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
Related authors
No articles found.
Douglas I. Kelley, Chantelle Burton, Francesca Di Giuseppe, Matthew W. Jones, Maria L. F. Barbosa, Esther Brambleby, Joe R. McNorton, Zhongwei Liu, Anna S. I. Bradley, Katie Blackford, Eleanor Burke, Andrew Ciavarella, Enza Di Tomaso, Jonathan Eden, Igor José M. Ferreira, Lukas Fiedler, Andrew J. Hartley, Theodore R. Keeping, Seppe Lampe, Anna Lombardi, Guilherme Mataveli, Yuquan Qu, Patrícia S. Silva, Fiona R. Spuler, Carmen B. Steinmann, Miguel Ángel Torres-Vázquez, Renata Veiga, Dave van Wees, Jakob B. Wessel, Emily Wright, Bibiana Bilbao, Mathieu Bourbonnais, Cong Gao, Carlos M. Di Bella, Kebonye Dintwe, Victoria M. Donovan, Sarah Harris, Elena A. Kukavskaya, Aya Brigitte N'Dri, Cristina Santín, Galia Selaya, Johan Sjöström, John T. Abatzoglou, Niels Andela, Rachel Carmenta, Emilio Chuvieco, Louis Giglio, Douglas S. Hamilton, Stijn Hantson, Sarah Meier, Mark Parrington, Mojtaba Sadegh, Jesus San-Miguel-Ayanz, Fernando Sedano, Marco Turco, Guido R. van der Werf, Sander Veraverbeke, Liana O. Anderson, Hamish Clarke, Paulo M. Fernandes, and Crystal A. Kolden
Earth Syst. Sci. Data, 17, 5377–5488, https://doi.org/10.5194/essd-17-5377-2025, https://doi.org/10.5194/essd-17-5377-2025, 2025
Short summary
Short summary
The second State of Wildfires report examines extreme wildfire events from 2024 to early 2025. It analyses key regional events in Southern California, Northeast Amazonia, Pantanal–Chiquitano, and the Congo Basin, assessing their drivers and predictability and attributing them to climate change and land use. Seasonal outlooks and decadal projections are provided. Climate change greatly increased the likelihood of these fires, and without strong mitigation, such events will become more frequent.
Renata Moura da Veiga, Celso von Randow, Chantelle Burton, Douglas I. Kelley, Manoel Cardoso, and Fabiano Morelli
Nat. Hazards Earth Syst. Sci., 25, 3581–3601, https://doi.org/10.5194/nhess-25-3581-2025, https://doi.org/10.5194/nhess-25-3581-2025, 2025
Short summary
Short summary
We systematically reviewed 77 papers to understand the Cerrado’s fire emissions within the global carbon budget by evaluating how fire parameters can inform emission estimates and mitigation strategies. Estimating fire emissions in the Cerrado requires a holistic approach, combining fire carbon emission estimates, fire dynamic parameters, and fire management and policy. We highlight key research gaps that could provide more comprehensive insights into accounting for fire emissions in the Cerrado.
Cecilia Chavana-Bryant, Phil Wilkes, Wanxin Yang, Andrew Burt, Peter Vines, Amy C. Bennett, Georgia C. Pickavance, Declan L. M. Cooper, Simon L. Lewis, Oliver L. Phillips, Benjamin Brede, Alvaro Lau, Martin Herold, Iain McNicol, Edward T. A. Mitchard, David A. Coomes, Toby Jackson, Loic Makaga, Heddy O. Milamizokou Napo, Alfred Ngomanda, Stephan Ntie, Vincent Medjibe, Pacome Dimbonda, Luna Soenens, Virginie Daelemans, Laetitia Proux, Reuben Nilus, Nicolas Labriere, Kathryn Jeffery, David F. R. P. Burslem, Daniel Clewley, David Moffat, Lan Qie, Harm Bartholomeus, Vincent Gregoire, Nicolas Barbier, Geraldine Derroire, Katharine Abernethy, Klaus Scipal, and Mat Disney
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-67, https://doi.org/10.5194/essd-2025-67, 2025
Preprint under review for ESSD
Short summary
Short summary
The ForestScan project provides a comprehensive set of datasets of tropical forest 3D structural measurements using terrestrial, unpiloted aerial vehicle and aerial laser scanning, plus tree census data. Collected at three sites in French Guiana, Gabon, and Malaysia, these datasets are crucial for calibrating and validating earth observation-derived forest biomass estimates, therefore, expanding and enhancing their use, and aiding global conservation efforts.
