Dominant role of the non-forest woody vegetation in the post 2015/16 El Niño tropical carbon recovery.

The extreme dry and hot 2015/16 El Niño episode caused large losses in tropical live aboveground carbon (AGC) stocks. Followed by climatic conditions conducive to high vegetation productivity since 2016, tropical AGC are expected to recover from large losses during the El Niño episode; however, the recovery rate and its spatial distribution remain unknown. Here, we used low-frequency microwave satellite data to track AGC changes, and showed that tropical AGC stocks returned to pre-El Niño levels by the end of 2020, resulting in an AGC sink of 0.18 0.14 0.26 $$ {0.18}_{0.14}^{0.26} $$ Pg C year-1 during 2014-2020. This sink was dominated by strong AGC increases ( 0.61 0.49 0.84 $$ {0.61}_{0.49}^{0.84} $$ Pg C year-1) in non-forest woody vegetation during 2016-2020, compensating the forest AGC losses attributed to the El Niño event, forest loss, and degradation. Our findings highlight that non-forest woody vegetation is an increasingly important contributor to interannual to decadal variability in the global carbon cycle.

Facing the challenge of NDVI dataset consistency for improved characterization of vegetation response to climate variability.

Non-linear trend detection in Earth observation time series has become a standard method to characterize changes in terrestrial ecosystems. However, results are largely dependent on the quality and consistency of the input data, and only few studies have addressed the impact of data artifacts on the interpretation of detected abrupt changes. Here we study non-linear dynamics and turning points (TPs) of temperate grasslands in East Eurasia using two independent state-of-the-art satellite NDVI datasets (CGLS v3 and MODIS C6) and explore the impact of water availability on observed vegetation changes during 2001-2019. By applying the Break For Additive Season and Trend (BFAST01) method, we conducted a classification typology based on vegetation dynamics which was spatially consistent between the datasets for 40.86 % (459,669 km2) of the study area. When considering also the timing of the TPs, 27.09 % of the pixels showed consistent results between datasets, suggesting that careful interpretation was needed for most of the areas of detected vegetation dynamics when applying BFAST to a single dataset. Notably, for these areas showing identical typology we found that interrupted decreases in vegetation productivity were dominant in the transition zone between desert and steppes. Here, a strong link with changes in water availability was found for >80 % of the area, indicating that increasing drought stress had regulated vegetation productivity in recent years. This study shows the necessity of a cautious interpretation of the results when conducting advanced characterization of vegetation response to climate variability, but at the same time also the opportunities of going beyond the use of single dataset in advanced time-series approaches to better understanding dryland vegetation dynamics for improved anthropogenic interventions to combat vegetation productivity decrease.

Global area boom for greenhouse cultivation revealed by satellite mapping.

Greenhouse cultivation has been expanding rapidly in recent years, yet little knowledge exists on its global extent and expansion. Using commercial and freely available satellite data combined with artificial intelligence techniques, we present a global assessment of greenhouse cultivation coverage and map 1.3 million hectares of greenhouse infrastructures in 2019, a much larger extent than previously estimated. Our analysis includes both large (61%) and small-scale (39%) greenhouse infrastructures. Examining the temporal development of the 65 largest clusters (>1,500 ha), we show a recent upsurge in greenhouse cultivation in the Global South since the 2000s, including a dramatic increase in China, accounting for 60% of the global coverage. We emphasize the potential of greenhouse infrastructures to enhance food security but raise awareness of the uncertain environmental and social implications that may arise from this expansion. We further highlight the gap in spatio-temporal datasets for supporting future research agendas on this critical topic.

Changes in vegetation-water response in the Sahel-Sudan during recent decades.

