Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Widespread decline of Congo rainforest greenness in the past decade

Nature volume 509, pages 86–90 (2014)Cite this article

Subjects

Abstract

Tropical forests are global epicentres of biodiversity and important modulators of climate change1, and are mainly constrained by rainfall patterns1,2,3. The severe short-term droughts that occurred recently in Amazonia have drawn attention to the vulnerability of tropical forests to climatic disturbances4,5,6,7,8,9. The central African rainforests, the second-largest on Earth, have experienced a long-term drying trend10,11 whose impacts on vegetation dynamics remain mostly unknown because in situ observations are very limited. The Congolese forest, with its drier conditions and higher percentage of semi-evergreen trees12,13, may be more tolerant to short-term rainfall reduction than are wetter tropical forests11, but for a long-term drought there may be critical thresholds of water availability below which higher-biomass, closed-canopy forests transition to more open, lower-biomass forests1,2,14. Here we present observational evidence for a widespread decline in forest greenness over the past decade based on analyses of satellite data (optical, thermal, microwave and gravity) from several independent sensors over the Congo basin. This decline in vegetation greenness, particularly in the northern Congolese forest, is generally consistent with decreases in rainfall, terrestrial water storage, water content in aboveground woody and leaf biomass, and the canopy backscatter anomaly caused by changes in structure and moisture in upper forest layers. It is also consistent with increases in photosynthetically active radiation and land surface temperature. These multiple lines of evidence indicate that this large-scale vegetation browning, or loss of photosynthetic capacity, may be partially attributable to the long-term drying trend. Our results suggest that a continued gradual decline of photosynthetic capacity and moisture content driven by the persistent drying trend could alter the composition and structure of the Congolese forest to favour the spread of drought-tolerant species1,2,14.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: April–May–June rainfall anomalies and linear trends per decade.
Figure 2: Spatial patterns of linear trends per decade in April–May–June for the period 2000–2012.
Figure 3: Regional mean anomalies in EVI, rainfall, TWS, CBA and VOD.
Figure 4: VOD anomalies and linear trends per decade in April–May–June for the period 1988–2010.

Similar content being viewed by others

References

  1. Lewis, S. L. Tropical forests and the changing earth system. Phil. Trans. R. Soc. Lond. B 361, 195–210 (2006)

    Article  Google Scholar 

  2. Enquist, B. J. & Enquist, C. A. F. Long-term change within a neotropical forest: assessing differential functional and floristic responses to disturbance and drought. Glob. Change Biol. 17, 1408–1424 (2011)

    Article  ADS  Google Scholar 

  3. Lewis, S. L. et al. Above-ground biomass and structure of 260 African tropical forests. Phil. Trans. R. Soc. Lond. B 368, 20120295 (2013)

    Article  Google Scholar 

  4. Brando, P. M. et al. Seasonal and interannual variability of climate and vegetation indices across the Amazon. Proc. Natl Acad. Sci. USA 107, 14685–14690 (2010)

    Article  ADS  CAS  Google Scholar 

  5. Xu, L. et al. Widespread decline in greenness of Amazonian vegetation due to the 2010 drought. Geophys. Res. Lett. 38, L07402 (2011)

    Article  ADS  Google Scholar 

  6. Samanta, A., Ganguly, S., Vermote, E., Nemani, R. R. & Myneni, R. B. Why is remote sensing of Amazon forest greenness so challenging? Earth Interact. 16, 1–14 (2012)

    Article  Google Scholar 

  7. Atkinson, P. M., Dash, J. & Jeganathan, C. Amazon vegetation greenness as measured by satellite sensors over the last decade. Geophys. Res. Lett. 38, L19105 (2011)

    Article  ADS  Google Scholar 

  8. Saatchi, S. et al. Persistent effects of a severe drought on Amazonian forest canopy. Proc. Natl Acad. Sci. USA 110, 565–570 (2013)

    Article  ADS  CAS  Google Scholar 

  9. Morton, D. C. et al. Amazon forests maintain consistent canopy structure and greenness during the dry season. Nature 506, 221–224 (2014)

