Rapidly warming temperatures in the Arctic are driving increasing tundra vegetation productivity, evidenced in both the satellite derived normalized difference vegetation index (NDVI) imagery and field studies. These trends, however, are not uniformly positive across the circumpolar Arctic. One notable region of negative linear NDVI trends that have persisted over the last 15 years is southwest Alaska's Yukon-Kuskokwim Delta (YKD). Negative NDVI trends in the YKD region appear inconsistent with our understanding since tundra vegetation is temperature-limited and air temperatures have increased on the YKD. Analysis over a 40-yr record from 1982 to 2021 reveals distinct decadal variability in the NDVI time series, which continues to produce negative linear trends. Similar decadal variability is also evident in summer warmth and 100-km coastal zone spring sea ice concentrations. This suggests that decadal climate variations can dominate the trends of NDVI through their influence on the drivers of tundra vegetation, namely, coastal sea ice concentrations and summer warmth. The relationships among sea ice, summer warmth, and NDVI have changed over the 40-yr record. Seasonality analysis since 1982 shows declining sea ice concentration in spring is followed by trends of increasing temperatures, but weakly declining NDVI during the growing season. An additional key finding is that since early 2010s, the relationships between sea ice concentration and summer warmth, and sea ice concentration and NDVI have strengthened, while the relationship between NDVI and summer warmth has weakened, indicating that temperature may no longer be the primary limiting factor for Arctic tundra vegetation on the YKD.

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.

This study applies an indicators framework to investigate climate drivers of tundra vegetation trends and variability over the 1982–2019 period. Previously known indicators relevant for tundra productivity (summer warmth index (SWI), coastal spring sea-ice (SI) area, coastal summer open-water (OW)) and three additional indicators (continentality, summer precipitation, and the Arctic Dipole (AD): second mode of sea level pressure variability) are analyzed with maximum annual Normalized Difference Vegetation Index (MaxNDVI) and the sum of summer bi-weekly (time-integrated) NDVI (TI-NDVI) from the Advanced Very High Resolution Radiometer time-series. Climatological mean, trends, and correlations between variables are presented. Changes in SI continue to drive variations in the other indicators. As spring SI has decreased, summer OW, summer warmth, MaxNDVI, and TI-NDVI have increased. However, the initial very strong upward trends in previous studies for MaxNDVI and TI-NDVI are weakening and becoming spatially and temporally more variable as the ice retreats from the coastal areas. TI-NDVI has declined over the last decade particularly over High Arctic regions and southwest Alaska. The continentality index (CI) (maximum minus minimum monthly temperatures) is decreasing across the tundra, more so over North America than Eurasia. The relationship has weakened between SI and SWI and TI-NDVI, as the maritime influence of OW has increased along with total precipitation. The winter AD is correlated in Eurasia with spring SI, summer OW, MaxNDVI, TI-NDVI, the CI and total summer precipitation. This winter connection to tundra emphasizes the role of SI in driving the summer indicators. The winter (DJF) AD drives SI variations which in turn shape summer OW, the atmospheric SWI and NDVI anomalies. The winter and spring indicators represent potential predictors of tundra vegetation productivity a season or two in advance of the growing season.

Vegetation seasonality assessment through remote sensing data is crucial to understand ecosystem responses to climatic variations and human activities at large-scales. Whereas the study of the timing of phenological events showed significant advances, their recurrence patterns at different periodicities has not been widely study, especially at global scale. In this work, we describe vegetation oscillations by a novel quantitative approach based on the spectral analysis of Normalized Difference Vegetation Index (NDVI) time series. A new set of global periodicity indicators permitted to identify different seasonal patterns regarding the intra-annual cycles (the number, amplitude, and stability) and to evaluate the existence of pluri-annual cycles, even in those regions with noisy or low NDVI. Most of vegetated land surface (93.18%) showed one intra-annual cycle whereas double and triple cycles were found in 5.58% of the land surface, mainly in tropical and arid regions along with agricultural areas. In only 1.24% of the pixels, the seasonality was not statistically significant. The highest values of amplitude and stability were found at high latitudes in the northern hemisphere whereas lowest values corresponded to tropical and arid regions, with the latter showing more pluri-annual cycles. The indicator maps compiled in this work provide highly relevant and practical information to advance in assessing global vegetation dynamics in the context of global change.