Study region: The Africa Sahel-Sudan region, defined by annual rainfall between 150 and 1200 mm. Study focus: Understanding the mechanism of vegetation response to water availability could help mitigate the potential adverse effects of climate change on global dryland ecosystems. In the Sahel-Sudan region, spatio-temporal changes and drivers of the vegetation-water response remain unclear. This study employs long-term satellite water and vegetation products as proxies of water availability and vegetation productivity to analyze changes in vegetation-water sensitivity and the cumulative effect duration (CED) representing a measure of the legacy effect of the impact of water constraints on vegetation. A random forest model was subsequently used to analyze potential climatic drivers of the observed vegetation response. New hydrological insights for the region: During 1982-2016 we found a significant decrease (p < 0.05) in the sensitivity of vegetation productivity to water constraints in 26% of the Sahel-Sudan region, while 9% of the area showed a significantly increased sensitivity, mainly in the sub-humid zone. We further showed that CED significantly increased and decreased, respectively in around 9% of the study area in both cases. Our climatic driver attribution analysis suggested the existence of varying underlying mechanisms governing vegetation productivity in response to water deficit across the Sahel-Sudan dryland ecosystems. Our findings emphasize the need for diverse strategies in sustainable ecosystem management to effectively address these varying mechanisms.

Critical thresholds for nonlinear responses of ecosystem water use efficiency to drought.

Climate change is expected to lead to greater variability in precipitation and drought in different regions. However, the responses of ecosystem carbon and water cycles (i.e., water use efficiency, WUE) to different levels of drought stress are not fully understood. Here, we examined the relationship between WUE and precipitation anomalies and identified the critical drought threshold (DrCW) above which WUE showed substantial decrease. The results revealed that 85.56 % of the study area had nonlinear WUE responses to drought stress; that is, the WUE decreased sustainably and steeply when the precipitation deficit exceeded the DrCW. DrCW indicates inflection points for changing ecosystem responses from relatively resistant to vulnerable to drought stress, thus providing an instructive early warning for intensifying suppressive impacts on vegetation growth. Additionally, DrCW varies across aridity gradients and among vegetation types. Based on the DrCW at the pixel level, the future eco-drought is projected to increase in >67 % of the study area under both the SSP2&RCP4.5 and SSP5&RCP8.5 scenarios by the end of the 21st century. Our study elucidates the response of the ecosystem function to drought and supports the development of accurate ecosystem adaptation policies for future drought stress.

The success of ecological engineering projects on vegetation restoration in China strongly depends on climatic conditions.

China has implemented extensive ecological engineering projects (EEPs) during recent decades to restore and enhance ecosystem functioning. However, the effectiveness of these interventions can vary due to factors such as local climate and specific project objectives. Here, we used two independent satellite remote sensing datasets, including the Global Inventory Monitoring and Modeling System (GIMMS) Normalized Difference Vegetation Index (NDVI) and vegetation optical depth from Ku-band (Ku-VOD), to investigate the vegetation trends in two hotspot regions of EEPs characterized by different climate conditions, i.e., the xeric/semi-xeric Loess Plateau and mesic southwest China. We found diverging vegetation greenness/biomass trend shift patterns in these two regions as a result of the combined effects of EEPs and climate variations, as indicated by changes in the Standardized Precipitation Evapotranspiration Index (SPEI). In the Loess Plateau, where no significant climate variations were observed, NDVI/Ku-VOD increased continuously after the implementation of key EEPs in 2000. Conversely, southwest China has experienced persistent drying since 2000, and vegetation greenness/biomass showed an increasing trend during the initial stages of ecological engineering implementation but subsequently reversed towards a decline due to the continued dry climatic conditions. We used the residual trend method to separate the influence of EEPs from climate variations on vegetation trends and found a positive effect of the ecological management practices in the Loess Plateau, yet a predominantly negative effect in the southwest China region, which means that projects implemented in southwest China did not lead to a long-term improvement in vegetation growth under the given climate conditions in southwest China. This adverse impact suggests that ecological engineering practices could potentially increase the ecosystem's vulnerability to droughts, owing to the increased transpirational water demands introduced by ecological engineering interventions. Our study highlights the importance of considering the expected occurrence and magnitude of climatic variability when implementing large-scale EEPs.

The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe.

Trees are an integral part in European landscapes, but only forest resources are systematically assessed by national inventories. The contribution of urban and agricultural trees to national-level carbon stocks remains largely unknown. Here we produced canopy cover, height and above-ground biomass maps from 3-meter resolution nanosatellite imagery across Europe. Our biomass estimates have a systematic bias of 7.6% (overestimation; R = 0.98) compared to national inventories of 30 countries, and our dataset is sufficiently highly resolved spatially to support the inclusion of tree biomass outside forests, which we quantify to 0.8 petagrams. Although this represents only 2% of the total tree biomass, large variations between countries are found (10% for UK) and trees in urban areas contribute substantially to national carbon stocks (8% for the Netherlands). The agreement with national inventory data, the scalability, and spatial details across landscapes, including trees outside forests, make our approach attractive for operational implementation to support national carbon stock inventory schemes.