    Article  ADS  CAS  Google Scholar 

  10. Malhi, Y. & Wright, J. Spatial patterns and recent trends in the climate of tropical rainforest regions. Phil. Trans. R. Soc. Lond. B 359, 311–329 (2004)

    Article  Google Scholar 

  11. Asefi-Najafabady, S. & Saatchi, S. Response of African humid tropical forests to recent rainfall anomalies. Phil. Trans. R Soc. B 368, 20120306 (2013)

    Article  PubMed  Google Scholar 

  12. Adams, J. The distribution and variety of equatorial rain forest. http://www.esd.ornl.gov/projects/qen/rainfo.html . (1998)

  13. Ashton M. S., Tyrrell M. L., Spalding D., Gentry B., eds. Managing Forest Carbon in a Changing Climate (Springer, 2012)

  14. Fauset, S. et al. Drought-induced shifts in the floristic and functional composition of tropical forests in Ghana. Ecol. Lett. 15, 1120–1129 (2012)

    Article  PubMed  Google Scholar 

  15. Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213 (2002)

    Article  ADS  Google Scholar 

  16. Myneni, R. B., Hall, F. G., Sellers, P. J. & Marshak, A. L. The meaning of spectral vegetation indices. IEEE Trans. Geosci. Remote Sens. 33, 481–486 (1995)

    Article  ADS  Google Scholar 

  17. Huete, A. R. et al. Amazon rainforests green-up with sunlight in dry season. Geophys. Res. Lett. 33, L06405 (2006)

    Article  ADS  Google Scholar 

  18. Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003)

    Article  ADS  CAS  PubMed  Google Scholar 

  19. de Wasseige, C., Bastin, D. & Defourny, P. Seasonal variation of tropical forest LAI based on field measurements in Central African Republic. Agric. For. Meteorol. 119, 181–194 (2003)

    Article  ADS  Google Scholar 

  20. Schneider, U. et al. GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor. Appl. Climatol. 115, 15–40 (2014)

    Article  ADS  Google Scholar 

  21. Adler, R. F. et al. The Version 2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979-present). J. Hydrometeorol. 4, 1147–1167 (2003)

    Article  ADS  Google Scholar 

  22. Huffman, G. J. et al. The TRMM Multi-satellite Precipitation Analysis: quasi-global, multi-year, combined-sensor precipitation estimates at fine scale. J. Hydrometeorol. 8, 38–55 (2007)

    Article  ADS  Google Scholar 

  23. Crowley, J. W., Mitrovica, J. X., Bailey, R. C., Tamisiea, M. E. & Davis, J. L. Land water storage within the Congo Basin inferred from GRACE satellite gravity data. Geophys. Res. Lett. 33, L19402 (2006)

    Article  ADS  Google Scholar 

  24. Reager, J. T. & Famiglietti, J. S. Characteristic mega-basin water storage behavior using GRACE. Wat. Resour. Res. 49, (2013)

  25. Liu, Y. Y., van Dijk, A. I. J. M., McCabe, M. F., Evans, J. P. & de Jeu, R. A. M. Global vegetation biomass change (1988–2008) and attribution to environmental and human drivers. Glob. Ecol. Biogeogr. 22, 692–705 (2013)

    Article  Google Scholar 

  26. Andela, N., Liu, Y. Y., van Dijk, A. I. J. M., de Jeu, R. A. M. & McVicar, T. R. Global changes in dryland vegetation dynamics (1988–2008) assessed by satellite remote sensing: comparing a new passive microwave vegetation density record with reflective greenness data. Biogeosciences 10, 6657–6676 (2013)

    Article  ADS  Google Scholar 

  27. Lucht, W., Schaphoff, S., Erbrecht, T., Heyder, U. & Cramer, W. Terrestrial vegetation redistribution and carbon balance under climate change. Carbon Balance Manag. 1, 6 (2006)

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Lee, H. et al. Characterization of terrestrial water dynamics in the Congo Basin using GRACE and satellite radar altimetry. Remote Sens. Environ. 115, 3530–3538 (2011)

    Article  ADS  Google Scholar 

  29. Valentini, R. et al. The full greenhouse gases budget of Africa: synthesis, uncertainties and vulnerabilities. Biogeosci. Discuss. 10, 8343–8413 (2013)

