Changes In Normalized Difference Vegetation Index Research Articles

Integrating fuzzy logic with piecewise linear regression for detecting vegetation greenness change in the Yukon River Basin, Alaska

Effective methods are required to detect vegetation changes and attribute these changes to different driving forces. This article presents a new hybrid change detection method by integrating fuzzy logic with piecewise linear regression (PLR) as applied to the Yukon River Basin (YRB) in Alaska, USA, to detect the changes in spatial and temporal patterns of vegetation greenness based on Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data from 1995 to 2004 (2000 data missing). Abrupt changes (or breakpoints), and speed and tendency of gradual changes in short-, mid-, or long-term segments, were simultaneously detected during the study period. Fire scars, precipitation, and temperature data were applied to explain the spatial and temporal patterns of breakpoints and temporal trends (i.e. slopes or change rates). Results showed no significant systematic temporal trend (p = 0.1) in more than 90% of the study area. Abrupt changes in NDVI were partly attributed to wildfire disturbances. The NDVI decline partly overlaid the fire scars provided by the Alaskan Fire Science Center in time and space. The temporal change rates of NDVI were closely related to the temporal trends of temperature/precipitation. The approach presented in this article can be effectively adapted to other study areas.

Increasing terrestrial vegetation activity of ecological restoration program in the Beijing–Tianjin Sand Source Region of China

China's capital city, Beijing, has been suffering from sandstorms due to grassland degradation and the large distribution of deserts in western and Northern China, named as the Beijing–Tianjin Sand Source Region (BTSSR). To improve the ecological condition in the BTSSR and to reduce its impacts, the Chinese government has adopted the Beijing–Tianjin Sand Source Control Program since 2001. It is necessary to rigorously evaluate the effectiveness of this 10 years’ program, not only as an essential topic of environmental change in an ecologically vulnerable area, but also as an important aspect of policy efficiency assessments. Toward this aim, this study assessed vegetation changes both temporally and spatially in the areas under the program from 2000 to 2010 with the Moderate-resolution Imaging Spectroradiometer (MODIS) monthly Normalized Difference Vegetation Index (NDVI) data and trend analysis method. The results showed an overall improvement and its spatial variation in vegetation activity. The annual NDVI increased by 0.0121year−1 over 64.33% of the total area, with the greatest increasing trend of NDVI occurring in the spring. However, the change in NDVI varied remarkably in space. This study identified a southwest-to-northeast band in the study area where NDVI decreased notably, while most of the BTSSR experienced a positive trend of NDVI. Although the cause of the increased NDVI in the BTSSR remains uncertain, drought may result in a non-significant increasing trend in vegetation activity and the ecological restoration program may be one of the main driving forces behind the increasing trend in vegetation activity. All of these findings will enrich our knowledge of human activities that impact vegetation in arid and semi-arid environments and will provide a scientific basis for the management of ecological restoration programs.

Topographic controls on vegetation index in a hilly landscape: a case study in the Jiaodong Peninsula, eastern China

This study examined topographic influence on spatial and temporal variability in the normalized difference vegetation index (NDVI) derived from the Satellite Pour l’Observation de la Terre-Vegetation at the regional and landscape scales in the Jiaodong Peninsula. The generalized additive models were used to quantify the spatial variation of NDVI attributable to local terrain and topographically related variables including altitude, exposure to incoming solar radiation, topographic wetness index, distance to the nearest stream and distance from the coast. NDVI distribution shows significant dependence on topography. The variables explained 38.3 % of variance in NDVI at the peninsula, and 30–45.3 % of variance in NDVI at the woodland, cropland, and grassland landscapes. At the Jiaodong Peninsula scale, NDVI is influenced primarily by distance from the coast. However, topographic wetness index has the most explanatory power for NDVI at the woodland, cropland, and grassland landscapes. Through a statistical nonparametric correlation analysis (Spearman’s r), the study indicates that spatial distribution of NDVI changes during the period 1998–2009 and future change trend of persistence determined by Hurst exponent is closely associated with topography and topography-based attribution. These results highlight the importance of topographic changes at landscape and regional scales as an important control factor on NDVI patterns.

