Season Normalized Difference Vegetation Index Research Articles

Satellite-indicated long-term vegetation changes and their drivers on the Mongolian Plateau

The Mongolian Plateau, comprising the nation of Mongolia and the Inner Mongolia Autonomous Region of China, has been influenced by significant climatic changes and intensive human activities. Previous satellite-based analyses have suggested an increasing tendency in the vegetation cover over recent decades. However, several ground-based observations have indicated a decline in vegetation production. This study aimed to explore long-term changes in vegetation greenness and land surface phenology in relation to changes in temperature and precipitation on the Plateau between 1982 and 2011 using the normalized difference vegetation index (NDVI). Across the Plateau, a significantly positive trend in the growing season (May–September) NDVI was observed from 1982 to 1998, but since that time, the NDVI has not shown a persistent increase, thus causing an insignificant trend over the entire study period. For the steppe vegetation (a major vegetation type on the Plateau), the NDVI increased significantly in spring but decreased in summer. Precipitation was the dominant factor related to changes in steppe vegetation. Warming in spring contributed to earlier vegetation green-up only in meadow steppe vegetation, implying that water deficiency in typical and desert steppe vegetation may eliminate the effect of warming. Our results also suggest a combined effect of climatic and non-climatic factors and highlight the need to examine the role of regional human activities in the control of vegetation dynamics.

Geo-spatial analysis of the temporal trends of kharif crop phenology metrics over India and its relationships with rainfall parameters

The Global Inventory Modeling and Mapping Studies bimonthly Normalized Difference Vegetation Index (NDVI) data of 8 × 8km spatial resolution for the period of 1982-2006 were analyzed to detect the trends of crop phenology metrics (start of the growing season (SGS), seasonal NDVI amplitude (AMP), seasonally integrated NDVI (SiNDVI)) during kharif season (June to October) and their relationships with the amount of rainfall and the number of rainy days over Indian subcontinent. Direction and magnitude of trends were analyzed at pixel level using the Mann-Kendall test and further assessed at meteorological subdivision level using field significance test (α = 0.1). Significant pre-occurrence of the SGS was observed over northern (Punjab, Haryana) and central (Marathwada, Vidarbha and Madhya Maharashtra) parts, whereas delay was found over southern (Rayalaseema, Coastal Andhra Pradesh) and eastern (Bihar, Gangetic West Bengal and Sub-Himalayan West Bengal) parts of India. North, west, and central India showed significant increasing trends of SiNDVI, corroborating the kharif food grain production performance during the time frame. Significant temporal correlation (α = 0.1) between the rainfall/number of rainy days and crop phenology metrics was observed over the rainfed region of India. About 35-40% of the study area showed significant correlation between the SGS and the rainfall/number of rainy days during June to August. June month rainfall/number of rainy days was found to be the most sensitive to the SGS. The amount of rainfall and the number of rainy days during monsoon were found to have significant influence over the SiNDVI in 24-30% of the study area. The crop phenology metrics had significant correlation with the number of rainy days over the larger areas than that of the rainfall amount.

NDVI-Based Vegetation Change in Inner Mongolia from 1982 to 2006 and Its Relationship to Climate at the Biome Scale

Based on the normalized difference vegetation index (NDVI), we analyzed vegetation change of the six major biomes across Inner Mongolia at the growing season and the monthly timescales and estimated their responses to climate change between 1982 and 2006. To reduce disturbance associated with land use change, those pixels affected by land use change from the 1980s to 2000s were excluded. At the growing season scale, the NDVI increased weakly in the natural ecosystems, but strongly in cropland. Interannual variations in the growing season NDVI for forest was positively linked with potential evapotranspiration and temperature, but negatively correlated with precipitation. In contrast, it was positively correlated with precipitation, but negatively related to potential evapotranspiration for other natural biomes, particularly for desert steppe. Although monthly NDVI trends were characterized as heterogeneous, corresponding to monthly variations in climate change among biome types, warming-related NDVI at the beginning of the growing season was the main contributor to the NDVI increase during the growing season for forest, meadow steppe, and typical steppe, but it constrained the NDVI increase for desert steppe, desert, and crop. Significant one-month lagged correlations between monthly NDVI and climate variables were found, but the correlation characteristics varied greatly depending on vegetation type.

Interannual variability and correlation of vegetation cover and precipitation in Eastern China

Based on the SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) data and daily precipitation data of 357 meteorological stations, the spatial and temporal variability of vegetation cover, measured by NDVI, and precipitation as well as their relationships are investigated in Eastern China, which is portioned into three subregions (regions I, II, and III), for the period 1998–2010. The results show that high NDVI values appear mainly in Northeastern China and in August while high precipitation (PRETOT) occurs in Southeastern China and in July (June for Southern China). Extreme precipitation days (RD95p) and amount (EPRETOT) coincide well with PRETOT. Extreme precipitation intensity (RINTEN) has a similar spatial variability to PRETOT but with a smaller seasonal variation than PRETOT. Growing season NDVI is positively correlated with PRETOT in 11.7 % of the study area (mostly in arid to subhumid regions of Northern China), where precipitation is a limiting factor for vegetation growth. In contrast, a negative correlation between growing season NDVI and PRETOT is found in 4.8 % of the study area, mostly in areas around the Yangtze River and deep Northeastern China. No significant correlations between these two variables are found for the other regions because vegetation response to precipitation is affected by other factors such as temperature, radiation, and human disturbance. On a monthly scale, there is a positive correlation between NDVI and PRETOT in May (for region II) and September (all subregions except region I). NDVI variations lag 1 month behind PRETOT in June (for region I) and October. Correlations between NDVI and RD95p, EPRETOT are similar to that with PRETOT, but the relationships between NDVI and RINTEN are relatively weaker than with PRETOT. This study provides the technical basis for agriculture development and ecological construction in Eastern China.

NDVI-based vegetation changes and their responses to climate change from 1982 to 2011: A case study in the Koshi River Basin in the middle Himalayas

Considerable researches during the past several decades have focused on monitoring changes in vegetation growth due to its important role in regulating the terrestrial carbon cycle and the climate system. In this study, we combined datasets of the satellite-derived Normalized Difference Vegetation Index (NDVI) and climatic factors to analyze spatio-temporal patterns of vegetation growth in 1982–2006 in the Koshi River Basin (KRB) in the middle Himalayas. In addition, the dataset from the global land surface satellite sensor from SPOT-4's Vegetation instrument in 1998–2011 was used to further verify the results of this study. At the regional scale, although a statistically significant increase in average growing season NDVI was observed (0.0008yr−1, P=0.03) during the entire study period, there existed three distinct periods with opposing trends. Growing season NDVI significantly increased in 1982–1994 (0.0019yr−1, P=0.03), and then decreased in 1994–2000 (−0.0058yr−1, P<0.001), while increased again in 2000–2006 (0.0049yr−1, P<0.001) and in 2000–2011 (0.0034yr−1, P<0.001). A sudden drop in NDVI in 1994–2000 largely contributed to these fluctuations in growing season NDVI over the KRB. Spring, summer and autumn NDVI significantly decreased in 1994–2000 (−0.0077yr−1, P<0.001; −0.0067yr−1, P<0.001; and −0.0155yr−1, P<0.001; respectively). Our further spatial analyses supported the existence of the sudden decrease in spring, summer and autumn NDVI in 1994–2000.

Reply to Shen et al.: No evidence to show nongrowing season NDVI affects spring phenology trend in the Tibetan Plateau over the last decade

We appreciate the comments on our article (1) from Shen et al. (2). They raise the issue of whether the nongrowing season (NGS) Normalized Difference Vegetation Index (NDVI) could affect spring phenology as interpreted from time series NDVI data. To address it, they compared the NGS NDVIs of two separate periods (1998–2000 and 2009–2011) and found a higher 2009–2011 NGS NDVI than that of 1998–2000, which was considered to negatively (e.g., advanced) bias retrieved spring phenology results [figure 1 A and B in the letter (2)]. We found that the NGS NDVI increase from 1998–2000 to 2009–2011 was overused to represent the trend from 1998 to 2011 because of the effects of outliers in 1998 and 2000 (Fig. 1A). We used snow depth data derived from passive microwave remote sensing for the studied area (3) and found that snow depth from January to April was significantly higher in 1998 and 2000 (Fig. 1B). The El Nino Southern Oscillation (ENSO) event resulted in the most severe snow storm in the 1997/1998 winter across the Tibetan Plateau (4), which led to lower NGS NDVI from January to April in 1998. The La Nina in year 1999/2000 also resulted in big snow and lower NGS NDVI on the plateau in 2000 (Fig. 1B). Actually, the trends of the NDVIs in February, March, and April were not statistically significant when we removed the outliers in 1998 and 2000 (Fig. 1C). January NDVI increasing was significant. However, it will not affect start of the vegetation growing season (SOS) retrieval because all of the green-up dates happened after March on the plateau.

No evidence of continuously advanced green-up dates in the Tibetan Plateau over the last decade

Zhang et al. (1) report that the green-up dates in the Tibetan Plateau (TP) have continuously advanced from 1982 to 2011. They retrieved green-up dates from the multiannual time-series of Normalized Difference Vegetation Index (NDVI) using the method proposed by Piao et al. (2). This method assumes that the nongrowing season NDVI values remain constant among the different years and thus requires sophisticated preprocess of the nongrowing season NDVI. Otherwise, the retrieved green-up dates by the step of polynomial fitting will be negatively biased (i.e., advanced) by the increase in the nongrowing season NDVI (Fig. 1A).

Climate warming shifts carbon allocation from stemwood to roots in calcium‐depleted spruce forests

Increased greening of northern forests, measured by the Normalized Difference Vegetation Index (NDVI), has been presented as evidence that a warmer climate has increased both net primary productivity (NPP) and the carbon sink in boreal forests. However, higher production and greener canopies may accompany changes in carbon allocation that favor foliage or fine roots over less decomposable woody biomass. Furthermore, tree core data throughout mid‐ and northern latitudes have revealed a divergence problem (DP), a weakening in tree ring responses to warming over the past half century that is receiving increasing attention, but remains poorly understood. Often, the same sites exhibit trend inconsistency phenomenon (TIP), namely positive, or no trends in growing season NDVI where negative trends in tree ring indexes are observed. Here we studied growth of two Norway spruce (Picea abies) stands in western Russia that exhibited both the DP and TIP but were subject to soil acidification and calcium depletion of differing timing and severity. Our results link the decline in radial growth starting in 1980 to a shift in carbon allocation from wood to roots driven by a combination of two factors: (a) soil acidification that depleted calcium and impaired root function and (b) earlier onset of the growing season that further taxed the root system. The latter change in phenology appears to act as a trigger at both sites to push trees into nutrient limitation as the demand for Ca increased with the longer growing season, thereby causing the shift in carbon allocation.

Prediction of Bluetongue virus seropositivity on pastoral properties in northern Australia using remotely sensed bioclimatic variables

To monitor Bluetongue virus (BTV) activity in northern and eastern Australia the National Arbovirus Monitoring Program (NAMP) collects data from a network of sentinel herds. Groups of young cattle, previously unexposed to infection, are regularly tested to detect evidence of seroconversion. While this approach has been successful in fulfilling international surveillance requirements, it is labour and cost intensive and operationally challenging in the remote area of the northern Australian rangelands.The aim of this study was to assess the suitability of remotely sensed data as a means for predicting the distribution of BTV seroprevalence. For the period 2000–2009, bioclimatic variables were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) data products for the entire Northern Territory. A generalised linear model, based on the seasonal Normalised Difference Vegetation Index (NDVI) and minimum land surface temperature, was developed to predict BTV seropositivity. The odds of seropositivity in locations with NDVI estimates >0.45 was 3.90 (95% CI 1.11 to 13.7) times that of locations where NDVI estimates were between 0 and 0.45. Unit increases in minimum night land surface temperature in the previous winter increased the odds of seropositivity by a factor of 1.40 (95% CI 1.02 to 1.91). The area under a Receiver Operator Characteristic curve generated on the basis of the model predictions was 0.8.Uncertainty in the model's predictions was attributed to the spatio-temporal inconsistency in the precision of the available serosurveillance data. The discriminatory ability of models of this type could be improved by ensuring that exact location details and date of NAMP BTV test events are consistently recorded.

Vegetation shifts observed in arctic tundra 17 years after fire

With anticipated climate change, tundra fires are expected to occur more frequently in the future, but data on the long-term effects of fire on tundra vegetation composition are scarce. This study addresses changes in vegetation structure that have persisted for 17 years after a tundra fire on the North Slope of Alaska. Fire-related shifts in vegetation composition were assessed from remote-sensing imagery and ground observations of the burn scar and an adjacent control site. Early-season remotely sensed imagery from the burn scar exhibits a low vegetation index compared with the control site, whereas the late-season signal is slightly higher. The range and maximum vegetation index are greater in the burn scar, although the mean annual values do not differ among the sites. Ground observations revealed a greater abundance of moss in the unburned site, which may account for the high early growing season normalized difference vegetation index (NDVI) anomaly relative to the burn. The abundance of graminoid species and an absence of Betula nana in the post-fire tundra sites may also be responsible for the spectral differences observed in the remotely sensed imagery. The partial replacement of tundra by graminoid-dominated ecosystems has been predicted by the ALFRESCO model of disturbance, climate and vegetation succession.

Vegetation dynamics and their response to freshwater inflow and climate variables in the Yellow River Delta, China

Based on SPOT Vegetation NDVI data, streamflow data and meteorological data, the variation of vegetation cover, measured by the Normalized Difference Vegetation Index (NDVI), and its response to freshwater inflow, precipitation and temperature in the Yellow River Delta and its buffer zones have been investigated for the period 1998–2009. The results show that NDVI has a remarkable regional and seasonal difference. The farther from the Yellow River Channel and the nearer to the Bohai Sea Coastline, the smaller the NDVI value, as influenced by the interaction between freshwater and saltwater on vegetation. Seasonally, high NDVI values appear in summer (August) and low in spring (April). From 1998 to 2009, growing season NDVI significantly increases in the Yellow River Delta. Summer and autumn NDVI have a similar trend pattern to growing season NDVI, while spring NDVI significantly decreases. NDVI shows different strengths of correlation with freshwater inflow, precipitation and temperature respectively and these correlations vary in different seasons and months. Freshwater inflow is a key factor for vegetation dynamics and NDVI variation. Climate features play a dominant role in seasonal variation in vegetation cover. However, the impacts of freshwater inflow and climate variables on vegetation have been greatly modified by a range of human activities such as land use pattern and land use change as well as water diversion from the Yellow River. Overall, the results of this study can be helpful for decision-making of regional ecological protection and economic development.

Phenological dynamics of irrigated and natural drylands in Central Asia before and after the USSR collapse

Central Asia has experienced drastic socio-economic, geopolitical, and ecological transitions within the last few decades. The USSR collapse in 1991 has led to widespread changes in land cover and land use due to economic and political transformations within the region. Management practices during and after the Soviet era have intensified ecological problems and demands on resources. Satellite derived vegetation greenness data offer insights into these dynamics by providing measurements linked to vegetation productivity and the timing of vegetation growth cycles, including the timing of greenness onset, peak, and senescence. The main research goals are to examine the impact of socio-economic and bioclimatic factors by characterizing interannual dynamics of regional land surface phenology. One of the longest available records (1981–2006) of geospatial time-series data of the biweekly Normalized Difference Vegetation Index (NDVI) were used to derive annual pheno-metrics for sites in Uzbekistan and Turkmenistan. Land cover types include irrigated agriculture, riparian zones, and arid desert regions. Statistical analysis showed significant differences between pre- and post-Soviet collapse seasonal NDVI trajectories and interannual variation in greenness onset and vegetation response. Changes in satellite-based land surface phenological information are attributed to differences in prevailing land management, climate, and socio-economic factors before and after the USSR collapse.

Responses of grassland and forest to temperature and precipitation changes in Northeast China

Using the Normalized Difference Vegetation Index (NDVI) as an indicator of vegetation growth, we explored the characteristics and differences in the response to drought of five vegetation biomes in Northeast China, including typical steppe, desert steppe, meadow steppe, deciduous coniferous forest and deciduous broad-leaved forest during the period 1982–2009. The results indicate that growing season precipitation may be the primary vegetation growth-limiting factor in grasslands. More than 70% of the temporal variations in NDVI can be explained by the amount of precipitation during the growing season in typical and desert steppes. During the same period, the mean temperature in the growing season could explain nearly 43% of the variations in the mean growing season NDVI and is therefore a dominant growth-limiting factor for forest ecosystems. Therefore, the NDVI trends differ largely due to differences in the vegetation growth-limiting factors of the different vegetation biomes. The NDVI responses to droughts vary in magnitude and direction and depend on the drought-affected areas of the five vegetation types. Specifically, the changes in NDVI are consistent with the variations in precipitation for grassland ecosystems. A lack of precipitation resulted in decreases in NDVI, thereby reducing vegetation growth in these regions. Conversely, increasing precipitation decreased the NDVI of forest ecosystems. The results also suggest that grasslands under arid and semi-arid environments may be more sensitive to drought than forests under humid environments. Among grassland ecosystems, desert steppe was most sensitive to drought, followed by typical steppe; meadow steppe was the least sensitive.

Time-series analysis of NDVI from AVHRR data over the Hindu Kush–Himalayan region for the period 1982–2006

The Hindu Kush–Himalayan (HKH) region with its surrounding mountains in central Asia is a region that has been warming at an alarming rate and is sensitive to climate change due to its heterogeneous terrain and high altitude. In a region where research is limited due to the paucity of field-based biophysical observations, analysis of remotely sensed data such as the normalized difference vegetation index (NDVI) can provide invaluable information on spatio-temporal patterns in linkages among land use, climate and vegetative phenological cycles, and trends in vegetative cover. In this study, NDVI data with 8 km spatial resolution for each 15 day composite period from 1982 to 2006 were analysed using a seasonal trend analysis technique, where the first step determines the annual mean and seasonal NDVI patterns across the HKH region. The second step analyses the non-parametric trends in magnitude and timing of the annual mean and seasonal NDVI cycle. The seasonal vegetation cycles were compared for the first and last ten years of the time series and were also analysed across areas undergoing significant change. Results indicated an overall greening trend in NDVI magnitude in most areas, particularly over open shrubland, grassland and cropland. Trends in the annual seasonal timing of NDVI indicated an earlier green-up for most parts of this region. Results also confirmed deforestation trends observed in a few states in northeastern India and Myanmar (Shan state) within the HKH region.

Long term data fusion for a dense time series analysis with MODIS and Landsat imagery in an Australian Savanna

The spatial resolution of Landsat imagery has proven to be well suited for the analysis of vegetation patterns and dynamics at regional scale; however, the low temporal frequency is often a limitation for the quantification of vegetation dynamics. The spatial and temporal adaptive reflectance fusion model (STARFM) combines moderate resolution imaging spectrometer (MODIS) and Landsat thematic mapper/enhanced thematic mapper plus (TM/ETM+) imagery to a high spatiotemporal resolution dataset. A time series of 333 STARFM images was generated between February 2000 and September 2007 (8-day interval) at Landsat spatial and spectral resolution for a 12×10 km heterogeneous test area within the North Queensland Savannas. Time series of observed Landsat and predicted STARFM images correlated high for each spectral band (0.89 to 0.99). The STARFM algorithm was tested in a regionalization study where sudden change events were analyzed for a pallustrine wetland. A MODIS subpixel analysis showed a very close relationship between STARFM normalized difference vegetation index (NDVI) data and MODIS NDVI data (root mean square error of 0.027). A phenological description of the major vegetation classes within the region revealed distinct differences and lag times within the ecosystem. The 2004 dry season NDVI minimum-map correlated highly with the validated 2004 foliage projective cover product (r2 = 0.92) from the Queensland Department of Environment and Resource Management.

