Yhasmin Mendes de Moura

Vegetation Dynamics and Rainfall Sensitivity of the Amazon

Significance Understanding the sensitivity of tropical vegetation to changes in precipitation is of key importance for assessing the fate of the Amazon rainforest and predicting atmospheric CO2 levels. Using improved satellite observations, we reconcile observational and modeling studies by showing that tropical vegetation is highly sensitive to changes in precipitation and El Niño events. Our results show that, since the year 2000, the Amazon forest has declined across an area of 5.4 million km2 as a result of well-described reductions in rainfall. We conclude that, if drying continues across Amazonia, which is predicted by several global climate models, this drying may accelerate global climate change through associated feedbacks in carbon and hydrological cycles. We show that the vegetation canopy of the Amazon rainforest is highly sensitive to changes in precipitation patterns and that reduction in rainfall since 2000 has diminished vegetation greenness across large parts of Amazonia. Large-scale directional declines in vegetation greenness may indicate decreases in carbon uptake and substantial changes in the energy balance of the Amazon. We use improved estimates of surface reflectance from satellite data to show a close link between reductions in annual precipitation, El Niño southern oscillation events, and photosynthetic activity across tropical and subtropical Amazonia. We report that, since the year 2000, precipitation has declined across 69% of the tropical evergreen forest (5.4 million km2) and across 80% of the subtropical grasslands (3.3 million km2). These reductions, which coincided with a decline in terrestrial water storage, account for about 55% of a satellite-observed widespread decline in the normalized difference vegetation index (NDVI). During El Niño events, NDVI was reduced about 16.6% across an area of up to 1.6 million km2 compared with average conditions. Several global circulation models suggest that a rise in equatorial sea surface temperature and related displacement of the intertropical convergence zone could lead to considerable drying of tropical forests in the 21st century. Our results provide evidence that persistent drying could degrade Amazonian forest canopies, which would have cascading effects on global carbon and climate dynamics.

View-illumination effects on hyperspectral vegetation indices in the Amazonian tropical forest

Abstract Because of the pointing capability of the Hyperion/Earth Observing-One (EO-1) to improve the revisit time of the scene, temporal series of narrowband vegetation indices (VIs) can be generated to study the phenology of the Amazonian tropical forests. In this study, 10 selected narrowband VIs calculated from Hyperion nadir and off-nadir data and from different view directions (forward scattering and backscattering) were analyzed for their sensitivity to view-illumination effects along the dry season on the Seasonal Semi-deciduous Forest. Data analysis was also supported by PROSAIL modeling to simulate the spectral response of this forest type in both directions. Hyperion and PROSAIL results showed that the Enhanced Vegetation Index (EVI) and Photochemical Reflectance Index (PRI) were the two more anisotropic VIs, whereas the Normalized Difference Vegetation Index (NDVI), Structure Insensitive Pigment Index (SIPI) and the Vogelmann Red Edge Index (VOG) were comparatively less sensitive to view-illumination effects. When compared to the other VIs and because of the greater dependence on the near-infrared (NIR) reflectance, EVI showed a different spectral behavior. EVI increased from forward scattering to backscattering and with decreasing solar zenith angle (SZA) towards the end of the local dry season, due to reduction in shading and enhancement of the illumination effects. On the other hand, PRI was higher with increasing shading in the forward scattering direction, as deduced from the PROSAIL simulation. Results emphasized the importance of taking into account bidirectional effects when analyzing temporal series of VIs collected over tropical forests by imaging spectrometers with pointing capability or even by multispectral sensors with large field-of-view (FOV).

Spectral anisotropy of subtropical deciduous forest using MISR and MODIS data acquired under large seasonal variation in solar zenith angle

