Global Inventory Monitoring And Modeling Studies Research Articles

Temporal and Spatial Change in Vegetation and Its Interaction with Climate Change in Argentina from 1982 to 2015

Studying vegetation change and its interaction with climate change is essential for regional ecological protection. Previous studies have demonstrated the impact of climate change on regional vegetation in South America; however, studies addressing the fragile ecological environment in Argentina are limited. Therefore, we assessed the vegetation dynamics and their climatic feedback in five administrative regions of Argentina, using correlation analysis and multiple regression analysis methods. The Normalized Difference Vegetation Index 3rd generation (NDVI3g) from Global Inventory Monitoring and Modeling Studies (GIMMS) and climatic data from the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) were processed. The NDVI of the 1982–2015 period in Argentina showed a downward trend, varying from −1.75 to 0.69/decade. The NDVI in Northeast Argentina (NEA), Northwest Argentina (NWA), Pampas, and Patagonia significantly decreased. Precipitation was negatively correlated with the NDVI in western Patagonia, whereas temperature and solar radiation were positively correlated with the NDVI. Extreme precipitation and drought were essential causes of vegetation loss in Patagonia. The temperature (73.09%), precipitation (64.02%), and solar radiation (73.27%) in Pampas, Cuyo, NEA, and NWA were positively correlated with the NDVI. However, deforestation and farming and pastoral activities have caused vegetation destruction in Pampas, NEA, and NWA. Environmental protection policies and deforestation regulations should be introduced to protect the ecological environment. The results of this study clarify the reasons for the vegetation change in Argentina and provide a theoretical reference for dealing with climate change.

Spatial and Temporal Changes in the Normalized Difference Vegetation Index and Their Driving Factors in the Desert/Grassland Biome Transition Zone of the Sahel Region of Africa

The ecological system of the desert/grassland biome transition zone is fragile and extremely sensitive to climate change and human activities. Analyzing the relationships between vegetation, climate factors (precipitation and temperature), and human activities in this zone can inform us about vegetation succession rules and driving mechanisms. Here, we used Landsat series images to study changes in the normalized difference vegetation index (NDVI) over this zone in the Sahel region of Africa. We selected 6315 sampling points for machine-learning training, across four types: desert, desert/grassland biome transition zone, grassland, and water bodies. We then extracted the range of the desert/grassland biome transition zone using the random forest method. We used Global Inventory Monitoring and Modelling Studies (GIMMS) data and the fifth-generation atmospheric reanalysis of the European Centre for Medium-Range Weather Forecasts (ERA5) meteorological assimilation data to explore the spatiotemporal characteristics of NDVI and climatic factors (temperature and precipitation). We used the multiple regression residual method to analyze the contributions of human activities and climate change to NDVI. The cellular automation (CA)-Markov model was used to predict the spatial position of the desert/grassland biome transition zone. From 1982 to 2015, the NDVI and temperature increased; no distinct trend was found for precipitation. The climate change and NDVI change trends both showed spatial stratified heterogeneity. Temperature and precipitation had a significant impact on NDVI in the desert/grassland biome transition zone; precipitation and NDVI were positively correlated, and temperature and NDVI were negatively correlated. Both human activities and climate factors influenced vegetation changes. The contribution rates of human activities and climate factors to the increase in vegetation were 97.7% and 48.1%, respectively. Human activities and climate factors together contributed 47.5% to this increase. The CA-Markov model predicted that the area of the desert/grassland biome transition zone in the Sahel region will expand northward and southward in the next 30 years.

Assessing extreme climatic changes on a monthly scale and their implications for vegetation in Central Asia

The arid and semi-arid areas of Central Asia are especially susceptible and vulnerable to climatic change, thus making understanding the relationship between extreme climate and vegetation dynamics in recent decades very important. Based on the maximum value composite, trend analysis, Mann-Kendall test, correlation analysis, and cross-correlation analysis, this study investigated the variations of extreme climatic indices and their influences on vegetation dynamics using the Unified Gauged-Based Analysis data from the NOAA Climate Prediction Center (CPC) and the Global Inventory Monitoring and Modeling Studies (GIMMS) normalized difference vegetation index (NDVI) series of 1982–2015. In general terms, it is suggested that Central Asia has experienced more extreme precipitation and high temperature events, especially in the mountainous regions. The vegetation in Central Asia has significantly increased during the past 34 years at a rate of 0.0006⋅year-1. The NDVI is significantly and positively related to extreme precipitation and temperature intensity indices on a monthly scale, with strong spatiotemporal heterogeneity. The influences of extreme precipitation indices mainly occurred in May and June, without time lag on a monthly scale, while extreme temperatures exhibited significant relationship with maximum NDVI in April, with a time lag of at least 1 month. Analyzing the relationship between extreme climatic factors and vegetation dynamics on a monthly scale can help us to better understand the main limiting factors for vegetation growth in different growth periods compared with those on an annual scale. In the most severely affected regions, adaptation methods must be initiated, especially for improving the speed of disaster relief and reducing socioeconomic losses. The findings of this study will provide vital information for the ecological protection and sustainable development of Central Asia.

Spatial and temporal variations in vegetation coverage observed using AVHRR GIMMS and Terra MODIS data in the mainland of China

ABSTRACTHuman activities and climate change have changed the vegetation in China. The analysis of the changes in vegetation that have occurred over the past 30 years in China remains a great challenge due to intense human activity and lack of field observations. The use of various Normalized Difference Vegetation Index (NDVI) datasets to study vegetation coverage changes has received much attention. In this paper, we selected the early versions of Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Monitoring and Modelling Studies (GIMMS), GIMMS3g (third generation GIMMS NDVI from AVHRR sensors) and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data including the fusion data (GIMMS+MODIS). We analysed spatial and temporal changes in vegetation cover in different ecosystems and basins in the mainland of China. Different contributions of ecosystems and variations in NDVI trends exist in different ecosystems in 17 basins. The results show that different NDVI from different data sources yield different results: (1) Vegetation increased in 74.62–77.7% of the area of the Chinese mainland during 1982–2015, mainly in the Yellow River and the middle reaches of the Yangtze River basin; (2) 2000–2017 MODIS NDVI in mainland China has increased more area (79.67%). (3) Farmland and Forest ecosystems were significantly enhanced in the eastern monsoon region; (4) High-resolution NDVI can provide more information than domain average NDVI. GIMMS and MODIS NDVI data have complementary spatial and temporal distributions. Our study improves the understanding of vegetation dynamics over long time periods and large areas and, moreover, has potential for supporting ecological managers in mainland China.

Historical Trajectory in Vegetation Cover in Northeastern Namibia Based on AVHRR Satellite Imagery (1982–2015)

The negative impact of the reduction of vegetation cover is already being felt in the Zambezi Region in northeastern Namibia. The region has been undergoing various land cover changes in the past decades. To understand the historical trend of vegetation cover (increase or decrease), we analyzed 8-km resolution Global Inventory Monitoring and Modeling Studies (GIMMS) from the Advanced Very High Resolution Radiometer (AVHRR) and 0.25° × 0.25° (resampled to 8 km) resolution Global Precipitation Climatology Center (GPCC). We used the Time Series Segmented Residual Trends (TSS-RESTREND) method. We found that the general trajectory of vegetation cover was negative. Pixel-wise analysis and visual interpretation of historical images both revealed clear signs of vegetation cover change. We observed a single breakpoint in the vegetation trajectory which correlated to the 1991–1992 drought in southern Central Africa. Potential drivers of land cover change are the (il)legal expansion of subsistence farming, population growth, and wood extraction. These findings will serve as a reference for decision makers and policymakers. To better understand the human-induced land cover change at the micro scale and sub-regional level, we recommend using higher resolution remote sensing datasets and historical documents to assess the effect of demographic change, disease, civil unrest, and war.

Vegetation Dynamics and Diverse Responses to Extreme Climate Events in Different Vegetation Types of Inner Mongolia

As the global climate has changed, studies on the relationship between vegetation and climate have become crucial. We analyzed the long-term vegetation dynamics and diverse responses to extreme climate changes in Inner Mongolia, based on long-term Global Inventory Monitoring and Modelling Studies (GIMMS) NDVI3g datasets, as well as the eight extreme precipitation indices and six extreme temperature indices that are highly correlated with the occurrence of droughts or floods, heat or cold temperature disasters, and vegetation growth in Inner Mongolia. These datasets were analyzed using linear regression analysis, the Hurst exponent index, residual analysis, and the Pearson correlation analysis. The results showed the following: (1) The vegetation dynamical changes exhibited trends of improvement during 1982 to 2015, and 68% of the vegetation growth changes in Inner Mongolia can be explained by climate changes. (2) The extreme precipitation indices exhibited a slight change, except for the annual total wet–day precipitation (PRCPTOT). The occurrence of extreme cold temperatures showed a significant decline, while the occurrence of extreme warm temperatures showed an upward trend in Inner Mongolia. (3) The typical steppe, desert steppe, and forest steppe regions are more sensitive to extreme large precipitation, and the forest regions are more sensitive to extreme warm temperatures. (4) Extreme precipitation exhibits a one-month lagged effect on vegetation that is larger than the same-month effects on the grassland system. Extreme temperature exhibits same-month effects on vegetation, which are larger than the one-month lagged effects on the forest system. Therefore, studies of the relationship between extreme climate indices and vegetation are important for performing risk assessments of droughts, floods, and other related natural disasters.

Inter-annual and seasonal trends of vegetation condition in the Upper Blue Nile (Abay) Basin: dual-scale time series analysis

Abstract. A long-term decline in ecosystem functioning and productivity, often called land degradation, is a serious environmental challenge to Ethiopia that needs to be understood so as to develop sustainable land use strategies. This study examines inter-annual and seasonal trends of vegetation cover in the Upper Blue Nile (UBN) or Abbay Basin. The Advanced Very High Resolution Radiometer (AVHRR)-based Global Inventory, Monitoring, and Modeling Studies (GIMMS) normalized difference vegetation index (NDVI) was used for long-term vegetation trend analysis at low spatial resolution. Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data (MOD13Q1) were used for medium-scale vegetation trend analysis. Harmonic analyses and non-parametric trend tests were applied to both GIMMS NDVI (1981–2006) and MODIS NDVI (2001–2011) data sets. Based on a robust trend estimator (Theil–Sen slope), most parts of the UBN (~ 77 %) showed a positive trend in monthly GIMMS NDVI, with a mean rate of 0.0015 NDVI units (3.77 % yr−1), out of which 41.15 % of the basin depicted significant increases (p < 0.05), with a mean rate of 0.0023 NDVI units (5.59 % yr−1) during the period. However, the MODIS-based vegetation trend analysis revealed that about 36 % of the UBN showed a significant decreasing trend (p < 0.05) over the period 2001–2011 at an average rate of 0.0768 NDVI yr−1. This indicates that the greening trend of the vegetation condition was followed by decreasing trend since the mid-2000s in the basin, which requires the attention of land users and decision makers. Seasonal trend analysis was found to be very useful to identify changes in vegetation condition that could be masked if only inter-annual vegetation trend analysis was performed. Over half (60 %) of the Abay Basin was found to exhibit significant trends in seasonality over the 25-year period (1982–2006). About 17 and 16 % of the significant trends consisted of areas experiencing a uniform increase in NDVI throughout the year and extended growing season, respectively. These areas were found primarily in shrubland and woodland regions. The study demonstrated that integrated analysis of inter-annual and intra-annual trends based on GIMMS and MODIS enables a more robust identification of changes in vegetation condition.

NDVI‐indicated long‐term vegetation dynamics in Mongolia and their response to climate change at biome scale

ABSTRACTBased on the vegetation map of Mongolia, Global Inventory Monitoring and Modelling Studies (GIMMS) normalized difference vegetation index (NDVI) (1982–2006), the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (2000–2010), and temperature and precipitation data derived from 60 meteorological stations, this study has thoroughly examined vegetation dynamics in Mongolia and their responses to regional climate change at biome scale. To ensure continuity and consistency between the two NDVI datasets, the MODIS NDVI was first calibrated to the GIMMS NDVI based on the overlapping period of 2000–2006. Good calibration results with R2 values of 0.86–0.98 between the two NDVI datasets were obtained and can detect subtle trends in the long‐term vegetation dynamics of Mongolia. The results indicated that for various biomes, although NDVI changes during 1982–2010 showed great variation, vegetation greening for all biomes in Mongolia seem to have stalled or even decreased since 1991–1994, particularly for meadow steppe (0.0015 year−1), typical steppe (−0.0010 year−1), and desert steppe (−0.0008 year−1), which is an apparent turning point (TP) of the vegetation growth trend in Mongolia. A pronounced drying trend (from −4.399 mm year−1 in meadow steppe since 1990 to −2.445 mm year−1 in alpine steppe since 1993) occurred between 1990 and 1994, and persistently warming temperatures (0.015 °C year−1 in alpine steppe to 0.070 °C year−1 in forest and meadow steppe) until recently have likely played a major role in this NDVI trend reversal. However, the NDVI TP varied by biome, month, and climate and was not coupled exactly with climatic variables. The impact on climate of both same‐time and lagged‐time temperature and precipitation effects also varied strongly across biomes and months. On the whole, climate‐related vegetation decline and associated potential desertification trends will likely be among the major sources of ecological pressure for each biome in Mongolia, which could intensify environmental problems like sandstorms in other East Asian regions.

Consistent shifts in spring vegetation green‐up date across temperate biomes in China, 1982–2006

Understanding spring phenology changes in response to the rapid climate change at biome-level is crucial for projecting regional ecosystem carbon exchange and climate-biosphere interactions. In this study, we assessed the long-term changes and responses to changing climate of the spring phenology in six temperate biomes of China by analyzing the global inventory monitoring and modeling studies (GIMMS) NOAA/AVHRR Normalized Difference Vegetation Index (NDVI) and concurrent mean temperature and precipitation data for 1982-2006. Results show that the spring phenology trends in the six temperate biomes are not continuous throughout the 25year period. The spring phenology in most areas of the six biomes showed obvious advancing trends (ranging from -0.09 to -0.65day/yr) during the 1980s and early 1990s, but has subsequently suffered consistently delaying trends (ranging from 0.22 to 1.22day/yr). Changes in spring (February-April) temperature are the dominating factor governing the pattern of spring vegetation phenology in the temperate biomes of China. The recently delayed spring phenology in these temperate biomes has been mainly triggered by the stalling or reversal of the warming trend in spring temperatures. Results in this study also reveal that precipitation during November-January can explain 16.1% (P<0.05), 20.9% (P<0.05) and 14.2% (P<0.05) of the variations in temperate deciduous forest (TDF), temperate steppe (TS), temperate desert (TD) respectively, highlighting the important role of winter precipitation in regulating changes in the spring vegetation phenology of water-limited biomes.

Increasing contamination might have delayed spring phenology on the Tibetan Plateau

Using the National Aeronautics and Space Administration (NASA) Global Inventory, Monitoring, and Modeling Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) dataset (1982–2006), Yu et al. (1) found that the trend of advancing spring phenology for alpine steppe and meadow was reversed around 2000, despite the increasing air temperature over the Tibetan Plateau. This phenomenon cannot be explained by changes in precipitation or snow melt. They speculated that the lack of fulfillment of a chilling requirement caused by warming winters could be the reason. This might have significant implications for the ecosystem and economy of the Tibetan Plateau. However, we …

Variations of NDVI Over Elevational Zones During the Past Two Decades and Climatic Controls in the Qilian Mountains, Northwestern China

Trends of Normalized Difference Vegetation Index (NDVI) from 1982 to 2006 in the upper mountainous areas of three inland river basins (Shiyanghe, Heihe, and Shulehe, from east to west) in the Qilian Mountains, northwestern China, were analyzed based on the Global Inventory Monitoring and Modeling Studies (GIMMS) NDVI data. The relationships between NDVI and climatic factors such as air temperature, precipitation, and evaporation were also analyzed. The results indicate that changes of NDVI over time had an obvious elevational difference. NDVI has decreased in the northern lower-elevation (<3000 m) areas, which account for 31% of the total area, and increased in the southern higher-elevation (3000–4100 m) areas, which occupy 32% of the total area. In addition, 37% of the area did not show an obvious change in NDVI and was distributed in the periglacial belts with elevations higher than 4100 m. The decrease of NDVI in the lower elevations was controlled by a decrease in precipitation and an increase in air temperatures, whereas the increase in the higher elevations was mainly controlled by an increase in air temperature alone. With a continuous increase in air temperature in the future, vegetation would suffer from more serious water stress in the elevations lower than 3000 m, but become more flourishing between 3000 and 4100 m in the Qilian Mountains. This information is critical for understanding how climate warming may affect hydrology and ecology in the Qilian Mountains and for managing water resources for the lowlands in the Hexi Corridor adjacent to the mountains.

Debating the greening vs. browning of the North American boreal forest: differences between satellite datasets