More than one quarter of Africa's tree cover is found outside areas previously classified as forest.

The consistent monitoring of trees both inside and outside of forests is key to sustainable land management. Current monitoring systems either ignore trees outside forests or are too expensive to be applied consistently across countries on a repeated basis. Here we use the PlanetScope nanosatellite constellation, which delivers global very high-resolution daily imagery, to map both forest and non-forest tree cover for continental Africa using images from a single year. Our prototype map of 2019 (RMSE = 9.57%, bias = -6.9%). demonstrates that a precise assessment of all tree-based ecosystems is possible at continental scale, and reveals that 29% of tree cover is found outside areas previously classified as tree cover in state-of-the-art maps, such as in croplands and grassland. Such accurate mapping of tree cover down to the level of individual trees and consistent among countries has the potential to redefine land use impacts in non-forest landscapes, move beyond the need for forest definitions, and build the basis for natural climate solutions and tree-related studies.

Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale.

Sustainable tree resource management is the key to mitigating climate warming, fostering a green economy, and protecting valuable habitats. Detailed knowledge about tree resources is a prerequisite for such management but is conventionally based on plot-scale data, which often neglects trees outside forests. Here, we present a deep learning-based framework that provides location, crown area, and height for individual overstory trees from aerial images at country scale. We apply the framework on data covering Denmark and show that large trees (stem diameter >10 cm) can be identified with a low bias (12.5%) and that trees outside forests contribute to 30% of the total tree cover, which is typically unrecognized in national inventories. The bias is high (46.6%) when our results are evaluated against all trees taller than 1.3 m, which involve undetectable small or understory trees. Furthermore, we demonstrate that only marginal effort is needed to transfer our framework to data from Finland, despite markedly dissimilar data sources. Our work lays the foundation for digitalized national databases, where large trees are spatially traceable and manageable.

Contrasting ecosystem vegetation response in global drylands under drying and wetting conditions.

Increasing aridity is one major consequence of ongoing global climate change and is expected to cause widespread changes in key ecosystem attributes, functions, and dynamics. This is especially the case in naturally vulnerable ecosystems, such as drylands. While we have an overall understanding of past aridity trends, the linkage between temporal dynamics in aridity and dryland ecosystem responses remain largely unknown. Here, we examined recent trends in aridity over the past two decades within global drylands as a basis for exploring the response of ecosystem state variables associated with land and atmosphere processes (e.g., vegetation cover, vegetation functioning, soil water availability, land cover, burned area, and vapor-pressure deficit) to these trends. We identified five clusters, characterizing spatiotemporal patterns in aridity between 2000 and 2020. Overall, we observe that 44.5% of all areas are getting dryer, 31.6% getting wetter, and 23.8% have no trends in aridity. Our results show strongest correlations between trends in ecosystem state variables and aridity in clusters with increasing aridity, which matches expectations of systemic acclimatization of the ecosystem to a reduction in water availability/water stress. Trends in vegetation (expressed by leaf area index [LAI]) are affected differently by potential driving factors (e.g., environmental, and climatic factors, soil properties, and population density) in areas experiencing water-related stress as compared to areas not exposed to water-related stress. Canopy height for example, has a positive impact on trends in LAI when the system is stressed but does not impact the trends in non-stressed systems. Conversely, opposite relationships were found for soil parameters such as root-zone water storage capacity and organic carbon density. How potential driving factors impact dryland vegetation differently depending on water-related stress (or no stress) is important, for example within management strategies to maintain and restore dryland vegetation.

Sub-continental-scale carbon stocks of individual trees in African drylands.