Land degradation has been highlighted as a key development challenge by numerous international bodies, including the United Nations Convention to Combat Desertification or the UNCCD, the Convention on Biological Diversity, the United Nations Framework Convention on Climate Change, and is one of the so-called Sustainable Development Goals. The UNCCD aims to avoid, reduce, and reverse land degradation, especially desertification and deforestation, by providing support to sustainable land management. Sustainable land management implements practices that maintain vegetative cover, build soil organic matter, make efficient use of inputs, such as water, nutrients and pesticides, and minimize off-site impacts. In the context of Land Degradation Neutrality or Sustainable Development Goal 15.3, three indicators have been identified to quantify land degradation: trends in land cover; trends in carbon stocks above and below ground; and trends in land productivity.

Potential climate drivers of Arctic tundra vegetation productivity are investigated to understand recent greening and browning trends documented by maximum normalized difference vegetation index (NDVI) (MaxNDVI) and time-integrated NDVI (TI-NDVI) for 1982-2015. Over this period, summer sea ice has continued to decline while oceanic heat content has increased. The increases in summer warmth index (SWI) and NDVI have not been uniform over the satellite record. SWI increased from 1982 to the mid-1990s and remained relatively flat from 1998 onwards until a recent upturn. While MaxNDVI displays positive trends from 1982-2015, TI-NDVI increased from 1982 until 2001 and has declined since. The data for the first and second halves of the record were analyzed and compared spatially for changing trends with a focus on the growing season. Negative trends for MaxNDVI and TI-NDVI were more common during 1999-2015 compared to 1982-1998.Trend analysis within the growing season reveals that sea ice decline was larger in spring for the 1982-1998 period compared to 1999-2015, while fall sea ice decline was larger in the later period. Land surface temperature trends for the 1982-1998 growing season are positive and for 1999-2015 are positive in May-June but weakly negative in July-August. Spring biweekly NDVI trends are positive and significant for 1982-1998, consistent with increasing open water and increased available warmth in spring. MaxNDVI trends for 1999-2015 display significant negative trends in May and the first half of June.Numerous possible drivers of early growing season NDVI decline coincident with warming temperatures are discussed, including increased standing water, delayed spring snow-melt, winter thaw events, and early snow melt followed by freezing temperatures. Further research is needed to robustly identify drivers of the spring NDVI decline.

The mechanisms driving trends and variability of the normalized difference vegetation index (NDVI) for tundra in Alaska along the Beaufort, east Chukchi, and east Bering Seas for 1982-2013 are evaluated in the context of remote sensing, reanalysis, and meteorological station data as well as regional modeling. Over the entire season the tundra vegetation continues to green; however, biweekly NDVI has declined during the early part of the growing season in all of the Alaskan tundra domains. These springtime declines coincide with increased snow depth in spring documented in northern Alaska. The tundra region generally has warmed over the summer but intraseasonal analysis shows a decline in midsummer land surface temperatures. The midsummer cooling is consistent with recent large-scale circulation changes characterized by lower sea level pressures, which favor increased cloud cover. In northern Alaska, the sea-breeze circulation is strengthened with an increase in atmospheric moisture/cloudiness inland when the land surface is warmed in a regional model, suggesting the potential for increased vegetation to feedback onto the atmospheric circulation that could reduce midsummer temperatures. This study shows that both large-and local-scale climate drivers likely play a role in the observed seasonality of NDVI trends.