Drought and spring cooling induced recent decrease in vegetation growth in Inner Asia

The response of vegetation growth to current climate change in Inner Asia (35–55°N, 45–120°E) was investigated by analyzing time series of the Normalized Difference Vegetation Index (NDVI) data from the Advanced Very High Resolution Radiometer (AVHRR) from 1982 to 2009. We found that at the regional scale, the greening trend observed during the 1980s was stalled in the 1990s. Different seasons, however, show different changes and mechanisms. Among the three seasons (spring, summer and autumn), summer has the earliest turning point (from greening to non-greening) in the early 1990s, as a result of summertime droughts, as indicated by Palmer Drought Severity Index (PDSI). Consistent with the change in summer NDVI, summer PDSI and precipitation significantly increased in the 1980s, but strongly decreased since the early 1990s at the regional scale. The negative effect of summer drought is particularly significant over dry regions such as eastern Kazakhstan, Mongolia and Inner Mongolia. However, in high altitude or high latitude regions (>50°N), summer vegetation growth is more strongly correlated with summer temperature rather than with summer PDSI. In spring, changes in vegetation growth are closely linked with temperature changes rather than droughts. Both spring temperature and spring NDVI, for instance, increased until the late 1990s and then decreased. Statistical analyses also show that spring NDVI is significantly correlated with spring temperature at the regional scale (P<0.05), implying that temperature is the dominant limiting factor for spring vegetation growth in most regions of Inner Asia except Turkmenistan and Uzbekistan, where spring PDSI shows significant positive correlation with spring NDVI. Further analyses of the response of vegetation to extreme high spring temperature in 1997 and extreme summer drought in 2001 exhibit a highly heterogeneous pattern.

Land-use and vegetation change detection in Plastira artificial lake catchment (Greece) by using remote-sensing and GIS techniques

Detecting changes in the land-use and vegetation conditions by using remote-sensing techniques is a common approach nowadays to assessing human-induced impacts in a specific area. For this purpose, a series of vegetation indices and change detection algorithms such as the normalized difference vegetation index (NDVI), Iterative Self-Organizing Data Analysis Technique (ISODATA), and k-means have been efficiently developed and used in many studies worldwide. However, identifying the driving forces for the estimated changes in land use and vegetation has always been a difficult and challenging task. In this study, Landsat and Système Pour l'Observation de la Terre (SPOT) images have been used to estimate the NDVI and land-use changes at the Plastira artificial lake catchment in the period 1984–2009. The recorded vegetation changes were correlated with a series of environmental and human-related parameters such as precipitation, temperature, specific land uses, and topography to identify the dominant factors of the aforementioned changes. This was done using both linear and geographically weighted regression methods. The results indicate that the precipitation and temperature fluctuations are strongly correlated with the vegetation conditions, whereas, as far as the topographic parameters are concerned, the aspect and slope affect mostly the particular vegetation index (the NDVI) of the study area.

NDVI, temperature and precipitation changes and their relationships with different vegetation types during 1998–2007 in Inner Mongolia, China

ABSTRACTBased on vegetation maps of Inner Mongolia, SPOT‐VEGETATION normalized difference vegetation index (NDVI) data, and temperature and precipitation data from 118 meteorological stations, this study analysed changes in NDVI, temperature and precipitation, and performed correlation analyses of NDVI, temperature and precipitation for eight different vegetation types during the growing seasons (April–October) of the period 1998–2007 in Inner Mongolia, China. We also investigated seasonal correlations and lag‐time effects, and our results indicated that for different vegetation types, NDVI changes during 1998–2007 showed great variation. NDVI correlated quite differently with temperature and precipitation, with obvious seasonal differences. Lag‐time effects also varied among vegetation types and seasons. On the whole, Inner Mongolia is becoming warmer, and drier for most regions, and ecological pressure in Inner Mongolia is increasing, and our focus on such issues is therefore important. Copyright © 2012 Royal Meteorological Society

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.