The distribution of dryland trees and their density, cover, size, mass and carbon content are not well known at sub-continental to continental scales1-14. This information is important for ecological protection, carbon accounting, climate mitigation and restoration efforts of dryland ecosystems15-18. We assessed more than 9.9 billion trees derived from more than 300,000 satellite images, covering semi-arid sub-Saharan Africa north of the Equator. We attributed wood, foliage and root carbon to every tree in the 0-1,000 mm year-1 rainfall zone by coupling field data19, machine learning20-22, satellite data and high-performance computing. Average carbon stocks of individual trees ranged from 0.54 Mg C ha-1 and 63 kg C tree-1 in the arid zone to 3.7 Mg C ha-1 and 98 kg tree-1 in the sub-humid zone. Overall, we estimated the total carbon for our study area to be 0.84 (±19.8%) Pg C. Comparisons with 14 previous TRENDY numerical simulation studies23 for our area found that the density and carbon stocks of scattered trees have been underestimated by three models and overestimated by 11 models, respectively. This benchmarking can help understand the carbon cycle and address concerns about land degradation24-29. We make available a linked database of wood mass, foliage mass, root mass and carbon stock of each tree for scientists, policymakers, dryland-restoration practitioners and farmers, who can use it to estimate farmland tree carbon stocks from tablets or laptops.

Recent decrease of the impact of tropical temperature on the carbon cycle linked to increased precipitation.

The atmospheric CO2 growth rate (CGR) variability is largely controlled by tropical temperature fluctuations. The sensitivity of CGR to tropical temperature [Formula: see text] has strongly increased since 1960, but here we show that this trend has ceased. Here, we use the long-term CO2 records from Mauna Loa and the South Pole to compute CGR, and show that [Formula: see text] increased by 200% from 1960-1979 to 1979-2000 but then decreased by 117% from 1980-2001 to 2001-2020, almost returning back to the level of the 1960s. Variations in [Formula: see text] are significantly correlated with changes in precipitation at a bi-decadal scale. These findings are further corroborated by results from a dynamic vegetation model, collectively suggesting that increases in precipitation control the decreased [Formula: see text] during recent decades. Our results indicate that wetter conditions have led to a decoupling of the impact of the tropical temperature variation on the carbon cycle.

Nation-wide mapping of tree-level aboveground carbon stocks in Rwanda.

Trees sustain livelihoods and mitigate climate change but a predominance of trees outside forests and limited resources make it difficult for many tropical countries to conduct automated nation-wide inventories. Here, we propose an approach to map the carbon stock of each individual overstory tree at the national scale of Rwanda using aerial imagery from 2008 and deep learning. We show that 72% of the mapped trees are located in farmlands and savannas and 17% in plantations, accounting for 48.6% of the national aboveground carbon stocks. Natural forests cover 11% of the total tree count and 51.4% of the national carbon stocks, with an overall carbon stock uncertainty of 16.9%. The mapping of all trees allows partitioning to any landscapes classification and is urgently needed for effective planning and monitoring of restoration activities as well as for optimization of carbon sequestration, biodiversity and economic benefits of trees.

Mapping global lake dynamics reveals the emerging roles of small lakes.

Lakes are important natural resources and carbon gas emitters and are undergoing rapid changes worldwide in response to climate change and human activities. A detailed global characterization of lakes and their long-term dynamics does not exist, which is however crucial for evaluating the associated impacts on water availability and carbon emissions. Here, we map 3.4 million lakes on a global scale, including their explicit maximum extents and probability-weighted area changes over the past four decades. From the beginning period (1984-1999) to the end (2010-2019), the lake area increased across all six continents analyzed, with a net change of +46,278 km2, and 56% of the expansion was attributed to reservoirs. Interestingly, although small lakes (<1 km2) accounted for just 15% of the global lake area, they dominated the variability in total lake size in half of the global inland lake regions. The identified lake area increase over time led to higher lacustrine carbon emissions, mostly attributed to small lakes. Our findings illustrate the emerging roles of small lakes in regulating not only local inland water variability, but also the global trends of surface water extent and carbon emissions.

Estimating herbaceous aboveground biomass in Sahelian rangelands using Structure from Motion data collected on the ground and by UAV.

