: The use of vegetation
indices in forested regions: issues of linearity and saturation, in:
IGARSS'97. 1997 IEEE International Geoscience and Remote Sensing Symposium
Proceedings, Remote Sensing – A Scientific Vision for Sustainable
Development, IGARSS'97, 1997 IEEE International Geoscience and Remote
Sensing Symposium Proceedings, Remote Sensing – A Scientific Vision for
Sustainable Development, 1966–1968, Vol. 4,
https://doi.org/10.1109/IGARSS.1997.609169, 1997.
Jung, M., Henkel, K., Herold, M., and Churkina, G.: Exploiting synergies of
global land cover products for carbon cycle modeling, Remote Sens.
Environ., 101, 534–553, https://doi.org/10.1016/j.rse.2006.01.020, 2006.
Kelley, D. I., Prentice, I. C., Harrison, S. P., Wang, H., Simard, M., Fisher, J. B., and Willis, K. O.: A comprehensive benchmarking system for evaluating global vegetation models, Biogeosciences, 10, 3313–3340, https://doi.org/10.5194/bg-10-3313-2013, 2013.
Kelley, D. I., Bistinas, I., Whitley, R., Burton, C., Marthews, T. R., and
Dong, N.: How contemporary bioclimatic and human controls change global fire
regimes, Nat. Clim. Change, 9, 690–696,
https://doi.org/10.1038/s41558-019-0540-7, 2019.
Kelley, D. I., Burton, C., Huntingford, C., Brown, M. A. J., Whitley, R., and Dong, N.: Technical note: Low meteorological influence found in 2019 Amazonia fires, Biogeosciences, 18, 787–804, https://doi.org/10.5194/bg-18-787-2021, 2021.
Korhonen, L., Korhonen, K., Rautiainen, M., and Stenberg, P.: Estimation of
forest canopy cover: a comparison of field measurement techniques, Silva Fenn., 40, 315,
https://doi.org/10.14214/sf.315, 2006.
Kumar, S. S., Hanan, N. P., Prihodko, L., Anchang, J., Ross, C. W., Ji, W.,
and Lind, B. M.: Alternative Vegetation States in Tropical Forests and
Savannas: The Search for Consistent Signals in Diverse Remote Sensing Data,
Remote Sens., 11, 815,
https://doi.org/10.3390/rs11070815, 2019.
Lary, D. and Lait, L.: Using probability distribution functions for
satellite validation, IEEE T. Geosci. Remote Sens.,
44, 1359–1366, https://doi.org/10.1109/TGRS.2005.860662, 2006.
Lasslop, G., Moeller, T., D'Onofrio, D., Hantson, S., and Kloster, S.: Tropical climate–vegetation–fire relationships: multivariate evaluation of the land surface model JSBACH, Biogeosciences, 15, 5969–5989, https://doi.org/10.5194/bg-15-5969-2018, 2018.
Lasslop, G., Hantson, S., Harrison, S. P., Bachelet, D., Burton, C., Forkel,
M., Forrest, M., Li, F., Melton, J. R., Yue, C., Archibald, S., Scheiter,
S., Arneth, A., Hickler, T., and Sitch, S.: Global ecosystems and fire:
Multi-model assessment of fire-induced tree-cover and carbon storage
reduction, Glob. Change Biol., 26, 5027–5041, https://doi.org/10.1111/gcb.15160, 2020.
Lloyd, J., Bird, M. I., Vellen, L., Miranda, A. C., Veenendaal, E. M.,
Djagbletey, G., Miranda, H. S., Cook, G., and Farquhar, G. D.: Contributions
of woody and herbaceous vegetation to tropical savanna ecosystem
productivity: a quasi-global estimate, Tree Physiol., 28, 451–468,
https://doi.org/10.1093/treephys/28.3.451, 2008.
Lopez-Gonzalez, G., Lewis, S. L., Burkitt, M., and Phillips, O. L.:
ForestPlots.net: a web application and research tool to manage and analyse
tropical forest plot data, J. Veg. Sci., 22, 610–613, https://doi.org/10.1111/j.1654-1103.2011.01312.x, 2011.