RIPARIAN VEGETATION STATUS AND RATES OF WATER USE FROM SATELLITE DATA

Abstract. Hydrology and water availability are key determinants of the health of riparian vegetation systems, and an understanding of the interactions between vegetation and hydrology is a prerequisite for the maintenance and improvement of these systems under managed water regimes. Changes to natural flooding cycles, caused by the regulation of river flows and irrigation activities, have changed the composition and amount of vegetation, and the distribution of species within riparian areas (Chong and Ladson, 2003; Lawrence and Colloff, 2008). The extent and frequency of flooding cycles are key issues for the health of riparian ecosystems under controlled water management regimes. This paper demonstrates the potential contribution of satellite-based measurements to an improved understanding of the changes in vegetation status of riparian systems, and, also, of their water requirement. Evapotranspiration (ET) and Normalised Difference Vegetation Index (NDVI) of the Barmah Forest were derived from satellite imagery over a number of years. NDVI provided a general measure of vegetation status and cover. ET measures provided an indication of the availability of water to the existing vegetation, and an assessment of areas under water-stress (Anderson et al., 2012). Previous work has demonstrated that these indicators provide a comprehensive measure of riparian vegetation status (Sheffield et al., 2011), and estimates of vegetation water requirement (Whitfield et al., 2010a; Sheffield et al., 2011). This paper addresses changes in NDVI and ET rate of major vegetation classes in the Barmah Forest over the period, 1993 – 2008. Measures of ET and NDVI, analysed in conjunction with rainfall and river flow data, provided insights into the response of vegetation to changes in water availability, which may be used to evaluate impacts of management practices and water regime within riparian zones.

Non-linear response of vegetation to coherent warming over northern high latitudes

This study evaluates the large-scale changes in vegetation greenness at northern high latitudes (>60° N) using satellite-measured normalized difference vegetation index (NDVI) and station-merged temperature, precipitation and soil moisture for the period 1982–2008. During this 27-year period, although coherent warming trends were observed at most of the high latitudes, changes in the NDVI showed apparent spatial and temporal heterogeneity. In particular, changes in the hemispheric mean NDVI increased until 1997, but decreased thereafter. Maximum covariance analysis, which is a statistical method to detect large-scale covariability between two variables over time, reveals significant relationships between NDVI and soil moisture (and/or precipitation) in the regions of negative NDVI trends. These results further suggest that local moisture availability also plays a considerable role in the large-scale changes in vegetation as well as coherent warming over the northern high latitudes.

Remote sensing analysis of forest damage by selection logging in the Kabaung Reserved Forest, Bago Mountains, Myanmar

Selection logging is a principal management scheme in natural teak-bearing forests in Myanmar. Monitoring the spatial extent and intensity of selection logging is important for sustainable forest management. This study applied the normalized difference vegetation index (NDVI) image differencing method using two SPOT-5 pan-sharpened images (2.5 m spatial resolution) taken in October 2007 and January 2009 to analyze canopy changes associated with damage from forest harvesting. According to the pixel-based analysis, NDVI changes were larger in most logging road/log landing points whereas smaller NDVI changes were seen in most unlogged points. NDVI changes in teak stump areas were related to distance from a logging road and the number of stumps within the estimated crown area (a circular area with a 10 m radius from the center of each stump). A Fisher's exact test showed that one of the main factors causing the high NDVI change in teak stump areas was the effect of road construction. The distribution pattern of teak stumps indicated that teak stumps with estimated crown areas that contained more than one stump had high mean change in NDVI. The spectral difference between before and after logging revealed that logging roads had a greater effect on canopy changes than teak stumps.

Effects of El Niño on spring phenology of the highest mountain in north-east Asia

Normalized difference vegetation index (NDVI) data on the highest mountain in north-east Asia were analysed to understand their temporal variability and response to large-scale El Niño–Southern Oscillation (ENSO) events. We demonstrated that El Niño events played an important role in determining the phenology conditions in the Mt Baekdu area in north-east Asia. The analysis confirmed that the onset of phenological spring was earlier during ENSO years. This was evident from a negative trend of about −16 days for each increase of 1 in the ENSO index in year-to-year variations in spring timing and those in ENSO magnitudes. Over two decades, the phenological phases were negatively correlated with air temperature variations under atmospheric warming at the mountain. However, such changes in NDVI are not likely to be affected by changes in local precipitation, as inferred from the analysis of forest types in this area. On the basis of NDVI changes during ENSO years, the results of this study emphasized the importance of the elevation effect and forest types on the ecological response. Moreover, we addressed a significant remote connection between local phenology at the highest mountain in north-east Asia and large-scale atmospheric and oceanic phenomena.

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.