Herbaceous aboveground biomass (HAB) is a key indicator of grassland vegetation and indirect estimation tools, such as remote sensing imagery, increase the potential for covering larger areas in a timely and cost-efficient way. Structure from Motion (SfM) is an image analysis process that can create a variety of 3D spatial models as well as 2D orthomosaics from a set of images. Computed from Unmanned Aerial Vehicle (UAV) and ground camera measurements, the SfM potential to estimate the herbaceous aboveground biomass in Sahelian rangelands was tested in this study. Both UAV and ground camera recordings were used at three different scales: temporal, landscape, and national (across Senegal). All images were processed using PIX4D software (photogrammetry software) and were used to extract vegetation indices and heights. A random forest algorithm was used to estimate the HAB and the average estimation errors were around 150 g m-² for fresh mass (20% relative error) and 60 g m-² for dry mass (around 25% error). A comparison between different datasets revealed that the estimates based on camera data were slightly more accurate than those from UAV data. It was also found that combining datasets across scales for the same type of tool (UAV or camera) could be a useful option for monitoring HAB in Sahelian rangelands or in other grassy ecosystems.

Groundwater arsenic content related to the sedimentology and stratigraphy of the Red River delta, Vietnam.

Arsenic (As) is highly toxic and over 100 million people living on the floodplains of Asia are exposed to excessive groundwater As. A very large spatial variability over small distances has been observed in the groundwater As concentrations. Advances in the prediction of the As distribution in aquifers would support drinking water management. The application of remote sensing of geomorphic paleo river features combined with geological, geophysical and archeological data and available groundwater As measurements may be used to predict groundwater As levels in rural areas, as shown by the example from the Red River delta, Vietnam. Groundwater in sediments deposited in the marine environment is low in As, probably due to the precipitation of As in sulfide minerals under anoxic conditions. Groundwater As levels in freshwater alluvial deposits in undisturbed floodplain areas are slightly increased and the highest As concentrations are associated with meander belts. The meander belts remain clearly visible in remote sensing and may well reflect the youngest preserved alluvial sediments. High As levels in the meander belt aquifers are probably related to the availability of highly reactive organic matter and consequent reduction of iron oxyhydroxides and As release. Furthermore, given similar hydrogeological conditions, the extent of flushing of As from the youngest alluvial sands is limited compared to the older Pleistocene sands. Even within abandoned meander belts a high spatial variability of As concentrations was observed. The younger channel belts (<1 ka BP) and old Holocene aquifers below undisturbed floodplain environments deposited during a period with high sea level host groundwater enriched in As. Low As groundwater is found in sandy channel belts deposited during the regression of the sea and in Pleistocene islands preserved within the floodplain. The decisive influence of the depositional environment of the aquifer sediments on groundwater As content is revealed.

Response to concerns about the African fire trends controlled by precipitation over recent decades.

Croplands expanded in Africa over recent decades, even though the increasing trends are spatially heterogeneous.

A global increase in tree cover extends the growing season length as observed from satellite records.

Plant phenology provides information on the seasonal dynamics of plants, and changes herein are important for understanding the impact of climate change and human management on the biosphere. Land surface phenology is the study of plant phenology across large spatial scales estimated by satellite observations. However, satellite observations (pixels) are often composed of a mixture of vegetation types, like woody vegetation and herbaceous vegetation, having different phenological characteristics. Therefore, any changes in tree cover presumably impact land surface phenology, as trees usually have a different seasonal cycle compared to herbaceous vegetation. On the other hand, changes in land surface phenology are often interpreted as a result of climate change-induced impacts on the photosynthetic activity of vegetation. Therefore, it is important to better understand the role of changes in vegetation cover (here, the proportion between tree and short vegetation cover) in satellite-derived land surface phenology analysis. We studied the impact of changes in tree cover on satellite observed land surface phenology at a global scale over the past three decades. We found an extension of the growing season length in 36.6% of the areas where tree cover increased, whereas only 20.1% of the areas where tree cover decreased showed an increase in growing season length. Furthermore, the ratio between tree cover and short vegetation cover was found to affect changes in the length of the growing season, with the denser tree cover showing a more pronounced extension of the growing season length (especially in boreal forests). These results highlight the importance of changes in tree cover when analyzing the impact of climate change on vegetation phenology. Our study thereby addresses a critical knowledge gap for an improved understanding of changes in land surface phenology during recent decades in the context of climate and human-induced global land cover change.