AbstractA number of remote sensing studies have evaluated the temporal trends of the normalized difference vegetation index (NDVI or vegetation greenness) in the North American boreal forest during the last two decades, often getting quite different results. To examine the effect that the use of different datasets might be having on the estimated trends, we compared the temporal trends of recently burned and unburned sites of boreal forest in central Canada calculated from two datasets: the Global Inventory, Monitoring, and Modeling Studies (GIMMS), which is the most commonly used 8 km dataset, and a new 1 km dataset developed by the Canadian Centre for Remote Sensing (CCRS). We compared the NDVI trends of both datasets along a fire severity gradient in order to evaluate the variance in regeneration rates. Temporal trends were calculated using the seasonal Mann–Kendall trend test, a rank‐based, nonparametric test, which is robust against seasonality, nonnormality, heteroscedasticity, missing values, and serial dependence. The results showed contrasting NDVI trends between the CCRS and the GIMMS datasets. The CCRS dataset showed NDVI increases in all recently burned sites and in 50% of the unburned sites. Surprisingly, the GIMMS dataset did not capture the NDVI recovery in most burned sites and even showed NDVI declines in some burned sites one decade after fire. Between 50% and 75% of GIMMS pixels showed NDVI decreases in the unburned forest compared with &lt;1% of CCRS pixels. Being the most broadly used dataset for monitoring ecosystem and carbon balance changes, the bias towards negative trends in the GIMMS dataset in the North American boreal forest has broad implications for the evaluation of vegetation and carbon dynamics in this region and globally.

Effects of precipitation, temperature and topographic parameters on evergreen vegetation greenery in the Western Ghats, India

AbstractBi‐weekly National Oceanic and Atmospheric Administration‐advanced very high‐resolution radiometer (NOAA‐AVHRR) satellite data covering a fourteen‐year time period (1990–2003) were used to examine spatial patterns in the normalized difference vegetation index (NDVI) and their relationships with environmental variables covering tropical evergreen forests of the Western Ghats, India. NDVI values and corresponding environmental variables were extracted from 23 different forested sites using the NOAA‐AVHRR global inventory monitoring and modelling studies (GIMMS) dataset. We specifically used the partial least square (PLS) multivariate regression technique that combines features from principal component analysis and multiple regression to link spatial patterns in NDVI with the environmental variables. PLS regression analysis suggested the two‐component model to be the best model, explaining nearly 71% of the variance in the NDVI datasets with relatively good R2 value of 0.78 and a predicted R2 value of 0.74. The most important positive predictors for NDVI included Riva's continentality index, precipitation indicators summed over different quarters, average precipitation and elevation. Also, the results from PLS regression clearly suggested that bio‐climatic indicators that relied only on precipitation parameters had much more positive influence than indicators that combined both temperature and precipitation together. These results highlight the climatic controls of vegetation vigor in evergreen forests and have implications for monitoring bio‐spheric activity, developing prognostic phenology models and deriving land cover maps in the Western Ghats region of India. Copyright © 2008 Royal Meteorological Society

Assessing vegetation dynamics impacted by climate change in the southwestern karst region of China with AVHRR NDVI and AVHRR NPP time-series

The relationship between climate change and vegetation dynamics in the southwestern karst region of China has been identified by recent studies. Based on previous researches and AVHRR (Advanced Very High Resolution Radiometer) GIMMS (Global Inventory Monitoring and Modeling Studies) NDVI (Normalized Difference Vegetation Index) (1982–2003) and AVHRR GloPEM (Global Production Efficiency Model) NPP (Net Primary Production) (1981–2000) datasets, vegetation dynamics impacted by climate change in the southwestern karst region of China were assessed. The results show that: (1) since the early 1980s, both vegetation cover density and net primary production have insignificant ascending tendencies. However, the inter-annual variation rates of vegetation indexes have apparent spatial differentiations; (2) the correlation coefficients between the inter-annual variations of vegetation indexes and the inter-annual variations of climate factors vary geographically; (3) as indicated by NDVI and NPP, various vegetation types have different responses to climate change, and the annual mean temperature variation has more significant impact on vegetation dynamics than the annual precipitation variation in the study area; (4) distribution laws of correlation coefficients between the inter-annual variations of vegetation indexes and the inter-annual variations of climate factors in different climate conditions are apparent. All these findings will enrich our knowledge of the natural forces which impact the stability of the karst ecosystems and provide scientific basis for the management of the karst ecosystems.

Interannual covariability between actual evapotranspiration and PAL and GIMMS NDVIs of northern Asia

This study examined the covariability between interannual changes in the normalized difference vegetation index (NDVI) and actual evapotranspiration (ET). To reduce possible uncertainty in the NDVI time series, two NDVI datasets derived from Pathfinder AVHRR Land (PAL) data and the Global Inventory Monitoring and Modeling Studies (GIMMS) group were used. Analyses were conducted using data over northern Asia from 1982 to 2000. Interannual changes over 19 years in the PAL-NDVI and GIMMS-NDVI were compared with interannual changes in ET estimated from model-assimilated atmospheric data and gridded precipitation data. For both NDVI datasets, the annual maximum correlation with ET occurred in June, which is the beginning of the vegetation growing season. The PAL and GIMMS datasets showed a significant, positive correlation between interannual changes in the NDVI and ET over most of the vegetated land area in June. These results suggest that interannual changes in vegetation activity predominantly control interannual changes in ET in June. Based on analyses of interannual changes in temperature, precipitation, and the NDVI in June, the study area can be roughly divided into two regions, the warmth-dominated northernmost region and the wetness-dominated southern region, indicating that interannual changes in vegetation and the resultant interannual changes in ET are controlled by warmth and wetness in these two regions, respectively.