Lopez-Gonzalez, G., Lewis, S. L., Burkitt, M., Baker T. R., and Phillips, O. L.:
ForestPlots.net Database, https://www.forestplots.net
(last access: 14 April 2020), 2009.
Miles, L., Newton, A. C., DeFries, R. S., Ravilious, C., May, I., Blyth, S.,
Kapos, V., and Gordon, J. E.: A global overview of the conservation status of
tropical dry forests, J. Biogeogr., 33, 491–505,
https://doi.org/10.1111/j.1365-2699.2005.01424.x, 2006.
Montesano, P., Nelson, R., Sun, G., Margolis, H., Kerber, A., and Ranson, K.
J.: MODIS tree cover validation for the circumpolar taiga–tundra transition
zone, Remote Sens. Environ., 113, 2130–2141,
https://doi.org/10.1016/j.rse.2009.05.021, 2009.
Montesano, P., Neigh, C., Sexton, J., Feng, M., Channan, S., Ranson, K., and
Townshend, J.: Calibration and Validation of Landsat Tree Cover in the
Taiga–Tundra Ecotone, Remote Sens., 8, 551, https://doi.org/10.3390/rs8070551,
2016.
Pennington, R. T., Lehmann, C. E. R., and Rowland, L. M.: Tropical savannas
and dry forests, Curr. Biol., 28, R541–R545,
https://doi.org/10.1016/j.cub.2018.03.014, 2018.
Rabin, S. S., Melton, J. R., Lasslop, G., Bachelet, D., Forrest, M., Hantson, S., Kaplan, J. O., Li, F., Mangeon, S., Ward, D. S., Yue, C., Arora, V. K., Hickler, T., Kloster, S., Knorr, W., Nieradzik, L., Spessa, A., Folberth, G. A., Sheehan, T., Voulgarakis, A., Kelley, D. I., Prentice, I. C., Sitch, S., Harrison, S., and Arneth, A.: The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols with detailed model descriptions, Geosci. Model Dev., 10, 1175–1197, https://doi.org/10.5194/gmd-10-1175-2017, 2017.
Rautiainen, M., Stenberg, P., and Nilson, T.: Estimating canopy cover in
Scots pine stands, Silva Fenn., 39, 137–142,
https://doi.org/10.14214/sf.402, 2005.
R Core Team: R: A language and environment for statistical computing, R
Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (last access: 31 January 2020), 2018.
Rutten, G., Ensslin, A., Hemp, A., and Fischer, M.: Vertical and Horizontal
Vegetation Structure across Natural and Modified Habitat Types at Mount
Kilimanjaro, PLOS ONE, 10, e0138822, https://doi.org/10.1371/journal.pone.0138822,
2015.
Saatchi, S., Harris, N., Brown, S., Lefsky, M., Mitchard, E., Salas, W.,
Zutta, B., Buermann, W., Lewis, S., Hagen, S., Petrova, S., White, L.,
Silman, M., and Morel, A.: Benchmark map of forest carbon stocks in tropical
regions across three continents, P. Natl. Acad.
Sci. USA, 108, 9899–904,
https://doi.org/10.1073/pnas.1019576108, 2011.
Sankaran, M., Hanan, N., Scholes, R., Ratnam, J., Augustine, D., Cade, B.,
Gignoux, J., Higgins, S., Roux, X., Ludwig, F., Ardö, J., Banyikwa, F.,
Bronn, A., Bucini, G., Caylor, K., Coughenour, M., Diouf, A., Ekaya, W.,
Feral, C., and Zambatis, N.: Determinants of woody cover in African Savannas,
Nature, 438, 846–849, https://doi.org/10.1038/nature04070, 2006.
Sellar, A. A., Jones, C. G., Mulcahy, J. P., Tang, Y., Yool, A., Wiltshire,
A., O'Connor, F. M., Stringer, M., Hill, R., Palmieri, J., Woodward, S.,
Mora, L. de, Kuhlbrodt, T., Rumbold, S. T., Kelley, D. I., Ellis, R.,
Johnson, C. E., Walton, J., Abraham, N. L., Andrews, M. B., Andrews, T.,
Archibald, A. T., Berthou, S., Burke, E., Blockley, E., Carslaw, K., Dalvi,
M., Edwards, J., Folberth, G. A., Gedney, N., Griffiths, P. T., Harper, A.
