Normalized Difference Vegetation Index Trends Research Articles

Evaluation of Earth Observation based global long term vegetation trends — Comparing GIMMS and MODIS global NDVI time series

A new and updated version of the AVHRR (Advanced Very High Resolution Radiometer) based GIMMS (Global Inventory Modelling and Mapping Studies) NDVI (Normalized Difference Vegetation Index) dataset is now available covering 1981 to 2010 (GIMMS3g). Earlier versions of this global coverage 15-day composite dataset have been used for numerous local to global scale vegetation time series studies during recent years. However, several aspects of the AVHRR sensor design and data processing potentially introduce substantial noise into the NDVI dataset if not corrected for. The more recent NDVI dataset from Terra MODIS (Moderate Resolution Imaging Spectroradiometer) is considered an improvement over AVHRR data and with the release of GIMMS3g an overlapping period of 11years now provides a possibility to perform a robust evaluation of the accuracy of GIMMS3g data and derived trends. In this study the accuracy is evaluated by comparison with the global Terra MODIS NDVI (MOD13C2 Collection 5) data using linear regression trend analysis. The trends of GIMMS NDVI were found to be in overall acceptable agreement with MODIS NDVI data. A significant trend in NDVI (α=0.05) was found for 11.8% of the MODIS NDVI pixels on a global scale (5.4% characterised by positive trends and 6.3 with negative trends) whereas GIMMS NDVI analysis produced a total of 10.5% significant pixels (4.9% positive, 5.6% negative). However, larger differences were found for the Southern Hemisphere land masses (South America and Australia) and the high northern latitude Arctic regions. From a linear regression analysis the correlation coefficient between the two datasets was found to be highly significant for areas with a distinct phenological cycle. Discrepancies between the GIMMS and MODIS datasets were found in equatorial areas (broadleaved, evergreen forest), Arctic areas (sparse herbaceous or sparse shrub cover) and arid areas (herbaceous cover, closed–open). Linear regression of QA filtered Terra and Aqua MODIS NDVI (2003–2010) revealed similar inconsistencies for Arctic and equatorial areas suggesting that robust long-term NDVI trend estimates in these areas are difficult to obtain from both GIMMS and MODIS data. Additionally, GIMMS based NDVI trend analysis in arid areas of limited photosynthetic activity should be interpreted with caution. The regression coefficient (slope value) (p<0.01) was found to be close to 1 for most land cover types on a global scale (global land cover class average slope=1.00) suggesting overall compatibility between MODIS and GIMMS NDVI, but with land cover class specific variations (within class and between classes).

Causes of spring vegetation growth trends in the northern mid–high latitudes from 1982 to 2004

The Community Land Model version 4 (CLM4) is applied to explore the spatial–temporal patterns of spring (April–May) vegetation growth trends over the northern mid–high latitudes (NMH) (>25°N) between 1982 and 2004. During the spring season through the 23 yr period, both the satellite-derived and simulated normalized difference vegetation index (NDVI) anomalies show a statistically significant correlation and an overall greening trend within the study area. Consistently with the observed NDVI–temperature relation, the CLM4 NDVI shows a significant positive association with the spring temperature anomaly for the NMH, North America and Eurasia. Large study areas experience temperature discontinuity associated with contrasting NDVI trends. Before and after the turning point (TP) of the temperature trends, climatic variability plays a dominant role, while the other environmental factors exert minor effects on the NDVI tendencies. Simulated vegetation growth is broadly stimulated by the increasing atmospheric CO2. Trends show that nitrogen deposition increases NDVI mostly in southeastern China, and decreases NDVI mainly in western Russia after the temperature TP. Furthermore, land use-induced NDVI trends vary roughly with the respective changes in land management practices (crop areas and forest coverage). Our results highlight how non-climatic factors mitigate or exacerbate the impact of temperature on spring vegetation growth, particularly across regions with intensive human activity.

Impact of sensor degradation on the MODIS NDVI time series

Time series of satellite data provide unparalleled information on the response of vegetation to climate variability. Detecting subtle changes in vegetation over time requires consistent satellite-based measurements. Here, the impact of sensor degradation on trend detection was evaluated using Collection 5 data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on the Terra and Aqua platforms. For Terra MODIS, the impact of blue band (Band 3, 470nm) degradation on simulated surface reflectance was most pronounced at near-nadir view angles, leading to a 0.001–0.004yr−1 decline in Normalized Difference Vegetation Index (NDVI) under a range of simulated aerosol conditions and surface types. Observed trends in MODIS NDVI over North America were consistent with simulated results, with nearly a threefold difference in negative NDVI trends derived from Terra (17.4%) and Aqua (6.7%) MODIS sensors during 2002–2010. Planned adjustments to Terra MODIS calibration for Collection 6 data reprocessing will largely eliminate this negative bias in detection of NDVI trends.

