Trends In Vegetation Cover Research Articles

Vegetation Response to Recent Trends in Climate and Landuse Dynamics in a Typical Humid and Dry Tropical Region under Global Change

The influence of global change on vegetation cover and processes has drawn increasing attention in the past few decades. In this study, we used remotely sensed rainfall and land surface temperature to investigate the spatiotemporal pattern and trend in vegetation condition using NDVI as proxy from 2001 to 2017 in a humid and dry tropical region. We also determined the partial correlation coefficient of temperature and rainfall with NDVI and the response of NDVI to changes in landcover categories due to human activities. We found that the mean annual maximum NDVI was 0.42, decreasing at a rate of 0.06 per decade. About 27.4% of the area was found to have experienced a significant negative trend in vegetation cover, while only 0.34% exhibited significant increasing vegetation vigour. Land surface temperature increased at a mean rate of 0.75°C/decade, with higher rates in agriculture, savanna, settlements, woodlands, and riparian vegetation than in forest and mangrove vegetations. Precipitation also reduced at a mean rate of 58.69 mm/decade, with higher rates in agriculture savanna and riparian vegetation than in sahelian grasslands, mangrove, forest, and woodlands. NDVI was negatively correlated with temperature in savanna, settlements, degraded forest, and sahelian grasslands providing confirmation of ongoing land degradation. It was concluded that vegetation vigour will continue to decline under rainfall and increasing temperature conditions especially in dryer regions. The use of land surface temperature in this study is particularly valuable in highlighting areas where changes in NDVI occurred as a result of synergistic action between climate and human-induced landcover changes. Our findings underscore the importance of landuse policies that account for spatial variation in synergistic relationships between the nexus of climate and land conversion processes that influence vegetation cover change in different landcover types in tropical regions.

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

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

Estimating and Examining the Sensitivity of Different Vegetation Indices to Fractions of Vegetation Cover at Different Scaling Grids for Early Stage Acacia Plantation Forests Using a Fixed-Wing UAS

Understanding the information on land conditions and especially green vegetation cover is important for monitoring ecosystem dynamics. The fraction of vegetation cover (FVC) is a key variable that can be used to observe vegetation cover trends. Conventionally, satellite data are utilized to compute these variables, although computations in regions such as the tropics can limit the amount of available observation information due to frequent cloud coverage. Unmanned aerial systems (UASs) have become increasingly prominent in recent research and can remotely sense using the same methods as satellites but at a lower altitude. UASs are not limited by clouds and have a much higher resolution. This study utilizes a UAS to determine the emerging trends for FVC estimates at an industrial plantation site in Indonesia, which utilizes fast-growing Acacia trees that can rapidly change the land conditions. First, the UAS was utilized to collect high-resolution RGB imagery and multispectral images for the study area. The data were used to develop general land use/land cover (LULC) information for the site. Multispectral data were converted to various vegetation indices, and within the determined resolution grid (5, 10, 30 and 60 m), the fraction of each LULC type was analyzed for its correlation between the different vegetation indices (Vis). Finally, a simple empirical model was developed to estimate the FVC from the UAS data. The results show the correlation between the FVC (acacias) and different Vis ranging from R2 = 0.66–0.74, 0.76–0.8, 0.84–0.89 and 0.93–0.94 for 5, 10, 30 and 60 m grid resolutions, respectively. This study indicates that UAS-based FVC estimations can be used for observing fast-growing acacia trees at a fine scale resolution, which may assist current restoration programs in Indonesia.

