Drought impacts on the Amazon forest: the remote sensing perspective

Increasing and widespread vulnerability of intact tropical rainforests to repeated droughts

Intact tropical rainforests have been exposed to severe droughts in recent decades, which may threaten their integrity, their ability to sequester carbon, and their capacity to provide shelter for biodiversity. However, their response to droughts remains uncertain due to limited high-quality, long-term observations covering extensive areas. Here, we examined how the upper canopy of intact tropical rainforests has responded to drought events globally and during the past 3 decades. By developing a long pantropical time series (1992 to 2018) of monthly radar satellite observations, we show that repeated droughts caused a sustained decline in radar signal in 93%, 84%, and 88% of intact tropical rainforests in the Americas, Africa, and Asia, respectively. Sudden decreases in radar signal were detected around the 1997-1998, 2005, 2010, and 2015 droughts in tropical Americas; 1999-2000, 2004-2005, 2010-2011, and 2015 droughts in tropical Africa; and 1997-1998, 2006, and 2015 droughts in tropical Asia. Rainforests showed similar low resistance (the ability to maintain predrought condition when drought occurs) to severe droughts across continents, but American rainforests consistently showed the lowest resilience (the ability to return to predrought condition after the drought event). Moreover, while the resistance of intact tropical rainforests to drought is decreasing, albeit weakly in tropical Africa and Asia, forest resilience has not increased significantly. Our results therefore suggest the capacity of intact rainforests to withstand future droughts is limited. This has negative implications for climate change mitigation through forest-based climate solutions and the associated pledges made by countries under the Paris Agreement.

Assessing Drought Response in the Southwestern Amazon Forest by Remote Sensing and In Situ Measurements

Long-term meteorological analyzes suggest an increase in air temperature and a decrease in rainfall over the Amazon biome. The effect of these climate changes on the forest remains unresolved, because field observations on functional traits are sparse in time and space, and the results from remote sensing analyses are divergent. Then, we analyzed the drought response in a ‘terra firme’ forest fragment in the southwestern Amazonia, during an extreme drought event influenced by ENSO episode (2015/2017), focusing on stem growth, litter production, functional traits and forest canopy dynamics. We use the Moderate Resolution Imaging Spectroradiometer (MODIS), corrected by Multi-Angle Implementation of Atmospheric Correction (MAIAC) to generate the enhanced vegetation index (EVI) and green chromatic coordinate (Gcc) vegetation indices. We monitor stem growth and measure the functional traits of trees in situ, such as the potential at which the plant loses 50% of hydraulic conductivity (P50), turgor loss point (πTLP), hydraulic safety margin (HSM) and isohydricity. Our results suggest that: (a) during the dry season, there is a smooth reduction in EVI values (browning) and an increase in the wet season (greening); (b) in the dry season, leaf flush occurs, when the water table still has a quota at the limit of the root zone; (c) the forest showed moderate resistance to drought, with water as the primary limiting factor, and the thickest trees were the most resistant; and (d) a decline in stem growth post-El-Niño 2015/2016 was observed, suggesting that the persistence of negative rainfall anomalies may be as critical to the forest as the drought episode itself.

A copula model integrating atmospheric moisture demand and supply for vegetation vulnerability mapping

Drought caused by various meteorological factors negatively affects vegetation. Constructing a joint probability distribution between vegetation and drought information may be appropriate to understand the vulnerability of vegetation to drought. In this study, a copula-based trivariate joint probability model is proposed to investigate the effects of various aspects of meteorological drought on vegetation (vegetation drought). Because drought can be caused by insufficient precipitation or excessive evapotranspiration, the meteorological drought risk for vegetation was divided into two aspects (atmospheric moisture supply and moisture demand). The vulnerability of vegetation drought was mapped when two aspects of meteorological drought occurred separately or simultaneously at high spatial resolution using remote sensing data. The results revealed that the response of vegetation was significantly different depending on the climatic stressors. Although the sensitivity of vegetation to each drought condition varied from region to region, it was found that vegetation was more vulnerable to drought caused by atmospheric moisture demand in most regions of Far East Asia. It has also been shown that drought conditions, which overlapped with insufficient precipitation and excessive evapotranspiration, can drive vegetation to a far more lethal level. Meanwhile, through comparison with the existing VTCI, the proposed Normalized Vegetation Temperature Condition Index (nVTCI) was found to be able to more rationally monitor vegetation drought in the Far East Asian region.

Assessing the Potential of Downscaled Far Red Solar-Induced Chlorophyll Fluorescence from the Canopy to Leaf Level for Drought Monitoring in Winter Wheat

Solar-induced chlorophyll fluorescence (SIF) from ground, airborne, and satellite-based observations has been increasingly used in drought monitoring recently due to its close relationship with photosynthesis. SIF emissions respond rapidly to droughts, relative to the widely used vegetation indices (VIs), thus indicating their potential for early drought monitoring. The response of SIF to droughts can be attributed to the confounding effects of both the physiology and canopy structure. In order to reduce the reabsorption and scattering effects, the total emitted SIF (SIFtot) was proposed and served as a better tool to estimate GPP compared with the top-of-canopy SIF (SIFtoc). However, the response time and response magnitude of SIFtot to droughts and its relationships with the environmental parameters and soil moisture (SM) (i.e., the knowledge of drought monitoring using SIFtot) remains unclear. Here, the continuous ground data of F760toc (SIFtoc at 760 nm) from a nadir view that was downscaled to F760tot (SIFtot at 760 nm), NIRv, and the NDVI, SM, meteorological, and crop growth parameters were measured from four winter wheat plots with different intensities of drought (well-watered, moderate drought, severe drought, and extreme drought) over 2 months. The results indicated that F760tot was more closely correlated with the SM than the VIs at short time lags but weaker at longer time lags. The daily mean values of F760tot and NIRv were able to distinguish the differences between different drought levels, and F760tot responded quickly to the onset of drought, especially for the moderate drought intensity. These findings demonstrated that F760tot has potential for early drought monitoring and may contribute to mitigating the risk of agricultural drought.

A Study on Sensitivities of Tropical Forest GPP Responding to the Characteristics of Drought—A Case Study in Xishuangbanna, China

Droughts that occur in tropical forests (TF) are expected to significantly impact the gross primary production (GPP) and the capacity of carbon sinks. Therefore, it is crucial to evaluate and analyze the sensitivities of TF-GPP to the characteristics of drought events for understanding global climate change. In this study, the standardized precipitation index (SPI) was used to define the drought intensity. Then, the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM) was utilized to simulate the dynamic process of GPP corresponding to multi-gradient drought scenarios—rain and dry seasons × 12 level durations × 4 level intensities. The results showed that drought events in the dry season have a significantly greater impact on TF-GPP than drought events in the rainy season, especially short-duration drought events. Furthermore, the impact of drought events in the rainy season is mainly manifested in long-duration droughts. Due to abundant rainfall in the rainy season, only extreme drought events caused a significant reduction in GPP, while the lack of water in the dry season caused significant impacts due to light drought. Effective precipitation and soil moisture stock in the rainy season are the most important support for the tropical forest dry season to resist extreme drought events in the study area. Further water deficit may render the tropical forest ecosystem more sensitive to drought events.