Annual integral changes of time serial NDVI in mining subsidence area

By means of MORTRAN4 model of FLASSH module, an atmospheric correction of the SPOT II/IV data of four-year period for eight experimental coal faces of Lu'an mine area was done. The mining-affected zones were obtained with probability integral method. The normalized difference vegetation index (NDVI) and its mean were obtained by band ratio calculation at the mining subsidence area. Using FOURIER4 model of the fast Fourier transform (FFT) of discrete Fourier analysis theory, the mean NDVI time series data set was reconstructed. With the mean of NDVI transformed into amplitude and the phonological information transformed into phase, the law of NDVI variation by monthly, quarterly, annual were obtained. The annual integral of the time series of NDVI Fourier function (ANDVI) was obtained. The results show that an experience formula of ANDVI variation accompanies with mining time; A fast estimation formula of NPP by means of a coefficient of mining disturbance such as γ.

AN ALTERNATIVE METHOD OF REMOTE SENSING AND GIS FOR ASSESSING AGRICULTURAL DROUGHT IN UPPER BRANTAS WATERSHED, INDONESIA

In Indonesia, drought disasters have been reoccurring more frequently in recent years. The 1997-1998 El Nino had caused the worst drought to Indonesia in the last 50 years and disrupted rice production. Remote sensing (RS) and geographic informa-tion system (GIS) provide good capability to achieve spatially distributed information over wide area coverage and multi-temporal data to give sufficient information to anticipate those situations. The study aimed to develop a method using GIS combined with satellite data for monitoring and assessing agri-cultural drought in Brantas Watershed, Indonesia. The drought factors were determined based on expert knowledge analysis. Risk assessment method was developed using weighting which is determined based on significant factors of drought, i.e. rainfall pattern, irrigation status, ground water capacity, soil drainage, and land cover. Satellite data were used to analyze the characteristics of temporal variations of normalized difference vegetation index (NDVI) against drought factors. Weighting scores were determined by analyzing NDVI character using changes in NDVI and normal line diagram of each factor. The accuracy of drought risk map was evaluated by comparing drought risk level and NDVI value. The results indicated that expert knowledge analysis of the drought factors showed significant influence on NDVI value. Drought risk and drought status showed a high positive correlation with R2 = 0.85 for NOAA AVHRR, meaning that there is a significant correlation between the two (r = 0.92). The results of this study can be used to determine spatially location of drought-prone areas based on bio-physical factor causes. Therefore, it can be make recom-mendation for prevention of agricultural drought in the future.&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;

AN ALTERNATIVE METHOD OF REMOTE SENSING AND GIS FOR ASSESSING AGRICULTURAL DROUGHT IN UPPER BRANTAS WATERSHED, INDONESIA

In Indonesia, drought disasters have been reoccurring more frequently in recent years. The 1997-1998 El Nino had caused the worst drought to Indonesia in the last 50 years and disrupted rice production. Remote sensing (RS) and geographic informa-tion system (GIS) provide good capability to achieve spatially distributed information over wide area coverage and multi-temporal data to give sufficient information to anticipate those situations. The study aimed to develop a method using GIS combined with satellite data for monitoring and assessing agri-cultural drought in Brantas Watershed, Indonesia. The drought factors were determined based on expert knowledge analysis. Risk assessment method was developed using weighting which is determined based on significant factors of drought, i.e. rainfall pattern, irrigation status, ground water capacity, soil drainage, and land cover. Satellite data were used to analyze the characteristics of temporal variations of normalized difference vegetation index (NDVI) against drought factors. Weighting scores were determined by analyzing NDVI character using changes in NDVI and normal line diagram of each factor. The accuracy of drought risk map was evaluated by comparing drought risk level and NDVI value. The results indicated that expert knowledge analysis of the drought factors showed significant influence on NDVI value. Drought risk and drought status showed a high positive correlation with R2 = 0.85 for NOAA AVHRR, meaning that there is a significant correlation between the two (r = 0.92). The results of this study can be used to determine spatially location of drought-prone areas based on bio-physical factor causes. Therefore, it can be make recom-mendation for prevention of agricultural drought in the future.&lt;br /&gt;&lt;br /&gt;&lt;br /&gt;

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.