    Article  ADS  Google Scholar 

  30. Nepstad, D. C., Tohver, I. M., Ray, D., Moutinho, P. & Cardinot, G. Mortality of large trees and lianas following experimental drought in an amazon forest. Ecology 88, 2259–2269 (2007)

    Article  PubMed  Google Scholar 

  31. Wan, Z. New refinements and validation of the MODIS land surface temperature/emissivity products. Remote Sens. Environ. 112, 59–74 (2008)

    Article  ADS  Google Scholar 

  32. Friedl, M. A. et al. MODIS Collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010)

    Article  ADS  Google Scholar 

  33. DiMiceli, C. M. et al. Annual Global Automated MODIS Vegetation Continuous Fields (MOD44B) at 250 m Spatial Resolution for Data Years Beginning Day 65, 2000-2010, Collection 5 Percent Tree Cover. (Univ. Maryland, 2011) ; http://www.landcover.org/data/vcf

  34. King, M. D. et al. Cloud and aerosol properties, precipitable water and profiles of temperature and water vapor from MODIS. IEEE Trans. Geosci. Rem. Sens. 41, 442–458 (2003)

    Article  ADS  Google Scholar 

  35. Remer, L. A. et al. MODIS aerosol algorithm, products and validation. J. Atmos. Sci. 62, 947–973 (2005)

    Article  ADS  Google Scholar 

  36. Platnick, S. et al. The MODIS cloud products: algorithms and examples from Terra. IEEE Trans. Geosci. Rem. Sens. 41, 459–473 (2003)

    Article  ADS  Google Scholar 

  37. Hansen, M. C. et al. A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin. Remote Sens. Environ. 112, 2495–2513 (2008)

    Article  ADS  Google Scholar 

  38. van Leeuwen, T. T. et al. Optimal use of land surface temperature data to detect changes in tropical forest cover. J. Geophys. Res. 116, G02002 (2011)

    Article  ADS  Google Scholar 

  39. Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations. Int. J. Climatol. 34, 623–642 (2013)

    Article  Google Scholar 

  40. Landerer, F. W. & Swenson, S. C. Accuracy of scaled GRACE terrestrial water storage estimates. Water Resour. Res. 48, W04531 (2012)

    Article  ADS  Google Scholar 

  41. Swenson, S. C. & Wahr, J. Post-processing removal of correlated errors in GRACE data. Geophys. Res. Lett. 33, L08402 (2006)

    ADS  Google Scholar 

  42. Wielicki, B. A. et al. Clouds and the Earth's Radiant Energy System (CERES): an Earth observing system experiment. Bull. Am. Meteorol. Soc. 77, 853–868 (1996)

    Article  ADS  Google Scholar 

  43. Kirdyashev, K. P., Chukhlantsev, A. A. & Shutko, A. M. Microwave radiation of the Earth's surface in the presence of a vegetation cover. Radio Eng. Electron. Phys. 24, 37–44 (1979)

    Google Scholar 

  44. Kerr, Y. H. & Njoku, E. G. A semiempirical model for interpreting microwave emission from semiarid land surfaces as seen from space. IEEE Trans. Geosci. Remote Sens. 28, 384–393 (1990)

    Article  ADS  Google Scholar 

  45. Jackson, T. J. & Schmugge, T. J. Vegetation effects on the microwave emission of soils. Remote Sens. Environ. 36, 203–212 (1991)

    Article  ADS  Google Scholar 

  46. Shi, J. C. et al. Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E. Remote Sens. Environ. 112, 4285–4300 (2008)

    Article  ADS  Google Scholar 

  47. Jones, M. O., Jones, L. A., Kimball, J. S. & McDonald, K. C. Satellite passive microwave remote sensing for monitoring global land surface phenology. Remote Sens. Environ. 115, 1102–1114 (2011)

    Article  ADS  Google Scholar 

  48. Liu, Y. Y., de Jeu, R. A. M., McCabe, M. F., Evans, J. P. & van Dijk, A. I. J. M. Global long-term passive microwave satellite-based retrievals of vegetation optical depth. Geophys. Res. Lett. 38, L18402 (2011)

