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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.

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.

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.

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.

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.

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.

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.

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.

It is often assumed that the Normalized Difference Vegetation Index (NDVI) can be equated to aboveground plant biomass, but such a relationship has never been quantified at a global biome scale. We sampled aboveground plant biomass (phytomass) at representative zonal sites along two trans-Arctic transects, one in North America and one in Eurasia, and compared these data to satellite-derived NDVI. The results showed a remarkably strong correlation between total aboveground phytomass sampled at the peak of summer and the maximum annual NDVI (R 2 = 0.94, p < 0.001). The relationship was almost identical for the North America and Eurasia transects. The NDVI–phytomass relationship was used to make an aboveground phytomass map of the tundra biome. The approach uses a new and more accurate NDVI data set for the Arctic (GIMMS3g) and a sampling protocol that employs consistent methods for site selection, clip harvest and sorting and weighing of plant material. Extrapolation of the results to zonal landscape-level ...

Linkages between diminishing Arctic sea ice and changes in Arctic terrestrial ecosystems have not been previously demonstrated. Here, the authors use a newly available Arctic Normalized Difference Vegetation Index (NDVI) dataset (a measure of vegetation photosynthetic capacity) to document coherent temporal relationships between near-coastal sea ice, summer tundra land surface temperatures, and vegetation productivity. The authors find that, during the period of satellite observations (1982–2008), sea ice within 50 km of the coast during the period of early summer ice breakup declined an average of 25% for the Arctic as a whole, with much larger changes in the East Siberian Sea to Chukchi Sea sectors (>44% decline). The changes in sea ice conditions are most directly relevant and have the strongest effect on the villages and ecosystems immediately adjacent to the coast, but the terrestrial effects of sea ice changes also extend far inland. Low-elevation (70%). The NDVI has increased across most of the Arctic, with some exceptions over land regions along the Bering and west Chukchi Seas. The greatest change in absolute maximum NDVI occurred over tundra in northern Alaska on the Beaufort Sea coast [+0.08 Advanced Very High Resolution Radiometer (AVHRR) NDVI units]. When expressed as percentage change, large NDVI changes (10%–15%) occurred over land in the North America High Arctic and along the Beaufort Sea. Ground observations along an 1800-km climate transect in North America support the strong correlations between satellite NDVI observations and summer land temperatures. Other new observations from near the Lewis Glacier, Baffin Island, Canada, document rapid vegetation changes along the margins of large retreating glaciers and may be partly responsible for the large NDVI changes observed in northern Canada and Greenland. The ongoing changes to plant productivity will affect many aspects of Arctic systems, including changes to active-layer depths, permafrost, biodiversity, wildlife, and human use of these regions. Ecosystems that are presently adjacent to year-round (perennial) sea ice are likely to experience the greatest changes.

Global climate variability is currently a topic of high scientific and public interest, with potential ramifications for the Earth's ecologic systems and policies governing world economy. Across the broad spectrum of global climate variability, the least well understood time scale is that of decade-to-century.1 The bases for investigating past changes across that period band are the records of annual mean Global Surface Temperature Anomaly (GSTA) time series, produced variously in many painstaking efforts.2–5 However, due to incipient instrument noise, the uneven distribution of sensors spatially and temporally, data gaps, land urbanization, and bias corrections to sea surface temperature, noise and uncertainty continue to exist in all data sets.1, 2, 6–8 Using the Empirical Mode Decomposition method as a filter, we can reduce this noise and uncertainty and produce a cleaner annual mean GSTA dataset. The noise in the climate dataset is thus reduced by one-third and the difference between the new and the commonly used, but unfiltered time series, ranges up to 0.1506°C, with a standard deviation up to 0.01974°C, and an overall mean difference of only 0.0001°C. Considering that the total increase of the global mean temperature over the last 150 years to be only around 0.6°C, we believe this difference of 0.1506°C is significant.

Famine early warning organizations use data from multiple disciplines to assess food insecurity of communities and regions in less-developed parts of the world. Here we present a model that integrates information on the suitability of the growing season and millet prices in the dry central and northern areas of West Africa. The model is used to create spatially continuous maps of millet prices. By coupling the model with remote sensing vegetation data estimated one to four months into the future, we create a leading indicator of potential price movements for early warning of food crises.

Systematic evaluation of food security throughout the Sahel has been attempted for nearly two decades. Food security analyses have used both food prices to determine the ability of the population to access food, and satellite-derived vegetation indices that measure vegetation production to establish how much food is available each year. The relationship between these two food security indicators is explored here using correspondence analysis and through the use of Markov chain models. Two sources of quantitative data were used: 8 km normalized difference vegetation index (NDVI) data from the Advanced Very High Resolution Radiometers (AVHRR) carried on the NOAA series of satellites, and monthly millet prices from 445 markets in Mali, Niger and Burkina Faso. The results show that the growing season vegetation production is related to the price of millet at the annual and the seasonal time scales. If the growing season was characterized by erratic, sparse rainfall, it resulted in higher prices, and well-distributed, abundant rainfall resulted in lower prices. The correspondence between vegetation production and millet prices is used to produce maps of millet prices for West Africa.

Daily daytime Advanced Very High Resolution Radiometer (AVHRR) 4‐km global area coverage data have been processed to produce a Normalized Difference Vegetation Index (NDVI) 8‐km equal‐area dataset from July 1981 through December 2004 for all continents except Antarctica. New features of this dataset include bimonthly composites, NOAA‐9 descending node data from August 1994 to January 1995, volcanic stratospheric aerosol correction for 1982–1984 and 1991–1993, NDVI normalization using empirical mode decomposition/reconstruction to minimize varying solar zenith angle effects introduced by orbital drift, inclusion of data from NOAA‐16 for 2000–2003 and NOAA‐17 for 2003–2004, and a similar dynamic range with the MODIS NDVI. Two NDVI compositing intervals have been produced: a bimonthly global dataset and a 10‐day Africa‐only dataset. Post‐processing review corrected the majority of dropped scan lines, navigation errors, data drop outs, edge‐of‐orbit composite discontinuities, and other artefacts in the compos...

An increase in photosynthetic activity of the northern hemisphere terrestrial vegetation, as derived from satellite observations, has been reported in previous studies. The amplitude of the seasonal cycle of the annually detrended atmospheric CO 2 in the northern hemisphere (an indicator of biospheric activity) also increased during that period. We found, by analyzing the annually detrended CO 2 record by season, that early summer (June) CO 2 concentrations indeed decreased from 1985 to 1991, and they have continued to decrease from 1994 up to 2002. This decrease indicates accelerating springtime net CO 2 uptake. However, the CO 2 minimum concentration in late summer (an indicator of net growing-season uptake) showed no positive trend since 1994, indicating that lower net CO 2 uptake during summer cancelled out the enhanced uptake during spring. Using a recent satellite normalized difference vegetation index data set and climate data, we show that this lower summer uptake is probably the result of hotter and drier summers in both mid and high latitudes, demonstrating that a warming climate does not necessarily lead to higher CO 2 growing-season uptake, even in high-latitude ecosystems that are considered to be temperature limited.

A consistent, 2-decade or longer vegetation record is needed to detect trends in global land cover and climate change. With the longest record starting in 1981, vegetation data from the Advanced Very High Resolution Radiometer (AVHRR) have played a key role in detecting changes in vegetation caused by global temperature increases. NASA's Global Inventory Modeling and Mapping Studies (GIMMS) group has recently produced a new global vegetation data set at 8-km resolution from 1981 to 2004. Maximizing the length, stability, and quality of the AVHRR data set, the GIMMS Normalized Difference Vegetation Index (NDVI) data will enable new Earth science conclusions and continuous monitoring of vegetation dynamics during the next decade.

We use spatially continuous satellite data as a correlate of precipitation within tropical Africa and show that the majority of documented Ebola hemorrhagic fever outbreaks were closely associated with sharply drier conditions at the end of the rainy season. We propose that these trigger events may enhance transmission of Ebola virus from its cryptic reservoir to humans. These findings suggest specific directions to help understand the sylvatic cycle of the virus and may provide early warning tools to detect possible future outbreaks of this enigmatic disease.

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 19822015. 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 19822015, 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 19992015 compared to 19821998.

Normalized difference vegetation index data from the polar-orbiting National Oceanic and Atmospheric Administration meteorological satellites from 1982 to 1999 show significant variations in photosynthetic activity and growing season length at latitudes above 35 degrees N. Two distinct periods of increasing plant growth are apparent: 1982-1991 and 1992-1999, separated by a reduction from 1991 to 1992 associated with global cooling resulting from the volcanic eruption of Mt. Pinatubo in June 1991. The average May to September normalized difference vegetation index from 45 degrees N to 75 degrees N increased by 9% from 1982 to 1991, decreased by 5% from 1991 to 1992, and increased by 8% from 1992 to 1999. Variations in the normalized difference vegetation index were associated with variations in the start of the growing season of -5.6, +3.9, and -1.7 days respectively, for the three time periods. Our results support surface temperature increases within the same period at higher northern latitudes where temperature limits plant growth.

Smoothness membership in Besov spaces B/sub q//sup /spl alpha//(I) is used to compare the spatial coherence of satellite images. Smoothness is given by a complexity index computed as the rate of decay of the approximation error /spl epsiv/(M) when the image is approximated by its M-largest quantized wavelet coefficient. The technique was applied to a set of nine normalized difference vegetation index (NDVI) time series data as a quantitative quality measure of spatial coherence. The NDVI data set comprises different compositing and atmospheric correction techniques. The estimates of the complexity index give a quantitative measure of the performance of these techniques that agrees well with visual evaluation and with the physics of the image collection process. The authors demonstrate the maximum value NDVI composites with Rayleigh, ozone, and water vapor correction consistently provide the highest spatial coherence among the compositing and atmospheric correction techniques evaluated. They also show the complexity index is regionally dependent and is higher in dry periods than in wet periods where residual cloud interference is more likely to appear.

Hierarchical foreground and background analysis (HFBA) was used to discriminate soil properties from two valleys in the Santa Monica Mountains Recreation Area, California. The analysis was organized in two levels. First, spectral data from laboratory measured soil samples were used to train a vector in AVIRIS data for classifying the soils between valleys. The prediction of organic matter and iron contents is performed at a second level of resolution. Results showed that, in the laboratory, soils could be classified at a high level of accuracy. When applied to the image, the spatial predictions of organic matter and iron content were consistent for the first level of classification. The ranges of predicted organic matter and iron contents developed at the second level of classification were also consistent with the magnitude and distribution of field samples. The presence of vegetation and the steep terrain affect adversely the ability to resolve these soil properties.

Predicting fire hazard in fire-prone ecosystems in urbanized landscapes, such as the chaparral systems of California, is critical to risk assessment and mitigation. Understanding the dynamics of fire spread, topography and vegetation condition are necessary to increase the accuracy of fire risk assessment. One vital input to fire models is spatial and temporal estimates of canopy water content. However, timely estimates of such a dynamic ecosystem property cannot be provided for more than periodic point samples using ground based methods. This study examined the potential of three quasiphysical methods for estimating water content using remotely sensed Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data of chaparral systems in the Santa Monica Mountains, California. We examined estimates of water content at the leaf, canopy, and image level and compared them to each other and to ground-based estimates of plant water content. These methods predicted water content (with R2 between 0.62 and 0.95) but differ in their ease of use and the need for ancillary data inputs. The prospect for developing regional estimates for canopy water content at high spatial resolution (20 m) from high resolution optical sensors appears promising.

A hierarchical procedure was developed for quantitative estimation of foliar chemistry from remote reflectance spectra. The authors based their analysis on a new methodology called Hierarchical Foreground and Background Analysis (HFBA) that derives sequentially a series of weighting vectors which simultaneously extract important discriminant features, in this case, leaf anatomy and chemical concentration at different levels of detection from the spectral information. In this study, they focused on the application of detecting carbon, cellulose, nitrogen concentrations, and water content. The goal of the derived vectors is twofold: 1) create a robust detection and classification system of constituent materials and 2) create a good information packing system that minimizes extraneous undesired interference, like noise, in the analysis. In their study, two data sets were examined: a fresh leaf (FL) data set, LOPEx, and a dry leaf data set, Blackhawk Island (BH), WI. The authors tested the robustness of the derived vectors with four other data sets: fresh leaf data from Jasper Ridge Biological Preserve (JRBP), Santa Monica Mountains (SMM), CA and dry leaf data from two ACCP sites Howland and Harvard Forest. The results support the robustness of the HFBA system and demonstrate an advantage in classification accuracy as well as in predicting the biochemical composition (subsequent levels) over classical forms of analysis that ignore effects of the nonlinear variation that contribute to reflectance at different (subpixel and spectral) scales, HFBA primarily deals with the spectral scaling issue.