Rangeland Livestock Production in Relation to Climate and Vegetation Trends in New Mexico

A large statewide historical database involving livestock numbers, vegetation cover, precipitation, air temperature, and drought frequency and severity allowed us to explore relationships between climate and rangeland livestock grazing levels and livestock productivity from 1920 to 2017. Trends in vegetation cover and livestock grazing levels from 1984 to 2017 were also explored. Our climate time series was divided into two periods, 1920−1975 and 1976−2017, based on an apparent accelerated increase in mean annual air temperatures that began in the mid-1970s. Both mean annual precipitation (MAP) and mean annual air temperature (MAT) differed (P ≤ 0.05) between the two periods. MAP and MAT were 9.6% and 3.4% higher in period 2 compared with period 1, respectively. From the 1920s to 2010s the livestock grazing level and weaned calf numbers fell 30% and 40%, respectively, despite a significant increase in MAP. Long-term declines in livestock grazing levels and in weaned calf numbers were significantly (P ≤ 0.05) correlated with increasing MAT (r = −0.34 and r = −0.43, respectively). No long-term trends (1984–2017) in woody or perennial herbaceous cover were detected at the level of the entire state of New Mexico. Woody plant cover dynamics for New Mexico were not related to livestock grazing levels. However, at the county level we detected a 2% increase in woody plant cover coupled with a 9% decrease in cattle animal units between 2000 and 2002 and 2015 and 2017 for 19 select counties well distributed across New Mexico. Increases in woody plant cover varied greatly among counties and were higher for eastern than western New Mexico. Both global and New Mexico data show the climate warming trend is accelerating. Our findings have relevance to several other parts of the world because New Mexico occurs at midlatitude, has varied topography and climatic conditions, and several different range vegetation types.

Time series harmonic regression analysis reveals seasonal vegetation productivity trends in semi-arid savannas

Vegetation cover in savannas is characterized by high spatial heterogeneity driven by natural and anthropogenic drivers acting at multiple spatial and temporal scales. This research article identifies trends in vegetation cover and productivity in response to both land management and variable precipitation through a methodological approach that incorporates vegetation transect fieldwork with satellite imagery time series analysis. The phenological cycles of semi-arid savanna vegetation were analyzed over 27 years for both a protected area and a privately-owned property in order to capture multiple vegetation cover categories within the eastern Edwards Plateau ecoregion of central Texas. Line-intercept vegetation transects were established across these two sites in 2015, with ground cover and structural vegetation measurements collected along each transect. These measurements were classified based upon distinct vegetation cover categories: woodland, intermediate, open, and open disturbed. Time series analysis of vegetation productivity utilized 111 multi-season Landsat TM5 and Landsat 8 images that captured spring, fall, and winter seasonalities across the 27-year time period spanning from 1988 to 2015, with the Soil-Adjusted Vegetation Index (SAVI) calculated as an indicator of vegetative productivity. Harmonic regression was applied to this time series in order to determine the relationship between time of year with respect to growing season and productivity level across vegetation cover types. Fixed mean harmonic amplitudes for calendar year productivity were calculated across the entire time period as well as across three natural breaks-based sub-divisions of the 27-year period. Mean amplitudes per transect were then plotted against field-truthed tree-grass ratios in order to determine the relationship between tree and grass cover and mean harmonic SAVI amplitude. Harmonic regression identified inter- and intra-annual seasonal variability across cover types, with woodland sites exhibiting much lower variability in SAVI levels than open sites. These results provide insight into the phenological response of vegetation cover types typical of semi-arid savannas to spatially and temporally variable precipitation levels and land management schemes, allowing for better informed management and monitoring strategies for these environments.

Relating vegetation condition to grazing management systems in the central Keiskamma Catchment, Eastern Cape Province, South Africa

AbstractVegetation degradation has been identified as a serious environmental problem, around the communal villages of South Africa. An investigation of the relationship between vegetation condition and local grazing management systems was undertaken across the communal villages of the central Keiskamma Catchment, Eastern Cape Province. The hypothesis that “differences in grazing management strategies may explain the variations in vegetation condition within these communal villages” was tested. Landsat TM imagery of 1984 and 1999, in conjunction with SPOT‐4 imagery of 2011, was used to assess the spatial and temporal trends in vegetation. Structured interviews were administered among communal village authorities to obtain information regarding the functionality of local grazing management structures. Using the logistic regression in IDRISI Selva remote sensing software, relationships between vegetation condition and grazing management systems were analysed.Spatial and temporal trends in vegetation cover revealed a drastic reduction in fair vegetation, an increase in extremely degraded vegetation and bare/eroded surfaces, particularly in villages with ineffective rangeland management practices. The hypothesis was accepted; a distinct relationship between the respective grazing practices and vegetation condition was identified. Variations in the functionality of local grazing management structures in the communal villages account for the vegetation condition disparities observed. A revitalisation of these structures is key in vegetation restoration endeavours.