    ADS  Google Scholar 

  49. Liu, Y. Y. et al. Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals. Hydrol. Earth Syst. Sci. 15, 425–436 (2011)

    Article  ADS  Google Scholar 

  50. Liu, Y. Y. et al. Trend-preserving blending of passive and active microwave soil moisture retrievals. Remote Sens. Environ. 123, 280–297 (2012)

    Article  ADS  Google Scholar 

  51. Halldor, B. & Venegas, S. A. A Manual for EOF and SVD Analyses of Climate Data. (Report No. 97-1, Centre for Climate and Global Change Research, McGill Univ., 1997)

    Google Scholar 

  52. Zhou, L., Tian, Y., Chen, H., Dai, Y. & Harris, R. A. Effects of topography on assessing wind farm impacts using MODIS data. Earth Interact. 17, 1–18 (2013)

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by the NOAA NESDIS project (NA11NES4400010) and by the startup funds provided by the University at Albany, State University of New York. R.B.M. was funded by NASA’s Earth Science Division. The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official NOAA or US Government position, policy, or decision. H.C. is supported by the National Natural Science Foundation of China (grant number 41230422).

Author information

Authors and Affiliations

  1. Department of Atmospheric and Environmental Sciences, University at Albany, State University of New York (SUNY), Albany, 12222, New York, USA

    Liming Zhou

  2. I. M. Systems Group (IMSG), National Oceanic and Atmospheric Administration/National Environmental Satellite, Data, and Information Service/The Center for Satellite Applications and Research (NOAA/NESDIS/STAR), 5830 University Research Court, College Park, 20740, Maryland, USA

    Yuhong Tian

  3. Department of Earth and Environment, Boston University, Boston, 02215, Massachusetts, USA

    Ranga B. Myneni

  4. Laboratoire des Sciences du Climat et de l’Environnement (LSCE), CEA-CNRS-UVSQ, 91191 Gif sur Yvette Cedex, France ,

    Philippe Ciais

  5. Jet Propulsion Laboratory, Pasadena, 91109, California, USA

    Sassan Saatchi

  6. ARC Centre of Excellence for Climate Systems Science & Climate Change Research Centre, University of New South Wales, Sydney, 2052, New South Wales, Australia

    Yi Y. Liu

  7. Department of Ecology, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China,

    Shilong Piao

  8. Key Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Haishan Chen

  9. NASA Goddard Space Flight Center, Code 619, Greenbelt, 20771, Maryland, USA

    Eric F. Vermote

  10. Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, 29599, North Carolina, USA

    Conghe Song

  11. School of Forestry and Landscape Architecture, Anhui Agricultural University, Hefei, Anhui 230036, China ,

    Conghe Song

  12. Institute for the Environment, University of North Carolina at Chapel Hill, Chapel Hill, 29599, North Carolina, USA

    Taehee Hwang

Authors
  1. Liming Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuhong Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ranga B. Myneni
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Philippe Ciais
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sassan Saatchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yi Y. Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shilong Piao
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Haishan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Eric F. Vermote
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Conghe Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Taehee Hwang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L.Z. and Y.T. contributed the central idea, analysed most of the data, and wrote the initial draft of the paper. The remaining authors contributed to refining the ideas, carrying out additional analyses and finalizing this paper.

Corresponding author

Correspondence to Liming Zhou.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Statistics of MODIS data quality and MODIS high-quality EVI mask.

a–d, Seasonal statistics of the total number of high-quality MODIS EVI composites over forested pixels at 0.05° resolution in central tropical Africa (7° S–7° N, 5–31 °E) for the period 2000–2012. For each season, there are up to 39 EVI composites (three composites per year multiplied by 13 years) for every pixel. e, The climatology of MODIS percentage forest cover at 0.05° resolution. f, The high-quality MODIS April–May–June EVI mask at 0.25° resolution over the intact Congo forest (6° S–5° N, 14–31° E) used in the analysis (see details in Methods).