JA, Sanchez‐Azofeifa GA, De Deurwaerder HPT, Krishna Moorthy SM, Schnitzer SA, Marvin DC, Longo M, Liu C, Broadbent EN, Almeyda Zambrano AM, Muller‐Landau HC, Detto M, Verbeeck H. Liana optical traits increase tropical forest albedo and reduce ecosystem productivity

Lianas are a key growth form in tropical forests. Their lack of self-supporting tissues and their vertical position on top of the canopy make them strong competitors of resources. A few pioneer studies have shown that liana optical traits differ on average from those of colocated trees. Those trait discrepancies were hypothesized to be responsible for the competitive advantage of lianas over trees. Yet, in the absence of reliable modelling tools, it is impossible to unravel their impact on the forest energy balance, light competition, and on the liana success in Neotropical forests. To bridge this gap, we performed a meta-analysis of the literature to gather all published liana leaf optical spectra, as well as all canopy spectra measured over different levels of liana infestation. We then used a Bayesian data assimilation framework applied to two radiative transfer models (RTMs) covering the leaf and canopy scales to derive tropical tree and liana trait distributions, which finally informed a full dynamic vegetation model. According to the RTMs inversion, lianas grew thinner, more horizontal leaves with lower pigment concentrations. Those traits made the lianas very efficient at light interception and significantly modified the forest energy balance and its carbon cycle. While forest albedo increased by 14% in the shortwave, light availability was reduced in the understorey (-30% of the PAR radiation) and soil temperature decreased by 0.5°C. Those liana-specific traits were also responsible for a significant reduction of tree (-19%) and ecosystem (-7%) gross primary productivity (GPP) while lianas benefited from them (their GPP increased by +27%). This study provides a novel mechanistic explanation to the increase in liana abundance, new evidence of the impact of lianas on forest functioning, and paves the way for the evaluation of the large-scale impacts of lianas on forest biogeochemical cycles.

A comprehensive framework for seasonal controls of leaf abscission and productivity in evergreen broadleaved tropical and subtropical forests

Relationships among productivity, leaf phenology, and seasonal variation in moisture and light availability are poorly understood for evergreen broadleaved tropical/subtropical forests, which contribute 25% of terrestrial productivity. On the one hand, as moisture availability declines, trees shed leaves to reduce transpiration and the risk of hydraulic failure. On the other hand, increases in light availability promote the replacement of senescent leaves to increase productivity. Here, we provide a comprehensive framework that relates the seasonality of climate, leaf abscission, and leaf productivity across the evergreen broadleaved tropical/subtropical forest biome. The seasonal correlation between rainfall and light availability varies from strongly negative to strongly positive across the tropics and maps onto the seasonal correlation between litterfall mass and productivity for 68 forests. Where rainfall and light covary positively, litterfall and productivity also covary positively and are always greater in the wetter sunnier season. Where rainfall and light covary negatively, litterfall and productivity are always greater in the drier and sunnier season if moisture supplies remain adequate; otherwise productivity is smaller in the drier sunnier season. This framework will improve the representation of tropical/subtropical forests in Earth system models and suggests how phenology and productivity will change as climate change alters the seasonality of cloud cover and rainfall across tropical/subtropical forests.

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Three climate-phenology regimes are identified across tropical and subtropical forest biomes

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Where light and water limit plant in dry season, litterfall and productivity peak in sunny wet season

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Where light or water alternately limits plant, productivity peaks in wet season with low litterfall

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Where water does not limit plant, litterfall and productivity peak in sunny dry season

Analysis of how the spatial and temporal patterns of fire and their bioclimatic and anthropogenic drivers vary across the Amazon rainforest in El Niño and non-El Niño years

In the past two decades, Amazon rainforest countries (Brazil, Bolivia, Colombia, Ecuador, Guyana, Peru and Venezuela) have experienced a substantial increase in fire frequency due to the changes in the patterns of different anthropogenic and climatic drivers. This study examines how both fire dynamics and bioclimatic factors varied based on the season (wet season and dry season) El Niño years across the different countries and ecosystems within the Amazon rainforest. Data from publicly available databases on forest fires (Global Fire Atlas) and bioclimatic, topographic and anthropogenic variables were employed in the analysis. Linear mixed-effect models discovered that year type (El Niño vs. non-El Niño), seasonality (dry vs. wet), land cover and forest strata (in terms of canopy cover and intactness) and their interactions varied across the Amazonian countries (and the different ecosystems) under consideration. A machine learning model, Multivariate Adaptive Regression Spline (MARS), was utilized to determine the relative importance of climatic, topographic, forest structure and human modification variables on fire dynamics across wet and dry seasons, both in El Niño and non-El Niño years. The findings of this study make clear that declining precipitation and increased temperatures have strong impact on fire dynamics (size, duration, expansion and speed) for El Niño years. El Niño years also saw greater fire sizes and speeds as compared to non-El Niño years. Dense and relatively undisturbed forests were found to have the lowest fire activity and increased human impact on a landscape was associated with exacerbated fire dynamics, especially in the El Niño years. Additionally, the presence of grass-dominated ecosystems such as grasslands also acted as a driver of fire in both El Niño and non-El Niño years. Hence, from a conservation perspective, increased interventions during the El Niño periods should be considered.

Reviewing the Potential of Sentinel-2 in Assessing the Drought

This paper systematically reviews the potential of the Sentinel-2 (A and B) in assessing drought. Research findings, including the IPCC reports, highlighted the increasing trend in drought over the decades and the need for a better understanding and assessment of this phenomenon. Continuous monitoring of the Earth’s surface is an efficient method for predicting and identifying the early warnings of drought, which enables us to prepare and plan the mitigation procedures. Considering the spatial, temporal, and spectral characteristics, the freely available Sentinel-2 data products are a promising option in this area of research, compared to Landsat and MODIS. This paper evaluates the recent developments in this field induced by the launch of Sentinel-2, as well as the comparison with other existing data products. The objective of this paper is to evaluate the potential of Sentinel-2 in assessing drought through vegetation characteristics, soil moisture, evapotranspiration, surface water including wetland, and land use and land cover analysis. Furthermore, this review also addresses and compares various data fusion methods and downscaling methods applied to Sentinel-2 for retrieving the major bio-geophysical variables used in the analysis of drought. Additionally, the limitations of Sentinel-2 in its direct applicability to drought studies are also evaluated.

Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask?

Estimates of Amazon rainforest gross primary productivity (GPP) differ by a factor of 2 across a suite of three statistical and 18 process models. This wide spread contributes uncertainty to predictions of future climate. We compare the mean and variance of GPP from these models to that of GPP at six eddy covariance (EC) towers. Only one model's mean GPP across all sites falls within a 99% confidence interval for EC GPP, and only one model matches EC variance. The strength of model response to climate drivers is related to model ability to match the seasonal pattern of the EC GPP. Models with stronger seasonal swings in GPP have stronger responses to rain, light, and temperature than does EC GPP. The model to data comparison illustrates a trade-off inherent to deterministic models between accurate simulation of a mean (average) and accurate responsiveness to drivers. The trade-off exists because all deterministic models simplify processes and lack at least some consequential driver or interaction. If a model's sensitivities to included drivers and their interactions are accurate, then deterministically predicted outcomes have less variability than is realistic. If a GPP model has stronger responses to climate drivers than found in data, model predictions may match the observed variance and seasonal pattern but are likely to overpredict GPP response to climate change. High or realistic variability of model estimates relative to reference data indicate that the model is hypersensitive to one or more drivers.

Assessing Freshwater Changes over Southern and Central Africa (2002–2017)

In large freshwater river basins across the globe, the composite influences of large-scale climatic processes and human activities (e.g., deforestation) on hydrological processes have been studied. However, the knowledge of these processes in this era of the Anthropocene in the understudied hydrologically pristine South Central African (SCA) region is limited. This study employs satellite observations of evapotranspiration (ET), precipitation and freshwater between 2002 and 2017 to explore the hydrological patterns of this region, which play a crucial role in global climatology. Multivariate methods, including the rotated principal component analysis (rPCA) were used to assess the relationship of terrestrial water storage (TWS) in response to climatic units (precipitation and ET). The use of the rPCA technique in assessing changes in TWS is warranted to provide more information on hydrological changes that are usually obscured by other dominant naturally-driven fluxes. Results show a low trend in vegetation transpiration due to deforestation around the Congo basin. Overall, the Congo (r2 = 76%) and Orange (r2 = 72%) River basins maintained an above-average consistency between precipitation and TWS throughout the study region and period. Consistent loss in freshwater is observed in the Zambezi (−9.9 ± 2.6 mm/year) and Okavango (−9.1 ± 2.5 mm/year) basins from 2002 to 2008. The Limpopo River basin is observed to have a 6% below average reduction in rainfall rates which contributed to its consistent loss in freshwater (−4.6 ± 3.2 mm/year) from 2006 to 2012.Using multi-linear regression and correlation analysis we show that ET contributes to the variability and distribution of TWS in the region. The relationship of ET with TWS (r = 0.5) and rainfall (r = 0.8) over SCA provides insight into the role of ET in regulating fluxes and the mechanisms that drive precipitation in the region. The moderate ET–TWS relationship also shows the effect of climate and anthropogenic influence in their interactions.

Long-Term Spatiotemporal Variation of Droughts in the Amazon River Basin

The Amazon River Basin (ARB) plays an important role in the hydrological cycle at the regional and global scales. According to the Intergovernmental Panel on Climate Change (IPCC), the incidence and severity of droughts could increase in this basin due to human-induced climate change. Therefore, the assessment of the impacts of extreme droughts in the ARB is of vital importance to develop appropriate drought mitigation strategies. The purpose of this study is to provide a comprehensive characterization of dry spells and extreme drought events in terms of occurrence, persistence, spatial extent, severity, and impacts on streamflow and vegetation in the ARB during the period 1901–2018. The Standardized Precipitation-Evapotranspiration Index (SPEI) at multiple time scales (i.e., 3, 6, and 12 months) was used as a drought index. A weak basin-wide drying trend was observed, but there was no evidence of a trend in extreme drought events in terms of spatial coverage, intensity, and duration for the period 1901–2018. Nevertheless, a progressive transition to drier-than-normal conditions was evident since the 1970s, coinciding with different patterns of coupling between the El Niño/Southern Oscillation (ENSO) phenomenon and the Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), and Madden–Julian Oscillation (MJO) as well as an increasing incidence of higher-than-normal surface air temperatures over the basin. Furthermore, a high recurrence of short-term drought events with high level of exposure to long-term drought conditions on the sub-basins Ucayali, Japurá-Caquetá, Jari, Jutaí, Marañón, and Xingu was observed in recent years. These results could be useful to guide social, economic, and water resource policy decision-making processes in the Amazon basin countries.

Competing effects of soil fertility and toxicity on tropical greening

Tropical forests are expected to green up with increasing atmospheric CO2 concentrations, but primary productivity may be limited by soil nutrient availability. However, rarely have canopy-scale measurements been assessed against soil measurements in the tropics. Here, we sought to assess remotely sensed canopy greenness against steep soil nutrient gradients across 50 1-ha mature forest plots in Panama. Contrary to expectations, increases in in situ extractable soil phosphorus (P) and base cations (K, Mg) corresponded to declines in remotely sensed mean annual canopy greenness (r2 = 0.77-0.85; p < 0.1), controlling for precipitation. The reason for this inverse relationship appears to be that litterfall also increased with increasing soil P and cation availability (r2 = 0.88-0.98; p < 0.1), resulting in a decline in greenness with increasing annual litterfall (r2 = 0.94; p < 0.1). As such, greater soil nutrient availability corresponded to greater leaf turnover, resulting in decreased greenness. However, these decreases in greenness with increasing soil P and cations were countered by increases in greenness with increasing soil nitrogen (N) (r2 = 0.14; p < 0.1), which had no significant relationship with litterfall, likely reflecting a direct effect of soil N on leaf chlorophyll content, but not on litterfall rates. In addition, greenness increased with extractable soil aluminum (Al) (r2 = 0.97; p < 0.1), but Al had no significant relationship with litterfall, suggesting a physiological adaptation of plants to high levels of toxic metals. Thus, spatial gradients in canopy greenness are not necessarily positive indicators of soil nutrient scarcity. Using a novel remote sensing index of canopy greenness limitation, we assessed how observed greenness compares with potential greenness. We found a strong relationship with soil N only (r2 = 0.65; p < 0.1), suggesting that tropical canopy greenness in Panama is predominantly limited by soil N, even if plant productivity (e.g., litterfall) responds to rock-derived nutrients. Moreover, greenness limitation was also significantly correlated with fine root biomass and soil carbon stocks (r2 = 0.62-0.71; p < 0.1), suggesting a feedback from soil N to canopy greenness to soil carbon storage. Overall, these data point to the potential utility of a remote sensing product for assessing belowground properties in tropical ecosystems.