Recent change of vegetation growth trend in China

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.

Monitoring responses of forest to climate variations by MODIS NDVI: a case study of Hun River upstream, northeastern China

This study analyzed the temporal variation of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) of Hun River upstream forest in northeastern China and its correlation with climate parameters (temperature and precipitation) during the period of 2000–2009. We examined the interannual variation of forest, seasonal variation of forest and lag effects of climate variables (temperature and precipitation) on forest using simple regression and correlation. The objective of this paper was to compare the results of our research and previous researches and to show that the conclusions derived from broad-scale researches provided a direction of policy, but the local details were essential to local management. We found that the annual mean NDVI was significantly correlated with annual mean temperature. The forests studied in our research showed insignificant increase trends except for Fraxinus spp. forest. We concluded that the temperature was the limiting factor of vegetation growth in our study area and the forest which was in the core geographic area of its distribution was resilient to climate variation. When seasonal variation was examined, we found the largest increase trend of seasonal mean NDVI was in winter. The result was different from the outcome of previous research at national scale. There were 3 months lag effects of climate variables on vegetation of our study area in summer and autumn, which was consistent with researches at broad scales. The reasons of both difference and indifference were discussed in this paper. We also got information about tree species for local management using MODIS NDVI. The results of this work suggested that information from local scales would be important complements to researches at broad scales and were essential for local managers.

Spatial and Temporal Relationships among NDVI, Climate Factors, and Land Cover Changes in Northeast Asia from 1982 to 2009

This study uses a multiple linear regression method to composite standard Normalized Difference Vegetation Index (NDVI) time series (1982-2009) consisting of three kinds of satellite NDVI data (AVHRR, SPOT, and MODIS). This dataset was combined with climate data and land cover maps to analyze growing season (June to September) NDVI trends in northeast Asia. In combination with climate zones, NDVI changes that are influenced by climate factors and land cover changes were also evaluated. This study revealed that the vegetation cover in the arid, western regions of northeast Asia is strongly influenced by precipitation, and with increasing precipitation, NDVI values become less influenced by precipitation. Spatial changes in the NDVI as influenced by temperature in this region are less obvious. Land cover dynamics also influence NDVI changes in different climate zones, especially for bare ground, cropland, and grassland. Future research should also incorporate higher-spatial-resolution data as well as other data types (such as greenhouse gas data) to further evaluate the mechanisms through which these factors interact.

Assessing vegetation dynamics and their relationships with climatic variability in Heilongjiang province, northeast China

In this study, the vegetation dynamics in Heilongjiang province and their relationships with climate variability were assessed using normalized difference vegetation index (NDVI) and meteorological datasets from 1981 to 2003. The conclusions from our results are as follows: (1) After 1981, vegetation cover, as indicated by the NDVI, exhibited an insignificant increasing tendency. However, the inter-annual variations of the NDVI showed apparent spatial differentiations. (2) The inter-annual changes of the NDVI were different from season to season. The spring and autumn NDVI values increased, while the summer and winter NDVI decreased. (3) The annual NDVI was significantly correlated with precipitation. Thus, as compared to temperature, precipitation was the dominant climatic factor affecting the vegetation dynamics in Heilongjiang province. (4) The trend in the NDVI showed a marked homogeneity corresponding to regional and seasonal variations in climate. Additionally, land use changes also play an important role in influencing the NDVI trends over some regions. All of these findings will enrich our knowledge of the natural forces that impact the stability of boreal ecosystems and provide a scientific basis for the environmental management in Heilongjiang province in response to climate change and human activities.