Seppe Lampe, Lukas Gudmundsson, Basil Kraft, Stijn Hantson, Douglas Kelley, Vincent Humphrey, Bertrand Le Saux, Emilio Chuvieco, and Wim Thiery
EGUsphere, https://doi.org/10.5194/egusphere-2025-3550, https://doi.org/10.5194/egusphere-2025-3550, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We introduce BuRNN, a model which estimates monthly burned area based on satellite observations and climate, vegetation, and socio-economic data using machine learning. BuRNN outperforms existing process-based fire models. However, the model tends to underestimate burned area in parts of Africa and Australia. We identify the extent of bare ground, the presence of grasses, and fire weather conditions (long periods of warm and dry weather) as key regional drivers of fire activity in BuRNN.
Simone Matias Reis, Yadvinder Malhi, Ben Hur Marimon Junior, Beatriz Schwantes Marimon, Huanyuan Zhang-Zheng, Igor Araújo, Renata Freitag, Edmar Almeida de Oliveira, Karine da Silva Peixoto, Luciana Januário de Souza, Ediméia Laura Souza da Silva, Eduarda Bernardes Santos, Kamila Parreira da Silva, Maélly Dállet Alves Gonçalves, Cécile Girardin, Cecilia Dahlsjö, Oliver L. Phillips, and Imma Oliveras Menor
Biogeosciences, 22, 3949–3964, https://doi.org/10.5194/bg-22-3949-2025, https://doi.org/10.5194/bg-22-3949-2025, 2025
Short summary
Short summary
The 2015–2016 El Niño caused severe droughts in tropical forests, but its impact on the Cerrado, Brazil's largest savanna, was unclear. Our study tracked the productivity of two key Cerrado vegetation types over 5 years. Before the El Niño, productivity was higher in the transitional forest–savanna, but it dropped sharply during the event. Meanwhile, the savanna showed minor changes. These findings suggest that transitional ecosystems are particularly vulnerable to drought and climate change.
Forrest M. Hoffman, Birgit Hassler, Ranjini Swaminathan, Jared Lewis, Bouwe Andela, Nathaniel Collier, Dóra Hegedűs, Jiwoo Lee, Charlotte Pascoe, Mika Pflüger, Martina Stockhause, Paul Ullrich, Min Xu, Lisa Bock, Felicity Chun, Bettina K. Gier, Douglas I. Kelley, Axel Lauer, Julien Lenhardt, Manuel Schlund, Mohanan G. Sreeush, Katja Weigel, Ed Blockley, Rebecca Beadling, Romain Beucher, Demiso D. Dugassa, Valerio Lembo, Jianhua Lu, Swen Brands, Jerry Tjiputra, Elizaveta Malinina, Brian Mederios, Enrico Scoccimarro, Jeremy Walton, Philip Kershaw, André L. Marquez, Malcolm J. Roberts, Eleanor O’Rourke, Elisabeth Dingley, Briony Turner, Helene Hewitt, and John P. Dunne
EGUsphere, https://doi.org/10.5194/egusphere-2025-2685, https://doi.org/10.5194/egusphere-2025-2685, 2025
Short summary
Short summary
As Earth system models become more complex, rapid and comprehensive evaluation through comparison with observational data is necessary. The upcoming Assessment Fast Track for the Seventh Phase of the Coupled Model Intercomparison Project (CMIP7) will require fast analysis. This paper describes a new Rapid Evaluation Framework (REF) that was developed for the Assessment Fast Track that will be run at the Earth System Grid Federation (ESGF) to inform the community about the performance of models.
Joao C. M. Teixeira, Chantelle Burton, Douglas I. Kelley, Gerd A. Folberth, Fiona M. O'Connor, Richard A. Betts, and Apostolos Voulgarakis
EGUsphere, https://doi.org/10.5194/egusphere-2025-3066, https://doi.org/10.5194/egusphere-2025-3066, 2025
Short summary
Short summary
Burnt areas produced by wildfires around the world are decreasing, especially in tropical regions, but many climate models fail to show this trend. Our study looks at whether adding a measure of human development to a fire model could improve its representation of these processes. We found that including these factors helped the model better match observations in many regions. This shows that human activity plays a key role in shaping fire trends.