Extended Data Figure 2 Spatial patterns of linear trends in April–May–June TWS (cm per decade) and correlation coefficients R between TWS and EVI for the period 2003–2012.

a–c, As in Fig. 2e but for TWS from individual data processing centres (the University of Texas’ Center for Space Research (CSR), NASA’s Jet Propulsion Laboratory (JPL) and Germany’s GeoForschungsZentrum (GFZ)). d, The topography of the Congo basin (http://en.wikipedia.org/wiki/File:Congo_Kinshasa_Topography.png). e, f, R between April–May–June EVI and ensemble-mean TWS in April–May–June (AMJ) and January–February–March (JFM). The significance level of R (its P value) is estimated using a two-tailed Student’s t-test. Pixels with a plus symbol have a linear trend or an R that is statistically significant at P < 0.1. The percentages of pixels with trends or R at P < 0.05 and P < 0.1 and the percentages of pixels with negative trends or positive R over the study region are shown.

Extended Data Figure 3 Regional mean anomalies and linear trends per decade for COT and AOT (unitless) in April–May–June for the period 2000–2012.

a, b, As in Fig. 3a. c, d, As in Fig. 2a. The dramatic AOT increase in 2004 is due to volcanic eruptions of the mountains Nyamulagira and Nyiragongo, which are located on the eastern border of the study region, on 25 May 2004. However, if the year 2004 is excluded, the AOT changes little.

Extended Data Figure 4 Regional mean anomalies and linear trends per decade for PAR and LST in April–May–June for the period 2003–2012.

a, c, PAR (W m−2); b, d, LST (°C); a, b, as in Fig. 3c; c, d, as in Fig. 2b.

Extended Data Figure 5 Annual mean VOD anomalies (unitless; a) and linear trends per decade (b).

For the period 1988–2010 (as in Fig. 4).

Extended Data Figure 6 Spatial patterns of linear trends per decade in April–May–June for MODIS reflectance in the blue (BLU; a), red (RED; b) and near-infrared (NIR; c) spectral bands.

For the period 2000–2012 (as in Fig. 2a).

Extended Data Figure 7 Regional mean anomalies for MODIS EVI and reflectance in the blue (BLU), red (RED) and near-infrared (NIR) spectral bands.

For the period of 2000–2012 (a) (as in Fig. 3a) and 2003–2012 (b) (as in Fig. 3c).

Extended Data Figure 8 Simulated surface reflectance values in the MODIS red (RED; a), near-infrared (NIR; b) and blue (BLU; c) bands using the 6S radiative transfer code for 25% overestimation or 25% underestimation of AOT.

There are 30 cases (cases 1–10 correspond to a small AOT load, AOT = 0.1; cases 11–20 correspond to a medium AOT load, AOT = 0.3; cases 21–30 correspond to a large AOT load, AOT = 0.5) and the actual reflectance is 0.03, 0.3 and 0.02 in RED, NIR and BLU, respectively (see details in Supplementary Table 1).

Extended Data Figure 9 Regional mean anomalies (unitless) and linear trends per decade for MODIS EVI and NDVI.

For bidirectional reflectance distribution function (BRDF)-corrected EVI (a, c) calculated from MCD43C4 and for MODIS NDVI (b, d) from MOD13C2 (as in Extended Data Fig. 4).

Extended Data Figure 10 Temporal dynamics of vegetation for four Landsat 7 ETM+ scenes.

a, Locations of the Landsat scenes (P176R057, P177R057, P178R057 and P177R058). b, Mean temporal variations of NDVI for cloud-free pixels with NDVI ≥ 0.5 in the first of the image time series. c, Mean temporal variations of EVI for the same pixels as in b. d, Mean temporal trajectory of vegetation in the brightness–greenness space of the Tasseled Cap transformation (see details in Supplementary Information section D). A decrease in greenness associated with an increase in brightness signifies forest degradation.

Supplementary information

Supplementary Information

This file contains Supplementary Text, Supplementary Tables 1-4 and Supplementary References. (PDF 302 kb)

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhou, L., Tian, Y., Myneni, R. et al. Widespread decline of Congo rainforest greenness in the past decade. Nature 509, 86–90 (2014). https://doi.org/10.1038/nature13265

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature13265

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing