Widespread decline of Congo rainforest greenness in the past decade

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.

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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.

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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

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.

Correspondence to Liming Zhou.

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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.

ad, 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.

ac, 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.

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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

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