Maria Lucia Ferreira Barbosa, Douglas I. Kelley, Chantelle A. Burton, Igor J. M. Ferreira, Renata Moura da Veiga, Anna Bradley, Paulo Guilherme Molin, and Liana O. Anderson
Geosci. Model Dev., 18, 3533–3557, https://doi.org/10.5194/gmd-18-3533-2025, https://doi.org/10.5194/gmd-18-3533-2025, 2025
Short summary
Short summary
As fire seasons in Brazil become increasingly severe, confidently understanding the factors driving fires is more critical than ever. To address this challenge, we developed FLAME (Fire Landscape Analysis using Maximum Entropy), a new model designed to predict fires and to analyse the spatial influence of both environmental and human factors while accounting for uncertainties. By adapting the model to different regions, we can enhance fire management strategies, making FLAME a powerful tool for protecting landscapes in Brazil and beyond.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
Short summary
Short summary
Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth system models mainly due to partially incorporating CO2 effects and land cover changes rather than to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Inika Taylor, Douglas I. Kelley, Camilla Mathison, Karina E. Williams, Andrew J. Hartley, Richard A. Betts, and Chantelle Burton
EGUsphere, https://doi.org/10.5194/egusphere-2025-720, https://doi.org/10.5194/egusphere-2025-720, 2025
Short summary
Short summary
Climate change is reshaping fire seasons worldwide and, in many places, increasing fire weather risk. We use climate model simulations to project future changes in fire danger at different levels of global warming, focusing on Australia, Brazil, and the USA. Keeping warming below 2 °C significantly limits the increase in fire risk, but even at 1.5 °C, fire seasons lengthen, with more extreme conditions. However, low-fire weather periods remain, offering critical windows for fire management.
Matthew W. Jones, Douglas I. Kelley, Chantelle A. Burton, Francesca Di Giuseppe, Maria Lucia F. Barbosa, Esther Brambleby, Andrew J. Hartley, Anna Lombardi, Guilherme Mataveli, Joe R. McNorton, Fiona R. Spuler, Jakob B. Wessel, John T. Abatzoglou, Liana O. Anderson, Niels Andela, Sally Archibald, Dolors Armenteras, Eleanor Burke, Rachel Carmenta, Emilio Chuvieco, Hamish Clarke, Stefan H. Doerr, Paulo M. Fernandes, Louis Giglio, Douglas S. Hamilton, Stijn Hantson, Sarah Harris, Piyush Jain, Crystal A. Kolden, Tiina Kurvits, Seppe Lampe, Sarah Meier, Stacey New, Mark Parrington, Morgane M. G. Perron, Yuquan Qu, Natasha S. Ribeiro, Bambang H. Saharjo, Jesus San-Miguel-Ayanz, Jacquelyn K. Shuman, Veerachai Tanpipat, Guido R. van der Werf, Sander Veraverbeke, and Gavriil Xanthopoulos
Earth Syst. Sci. Data, 16, 3601–3685, https://doi.org/10.5194/essd-16-3601-2024, https://doi.org/10.5194/essd-16-3601-2024, 2024
Short summary
Short summary
This inaugural State of Wildfires report catalogues extreme fires of the 2023–2024 fire season. For key events, we analyse their predictability and drivers and attribute them to climate change and land use. We provide a seasonal outlook and decadal projections. Key anomalies occurred in Canada, Greece, and western Amazonia, with other high-impact events catalogued worldwide. Climate change significantly increased the likelihood of extreme fires, and mitigation is required to lessen future risk.
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
Earth Syst. Dynam., 14, 1295–1315, https://doi.org/10.5194/esd-14-1295-2023, https://doi.org/10.5194/esd-14-1295-2023, 2023
Short summary
Short summary
We investigate the flux of carbon from the atmosphere into the land surface and ocean for multiple models and over a range of future scenarios. We did this by comparing simulations after the same change in the global-mean near-surface temperature. Using this method, we show that the choice of scenario can impact the carbon allocation to the land, ocean, and atmosphere. Scenarios with higher emissions reach the same warming levels sooner, but also with relatively more carbon in the atmosphere.
Joao Carlos Martins Teixeira, Chantelle Burton, Douglas I. Kelly, Gerd A. Folberth, Fiona M. O'Connor, Richard A. Betts, and Apostolos Voulgarakis
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-136, https://doi.org/10.5194/bg-2023-136, 2023
Revised manuscript not accepted
Short summary
Short summary
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
Geosci. Model Dev., 16, 4249–4264, https://doi.org/10.5194/gmd-16-4249-2023, https://doi.org/10.5194/gmd-16-4249-2023, 2023
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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...
Altmetrics
Final-revised paper
Preprint