AimsStarter lactic acid bacteria in Cheddar cheese face physico-chemical stresses during manufacture and ageing that alter their abilities to survive and to interact with other bacterial populations. Nonstarter bacteria are derived from milk handling, cheese equipment and human contact during manufacture. Probiotic bacteria are added to foods for human health benefits that also encounter physiological stresses and microbial competition that may mitigate their survival during ageing. We added probiotic Lactobacillus acidophilus, Lactobacillus casei, Lactobacillus paracasei and Bifidobacterium animalis subsp. lactis to full-fat, reduced-fat and low-fat Cheddar cheeses, aiming to study their survival over 270days of ageing and to determine the role of the cheese matrix in their survival.Methods and ResultsProbiotic and other lactic acid bacterial populations were enumerated by quantitative PCR using primers specifically targeting the different bacterial genera or species of interest. Bifidobacteria were initially added at 10(6)CFU g(-1) cheese and survived variably in the different cheeses over the 270-day ageing process. Probiotic lactobacilli that were added at 10(7)CFU g(-1) cheese and incident nonstarter lactobacilli (initially at 10(8)CFU g(-1) cheese) increased by 10- to 100-fold over 270days. Viable bacterial populations were differentiated using propidium monoazide followed by species-specific qPCR assays, which demonstrated that the starter and probiotic microbes survived over ageing, independent of cheese type. Addition of probiotic bacteria, at levels 100-fold below that of starter bacteria, modified starter and nonstarter bacterial levels.ConclusionsWe demonstrated that starter lactococci, nonstarter lactobacilli and probiotic bacteria are capable of surviving throughout the cheesemaking and ageing process, indicating that delivery via hard cheeses is possible. Probiotic addition at lower levels may also alter starter and nonstarter bacterial survival.Significance and Impact of the StudyWe applied qPCR to study multispecies survival and viability and distinctly enumerated bacterial species in commercial-scale Cheddar cheese manufacture.

Long-term trends in photosynthetic capacity measured with the satellite-derived Normalized Difference Vegetation Index (NDVI) are usually associated with climate change. Human impacts on the global land surface are typically not accounted for. Here, we provide the first global analysis quantifying the effect of the earth's human footprint on NDVI trends. Globally, more than 20% of the variability in NDVI trends was explained by anthropogenic factors such as land use, nitrogen fertilization, and irrigation. Intensely used land classes, such as villages, showed the greatest rates of increase in NDVI, more than twice than those of forests. These findings reveal that factors beyond climate influence global long-term trends in NDVI and suggest that global climate change models and analyses of primary productivity should incorporate land use effects.

The NDVI3g time series is an improved 8-km normalized difference vegetation index (NDVI) data set produced from Advanced Very High Resolution Radiometer (AVHRR) instruments that extends from 1981 to the present. The AVHRR instruments have flown or are flying on fourteen polar-orbiting meteorological satellites operated by the National Oceanic and Atmospheric Administration (NOAA) and are currently flying on two European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) polar-orbiting meteorological satellites, MetOp-A and MetOp-B. This long AVHRR record is comprised of data from two different sensors: the AVHRR/2 instrument that spans July 1981 to November 2000 and the AVHRR/3 instrument that continues these measurements from November 2000 to the present. The main difficulty in processing AVHRR NDVI data is to properly deal with limitations of the AVHRR instruments. Complicating among-instrument AVHRR inter-calibration of channels one and two is the dual gain introduced in late 2000 on the AVHRR/3 instruments for both these channels. We have processed NDVI data derived from the Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) from 1997 to 2010 to overcome among-instrument AVHRR calibration difficulties. We use Bayesian methods with high quality well-calibrated SeaWiFS NDVI data for deriving AVHRR NDVI calibration parameters. Evaluation of the uncertainties of our resulting NDVI values gives an error of ± 0.005 NDVI units for our 1981 to present data set that is independent of time within our AVHRR NDVI continuum and has resulted in a non-stationary climate data set.

Pronounced increases in winter temperature result in lower seasonal temperature differences, with implications for vegetation seasonality and productivity. Research now indicates that temperature and vegetation seasonality in northern ecosystems have diminished to an extent equivalent to a southerly shift of 4°– 7° in latitude, and may reach the equivalent of up to 20° over the twenty-first century.

We compared tree-ring width (TRW) and maximum latewood density (MXD) chronologies to remotely sensed indices of productivity (NDVI) and snowmelt since 1981 and to the instrumental temperature record at four arctic treeline sites in North America. Our results show that at these sites, TRW chronologies reflect temperatures less consistently than the MXD chronologies do and that the NDVI does not correlate significantly with TRW at high-frequency, i.e. when comparing yearly values. In contrast, the MXD chronologies correlate positively and significantly with NDVI and temperature during the growing season at all sites. Neither TRW or MXD chronologies appeared consistently influenced by the annual timing of snowmelt. A comparison of tree-ring chronologies and temperatures since 1900 confirms that MXD has tracked growing season temperature at these treeline sites throughout the past century. A spatial evaluation of the correlations further reveals that each of the MXD chronologies investigated here reflects interannual variation in NDVI and growing season temperatures across a large geographic region. As a result, they collectively provide a spatially comprehensive record of historic early-season canopy status as well as growing season temperatures for the high latitudes of North America.