Using GRACE to quantify the depletion of terrestrial water storage in Northeastern Brazil: The Urucuia Aquifer System

Covering a plateau area of approximately 125,000 km², the Urucuia Aquifer System (UAS) represents a national strategic water resource in the drought-stricken Northeastern part of Brazil. Variations in terrestrial water storage (TWS) extracted using a three-model-ensemble from the Gravity Recovery and Climate Experiment (GRACE) mission showed a negative balance equal to water stress. Monthly GRACE-derived water storage changes from 2002 to 2014 were compared with those derived from an independent hydrologic water balance of the region using in situ measurements and estimated evapotranspiration rates. Trend analyses revealed a TWS depletion rate of 6.5 ± 2.6 mm yr^-1, but no significant decline in precipitation as observed from available data records. Water storage depletion was found to be driven by anthropogenic impacts rather than by natural climatic variability. The obtained results demonstrate that GRACE is able to adequately capture water storage changes at the subregional scale, particularly during dry seasons.

Performance of optimized hyperspectral reflectance indices and partial least squares regression for estimating the chlorophyll fluorescence and grain yield of wheat grown in simulated saline field conditions

To overcome the salinity threats to crop production in arid conditions, wheat cultivars should be developed with better performance with regard to key physiological traits. Although different chlorophyll fluorescence (ChlF) parameters, such as maximum quantum PSII photochemical efficiency (Fv/Fm), quantum yield of PSII (Φ_PSII), and non-photochemical quenching (NPQ) have been proven to be key physiological traits to improve salt tolerance, their evaluation is time-consuming. In this study, hyperspectral canopy reflectance was used to assess ChlF parameters and grain yield (GY) of two wheat cultivars growing in simulated saline field conditions and exposed to three salinity levels (control, 6.0 dS m^-1, and 12.0 dS m^-1). Different spectral reflectance indices (SRIs) were formulated as ratios based on contour maps and tested for their relationship with ChlF parameters. The performance of individual SRIs and partial least squares regression (PLSR) models based on ChlF parameters, all examined SRIs, or data fusion of combined ChlF and SRIs to estimate the GY was considered. All examined SRIs failed to assess Φ_PSII and NPQ under control condition, but most of them showed a moderate to strong relationship with both parameters under the salinity levels of 6.0 and 12.0 dS m^-1. The examined SRIs showed a moderate and strong relationship with Fv/Fm under conditions of 6.0 and 12.0 dS m^-1, respectively. Most SRIs correlated better with the three ChlF parameters for the salt-sensitive cultivar Sakha 61 than for the salt-tolerant cultivar Sakha 93. Several SRIs exhibited strong relationships with GY under the salinity levels of 6.0 and 12.0 dS m^-1 and for both cultivars. Overall, the PLSR models exhibited additional improvements for estimating and predicting GY in both calibration and validation datasets over that using individual SRIs. The PLSR model based on data fusion was the best model to accurately estimate GY in the validation model even under control conditions. This study, of a type rarely conducted in simulated saline field conditions, indicates that the ChlF parameters could be linked to hyperspectral reflectance data for the rapid and non-destructive assessment of photosynthetic status and prediction of wheat production under salt stress field conditions.

Downscaling satellite soil moisture using geomorphometry and machine learning

Annual soil moisture estimates are useful to characterize trends in the climate system, in the capacity of soils to retain water and for predicting land and atmosphere interactions. The main source of soil moisture spatial information across large areas (e.g., continents) is satellite-based microwave remote sensing. However, satellite soil moisture datasets have coarse spatial resolution (e.g., 25-50 km grids); and large areas from regional-to-global scales have spatial information gaps. We provide an alternative approach to predict soil moisture spatial patterns (and associated uncertainty) with higher spatial resolution across areas where no information is otherwise available. This approach relies on geomorphometry derived terrain parameters and machine learning models to improve the statistical accuracy and the spatial resolution (from 27km to 1km grids) of satellite soil moisture information across the conterminous United States on an annual basis (1991-2016). We derived 15 primary and secondary terrain parameters from a digital elevation model. We trained a machine learning algorithm (i.e., kernel weighted nearest neighbors) for each year. Terrain parameters were used as predictors and annual satellite soil moisture estimates were used to train the models. The explained variance for all models-years was >70% (10-fold cross-validation). The 1km soil moisture grids (compared to the original satellite soil moisture estimates) had higher correlations (improving from r2 = 0.1 to r2 = 0.46) and lower bias (improving from 0.062 to 0.057 m3/m3) with field soil moisture observations from the North American Soil Moisture Database (n = 668 locations with available data between 1991-2013; 0-5cm depth). We conclude that the fusion of geomorphometry methods and satellite soil moisture estimates is useful to increase the spatial resolution and accuracy of satellite-derived soil moisture. This approach can be applied to other satellite-derived soil moisture estimates and regions across the world.

Space-time analysis of vegetation trends and drought occurrence in domain area of tropical forest

The purpose of this study is to evaluate temporal trends in changes in vegetation patterns within the Sooretama Biological Reserve and its surroundings, located in Espirito Santo State, Brazil. The evaluation will be performed using the EVI and NDVI index of the MODIS sensor, the Mann-Kendall monotonic trend, Seasonal Trend Analysis methods, and monitoring drought events through the VCI drought index for the years 2007 through 2015. The tools utilized were the EVI and NDVI indexes of the MOD13Q1 product and LST from the MOD11A2 product. These indices were used in order to represent the dynamics of the study area biomass and then to analyze the drought occurrence using the index best-suited to the area of study, identified as VCI. The temporal trends in the data set were examined, pixel by pixel, by application of the Mann-Kendall monotonic technique, treating each pixel in space as a one-dimensional temporal series of 16-day cycles. To evaluate the seasonal trend, the analysis used the STA technique (Seasonal Trend Analysis) implemented in the ETM module. The characterization and spatial distribution of drought events were performed through the Vegetation Condition Index (VCI). The use of (a) images and seasonal curves produced by the monotonic trend of Mann-Kendall and (b) analysis of seasonal trends generated the response of the vegetation to climate variations. The VCI indicated a potential for drought occurrence analysis in regions and areas with different vegetation densities. So, the VCI can be used as a powerful tool to compose a comprehensive and early system alert of drought that can accompany the changes in spatial coverage of vegetation and severity of change. Lastly, the analysis of the data from the MODIS NDVI, EVI, and TST images indicated that the data is suitable to a space-time analysis of drought occurrences and vegetation trends.

Can We Use Satellite-Based FAPAR to Detect Drought?

Drought in Australia has widespread impacts on agriculture and ecosystems. Satellite-based Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) has great potential to monitor and assess drought impacts on vegetation greenness and health. Various FAPAR products based on satellite observations have been generated and made available to the public. However, differences remain among these datasets due to different retrieval methodologies and assumptions. The Quality Assurance for Essential Climate Variables (QA4ECV) project recently developed a quality assurance framework to provide understandable and traceable quality information for Essential Climate Variables (ECVs). The QA4ECV FAPAR is one of these ECVs. The aim of this study is to investigate the capability of QA4ECV FAPAR for drought monitoring in Australia. Through spatial and temporal comparison and correlation analysis with widely used Moderate Resolution Imaging Spectroradiometer (MODIS), Satellite Pour l’Observation de la Terre (SPOT)/PROBA-V FAPAR generated by Copernicus Global Land Service (CGLS), and the Standardized Precipitation Evapotranspiration Index (SPEI) drought index, as well as the European Space Agency’s Climate Change Initiative (ESA CCI) soil moisture, the study shows that the QA4ECV FAPAR can support agricultural drought monitoring and assessment in Australia. The traceable and reliable uncertainties associated with the QA4ECV FAPAR provide valuable information for applications that use the QA4ECV FAPAR dataset in the future.

Fragmentation, forest structure, and topography modulate impacts of drought in a tropical forest landscape

Climate models predict increases in drought conditions in many parts of the tropics. Yet the response of tropical forests to drought remains highly uncertain, especially with regards to the factors that generate spatial heterogeneity in drought response across landscapes. In this study, we used Landsat imagery to assess the impacts of a severe drought in 2015 across an ~80,000-ha landscape in Puerto Rico. Specifically, we asked whether drought effects varied systematically with topography and with forest age, height, and fragmentation. We quantified drought impacts using anomalies of two vegetation indices, the enhanced vegetation index (EVI) and normalized difference water index (NDWI), and fit random forest models of these metrics including slope, aspect, forest age, canopy height, and two indices of fragmentation as predictors. Drought effects were more severe on drier topographic positions, that is, steeper slopes and southwest-facing aspects, and in second-growth forests. Shorter and more fragmented forests were also more strongly affected by drought. We also assessed which factors were associated with stronger recovery from drought. Factors associated with more negative drought anomalies were also associated with more positive postdrought anomalies, suggesting that increased light availability as a result of drought led to high rates of recovery in forests more severely affected by drought. In general, recovery from drought was rapid across the landscape, with postdrought anomalies at or above average across the study area. This suggests that forests in Puerto Rico might be resilient to a single-year drought, though vulnerability to drought varies depending on forest characteristics and landscape position.

Widespread Decline in Vegetation Photosynthesis in Southeast Asia Due to the Prolonged Drought During the 2015/2016 El Niño

El Niño events are known to be associated with climate extremes and have substantial impacts on the global carbon cycle. The drought induced by strong El Niño event occurred in the tropics during 2015 and 2016. However, it is still unclear to what extent the drought could affect photosynthetic activities of crop and forest in Southeast Asia. Here, we used the satellite solar-induced chlorophyll fluorescence (SIF), which is a proxy of actual photosynthesis, along with traditional vegetation indices (Enhanced Vegetation Index, EVI) and total water storage to investigate the impacts of El Niño–induced droughts on vegetation productivity of the forest and crop in the Southeast Asia. We found that SIF was more sensitive to the water stress than traditional vegetation indices (EVI) to monitor drought for both evergreen broadleaf forest and croplands in Southeast Asia. The higher solar radiation partly offset the negative effects of droughts on the vegetation productivity, leading to a larger decrease of SIF yield (SIFyield) than SIF. Therefore, SIFyield had a larger reduction and was more sensitive to precipitation deficit than SIF during the drought. The comparisons of retrieved column-average dry-air mole fraction of atmospheric carbon dioxide with SIF demonstrated the reduction of CO2 uptake by vegetation in Southeast Asia during the drought. This study highlights that SIF is more beneficial than EVI to be an indicator to characterize and monitor the dynamics of drought in tropical vegetated regions.

Detecting Human Presence and Influence on Neotropical Forests with Remote Sensing

The Amazon, and Neotropical forests, are one of the most important global biomes because of their extent and unique biodiversity, as well as their importance to global climate and as a habitat and resource for humans. Unravelling the influence of human presence on these forests is fundamental to our understanding of the biodiversity, ecosystem function, and service-providing potential. Human presence in these tropical rainforests dates back 13,000 years, and the impacts of this presence are hotly debated. Some authors suggest persistent effects of pre-Columbian plant domestication on current Amazonian forest composition. Other authors suggest that post-Columbian influence on forest composition is orders of magnitude higher than that of pre-Columbian times. Evidence from remote sensing has become increasingly useful as a way to help settle these debates. Here we review past, current, and future uses of remote sensing technology to detect human infrastructure in the Amazon and other Neotropical forests over the several historical periods of human presence, from archaeological to post-modern societies. We define human presence in terms of activities that left behind a footprint, such as settlements, earth-mounds, roads, use of timber and fuelwood, agriculture, soil, etc. Lastly, we discuss opportunities and challenges for the use of remote sensing to provide data and information necessary to expand our understanding of the history of human occupation in the Neotropical forests, and how this human occupation might affect biodiversity. There have been many recent applications of remote sensing to the detection of Pre-Columbian human infrastructure, from visual inspection of aerial photographs over deforested sites to uses of LiDAR on airborne and UAV platforms to detect infrastructure and smaller settlements under the canopy. Similar efforts are yet to be conducted for the Post-Columbian period, especially during the colonization and imperialism periods. Finally, our knowledge of human impacts in the modern era (20th and 21st centuries) is not-surprisingly more extensive. Remote sensing is still under-used and extremely useful for this type of application, and new missions might provide solutions that were unavailable before. Yet systematic ground surveys are irreplaceable, and detection accuracies of human presence from the combination of remote sensing and ground surveys need to be improved. It is vital therefore to understand how Neotropical forest biodiversity has developed in the presence of people in the past, the implications of this for predicting future directions of change in the Amazon and elsewhere.