Land cover change on Mt. Pinatubo, the Philippines, monitored using ASTER VNIR

Ashfall and pyroclastic flows from the large eruption of June 1991 destroyed much of the vegetation on the flanks of Mt. Pinatubo. Subsequent vegetation recovery has helped stabilize slopes and reduce debris flow hazard. In this project, visible and near-infrared (VNIR) satellite imagery from the advanced spaceborne thermal emission and reflection radiometer (ASTER) captured at a similar time of year in 2001, 2004 and 2008 were used to quantify vegetation recovery within 22 upland watersheds on the mountain, 10–16 years after the eruption took place. Differences in the normalized difference vegetation index (NDVI) derived from these images were used to measure the areal extent of losses and gains in ground cover and derive average net rates of change in ground cover. The success of this approach was dependent on post-processing ASTER imagery to correct for the effects of variation in satellite-sun geometry and vegetation reflectance and to calibrate and adjust the derived NDVI images for the influence of different atmospheric conditions at the time of image capture. All watersheds showed a variable pattern of losses and gains in vegetation and ground cover. Losses were related to shifting cultivation practices and gully and channel migration, and these amounted to 1–12% of watershed areas. Gains were related to revegetation of pyroclastic flows, recent channel terraces, abandoned gardens and areas of burnt vegetation, and these amounted to 3–45% of watershed areas. Consistent overall net gains in ground cover were observed in all watersheds, with the average NDVI increasing by up to 0.074 over each consecutive 3-year period. The rates of change in NDVI were used to derive a vegetation recovery curve from bare ground. The result showed that it will take approximately 50 years for hillslopes to regain a dense vegetative cover in this climate. This supports published findings which indicate rapid recovery of vegetation in tropical environments following such large volcanic eruptions. Results additionally showed that the trajectory of change and the speed of recovery were influenced by terrain type, geology, watershed morphology and the activity of erosion and depositional processes. Prior to 2001, revegetation had been fastest on mountain slopes that helped protect vegetation from the effects of eruption. Gains in ground cover are now greatest in areas that were most impacted by the eruption.

Assessing growing season beginning and end dates and their relation to climate in Taiwan using satellite data

Due to the close relationship between climate and plant phenology, changes in plant phenological patterns have been used as a surrogate of climate change. We analysed Moderate Resolution Imaging Spectroradiometer (MODIS) images to investigate the onset, offset and length of growing season, as well as spatial and inter-annual patterns of Normalized Difference Vegetation Index (NDVI) across six types of vegetation/land use in Taiwan. Regression models indicate that temperature was moderately to strongly related to NDVI for each of the six vegetation/land-use types (coefficients of determination (R 2) = 0.45–0.86). There was a 1–2 month lag time between changes in temperature and NDVI in the forests that are distributed in mid- to high-elevation areas, but not in low-elevation unirrigated fields, paddy fields and urban areas. The relationship between precipitation and changes in NDVI was only significant for unirrigated fields and urban areas (R 2 = 0.37–0.43). Growing season ended considerably earlier at low elevations than at high elevations, possibly because of the earlier start and more severe dry period in low-elevation areas, such that the length of the growing season was longer in the forests than in the unirrigated fields, paddy fields and urban areas.

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.

Changing climate affects vegetation growth in the arid region of the northwestern China

The northwestern China is a typical dry-land region of Inner Asia, where significant climate change has been observed over the past several decades. How the regional vegetation, particularly the grassland-oasis-desert complex, responds to such climatic change is poorly understood. To address this question, we investigated spatio-temporal changes in vegetation growth and their responses to a changing climate by biome and bioregion, using satellite-sensed Normalized Difference Vegetation Index (NDVI) data from 1982 to 2003, along with corresponding climate data. Over the past 22 years, about 30% of the total vegetated area showed an annual increase of 0.7% in growing season NDVI. This trend occurred in all biomes and all bioregions except Sawuer, a subregion of the study area with no significant climate change. Further analyses indicated that NDVI change was highly correlated with the current precipitation and evapotranspiration in growing season but was not associated with temperature. We also found that NDVI was positively correlated with the preceding winter precipitation. These findings suggest that precipitation may be the key cause of vegetation growth in this area, even for mountain forests and grasslands, whose growth are often regarded to be limited by low temperate in winter and early spring.