This chapter explores the complex, and sometimes frustrating, world of data discovery, access, delivery and use by reference to the US National Aeronautics & Space Administration's (NASA's) public health applications portfolio in 2011. It also provides examples of global information system applications in health.

Vegetation productivity trends for the Arctic tundra are updated for the 1982–2011 period and examined in the context of land surface temperatures and coastal sea ice. Understanding mechanistic links between vegetation and climate parameters contributes to model advancements that are necessary for improving climate projections. This study employs remote sensing data: Global Inventory Modeling and Mapping Studies (GIMMS) Maximum Normalized Difference Vegetation Index (MaxNDVI), Special Sensor Microwave Imager (SSM/I) sea-ice concentrations, and Advanced Very High Resolution Radiometer (AVHRR) radiometric surface temperatures. Spring sea ice is declining everywhere except in the Bering Sea, while summer open water area is increasing throughout the Arctic. Summer Warmth Index (SWI—sum of degree months above freezing) trends from 1982 to 2011 are positive around Beringia but are negative over Eurasia from the Barents to the Laptev Seas and in parts of northern Canada. Eastern North America continues to show increased summer warmth and a corresponding steady increase in MaxNDVI. Positive MaxNDVI trends from 1982 to 2011 are generally weaker compared to trends from 1982–2008. So to better understand the changing trends, break points in the time series were quantified using the Breakfit algorithm. The most notable break points identify declines in SWI since 2003 in Eurasia and 1998 in Western North America. The Time Integrated NDVI (TI-NDVI, sum of the biweekly growing season values of MaxNDVI) has declined since 2005 in Eurasia, consistent with SWI declines. Summer (June–August) sea level pressure (slp) averages from 1999–2011 were compared to those from 1982–1998 to reveal higher slp over Greenland and the western Arctic and generally lower pressure over the continental Arctic in the recent period. This suggests that the large-scale circulation is likely a key contributor to the cooler temperatures over Eurasia through increased summer cloud cover and warming in Eastern North America from more cloud-free skies.

Satellite observations of surface reflected solar radiation contain information about variability in the absorption of solar radiation by vegetation. Understanding the causes of variability is important for models that use these data to drive land surface fluxes or for benchmarking prognostic vegetation models. Here we evaluated the interannual variability in the new 30.5-year long global satellite-derived surface reflectance index data, Global Inventory Modeling and Mapping Studies normalized difference vegetation index (GIMMS NDVI3g). Pearson's correlation and multiple linear stepwise regression analyses were applied to quantify the NDVI interannual variability driven by climate anomalies, and to evaluate the effects of potential interference (snow, aerosols and clouds) on the NDVI signal. We found ecologically plausible strong controls on NDVI variability by antecedent precipitation and current monthly temperature with distinct spatial patterns. Precipitation correlations were strongest for temperate to tropical water limited herbaceous systems where in some regions and seasons > 40% of the NDVI variance could be explained by precipitation anomalies. Temperature correlations were strongest in northern mid- to high-latitudes in the spring and early summer where up to 70% of the NDVI variance was explained by temperature anomalies. We find that, in western and central North America, winter-spring precipitation determines early summer growth while more recent precipitation controls NDVI variability in late summer. In contrast, current or prior wet season precipitation anomalies were correlated with all months of NDVI in sub-tropical herbaceous vegetation. Snow, aerosols and clouds as well as unexplained phenomena still account for part of the NDVI variance despite corrections. Nevertheless, this study demonstrates that GIMMS NDVI3g represents real responses of vegetation to climate variability that are useful for global models.

Breeding, propagation and survivorship of disease-transmitting arthropod vectors often depend on meteorological and environmental parameters. Statistical and biological modelling that incorporate satellite-acquired data provide useful tools for assessing and forecasting these diseases, assessing possible outbreak areas and providing vital information for policies and decision making.