Long-term increases in tropical flowering activity across growth forms in response to rising CO2 and climate change

Mounting evidence suggests that anthropogenic global change is altering plant species composition in tropical forests. Fewer studies, however, have focused on long-term trends in reproductive activity, in part because of the lack of data from tropical sites. Here, we analyze a 28-year record of tropical flower phenology in response to anthropogenic climate and atmospheric change. We show that a multidecadal increase in flower activity is most strongly associated with rising atmospheric CO₂ concentrations using yearly aggregated data. Compared to significant climatic factors, CO₂ had on average an approximately three-, four-, or fivefold stronger effect than rainfall, solar radiation, and the Multivariate ENSO Index, respectively. Peaks in flower activity were associated with greater solar radiation and lower rainfall during El Niño years. The effect of atmospheric CO₂ on flowering has diminished over the most recent decade for lianas and canopy trees, whereas flowering of midstory trees and shrub species continued to increase with rising CO₂ . Increases in flowering were accompanied by a lengthening of flowering duration for canopy and midstory trees. Understory treelets did not show increases in flowering but did show increases in duration. Given that atmospheric CO₂ will likely continue to climb over the next century, a long-term increase in flowering activity may persist in some growth forms until checked by nutrient limitation or by climate change through rising temperatures, increasing drought frequency and/or increasing cloudiness and reduced insolation.

Amazon drought and forest response: Largely reduced forest photosynthesis but slightly increased canopy greenness during the extreme drought of 2015/2016

Amazon droughts have impacted regional ecosystem functioning as well as global carbon cycling. The severe dry-season droughts in 2005 and 2010, driven by Atlantic sea surface temperature (SST) anomaly, have been widely investigated in terms of drought severity and impacts on ecosystems. Although the influence of Pacific SST anomaly on wet-season precipitation has been well recognized, it remains uncertain to what extent the droughts driven by Pacific SST anomaly could affect forest greenness and photosynthesis in the Amazon. Here, we examined the monthly and annual dynamics of forest greenness and photosynthetic capacity when Amazon ecosystems experienced an extreme drought in 2015/2016 driven by a strong El Niño event. We found that the drought during August 2015-July 2016 was one of the two most severe meteorological droughts since 1901. Due to the enhanced solar radiation during this drought, overall forest greenness showed a small increase, and 21.6% of forests even greened up (greenness index anomaly ≥1 standard deviation). In contrast, solar-induced chlorophyll fluorescence (SIF), an indicator of vegetation photosynthetic capacity, showed a significant decrease. Responses of forest greenness and photosynthesis decoupled during this drought, indicating that forest photosynthesis could still be suppressed regardless of the variation in canopy greenness. If future El Niño frequency increases as projected by earth system models, droughts would result in persistent reduction in Amazon forest productivity, substantial changes in tree composition, and considerable carbon emissions from Amazon.

Resource acquisition and reproductive strategies of tropical forest in response to the El Niño-Southern Oscillation

The El Niño-Southern Oscillation (ENSO) is the largest source of interannual climate variability in much of the tropics. We hypothesize that tropical plants exhibit interannual variation in reproduction and resource acquisition strategies driven by ENSO that mirrors their seasonal responses. We analyze the relationship of leaf and seed fall to climate variation over 30 years in a seasonally dry tropical forest in Panama where El Niño brings warm, dry, and sunny conditions. Elevated leaf fall precedes the onset of El Niño, and elevated seed production follows, paralleling associations with dry seasons. Our results provide evidence of a shift in allocation from leafing to fruiting in response to a warming phase of ENSO. This shift may enable plants to take advantage of higher light availability, while coping with higher atmospheric water demand and lower water supply. These findings might be an indicator of adaptive strategies to optimize reproduction and resource acquisition.

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.

Extreme weather and climate events with ecological relevance: a review

Robust evidence exists that certain extreme weather and climate events, especially daily temperature and precipitation extremes, have changed in regard to intensity and frequency over recent decades. These changes have been linked to human-induced climate change, while the degree to which climate change impacts an individual extreme climate event (ECE) is more difficult to quantify. Rapid progress in event attribution has recently been made through improved understanding of observed and simulated climate variability, methods for event attribution and advances in numerical modelling. Attribution for extreme temperature events is stronger compared with other event types, notably those related to the hydrological cycle. Recent advances in the understanding of ECEs, both in observations and their representation in state-of-the-art climate models, open new opportunities for assessing their effect on human and natural systems. Improved spatial resolution in global climate models and advances in statistical and dynamical downscaling now provide climatic information at appropriate spatial and temporal scales. Together with the continued development of Earth System Models that simulate biogeochemical cycles and interactions with the biosphere at increasing complexity, these make it possible to develop a mechanistic understanding of how ECEs affect biological processes, ecosystem functioning and adaptation capabilities. Limitations in the observational network, both for physical climate system parameters and even more so for long-term ecological monitoring, have hampered progress in understanding bio-physical interactions across a range of scales. New opportunities for assessing how ECEs modulate ecosystem structure and functioning arise from better scientific understanding of ECEs coupled with technological advances in observing systems and instrumentation.This article is part of the themed issue 'Behavioural, ecological and evolutionary responses to extreme climatic events'.

Human disturbance amplifies Amazonian El Niño-Southern Oscillation signal

The long-term interaction between human activity and climate is subject to increasing scrutiny. Humans homogenize landscapes through deforestation, agriculture, and burning and thereby might reduce the capacity of landscapes to provide archives of climate change. Alternatively, land-use change might overwhelm natural buffering and amplify latent climate signals, rendering them detectable. Here we examine a sub-annually resolved sedimentary record from Lake Sauce in the western Amazonian lowlands that spans 6900 years. Finely-laminated sediments were deposited from ca. 5000 years ago until the present, and human activity in the watershed was revealed through the presence of charcoal and maize agriculture. The laminations, analyzed for color content and bandwidth, showed distinctive changes that were coupled to more frequent occurrence of fossil maize pollen. As agricultural activity intensified ca. 2200 cal. BP, the 2- to 8-year periodicity characteristic of El Niño-Southern Oscillation became evident in the record. These agricultural activities appeared to have amplified an existing, but subtle climatic signal that was previously absorbed by natural vegetation. When agricultural activity slowed, or land use around Lake Sauce changed at ca. 800 cal. BP, the signal of El Niño-Southern Oscillation (ENSO) activity became erratic.

Light-driven growth in Amazon evergreen forests explained by seasonal variations of vertical canopy structure

Light-regime variability is an important limiting factor constraining tree growth in tropical forests. However, there is considerable debate about whether radiation-induced green-up during the dry season is real, or an apparent artifact of the remote-sensing techniques used to infer seasonal changes in canopy leaf area. Direct and widespread observations of vertical canopy structures that drive radiation regimes have been largely absent. Here we analyze seasonal dynamic patterns between the canopy and understory layers in Amazon evergreen forests using observations of vertical canopy structure from a spaceborne lidar. We discovered that net leaf flushing of the canopy layer mainly occurs in early dry season, and is followed by net abscission in late dry season that coincides with increasing leaf area of the understory layer. Our observations of understory development from lidar either weakly respond to or are not correlated to seasonal variations in precipitation or insolation, but are strongly related to the seasonal structural dynamics of the canopy layer. We hypothesize that understory growth is driven by increased light gaps caused by seasonal variations of the canopy. This light-regime variability that exists in both spatial and temporal domains can better reveal the drought-induced green-up phenomenon, which appears less obvious when treating the Amazon forests as a whole.