The dynamic of vegetation coverage and its response to climate factors in Inner Mongolia, China

Normalized Difference Vegetation Index (NDVI) is widely recognized as a good indicator of vegetation productivity. Diagnosing the NDVI trend and understanding climatic factors influences on NDVI can predict the productivity changes under different climatic scenarios. This paper examined NDVI dynamic and its response to climate factors during a 10 year period (1998–2008) in Inner Mongolia. The main findings are as follows: (1) The NDVI multi-scale characters can be revealed well by wavelet transform, and the average NDVI and the NDVI amplitude show a gradually decreased trend from northeast to southwest in Inner Mongolia during the past 10 years, furthermore, this trend is consistent with the heat and water distribution caused by latitude difference in north–south direction and Asia monsoon effect in east–west direction. (2) The relation between NDVI and temperature is the most close, followed by precipitation, sunshine hours and relative humidity. Different vegetation cover types show different strengths in correlation between NDVI and climate variables with the correlation values decreasing from forest, meadow steppe to desert steppe in whole. (3) The precipitation and temperature have the same change cycle, both nearly 290 days in the 20 selected stations. The NDVI has the same change cycle with the precipitation and temperature or either 10 days earlier or later than precipitation and temperature, which supports the significant correlation between NDVI and its climatic factors from a new perspective. The nearly 290 days change cycle implies that the vegetation growth cycle is nearly 10 months and there are no obvious differences change cycles in different vegetations. (4) Vegetation dynamic is significantly correlated to the temperature and precipitation at the time scale of 10, 20, 40, 80, 160, and 320-day, respectively, and the S3 scale (i.e., the time scale of 80-day), nearly 3 months (one season), is most significant and suitable for evaluating the vegetation dynamic to climatic factors.

Changes in satellite‐derived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006

AbstractMonitoring changes in vegetation growth has been the subject of considerable research during the past several decades, because of the important role of vegetation in regulating the terrestrial carbon cycle and the climate system. In this study, we combined datasets of satellite‐derived Normalized Difference Vegetation Index (NDVI) and climatic factors to analyze spatio‐temporal patterns of changes in vegetation growth and their linkage with changes in temperature and precipitation in temperate and boreal regions of Eurasia (&gt; 23.5°N) from 1982 to 2006. At the continental scale, although a statistically significant positive trend of average growing season NDVI is observed (0.5 × 10−3 year−1, P = 0.03) during the entire study period, there are two distinct periods with opposite trends in growing season NDVI. Growing season NDVI has first significantly increased from 1982 to 1997 (1.8 × 10−3 year−1, P &lt; 0.001), and then decreased from 1997 to 2006 (−1.3 × 10−3 year−1, P = 0.055). This reversal in the growing season NDVI trends over Eurasia are largely contributed by spring and summer NDVI changes. Both spring and summer NDVI significantly increased from 1982 to 1997 (2.1 × 10−3 year−1, P = 0.01; 1.6 × 10−3 year−1P &lt; 0.001, respectively), but then decreased from 1997 to 2006, particularly summer NDVI which may be related to the remarkable decrease in summer precipitation (−2.7 mm yr−1, P = 0.009). Further spatial analyses supports the idea that the vegetation greening trend in spring and summer that occurred during the earlier study period 1982–1997 was either stalled or reversed during the following study period 1997–2006. But the turning point of vegetation NDVI is found to vary across different regions.

Evolution of vegetation activity on vegetated, eroded, and erosion risk areas in the central Spanish Pyrenees, using multitemporal Landsat imagery

AbstractThe temporal evolution of vegetation activity on various land cover classes in the Spanish Pyrenees was analyzed. Two time series of the normalized difference vegetation index (NDVI) were used, corresponding to March (early spring) and August (the end of summer). The series were generated from Landsat TM and Landsat ETM+ images for the period 1984–2007. An increase in the NDVI in March was found for vegetated areas, and the opposite trend was found in both March and August for degraded areas (badlands and erosion risk areas). The rise in minimum temperature and the time variation of the cloud cover during the study period appears to be the most important factors explaining increased NDVI in the vegetated areas. In degraded areas, no climatic or topographic variable was associated with the negative NDVI trend, which may be related to erosion processes taking place in these regions. Copyright © 2010 John Wiley &amp; Sons, Ltd.

High-latitude tree growth and satellite vegetation indices: Correlations and trends in Russia and Canada (1982–2008)

[1] Vegetation in northern high latitudes affects regional and global climate through energy partitioning and carbon storage. Spaceborne observations of vegetation, largely based on the normalized difference vegetation index (NDVI), suggest decreased productivity during recent decades in many regions of the Eurasian and North American boreal forests. To improve interpretation of NDVI trends over forest regions, we examined the relationship between NDVI from the advanced very high resolution radiometers and tree ring width measurements, a proxy of tree productivity. We collected tree core samples from spruce, pine, and larch at 22 sites in northeast Russia and northwest Canada. Annual growth rings were measured and used to generate site‐level ring width index (RWI) chronologies. Correlation analysis was used to assess the association between RWI and summer NDVI from 1982 to 2008, while linear regression was used to examine trends in both measurements. The correlation between NDVI and RWI was highly variable across sites, though consistently positive (r = 0.43, SD = 0.19, n = 27). We observed significant temporal autocorrelation in both NDVI and RWI measurements at sites with evergreen conifers (spruce and pine), though weak autocorrelation at sites with deciduous conifers (larch). No sites exhibited a positive trend in both NDVI and RWI, although five sites showed negative trends in both measurements. While there are technological and physiological limitations to this approach, these findings demonstrate a positive association between NDVI and tree ring measurements, as well as the importance of considering lagged effects when modeling vegetation productivity using satellite data.

Impacts of changes in climate variability on regional vegetation in China: NDVI‐based analysis from 1982 to 2000

AbstractThree methods were used to distinguish the characteristics of changes in climate variability and normalized difference vegetation index (NDVI) during the period from 1982 to 2000 in China. Great changes in climate variability and an increased trend in NDVI were observed. The changes in precipitation variability were greater than the changes in temperature variability in each month, which is attributed to changes in the monsoon system in East Asia. The abrupt changes in climate and NDVI were more significant in 1983 than in the other years due to the impacts of El Niño/Southern Oscillation (ENSO). Using these results, the influences of changes in climate variability on vegetation were studied in the whole nation, and eight regions were defined according to the vegetation division map of China. The results show that abrupt climate changes at a small scale cannot cause abrupt NDVI changes directly. At a nationwide level, over a longer time scale the persistence of above/below average temperature determines the changes in NDVI; at a shorter time scale, changes in the magnitude of precipitation influence NDVI significantly. Such regional climate variability affects vegetation in different ways owing to the diversity of vegetation types, climatic conditions and topography of the land.

The Browning of Alaska’s Boreal Forest

We used twelve Landsat scenes from the 1980s–2009 and regional 2000–2009 MODIS data to examine the long-term trend in the normalized difference vegetation index (NDVI) within unburned areas of the Alaskan boreal forest. Our analysis shows that there has been a declining trend in NDVI in this region, with the strongest “browning trend” occurring in eastern Alaska where the climate during the growing season is relatively dry and warm. Possible reasons for the "browning trend" are decreased vegetation due to temperature-induced drought stress and increased infestations of insect pests.

A latitudinal gradient in tree growth response to climate warming in the Siberian taiga

We investigated the climate response of three Siberian taiga species, Larix cajanderi, Picea obovata, and Pinus sylvestris, across a latitudinal gradient in central Siberia. We hypothesized that warming is more frequently associated with increased growth for evergreen conifers (P. obovata and P. sylvestris) than for L. cajanderi, and for northern than for southern sites; we also hypothesized that increased growth is associated with a positive trend in normalized difference vegetation index (NDVI). In mixed stands, growth of L. cajanderi and P. obovata increased over time, but the larger growth increases in P. obovata may presage a shift in competitive balance between these species. Climate response varied among and within populations of all species, and positive responses to temperature prevailed at northern sites, where trees grew faster in years with warm early summers. Negative responses to warming declined along the south to north latitudinal gradient. We observed considerable variability in climate response within populations which even exceeded that among species or sites. Tree response to climate was correlated with NDVI trends, indicating that patterns of tree-growth response to climate were indicative of a coherent landscape-scale response to warming. Our findings suggest that increased productivity with warming is likely only in the northern reaches of the Siberian taiga. An increased prevalence of evergreen conifers in areas currently dominated by deciduous Larix species also seems likely.

Recent trends in changes of vegetation over East Asia coupled with temperature and rainfall variations

In this study, we investigated whether long‐term normalized difference vegetation index (NDVI) data show climate change signals after the mid‐1990s which are inferred from other studies on changing trends in precipitation and dust frequencies. In doing so, mean NDVI data for the growing seasons (April–October) from 1982 to 2006 were used for examining the spatiotemporal variations in the vegetation over East Asia, in conjunction with precipitation and temperature data. Results indicate that there was a prominent change in the trend of NDVI around the mid‐1990s: a pronounced positive trend over most of the East Asian domain before the mid‐1990s (1982–1996) and a reverse (or weakened) trend after the mid‐1990s (1997–2006). The reverse trend is evident over the higher‐latitude regions north of 50°N and the eastern Mongolian border area. The EOF and SVD analysis suggest that the dominant warming trend until the mid‐1990s led to the increased NDVI over the high‐latitude regions. However, after the mid‐1990s, the reverse NDVI trend found primarily in the east of Lake Baikal and the arid and semiarid regions south of 50°N seems to be closely linked to local precipitation changes occurred abruptly in the mid‐1990s. However, precipitation influences on the reverse NDVI changes are not clear over the high‐latitude regions north of 50°N.

Migration and environment in Ghana: a cross-district analysis of human mobility and vegetation dynamics

Migration—environment linkages are at the centre of media attention because of public concern about climate change and a perceived “flooding” of migrants from less developed countries into more affluent parts of the world. In the past few years, a substantial body of conceptual literature about environmentally induced migration has evolved, but there is still a paucity of empirical work in this area. Moreover, the environmental causes of migration have been studied largely in isolation of the environmental consequences. In this paper we present an analysis of migration and vegetation dynamics for one country (Ghana) to assess four migration—environment linkages. On the one hand, we look at two environmental drivers of migration: environmental push and pull. On the other hand, we look at the environmental impact of migration on source and destination areas. Census data at the district level (N=110) are used to map domestic migration flows in Ghana, which are then related to vegetation dynamics retrieved from a remotely sensed Normalized Difference Vegetation Index (NDVI) dataset (1981— 2006). The analysis shows that at the national level, there are significant but weak correlations between migration and vegetation cover and trends therein. Districts with a migration deficit (more out-migration than in-migration) tend to be more sparsely vegetated and have experienced a more positive NDVI trend over the past quarter century than districts with a migration surplus. A disaggregation of data in three principle migration systems shows stronger correlations. Namely that north—south migration and cocoa frontier settlement have important environmental dimensions, but environmental factors do not seem to play a major role in migration to the capital, Accra. An important insight from this paper is that migration flows in Ghana can be explained partly by vegetation dynamics but are also strongly related to rural population densities. This is because access to natural resources is often more important than the scarcity or abundance of natural resources per se. This study further shows that satellite remote sensing can provide valuable input to analyses of migration—environment linkages.

Aridification determines changes in forest growth in Pinus halepensis forests under semiarid Mediterranean climate conditions

Using a set of 16 Landsat TM and Landsat ETM+ images from 1984 to 2006, we tested whether climate trends in the last three decades differentially controlled the vegetal activity of eight Pinus halepensis forests located across a marked bioclimatic gradient. Our results show spatial differences in trends in the normalized difference vegetation index (NDVI) between 1984 and 2006, which were highly related to the spatial distribution of aridity. There was a strong correlation between the change in the NDVI values and the climatic water balance in each forest ( R = 0.86, p = 0.006). A large increment of the NDVI was observed in forests located in the most humid areas, whereas those located in the most arid areas showed a slight decrease in the NDVI. We used a forest growth simulation model (GOTILWA+) to estimate the leaf area index (LAI) in each of the eight analyzed forests, and found similar results to those obtained for the NDVI. Significant correlation was found between the spatial pattern of NDVI and LAI trends between 1984 and 2006 ( R = 0.82, p = 0.01). The climate evolution in the last four decades explains the observed and modeled changes in the NDVI and the LAI. In agreement with other studies, we showed that the general response of the forests to more favorable conditions (in terms of temperature increment and CO 2 fertilization) is increased leaf activity and biomass. Nevertheless, in forests located in the most arid areas the positive trend observed in the potential evapotranspiration rates increased water stress, and had a negative effect on forest growth. Given the future predictions of warming and declining precipitation from global climate models for the Mediterranean region, an increase in stress conditions affecting these forests is expected, which will affect their growth and survival, mainly in the most arid areas.