Atmospheric conditions for monitoring the long-term vegetation dynamics in the Amazon using normalized difference vegetation index

This study examined the effect of biomass-burning aerosols and clouds on the temporal dynamics of the normalized difference vegetation index (NDVI) exhibited by two widely used, time-series NDVI data products: the Pathfinder AVHRR land (PAL) dataset and the NASA Global Inventory Monitoring and Modeling Studies (GIMMS) dataset. The PAL data are 10-day maximum-value NDVI composites from 1982 to 1999 with corrections for Rayleigh scattering and ozone absorption. The GIMMS data are 15-day maximum-value NDVI composites from 1982 to 1999. In our analysis, monthly maximum-value NDVI was extracted from these datasets. The effects were quantified by comparing time-series of NDVI from PAL and GIMMS with observations from the SPOT/VEGETATION sensor and aerosol index data from the Total Ozone Mapping Spectrometer (TOMS), and results from radiative transfer simulation. Our analysis suggests that the substantial large-scale NDVI seasonality observed in the south and east Amazon forest region with PAL and GIMMS is primarily caused by variations in atmospheric conditions associated with biomass-burning aerosols and cloudiness. Reliable NDVI data can be typically acquired from April to July when such effects are relatively low, whereas there is a few effective NDVI data from September to December. In the central Amazon forest region, where aerosol loads are relatively low throughout the year, large-scale NDVI seasonality results primarily from seasonal variations in cloud cover. Careful treatment of these aerosol and cloud effects is required when using NDVI from PAL and GIMMS (or other source) to determine large-scale seasonal and interannual dynamics of vegetation greenness and ecosystem–atmosphere CO 2 exchange in the Amazon region.

Relation between interannual variations in satellite measures of northern forest greenness and climate between 1982 and 1999

This paper analyzes the relation between satellite‐based measures of vegetation greenness and climate by land cover type at a regional scale (2° × 2° grid boxes) between 1982 and 1999. We use the normalized difference vegetation index (NDVI) from the Global Inventory Monitoring and Modeling Studies (GIMMS) data set to quantify climate‐induced changes in terrestrial vegetation. Climatic conditions are represented with monthly data for land surface air temperature and precipitation. The relation between NDVI and the climate variables is represented using a quadratic specification, which is consistent with the notion of a physiological optimum. The effects of spatial heterogeneity and unobserved variables are estimated with specifications and statistical techniques that allow coefficients to vary among grid boxes. Using this methodology, we are able to estimate statistically meaningful relations between NDVI and climate during spring, summer, and autumn for forests between 40°N and 70°N in North America and Eurasia. Of the variables examined, changes in temperature account for the largest fraction of the change in NDVI between the early 1980s and the late 1990s. Changes in stratospheric aerosol optical depth and precipitation have a smaller effect, while artifacts associated with variations in solar zenith angle are negligible. These results indicate that temperature changes between the early 1980s and the late 1990s are responsible for much of the observed increase in satellite measures of northern forest greenness.

A global 1° by 1° NDVI data set for climate studies derived from the GIMMS continental NDVI data

Abstract A nine-year (1982–1990) global normalized difference vegetation index (NDVI) data set with a spatial resolution of 1° by 1° and a temporal resolution of one month was compiled for use in climate studies. This data set was derived from higher resolution (5–8 km) monthly continental NDVI data sets that have been processed and archived by the Global Inventory Monitoring and Modelling Studies (GIMMS) group at NASA/Goddard Space Flight Center. The continental GIMMS NDVI data sets were calculated from Global Area Coverage (GAC) data collected at daily intervals by the Advanced Very High Resolution Radiometer (AVHRR) onboard the NOAA-7, -9 and -11 satellites The global 1° by 1° NDVI data set was produced to calculate land surface parameters for use within general circulation model J of the atmosphere (GCM). In view of this quantitative application, an evaluation is given of the representation by the NDVI data of the spectral properties of vegetation at the landsurface. Errors are defined as deviations f...