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.

Global temperature is increasing, especially over Northern lands (>50 N), owing to positive feedbacks. As this increase is most pronounced in winter, temperature seasonality (ST)—conventionally defined as the difference between summer and winter temperatures—is diminishing over time, a phenomenon that is analogous to its equatorward decline at an annual scale. The initiation, termination and performance of vegetation photosynthetic activity are tied to threshold temperatures. Trends in the timing of these thresholds andcumulative temperatures above them may alter vegetation productivity, or modify vegetation seasonality (SV), over time. The relationship between ST and SV is critically examined here with newly improved ground and satellite data sets. The observed diminishment of ST and SV is equivalent to 4 and 7 (5 and 6 ) latitudinal shift equatorward during the past 30 years in the Arctic (boreal) region. Analysis of simulations from 17 state-of-the-art climate models4 indicates an additional ST diminishment equivalent to a 20 equatorward shift could occur this century. How SV will change in response to such large projected ST declines and the impact this will have on ecosystem services5 are not well understood. Hence the need for continued monitoring6 of northern lands as their seasonal temperature profiles evolve to resemble those further south.

The amplitude of the CO 2 seasonal cycle at the Mauna Loa Observatory (MLO) increased from the early 1970s to the early 1990s but decreased thereafter despite continued warming over northern continents. Because of its location relative to the large-scale atmospheric circulation, the MLO receives mainly Eurasian air masses in the northern hemisphere (NH) winter but relatively more North American air masses in NH summer. Consistent with this seasonal footprint, our findings indicate that the MLO amplitude registers North American net carbon uptake during the warm season and Eurasian net carbon release as well as anomalies in atmospheric circulation during the cold season. From the early 1970s to the early 1990s, our analysis was consistent with that of Keeling et al. [Keeling CD, Chin JFS, Whorf TP (1996) Nature 382:146–149], suggesting that the increase in the MLO CO 2 amplitude is dominated by enhanced photosynthetic drawdown in North America and enhanced respiration in Eurasia. In contrast, the recent decline in the CO 2 amplitude is attributed to reductions in carbon sequestration over North America associated with severe droughts from 1998 to 2003 and changes in atmospheric circulation leading to decreased influence of Eurasian air masses. With the return of rains to the U.S. in 2004, both the normalized difference vegetation index and the MLO amplitude sharply increased, suggesting a return of the North American carbon sink to more normal levels. These findings indicate that atmospheric CO 2 measurements at remote sites can continue to play an important role in documenting changes in land carbon flux, including those related to widespread drought, which may continue to worsen as a result of global warming.

The goal of NASA's land long term data record (LTDR) project is to produce a consistent long term data set from the AVHRR and MODIS instruments for land climate studies. The project will create daily surface reflectance and normalized difference vegetation index (NDVI) products at a resolution of 0.05deg, which is identical to the climate modeling grid (CMG) used for MODIS products from EOS Terra and Aqua. Higher order products such as burned area, land surface temperature, albedo, bidirectional reflectance distribution function (BRDF) correction, leaf area index (LAI), and fraction of photosynthetically active radiation absorbed by vegetation (fPAR), will be created. The LTDR project will reprocess global area coverage (GAC) data from AVHRR sensors onboard NOAA satellites by applying the preprocessing improvements identified in the AVHRR Pathfinder II project and atmospheric and BRDF corrections used in MODIS processing. The preprocessing improvements include radiometric in-flight vicarious calibration for the visible and near infrared channels and inverse navigation to relate an Earth location to each sensor instantaneous field of view (IFOV). Atmospheric corrections for Rayleigh scattering, ozone, and water vapor are undertaken, with aerosol correction being implemented. The LTDR also produces a surface reflectance product for channel 3 (3.75 mum). Quality assessment (QA) is an integral part of the LTDR production system, which is monitoring temporal trends in the AVHRR products using time-series approaches developed for MODIS land product quality assessment. The land surface reflectance products have been evaluated at AERONET sites. The AVHRR data record from LTDR is also being compared to products from the PAL (pathfinder AVHRR land) and GIMMS (global inventory modeling and mapping studies) systems to assess the relative merits of this reprocessing vis-a-vis these existing data products. The LTDR products and associated information can be found at http://ltdr.nascom.nasa.gov/ltdr/ ltdr.html.