Abstract Recent studies in Amazonian tropical evergreen forests using the Multi-angle Imaging SpectroRadiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) have highlighted the importance of considering the view-illumination geometry in satellite data analysis. However, contrary to the observed for evergreen forests, bidirectional effects have not been evaluated in Brazilian subtropical deciduous forests. In this study, we used MISR data to characterize the reflectance and vegetation index anisotropies in subtropical deciduous forest from south Brazil under large seasonal solar zenith angle (SZA) variation and decreasing leaf area index (LAI) from the summer to winter. MODIS data were used to observe seasonal changes in the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Topographic effects on their determination were inspected by dividing data from the summer to winter and projecting results over a digital elevation model (DEM). By using the PROSAIL, we investigated the relative contribution of LAI and SZA to vegetation indices (VI) of deciduous forest. We also simulated and compared the MISR NDVI and EVI response of subtropical deciduous and tropical evergreen forests as a function of the large seasonal SZA amplitude of 33°. Results showed that the MODIS-MISR NDVI and EVI presented higher values in the summer and lower ones in the winter with decreasing LAI and increasing SZA or greater amounts of canopy shadows viewed by the sensors. In the winter, NDVI reduced local topographic effects due to the red-near infrared (NIR) band normalization. However, the contrary was observed for the three-band EVI that enhanced local variations in shaded and sunlit surfaces due to its strong dependence on the NIR band response. The reflectance anisotropy of the MISR bands increased from the summer to winter and was stronger in the backscattering direction at large view zenith angles (VZA). EVI was much more anisotropic than NDVI and the anisotropy increased from the summer to winter. It also increased from the forward scatter to the backscattering direction with the predominance of sunlit canopy components viewed by MISR, especially at large VZA. Modeling PROSAIL results confirmed the stronger anisotropy of EVI than NDVI for the subtropical deciduous and tropical evergreen forests. PROSAIL showed that LAI and SZA are coupled factors to decrease seasonally the VIs of deciduous forest with the first one having greater importance than the latter. However, PROSAIL seasonal variations in VIs were much smaller than those observed with MODIS data probably because the effects of shadows in heterogeneous canopy structures or/and cast by emergent trees and from local topography were not modeled.

Consistency of Vegetation Index Seasonality Across the Amazon Rainforest

Vegetation indices (VIs) calculated from remotely sensed reflectance are widely used tools for characterizing the extent and status of vegetated areas. Recently, however, their capability to monitor the Amazon forest phenology has been intensely scrutinized. In this study, we analyze the consistency of VIs seasonal patterns obtained from two MODIS products: the Collection 5 BRDF product (MCD43) and the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC). The spatio-temporal patterns of the VIs were also compared with field measured leaf litterfall, gross ecosystem productivity and active microwave data. Our results show that significant seasonal patterns are observed in all VIs after the removal of view-illumination effects and cloud contamination. However, we demonstrate inconsistencies in the characteristics of seasonal patterns between different VIs and MODIS products. We demonstrate that differences in the original reflectance band values form a major source of discrepancy between MODIS VI products. The MAIAC atmospheric correction algorithm significantly reduces noise signals in the red and blue bands. Another important source of discrepancy is caused by differences in the availability of clear-sky data, as the MAIAC product allows increased availability of valid pixels in the equatorial Amazon. Finally, differences in VIs seasonal patterns were also caused by MODIS collection 5 calibration degradation. The correlation of remote sensing and field data also varied spatially, leading to different temporal offsets between VIs, active microwave and field measured data. We conclude that recent improvements in the MAIAC product have led to changes in the characteristics of spatio-temporal patterns of VIs seasonality across the Amazon forest, when compared to the MCD43 product. Nevertheless, despite improved quality and reduced uncertainties in the MAIAC product, a robust biophysical interpretation of VIs seasonality is still missing.

Spectral/textural attributes from ALI/EO-1 for mapping primary and secondary tropical forests and studying the relationships with biophysical parameters

We analysed spectral and textural attributes from the Advanced Land Imager (ALI)/EO-1 for land-cover mapping and inspected their correlation with biophysical parameters of primary and secondary forests from Eastern Amazon. An artificial neural network (ANN) technique selected the most relevant spectral/textural attributes, which were combined for classification of the ALI scene. From the ANN land-cover map, areas classified as primary forest (PF), initial (SS1), intermediate (SS2) and advanced (SS3) stages of secondary succession were studied. Biophysical parameters were determined from field inventory of 40 sample plots. Results showed an overall classification accuracy of 79% using reflectance and 89% using the combined data set. The combined data set included the reflectance of ALI bands 3–9 and the texture metrics mean (bands 3–4; 6–8) and dissimilarity (band 8). The reflectance of the near-infrared/shortwave infrared bands and their texture mean decreased from SS1 to SS3/PF. The gradient between primary and secondary forests controlled the correlations of reflectance with biophysical parameters. While the aboveground biomass, basal area, leaf area index, tree height and canopy cover increased from SS1 to SS3/PF, the reflectance decreased with the development of canopy structure and the resultant canopy shadows. The mean was the only texture metric correlated with biophysical parameters.