Vulnerability assessment of the global water erosion tendency: Vegetation greening can partly offset increasing rainfall stress

AbstractGlobal water erosion is a grave threat to the sustainability of agriculture. Regional climate change may aggravate the threat of erosion, whereas global vegetation greening (an increasing trend in vegetation cover) may act as a mitigation to the threat. On the basis of the Revised Universal Soil Loss Equation, we assessed the vulnerability of the global water erosion during 1982–2015. A contribution index was built to describe the influences of rainfall erosivity and cover management on global water erosion vulnerability changes during 1982–2015. The research objective was to explore the spatial pattern of global water erosion vulnerability change in recent decades and to identify the factor that has contributed most to the vulnerability change, and three main results were obtained. (a) The global water erosion vulnerability increased over 51% of the surface during 1982–2015, and the surface with significant decreasing trends only accounted for 12% of the middle and highly sensitive areas. (b) Rainfall erosivity contributed more than cover management for absolute value except in the Amazon, Congo Basin, and Southeast Asia, whereas the contribution of cover management was increasing. (c) Vegetation greening can partly offset the stress caused by climate change. The global water erosion vulnerability decreased more than increased in sensitive area. Consequently, enhancing the vegetation growth in the highly sensitive water erosion region could reduce the erosion threat at large scale.

Tempo-spatial changes of vegetation coverage using remote sensing in Altay, China

Vegetation dynamics are very sensitive to climate change, especially in arid and semi-arid regions. In this paper, the Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to evaluate vegetation coverage area in Altay Prefecture during the 2000 to 2015. Furthermore, the patterns of tempo-spatial variations in vegetation coverage were analysed. The vegetation coverage area significantly changed in the past 16 years and the average percentage of vegetation was 35.72%. The results also indicated that the trend of vegetation cover had obvious discrepancy in various altitude ranges.

Assessing remotely sensed temperature humidity index as human comfort indicator relative to landuse landcover change in Abuja, Nigeria

Abuja, Nigeria’s capital city has witnessed remarkable expansion, growth and developmental activities since its inception in 1976. This mass inrush and settlement of people often leads to the replacement of the natural land cover with urban infrastructural facilities resulting in micro-climate change and worsening thermal environmental conditions. It is therefore pertinent to study the thermal environment of the city if Abuja will avoid the associated problems of growing and expanding city like many others in the world. Thus this study examines the use of remote sensing and geographic information system in mapping the temperature humidity index (THI) as human comfort indicator relative to land use land cover change in Abuja using Landsat Thematic Mapper, Enhanced Thematic Mapper + and Landsat 8 Operational Land Imager/Thermal Infrared Sensor data of 1987, 1999, 2009 and 2014. This is to assess the changes that have taken place between these periods. Subsequently, an attempt was made at comparing differences in the THI between five different land cover classes: water body, built-up area, closed canopy vegetation, open vegetation and waste land. The results show that the declining trend of vegetation cover between 1987–1999, and 2009–2014 saw a rise in the region with THI > 26 (discomfort) while the periods between 1999 and 2009 where there was a remarkable improvement in the vegetation cover saw an increase in region where comfortable condition is achieved. The discomfort index class category is the most dominant across most of the land cover classes except in the water body and closed canopy class categories. This order of dominance is the highest in wasteland and built-up areas land use/land cover categories.