Evaluating the Consistency of the 1982–1999 NDVI Trends in the Iberian Peninsula across Four Time-series Derived from the AVHRR Sensor: LTDR, GIMMS, FASIR, and PAL-II

Successive efforts have processed the Advanced Very High Resolution Radiometer (AVHRR) sensor archive to produce Normalized Difference Vegetation Index (NDVI) datasets (i.e., PAL, FASIR, GIMMS, and LTDR) under different corrections and processing schemes. Since NDVI datasets are used to evaluate carbon gains, differences among them may affect nations’ carbon budgets in meeting international targets (such as the Kyoto Protocol). This study addresses the consistency across AVHRR NDVI datasets in the Iberian Peninsula (Spain and Portugal) by evaluating whether their 1982–1999 NDVI trends show similar spatial patterns. Significant trends were calculated with the seasonal Mann-Kendall trend test and their spatial consistency with partial Mantel tests. Over 23% of the Peninsula (N, E, and central mountain ranges) showed positive and significant NDVI trends across the four datasets and an additional 18% across three datasets. In 20% of Iberia (SW quadrant), the four datasets exhibited an absence of significant trends and an additional 22% across three datasets. Significant NDVI decreases were scarce (croplands in the Guadalquivir and Segura basins, La Mancha plains, and Valencia). Spatial consistency of significant trends across at least three datasets was observed in 83% of the Peninsula, but it decreased to 47% when comparing across the four datasets. FASIR, PAL, and LTDR were the most spatially similar datasets, while GIMMS was the most different. The different performance of each AVHRR dataset to detect significant NDVI trends (e.g., LTDR detected greater significant trends (both positive and negative) and in 32% more pixels than GIMMS) has great implications to evaluate carbon budgets. The lack of spatial consistency across NDVI datasets derived from the same AVHRR sensor archive, makes it advisable to evaluate carbon gains trends using several satellite datasets and, whether possible, independent/additional data sources to contrast.

Evaluating the impacts of climate and elevated carbon dioxide on tropical rainforests of the western Amazon basin using ecosystem models and satellite data

AbstractForest inventories from the intact rainforests of the Amazon indicate increasing rates of carbon gain over the past three decades. However, such estimates have been questioned because of the poor spatial representation of the sampling plots and the incomplete understanding of purported mechanisms behind the increases in biomass. Ecosystem models, when used in conjunction with satellite data, are useful in examining the carbon budgets in regions where the observations of carbon flows are sparse. The purpose of this study is to explain observed trends in normalized difference vegetation index (NDVI) using climate observations and ecosystem models of varying complexity in the western Amazon basin for the period of 1984–2002. We first investigated trends in NDVI and found a positive trend during the study period, but the positive trend in NDVI was observed only in the months from August to December. Then, trends in various climate parameters were calculated, and of the climate variables considered, only shortwave radiation was found to have a corresponding significant positive trend. To compare the impact of each climate component, as well as increasing carbon dioxide (CO2) concentrations, on evergreen forests in the Amazon, we ran three ecosystem models (CASA, Biome‐BGC, and LPJ), and calculated monthly net primary production by changing a climate component selected from the available climate datasets. As expected, CO2 fertilization effects showed positive trends throughout the year and cannot explain the positive trend in NDVI, which was observed only for the months of August to December. Through these simulations, we demonstrated that the positive trend in shortwave radiation can explain the positive trend in NDVI observed for the period from August to December. We conclude that the positive trend in shortwave radiation is the most likely driver of the increasing trend in NDVI and the corresponding observed increases in forest biomass.

Trajectory-based warm season grassland mapping in Missouri prairies with multi-temporal ASTER imagery

Tallgrass prairie in North America has been largely converted to croplands and cool season grasslands. In Missouri, only 0.5% of the tallgrass prairie remains in a form of isolated prairie islands. This study sought to delineate prairie-native warm season grass (WSG) from cool season grass (CSG) using five ASTER images acquired on 03/11/2008, 05/12/2007, 07/12/2007, 08/16/2007 and 10/19/2007. Temporal trajectories of the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) were extracted to examine temporal variation of WSG and CSG grasslands in a growth cycle. It was found that the spring–summer period revealed maximal spectral differences between these two grass types. CSG reached peak NDVI in May while WSG tended to have peak NDMI in July. NDVI was more useful than NDMI in summer–fall. The NDVI trends in this period varied with both phenology and grassland treatments such as haying and grazing, which provided additional information in WSG/CSG delineation. A hierarchical decision tree was developed to delineate WSG and CSG grasslands in a 3-tier process. The WSG lands including those publically conserved prairies were identified in the lower Osage Plain in southwest Missouri. The accuracies were about 80% and could be improved with more frequent satellite observations. The trajectory-based decision tree in this study provides a robust approach of long-term WSG/CSG mapping.