B., Hendry, M. A., Hewitt, A. J., Johnson, B., Jones, A., Jones, C. D.,
Keeble, J., Liddicoat, S., Morgenstern, O., Parker, R. J., Predoi, V.,
Robertson, E., Siahaan, A., Smith, R. S., Swaminathan, R., Woodhouse, M. T.,
Zeng, G., and Zerroukat, M.: UKESM1: Description and Evaluation of the U.K.
Earth System Model, J. Adv. Model. Earth Sy., 11,
4513–4558, https://doi.org/10.1029/2019MS001739, 2019.
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 (data available at: https://e4ftl01.cr.usgs.gov/MEASURES/GFCC30TC.003/, last access: 3 March 2022).
Smith, J., Wickham, J., Stehman, S., and Yang, L.: Impacts of Patch Size and
Land-Cover Heterogeneity on Thematic Image Classification Accuracy,
Photogramm. Eng. Rem. S., 68, 65–70, 2002.
Solofondranohatra, C. L., Vorontsova, M. S., Hackel, J., Besnard, G., Cable,
S., Williams, J., Jeannoda, V., and Lehmann, C. E. R.: Grass Functional
Traits Differentiate Forest and Savanna in the Madagascar Central Highlands,
Front. Ecol. Evol., 6, 184, https://doi.org/10.3389/fevo.2018.00184, 2018.
Song, X. P., Huang, C., Feng, M., Sexton, J. O., Channan, S., and Townshend,
J. R.: Integrating global land cover products for improved forest cover
characterisation: an application in North America, Int. J.
Digit. Earth, 7, 709–724, https://doi.org/10.1080/17538947.2013.856959, 2014.
Stan Development Team: RStan: The R Interface to Stan, http://mc-stan.org/ (last access: 31 January 2020), 2019.
Sulla-Menashe, D. and Friedl, M. A.: User Guide to Collection 6 MODIS Land
Cover (MCD12Q1 and MCD12C1) Product, https://lpdaac.usgs.gov/documents/112/MOD44B_User_Guide_V6.pdf (last access: 26 February 2022), 2018.
Sulla-Menashe, D., Gray, J. M., Abercrombie, S. P., and Friedl, M. A.:
Hierarchical mapping of annual global land cover 2001 to present: The MODIS
Collection 6 Land Cover product, Remote Sens. Environ., 222,
183–194, https://doi.org/10.1016/j.rse.2018.12.013, 2019.
Tang, H., Armston, J., Hancock, S., Marselis, S., Goetz, S., and Dubayah,
R.: Characterizing global forest canopy cover distribution using spaceborne
lidar, Remote Sens. Environ., 231, 111–262,
https://doi.org/10.1016/j.rse.2019.111262, 2019a.
Tang, H., Song, X.-P., Zhao, F. A., Strahler, A. H., Schaaf, C. L., Goetz,
S., Huang, C., Hansen, M. C., and Dubayah, R.: Definition and measurement of
tree cover: A comparative analysis of field-, lidar- and landsat-based tree
cover estimations in the Sierra national forests, USA, Agr.
Forest Meteorol., 268, 258–268, https://doi.org/10.1016/j.agrformet.2019.01.024,
2019b.
Taylor, C., de Jeu, R., Guichard, F., Harris, P. P., and Dorigo, W. A.:
Afternoon rain more likely over drier soils, Nature, 489, 423–426,
https://doi.org/10.1038/nature11377, 2012.
Torello-Raventos, M., Feldpausch, T., Veenendaal, E., Schrodt, F., Saiz, G.,
Domingues, T., Djagbletey, G., Ford, A., Kemp, J., Marimon, B.,
Marimon-Junior, B. H., Lenza, E., A Ratter, J., Maracahipes, L., Sasaki, D.,
Sonké, B., Zapfack, L., Taedoumg, H., Daniel, V., and Lloyd, J.: On the
delineation of tropical vegetation types with an emphasis on forest/savanna
transitions, Plant Ecol. Divers., 6, 101–137,
https://doi.org/10.1080/17550874.2012.762812, 2013 (data available at: https://www.forestplots.net, last access: 31 October 2021).