Following a site-specific secondary succession in the Amazon using the Landsat CDR product and field inventory data

Secondary forests cover large areas and are strong carbon sinks in tropical regions. They are important for ecosystem functioning, biodiversity conservation, watershed protection, and recovery of soil fertility. In this study, we used the Surface Reflectance Climate Data Record (CDR) product from 16 Thematic Mapper (TM)/Landsat-5 images (1984–2010) to continuously track the secondary succession (SS) of a forest following land abandonment in 1980. Changes in canopy structure and floristic composition were analysed using data from four field inventories (1995, 2002, 2007, and 2012). To characterize variations in brightness, greenness, spectral reflectance, and shadows with the natural regeneration of vegetation, we applied tasselled cap transformations, principal component analysis (PCA), and linear spectral mixture models to the TM datasets. Shade fractions were plotted over time and correlated with the enhanced vegetation index (EVI) and the normalized difference vegetation index (NDVI). Because image texture may reflect the variability of the successional process, eight co-occurrence-based filter metrics were calculated for selected TM bands and plotted as a function of time since abandonment. The successional forest was compared to a nearby primary reference forest (PF) and had differences in the spectral and textural means evaluated using analysis of variance (ANOVA). The results showed increases of 35% and 10.4% over time in basal area and tree height, respectively. Species richness within the assemblage of sampling units increased from 14 to 71 between 1995 and 2012, and this trend was also confirmed using an individual-based rarefaction analysis. Species richness in 2012 was still lower than that observed in the PF site, which presented greater amounts of aboveground biomass (336.4 ± 17.0 ton ha−1 for PF versus 98.5 ± 21.4 ton ha−1 for SS in 2012). Brightness and greenness tasselled cap differences between the SS and PF rapidly decreased from 1984 (SS at the age of 4 years) to 1991 (age of 11 years). Brightness also decreased from 1997 to 2003, as indicated by PC1 scores and surface reflectance of the TM bands 4 (near infrared) and 5 (shortwave infrared). Spectral mixture shade fraction increased from young to old successional stages with strata composition and canopy structure development, whereas NDVI and EVI decreased over time. Because EVI was strongly dependent on near infrared reflectance (r = + 0.96), it was also much more strongly correlated with the shade fraction (r = −0.93) than NDVI. Except for the image texture mean that decreased from young to old successional stages in TM bands 4 and 5, no clear trend was observed in the remaining texture metrics over the time period of vegetation regeneration. Overall, due to structural-floristic and spectral/textural differences with the PF, the SS site was still distinguishable using Landsat data 30 years after land abandonment. Most of the spectral metric means between PF and SS were significantly different over time at 0.01 significance level, as indicated by ANOVA.

Progress in Remote Sensing of Photosynthetic Activity over the Amazon Basin

Although quantifying the massive exchange of carbon that takes place over the Amazon Basin remains a challenge, progress is being made as the remote sensing community moves from using traditional, reflectance-based vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), to the more functional Photochemical Reflectance Index (PRI). This new index, together with satellite-derived estimates of canopy light interception and Sun-Induced Fluorescence (SIF), provide improved estimates of Gross Primary Production (GPP). This paper traces the development of these new approaches, compares the results of their analyses from multiple years of data acquired across the Amazon Basin and suggests further improvements in instrument design, data acquisition and processing. We demonstrated that our estimates of PRI are in generally good agreement with eddy-flux tower measurements of photosynthetic light use efficiency (ε) at four sites in the Amazon Basin: r2 values ranged from 0.37 to 0.51 for northern flux sites and to 0.78 for southern flux sites. This is a significant advance over previous approaches seeking to establish a link between global-scale photosynthetic activity and remotely-sensed data. When combined with measurements of Sun-Induced Fluorescence (SIF), PRI provides realistic estimates of seasonal variation in photosynthesis over the Amazon that relate well to the wet and dry seasons. We anticipate that our findings will steer the development of improved approaches to estimate photosynthetic activity over the tropics.

Vulnerability of Amazonian forests to repeated droughts

Extreme droughts have been recurrent in the Amazon over the past decades, causing socio-economic and environmental impacts. Here, we investigate the vulnerability of Amazonian forests, both undisturbed and human-modified, to repeated droughts. We defined vulnerability as a measure of (i) exposure, which is the degree to which these ecosystems were exposed to droughts, and (ii) its sensitivity, measured as the degree to which the drought has affected remote sensing-derived forest greenness. The exposure was calculated by assessing the meteorological drought, using the standardized precipitation index (SPI) and the maximum cumulative water deficit (MCWD), which is related to vegetation water stress, from 1981 to 2016. The sensitivity was assessed based on the enhanced vegetation index anomalies (AEVI), derived from the newly available Moderate Resolution Imaging Spectroradiometer (MODIS)/Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) product, from 2003 to 2016, which is indicative of forests photosynthetic capacity. We estimated that 46% of the Brazilian Amazon biome was under severe to extreme drought in 2015/2016 as measured by the SPI, compared with 16% and 8% for the 2009/2010 and 2004/2005 droughts, respectively. The most recent drought (2015/2016) affected the largest area since the drought of 1981. Droughts tend to increase the variance of the photosynthetic capacity of Amazonian forests as based on the minimum and maximum AEVI analysis. However, the area showing a reduction in photosynthetic capacity prevails in the signal, reaching more than 400 000 km2 of forests, four orders of magnitude larger than areas with AEVI enhancement. Moreover, the intensity of the negative AEVI steadily increased from 2005 to 2016. These results indicate that during the analysed period drought impacts were being exacerbated through time. Forests in the twenty-first century are becoming more vulnerable to droughts, with larger areas intensively and negatively responding to water shortage in the region. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.