[1] We use statistical techniques to quantify the effect of interannual variations in vegetation within land covers on surface temperature in North America and Eurasia from satellite measures of surface greenness and ground based meteorological observations. During the winter, reductions in the extent of snow cover cause (in a statistical sense) temperature to rise. During the summer, increases in terrestrial vegetation within land covers cause (in a statistical sense) temperature to fall. Temperature-induced increases in vegetation have slowed increases in surface temperature, but this feedback may be limited by the range over which temperature has a positive effect on vegetation.

In Proceedings of the IEEE 2002 International Geoscience and Remote Sensing Symposium, IGARSS'02, Toronto, Canada, 24-28th June, 2002., Boreal wetland ecosystems cover an estimated 90 x 106 ha, about 36% of global wetlands, and are a major source of trace gases emissions to the atmosphere [1]. Four to 20 percent of the global emission of methane to the atmosphere comes from wetlands north of 40°N latitude [2]. Large uncertainties in emissions exist because of large spatial and temporal variation in the production and consumption of methane. Accurate knowledge of the areal extent of open water and inundated vegetation is critical to estimating magnitudes of trace gas emissions. Improvements in land cover mapping have been sought using physical-modeling approaches [3],[4], neural networks [5],[6], and active-microwave [7], examples that demonstrate the difficulties of separating open water, inundated vegetation and dry upland vegetation. Here we examine the feasibility of using a support vector machine to classify POLDER data representing open water, inundated vegetation and dry upland vegetation.

We present a hierarchical classification technique that discriminates broad categories of surface materials in terms of ground true features, such as water, vegetation, and soils from spectral information. Subsequently, we further discriminate these materials and extract finer ground features, like chemistries, peculiar to each. The interaction at various scales of the 3D spatial and the spectral domains is decomposed by using wavelet tools to address scale dependencies in the spatial domain, a robust spectral unmixing technique, called Hierarchical Foreground Background Analysis (HFBA) along the spectral axis. HFBA sequentially derives a series of weighting vectors discriminating features at different levels of detection: (1) constituent materials, (2) types within constituents, and (3) chemistries peculiar to each type. Our goal is two-fold. First, we present the combination of HFBA and wavelets as a supervised classification technique validating the categories imposed by the supervised classification, and manifesting clusters which can refine the classification at different scales. Second, we identify spectral redundancies between hyperspectral and multispectral information, studying mixture at different spatial/spectral resolutions and assess whether targeted features may be extracted as efficiently from multispectral data as they could be from hyperspectral data. Results on AVIRIS and simulated MODIS data illustrate the robustness and effectivity of the technique.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Background Recent clusters of outbreaks of mosquito-borne diseases (Rift Valley fever and chikungunya) in Africa and parts of the Indian Ocean islands illustrate how interannual climate variability influences the changing risk patterns of disease outbreaks. Although Rift Valley fever outbreaks have been known to follow periods of above-normal rainfall, the timing of the outbreak events has largely been unknown. Similarly, there is inadequate knowledge on climate drivers of chikungunya outbreaks. We analyze a variety of climate and satellite-derived vegetation measurements to explain the coupling between patterns of climate variability and disease outbreaks of Rift Valley fever and chikungunya. Methods and Findings We derived a teleconnections map by correlating long-term monthly global precipitation data with the NINO3.4 sea surface temperature (SST) anomaly index. This map identifies regional hot-spots where rainfall variability may have an influence on the ecology of vector borne disease. Among the regions are Eastern and Southern Africa where outbreaks of chikungunya and Rift Valley fever occurred 2004–2009. Chikungunya and Rift Valley fever case locations were mapped to corresponding climate data anomalies to understand associations between specific anomaly patterns in ecological and climate variables and disease outbreak patterns through space and time. From these maps we explored associations among Rift Valley fever disease occurrence locations and cumulative rainfall and vegetation index anomalies. We illustrated the time lag between the driving climate conditions and the timing of the first case of Rift Valley fever. Results showed that reported outbreaks of Rift Valley fever occurred after ∼3–4 months of sustained above-normal rainfall and associated green-up in vegetation, conditions ideal for Rift Valley fever mosquito vectors. For chikungunya we explored associations among surface air temperature, precipitation anomalies, and chikungunya outbreak locations. We found that chikungunya outbreaks occurred under conditions of anomalously high temperatures and drought over Eastern Africa. However, in Southeast Asia, chikungunya outbreaks were negatively correlated (p, Author Summary Interannual climate variability associated with the El Niño/Southern Oscillation (ENSO) phenomenon and regional climatic circulation mechanisms in the equatorial Indian Ocean result in significant rainfall and ecological anomaly patterns that are major drivers of spatial and temporal patterns of mosquito-borne disease outbreaks. Correlation and regression analyses of long time series rainfall, vegetation index, and temperature data show that large scale anomalies occur periodically that may influence mosquito vector populations and thus spatial and temporal patterns of Rift Valley fever and chikungunya outbreaks. Rift Valley fever outbreak events occurred after a period of ∼3–4 months of persistent and above-normal rainfall that enabled vector habitats to flourish. On the other hand, chikungunya outbreaks occurred during periods of high temperatures and severe drought over East Africa and the western Indian Ocean islands. This is consistent with highly populated environmental settings where domestic and peri-domestic stored water containers were the likely mosquito sources. However, in Southeast Asia, approximately 52% of chikungunya outbreaks occurred during cooler-than-normal temperatures and were significantly negatively correlated with drought. Besides climate variability, other factors not accounted for such as vertebrate host immunity may contribute to spatio-temporal patterns of outbreaks.