Veenendaal, E. M., Torello-Raventos, M., Feldpausch, T. R., Domingues, T. F., Gerard, F., Schrodt, F., Saiz, G., Quesada, C. A., Djagbletey, G., Ford, A., Kemp, J., Marimon, B. S., Marimon-Junior, B. H., Lenza, E., Ratter, J. A., Maracahipes, L., Sasaki, D., Sonké, B., Zapfack, L., Villarroel, D., Schwarz, M., Yoko Ishida, F., Gilpin, M., Nardoto, G. B., Affum-Baffoe, K., Arroyo, L., Bloomfield, K., Ceca, G., Compaore, H., Davies, K., Diallo, A., Fyllas, N. M., Gignoux, J., Hien, F., Johnson, M., Mougin, E., Hiernaux, P., Killeen, T., Metcalfe, D., Miranda, H. S., Steininger, M., Sykora, K., Bird, M. I., Grace, J., Lewis, S., Phillips, O. L., and Lloyd, J.: Structural, physiognomic and above-ground biomass variation in savanna–forest transition zones on three continents – how different are co-occurring savanna and forest formations?, Biogeosciences, 12, 2927–2951, https://doi.org/10.5194/bg-12-2927-2015, 2015.
White, M., Shaw, J., and Ramsey, R.: Accuracy assessment of the vegetation
continuous field tree cover product using 3954 ground plots in the
south-western USA, Int. J. Remote Sens., 26, 2699–2704,
https://doi.org/10.1080/01431160500080626, 2005.
White, R. P., Murray, S., and Rohweder, M.: Pilot Analysis of Global
Ecosystems: Grassland Ecosystems, World Resources Institute, ISBN 1-56973-461-5, 2000.
Whitley, R., Beringer, J., Hutley, L. B., Abramowitz, G., De Kauwe, M. G., Evans, B., Haverd, V., Li, L., Moore, C., Ryu, Y., Scheiter, S., Schymanski, S. J., Smith, B., Wang, Y.-P., Williams, M., and Yu, Q.: Challenges and opportunities in land surface modelling of savanna ecosystems, Biogeosciences, 14, 4711–4732, https://doi.org/10.5194/bg-14-4711-2017, 2017.
Wiltshire, A. J., Burke, E. J., Chadburn, S. E., Jones, C. D., Cox, P. M., Davies-Barnard, T., Friedlingstein, P., Harper, A. B., Liddicoat, S., Sitch, S., and Zaehle, S.: JULES-CN: a coupled terrestrial carbon–nitrogen scheme (JULES vn5.1), Geosci. Model Dev., 14, 2161–2186, https://doi.org/10.5194/gmd-14-2161-2021, 2021.
Wuyts, B., Champneys, A. R., and House, J. I.: Amazonian forest-savanna
bistability and human impact, Nat. Commun., 8, 15519,
https://doi.org/10.1038/ncomms15519, 2017.
Xu, C., Hantson, S., Holmgren, M., van Nes, E. H., Staal, A., and Scheffer,
M.: Remotely sensed canopy height reveals three pantropical ecosystem
states, Ecology, 97, 2518–2521, https://doi.org/10.1002/ecy.1470, 2016.
Yang, X. and Crews, K.: Applicability analysis of MODIS tree cover product
in Texas savanna, Int. J. Appl. Earth Obs., 81, 186–194, https://doi.org/10.1016/j.jag.2019.05.003, 2019.
Short summary
The MODIS Vegetation Continuous Fields (VCF) product underestimates tree cover compared to field data and could be underestimating tree cover significantly across the tropics. VCF is used to represent land cover or validate model performance in many land surface and global vegetation models and to train finer-scaled Earth observation products. Because underestimation in VCF may render it unsuitable for training data and bias model predictions, it should be calibrated before use in the tropics.
The MODIS Vegetation Continuous Fields (VCF) product underestimates tree cover compared to field...
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