Ecosystem Performance Monitoring of Rangelands by Integrating Modeling and Remote Sensing

Abstract Monitoring rangeland ecosystem dynamics, production, and performance is valuable for researchers and land managers. However, ecosystem monitoring studies can be difficult to interpret and apply appropriately if management decisions and disturbances are inseparable from the ecosystem's climate signal. This study separates seasonal weather influences from influences caused by disturbances and management decisions, making interannual time-series analysis more consistent and interpretable. We compared the actual ecosystem performance (AEP) of five rangeland vegetation types in the Owyhee Uplands for 9 yr to their expected ecosystem performance (EEP). Integrated growing season Normalized Difference Vegetation Index data for each of the nine growing seasons served as a proxy for annual AEP. Regression-tree models used long-term site potential, seasonal weather, and land cover data sets to generate annual EEP, an estimate of ecosystem performance incorporating annual weather variations. The difference between AEP and EEP provided a performance measure for each pixel in the study area. Ecosystem performance anomalies occurred when the ecosystem performed significantly better or worse than the model predicted. About 14% of the Owyhee Uplands showed a trend of significant underperformance or overperformance ( P

High-resolution satellite data reveal an increase in peak growing season gross primary production in a high-Arctic wet tundra ecosystem 1992–2008

Arctic ecosystems play a key role in the terrestrial carbon cycle. Our aim was to combine satellite-based normalized difference vegetation index (NDVI) with field measurements of CO2 fluxes to investigate changes in gross primary production (GPP) for the peak growing seasons 1992–2008 in Rylekærene, a wet tundra ecosystem in the Zackenberg valley, north-eastern Greenland. A method to incorporate controls on GPP through satellite data is the light use efficiency (LUE) model, here expressed as GPP=ɛpeak×PARin×FAPARgreen_peak; where ɛpeak was peak growing season light use efficiency of the vegetation, PARin was incoming photosynthetically active radiation, and FAPARgreen_peak was peak growing season fraction of PAR absorbed by the green vegetation. The ɛpeak was measured for seven different high-Arctic plant communities in the field, and it was on average 1.63g CO2MJ−1. We found a significant linear relationship between FAPARgreen_peak measured in the field and satellite-based NDVI. The linear regression was applied to peak growing season NDVI 1992–2008 and derived FAPARgreen_peak was entered into the LUE-model. It was shown that when several empirical models are combined, propagation errors are introduced, which results in considerable model uncertainties. The LUE-model was evaluated against field-measured GPP and the model captured field-measured GPP well (RMSE was 192mg CO2m−2h−1). The model showed an increase in peak growing season GPP of 42mg CO2m−2h−1y−1 in Rylekærene 1992–2008. There was also a strong increase in air temperature (0.15°Cy−1), indicating that the GPP trend may have been climate driven.

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.

Variations in Anarthrophyllum rigidum radial growth, NDVI and ecosystem productivity in the Patagonian shrubby steppes

The lack of long-term records of productivity is a critical limitation to the study of ecosystem dynamics. Annual rings, a measure of growth in woody species, are a useful tool to document ecosystem dynamics. Time series of the Normalized Difference Vegetation Index (NDVI) provide estimates of ecosystem productivity through satellite-derived data on the fraction of photosynthetic active radiation absorbed by vegetation. In the Patagonian steppes, we relate changes in NDVI to interannual variations in the radial growth of the shrub Anarthrophyllum rigidum. A widely distributed network of 15 ring-width chronologies of A. rigidum was used to estimate changes in NDVI across the Patagonia steppe (35°–50°S). In most sites, interannual variations in shrub growth and NDVI are regulated by winter precipitation. The water accumulated in the soil during winter is used by A. rigidum during the growing season, concurrent with the maximum NDVI values. At 10 from the 15 selected sites, variations in the radial growth of A. rigidum explained between 23 and 62% of the total variance in seasonal NDVI, suggesting that the A. rigidum growth at some sites provides good estimates of productivity in the Patagonian shrubby steppes during the growing season. However, we were unable to determine clear relationships between radial growth and NDVI at high-elevation mountainous sites or where intensive grazing by sheep masked the effect of climate variability on shrub growth. We conclude that dendrochronological methods can complement other estimates to reconstruct variations of productivity, supplementing and extending the few short records available in the Patagonian steppe.

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.