Spectral diversity area relationships for assessing biodiversity in a wildland-agriculture matrix

Species-area relationships have long been used to assess patterns of species diversity across scales. Here, this concept is extended to spectral diversity using hyperspectral data collected by NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over western Michigan. This mixture of mesic forest and agricultural lands offers two end-points on the local-scale diversity continuum; one set of well-mixed forest patches and one set of highly homogeneous agricultural patches. Using the sum of the first three principal component values and the principal components' convex hull volume, spectral diversity was compared within and among these plots and to null expectations for perfectly random and perfectly patchy landscapes. Overall, the spectral diversity-area relationship confirms the patterns that would be expected for this landscape, but this application suggests that this approach could be extended to less well-understood landscapes and could reveal key insights about the relative importance of different drivers of community assembly, even in the absence of additional data about plant functional traits or species' identities.

A probabilistic assessment of the likelihood of vegetation drought under varying climate conditions across China

Climate change significantly impacts the vegetation growth and terrestrial ecosystems. Using satellite remote sensing observations, here we focus on investigating vegetation dynamics and the likelihood of vegetation-related drought under varying climate conditions across China. We first compare temporal trends of Normalized Difference Vegetation Index (NDVI) and climatic variables over China. We find that in fact there is no significant change in vegetation over the cold regions where warming is significant. Then, we propose a joint probability model to estimate the likelihood of vegetation-related drought conditioned on different precipitation/temperature scenarios in growing season across China. To the best of our knowledge, this study is the first to examine the vegetation-related drought risk over China from a perspective based on joint probability. Our results demonstrate risk patterns of vegetation-related drought under both low and high precipitation/temperature conditions. We further identify the variations in vegetation-related drought risk under different climate conditions and the sensitivity of drought risk to climate variability. These findings provide insights for decision makers to evaluate drought risk and vegetation-related develop drought mitigation strategies over China in a warming world. The proposed methodology also has a great potential to be applied for vegetation-related drought risk assessment in other regions worldwide.

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 NASA's 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.

Toward an integrated monitoring framework to assess the effects of tropical forest degradation and recovery on carbon stocks and biodiversity

Tropical forests harbor a significant portion of global biodiversity and are a critical component of the climate system. Reducing deforestation and forest degradation contributes to global climate-change mitigation efforts, yet emissions and removals from forest dynamics are still poorly quantified. We reviewed the main challenges to estimate changes in carbon stocks and biodiversity due to degradation and recovery of tropical forests, focusing on three main areas: (1) the combination of field surveys and remote sensing; (2) evaluation of biodiversity and carbon values under a unified strategy; and (3) research efforts needed to understand and quantify forest degradation and recovery. The improvement of models and estimates of changes of forest carbon can foster process-oriented monitoring of forest dynamics, including different variables and using spatially explicit algorithms that account for regional and local differences, such as variation in climate, soil, nutrient content, topography, biodiversity, disturbance history, recovery pathways, and socioeconomic factors. Generating the data for these models requires affordable large-scale remote-sensing tools associated with a robust network of field plots that can generate spatially explicit information on a range of variables through time. By combining ecosystem models, multiscale remote sensing, and networks of field plots, we will be able to evaluate forest degradation and recovery and their interactions with biodiversity and carbon cycling. Improving monitoring strategies will allow a better understanding of the role of forest dynamics in climate-change mitigation, adaptation, and carbon cycle feedbacks, thereby reducing uncertainties in models of the key processes in the carbon cycle, including their impacts on biodiversity, which are fundamental to support forest governance policies, such as Reducing Emissions from Deforestation and Forest Degradation.

Simulations of chlorophyll fluorescence incorporated into the Community Land Model version 4

Several studies have shown that satellite retrievals of solar-induced chlorophyll fluorescence (SIF) provide useful information on terrestrial photosynthesis or gross primary production (GPP). Here, we have incorporated equations coupling SIF to photosynthesis in a land surface model, the National Center for Atmospheric Research Community Land Model version 4 (NCAR CLM4), and have demonstrated its use as a diagnostic tool for evaluating the calculation of photosynthesis, a key process in a land surface model that strongly influences the carbon, water, and energy cycles. By comparing forward simulations of SIF, essentially as a byproduct of photosynthesis, in CLM4 with observations of actual SIF, it is possible to check whether the model is accurately representing photosynthesis and the processes coupled to it. We provide some background on how SIF is coupled to photosynthesis, describe how SIF was incorporated into CLM4, and demonstrate that our simulated relationship between SIF and GPP values are reasonable when compared with satellite (Greenhouse gases Observing SATellite; GOSAT) and in situ flux-tower measurements. CLM4 overestimates SIF in tropical forests, and we show that this error can be corrected by adjusting the maximum carboxylation rate (Vmax ) specified for tropical forests in CLM4. Our study confirms that SIF has the potential to improve photosynthesis simulation and thereby can play a critical role in improving land surface and carbon cycle models.

Climate phase drives canopy condition in a large semi-arid floodplain forest

To maintain and restore the ecological integrity of floodplains, allocating water for environmental benefits (i.e. environmental water) is widely practised globally. To efficiently manage the always limited environmental water, there is pressing need to advance our understanding of the ecological response to long-term climate cycles as evidence grows of intensification of extreme climatic events such as severe drought and heat waves. In this study, we assessed the alleviating effects of artificial flooding on drought impact using the canopy condition of the iconic river red gum forests in Australia's Murray Darling Basin (MDB). To achieve this, we jointly analysed spatial-temporal patterns of NDVI response and drought conditions for the period of 2000-2013, during which the MDB experienced an extreme dry-wet cycle. Our results indicated that while NDVI-derived canopy condition was better at the sites receiving environmental water during the dry phases, both watered and unwatered sites displayed great similarity in seasonality and trends. Furthermore, we did not find any significant difference in NDVI response of the canopy between the sites to suggest significant differences in ecosystem stability and resilience, with watered and unwatered sites showing similar responses to the extreme wet conditions as the drought broke. The highly significant relationship between long-term drought index and NDVI anomaly suggest that climate phase is the main forcing driving canopy condition in semi-arid floodplain forests.

Vegetation dynamics and rainfall sensitivity of the Amazon

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 km(2)) and across 80% of the subtropical grasslands (3.3 million km(2)). 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 km(2) 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.