Numerous studies have evaluated the dynamics of Arctic tundra vegetation throughout the past few decades, using remotely sensed proxies of vegetation, such as the normalized difference vegetation index (NDVI). While extremely useful, these coarse-scale satellite-derived measurements give us minimal information with regard to how these changes are being expressed on the ground, in terms of tundra structure and function. In this analysis, we used a strong regression model between NDVI and aboveground tundra phytomass, developed from extensive field-harvested measurements of vegetation biomass, to estimate the biomass dynamics of the circumpolar Arctic tundra over the period of continuous satellite records (1982-2010). We found that the southernmost tundra subzones (C-E) dominate the increases in biomass, ranging from 20 to 26%, although there was a high degree of heterogeneity across regions, floristic provinces, and vegetation types. The estimated increase in carbon of the aboveground live vegetation of 0.40 Pg C over the past three decades is substantial, although quite small relative to anthropogenic C emissions. However, a 19.8% average increase in aboveground biomass has major implications for nearly all aspects of tundra ecosystems including hydrology, active layer depths, permafrost regimes, wildlife and human use of Arctic landscapes. While spatially extensive on-the-ground measurements of tundra biomass were conducted in the development of this analysis, validation is still impossible without more repeated, long-term monitoring of Arctic tundra biomass in the field.

Using satellite-derived normalized difference vegetation index (NDVI) data, several previous studies have indicated that vegetation growth significantly increased in most areas of China during the period 1982–99. In this letter, we extended the study period to 2010. We found that at the national scale the growing season (April–October) NDVI significantly increased by 0.0007 yr−1 from 1982 to 2010, but the increasing trend in NDVI over the last decade decreased in comparison to that of the 1982–99 period. The trends in NDVI show significant seasonal and spatial variances. The increasing trend in April and May (AM) NDVI (0.0013 yr−1) is larger than those in June, July and August (JJA) (0.0003 yr−1) and September and October (SO) (0.0008 yr−1). This relatively small increasing trend of JJA NDVI during 1982–2010 compared with that during 1982–99 (0.0012 yr−1) (Piao et al 2003 J. Geophys. Res.—Atmos. 108 4401) implies a change in the JJA vegetation growth trend, which significantly turned from increasing (0.0039 yr−1) to slightly decreasing ( − 0.0002 yr−1) in 1988. Regarding the spatial pattern of changes in NDVI, the growing season NDVI increased (over 0.0020 yr−1) from 1982 to 2010 in southern China, while its change was close to zero in northern China, as a result of a significant changing trend reversal that occurred in the 1990s and early 2000s. In northern China, the growing season NDVI significantly increased before the 1990s as a result of warming and enhanced precipitation, but decreased after the 1990s due to drought stress strengthened by warming and reduced precipitation. Our results also show that the responses of vegetation growth to climate change vary across different seasons and ecosystems.