Scaling estimates of vegetation structure in Amazonian tropical forests using multi-angle MODIS observations

Detailed knowledge of vegetation structure is required for accurate modelling of terrestrial ecosystems, but direct measurements of the three dimensional distribution of canopy elements, for instance from LiDAR, are not widely available. We investigate the potential for modelling vegetation roughness, a key parameter for climatological models, from directional scattering of visible and near-infrared (NIR) reflectance acquired from NASAs Moderate Resolution Imaging Spectroradiometer (MODIS). We compare our estimates across different tropical forest types to independent measures obtained from: (1) airborne laser scanning (ALS), (2) spaceborne Geoscience Laser Altimeter System (GLAS)/ICESat, and (3) the spaceborne SeaWinds/QSCAT. Our results showed linear correlation between MODIS-derived anisotropy to ALS-derived entropy (r2= 0.54, RMSE=0.11), even in high biomass regions. Significant relationships were also obtained between MODIS-derived anisotropy and GLAS-derived entropy (0.52≤ r2≤ 0.61; p<0.05), with similar slopes and offsets found throughout the season, and RMSE between 0.26 and 0.30 (units of entropy). The relationships between the MODIS-derived anisotropy and backscattering measurements (σ0) from SeaWinds/QuikSCAT presented an r2 of 0.59 and a RMSE of 0.11. We conclude that multi-angular MODIS observations are suitable to extrapolate measures of canopy entropy across different forest types, providing additional estimates of vegetation structure in the Amazon.

Reply to Gonsamo et al.: Effect of the Eastern Atlantic-West Russia pattern on Amazon vegetation has not been demonstrated

Gonsamo et al. (1) use 8-km satellite data from advanced very high-resolution imaging spectroradiometer (AVHRR) global inventory modeling and mapping studies (GIMMS) to demonstrate the role of climatic oscillations, specifically the East Atlantic-West Russia (EA-WR) pattern, on interannual dynamics of Amazon greenness. Hilker et al. (2) do not investigate EA-WR but focus on the El Nino southern oscillation (ENSO), a pattern that is well known to affect climate throughout South America and the Pacific region (3). Gonsamo et al. (1) do not challenge these results but claim that EA-WR, more than ENSO, may “explain the entire ensuing year Amazon vegetation greenness dynamics.” We are unable to judge this claim based on our findings (2), but argue that the authors do not present a convincing case. EA-WR is a teleconnection pattern whose anomalies result in above-average temperatures over eastern Asia and below-average temperatures over large portions of western Russia and northeastern Africa. A connection between North Atlantic sea surface temperature (SST) and the likelihood of an El Nino onset has been demonstrated (4). A direct approach to prove the superior explanatory power of EA-WR compared with ENSO would have been to use the same normalized difference vegetation index (NDVI) dataset shown in figure 1 of Hilker et al. (2) and demonstrate a better correlation between NDVI and EA-WR. Figure 1 A–D in Gonsamo et al. actually confirms a stronger connection between annual precipitation and ENSO than with annual precipitation and EA-WR (1). The lack of correlation in figure 1 E–G of Gonsamo et al. is not surprising given the high noise level in AVHRR GIMMS that largely prevents detection of trends over tropical vegetation (5). Proof of statistical significance of changes in GIMMS NDVI is missing. The spatial patterns in figure 1H seem unconnected to those in figure 1 C and D, which begs the question of what climate factor, if not precipitation, drives those changes in NDVI. The connections between EA-WR and ensuing year precipitation and EA-WR and ensuing year NDVI (figure 1 I and J) seem to contradict the findings in figure 1 D and H; at the very least, the distinction between those figures is not clear. The Pearson R values presented in figure 1 I and J are extremely low. Parts J and K in figure 1 are not comparable because figure 1K shows monthly mean values (2), whereas figure 1J shows interannual variation. The intent of the analysis shown in figure 1K was to demonstrate that Amazon forests initially respond positively to seasonal reductions in rainfall, whereas grasslands respond negatively. Why the authors included this figure in the presented context is unclear. On a side note, Gonsamo et al. wrongly claim that Hilker et al. (2) demonstrate that a lack of correlation between moderate resolution imaging spectroradiometer (MODIS) NDVI and ENSO can be attributed to normalizing MODIS reflectance to a common view and sensor geometry (1). Hilker et al. demonstrate that directionally normalized NDVI observations show seasonal variation, contrary to previous findings (6).