Spatio-temporal analysis of remotely-sensed forest mortality associated with road de-icing salts

Forest mortality along highways has long been a concern in areas where de-icing compounds are applied during winter. This study combined the spatial advantage of high-resolution remote sensing imagery and the temporal advantage of long-term archival imagery to quantify forest mortality and to detect the subtle and chronic effects of road de-icing salts for a large mountain watershed in the Sierra Nevada Mountains, USA. IKONOS-derived mortality was used in a fine-scale spatial analysis to assess road proximity and roadside topography effects on forest mortality and to compare two potential mechanisms of de-icing salt damage, i.e. aerial deposition and soil uptake. These mechanisms were modeled using spatial proxy variables that were constructed from LiDAR topographical data. The analysis revealed a clear trend of increasing mortality with increasing potential for aerial deposition of de-icing salt onto tree crowns, mainly occurring within 10 m from roads. The effect of soil uptake of salt was weaker than that of aerial deposition but had a broader potential effect zone that extended to at least 100 m from roads. Landsat TM-derived mortality from 1989 to 2010 provided a long-term time series that indicated both immediate and lagged effects of salt application on forest mortality. Immediate effects of de-icing salt were only distinct in wet years when salt application and spray generation by passing traffic and snow plowing were likely high and other damaging factors, such as bark beetles or drought mortality, were likely weak. A strong and consistent one-year lag in the effect of salt application on incidence of mortality suggested that longer-term impacts of de-icing salt on forest health likely involved more complex pathways than simply aerial deposition. Our multi-scale remote sensing approach provided convincing evidence that de-icing salt was a significant factor for roadside forest mortality and allows for efficient future monitoring at the large-watershed scale.

Response of African humid tropical forests to recent rainfall anomalies

During the last decade, strong negative rainfall anomalies resulting from increased sea surface temperature in the tropical Atlantic have caused extensive droughts in rainforests of western Amazonia, exerting persistent effects on the forest canopy. In contrast, there have been no significant impacts on rainforests of West and Central Africa during the same period, despite large-scale droughts and rainfall anomalies during the same period. Using a combination of rainfall observations from meteorological stations from the Climate Research Unit (CRU; 1950-2009) and satellite observations of the Tropical Rainfall Measuring Mission (TRMM; 1998-2010), we show that West and Central Africa experienced strong negative water deficit (WD) anomalies over the last decade, particularly in 2005, 2006 and 2007. These anomalies were a continuation of an increasing drying trend in the region that started in the 1970s. We monitored the response of forests to extreme rainfall anomalies of the past decade by analysing the microwave scatterometer data from QuickSCAT (1999-2009) sensitive to variations in canopy water content and structure. Unlike in Amazonia, we found no significant impacts of extreme WD events on forests of Central Africa, suggesting potential adaptability of these forests to short-term severe droughts. Only forests near the savanna boundary in West Africa and in fragmented landscapes of the northern Congo Basin responded to extreme droughts with widespread canopy disturbance that lasted only during the period of WD. Time-series analyses of CRU and TRMM data show most regions in Central and West Africa experience seasonal or decadal extreme WDs (less than -600 mm). We hypothesize that the long-term historical extreme WDs with gradual drying trends in the 1970s have increased the adaptability of humid tropical forests in Africa to droughts.

Forest productivity and water stress in Amazonia: observations from GOSAT chlorophyll fluorescence

It is unclear to what extent seasonal water stress impacts on plant productivity over Amazonia. Using new Greenhouse gases Observing SATellite (GOSAT) satellite measurements of sun-induced chlorophyll fluorescence, we show that midday fluorescence varies with water availability, both of which decrease in the dry season over Amazonian regions with substantial dry season length, suggesting a parallel decrease in gross primary production (GPP). Using additional SeaWinds Scatterometer onboard QuikSCAT satellite measurements of canopy water content, we found a concomitant decrease in daily storage of canopy water content within branches and leaves during the dry season, supporting our conclusion. A large part (r(2) = 0.75) of the variance in observed monthly midday fluorescence from GOSAT is explained by water stress over moderately stressed evergreen forests over Amazonia, which is reproduced by model simulations that include a full physiological representation of photosynthesis and fluorescence. The strong relationship between GOSAT and model fluorescence (r(2) = 0.79) was obtained using a fixed leaf area index, indicating that GPP changes are more related to environmental conditions than chlorophyll contents. When the dry season extended to drought in 2010 over Amazonia, midday basin-wide GPP was reduced by 15 per cent compared with 2009.

Drought impact assessment from monitoring the seasonality of vegetation condition using long-term time-series satellite images: a case study of Mt. Kenya region

Drought-induced anomalies in vegetation condition over wide areas can be observed by using time-series satellite remote sensing data. Previous methods to assess the anomalies may include limitations in considering (1) the seasonality in terms of each vegetation-cover type, (2) cumulative damage during the drought event, and (3) the application to various types of land cover. This study proposed an improved methodology to assess drought impact from the annual vegetation responses, and discussed the result in terms of diverse landscape mosaics in the Mt. Kenya region (0.4° N 35.8° E ~ 1.6° S 38.4° E). From the 30-year annual rainfall records at the six meteorological stations in the study area, we identified 2000 as the drought year and 2001, 2004, and 2007 as the normal precipitation years. The time-series profiles of vegetation condition in the drought and normal precipitation years were obtained from the values of Enhanced Vegetation Index (EVI; Huete et al. 2002), which were acquired from Terra MODIS remote sensing dataset (MOD13Q1) taken every 16 days at the scale of 250-m spatial resolution. The drought impact was determined by integrating the annual differences in EVI profiles between drought and normal conditions, per pixel based on nearly same day of year. As a result, we successfully described the distribution of landscape vulnerability to drought, considering the seasonality of each vegetation-cover type at every MODIS pixel. This result will contribute to the large-scale landscape management of Mt. Kenya region. Future study should improve this method by considering land-use change occurred during the long-term monitoring period.

Persistent effects of a severe drought on Amazonian forest canopy

Recent Amazonian droughts have drawn attention to the vulnerability of tropical forests to climate perturbations. Satellite and in situ observations have shown an increase in fire occurrence during drought years and tree mortality following severe droughts, but to date there has been no assessment of long-term impacts of these droughts across landscapes in Amazonia. Here, we use satellite microwave observations of rainfall and canopy backscatter to show that more than 70 million hectares of forest in western Amazonia experienced a strong water deficit during the dry season of 2005 and a closely corresponding decline in canopy structure and moisture. Remarkably, and despite the gradual recovery in total rainfall in subsequent years, the decrease in canopy backscatter persisted until the next major drought, in 2010. The decline in backscatter is attributed to changes in structure and water content associated with the forest upper canopy. The persistence of low backscatter supports the slow recovery (>4 y) of forest canopy structure after the severe drought in 2005. The result suggests that the occurrence of droughts in Amazonia at 5-10 y frequency may lead to persistent alteration of the forest canopy.