The Dynamic Change of Vegetation Cover and Associated Driving Forces in Nanxiong Basin, China

Natural climate change and human activities are the main driving forces associated with vegetation coverage change. Nanxiong Basin is a key ecosystem-service area at the national level with a dense population and highly representative of red-bed basins, which are considered as fragile ecological units in humid regions. In this study, the authors aimed to determine the trends in vegetation cover change over past two decades and the associated driving forces in this study area. The Normalized Difference Vegetation Index (NDVI) of 2000–2015, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing dataset along with the application of statistical methods and GIS (geographic information system) techniques were used to quantify vegetation cover change. The results show that human-induced factors can explain most variations at sites with significant cover change. That is to say that human activities are the main drivers of vegetation dynamics in this study area, which shows a significant reduction trend in vegetation cover during the industrialization and urbanization processes of the study period and noticeable recovery trend in 2000–2015 under the plantation and enclosed forest policy.

Time Series Monitoring of Bush Encroachment by <i>Euclea divinorum</i> in Ol Pejeta Conservancy Laikipia, Kenya

Bush encroachment refers to the invasion of woody species in Savannah ecosystems driven by either anthropogenic and/or natural factors. This study sought to examine land cover changes and topographic features attributable to patterns of encroachment in Ol Pejeta Conservancy (OPC) where, <i>Euclea divinorum</i> unpalatable woody species has colonised former grasslands and other habitats which provide grazing grounds for herbivore wildlife species. Here, we monitored vegetation cover trends in the period 1987-2016 using five vegetation classes on Landsat images acquired during the dry season. Additionally, slope based NDVI maps and digital elevation models were used to identify topographic influences on vegetation change. Results revealed that <i>E. divinorum</i> increased significantly between 1987 and 2016 (Mann Kendall test for trend analysis tau 1, n=6, p&lt; 0.01). On the other hand, <i>Acacia drepanolobium</i> and <i>Acacia xanthophloea</i> decreased from 49.72% and 5.31% in 1987 to 17.00% and 0.29% in 2016 respectively. Further, areas in low elevation were more colonised by <i>E. divinorum</i>. The colonising <i>E. divinorum</i> is unpalatable hence lacks natural predators whilst, <i>A. drepanolobium</i> and <i>A. Xanthophloea</i> which are alternative herbivory species decreased. Understanding dynamics of woody vegetation in savannah is crucial for management of healthy and sustainable ecosystems.

From greening to browning: Catchment vegetation development and reduced S-deposition promote organic carbon load on decadal time scales in Nordic lakes.

Increased concentrations of dissolved organic carbon (DOC), often labelled “browning”, is a current trend in northern, particularly boreal, freshwaters. The browning has been attributed to the recent reduction in sulphate (S) deposition during the last 2 to 3 decades. Over the last century, climate and land use change have also caused an increasing trend in vegetation cover (“greening”), and this terrestrially fixed carbon represents another potential source for export of organic carbon to lakes and rivers. The impact of this greening on the observed browning of lakes and rivers on decadal time scales remains poorly investigated, however. Here, we explore time-series both on water chemistry and catchment vegetation cover (using NDVI as proxy) from 70 Norwegian lakes and catchments over a 30-year period. We show that the increase in terrestrial vegetation as well as temperature and runoff significantly adds to the reduced SO4-deposition as a driver of freshwater DOC concentration. Over extended periods (centuries), climate mediated changes in vegetation cover may cause major browning of northern surface waters, with severe impact on ecosystem productivity and functioning.

A comparison of pixel-based and object-based approaches for land use land cover classification in semi-arid areas, Sudan

This paper deals with the comparison between application of pixel-based and object- based approaches in land use land cover classification in semi-arid areas in Sudan. The second aim is to assess the accuracy of classification for each approach. The study was conducted in the gum arabic belt in North Kordofan State, which is affected by modifications in conditions and composition of vegetation cover trends. The study used ASTER L1B registered radiance at the sensor image acquired on (19.10.2010). The image was radiometrically corrected by using ENVI-FLAASH software. Subset with an area of (40880) ha was created. The image classification (pixel-based and object-based) and accuracy assessment were conducted. Total number of (47) GCPs were surveyed and used in accuracy assessment using ERDAS 9.1. Image segmentation process was implemented using Definiens eCognition 7.1 software. Segmentation level 4 of scale parameter 25 was selected for classification based on colour and form homogeneity. Land use land cover classes were derived by classification using the nearest neighbor classifier with membership functions (fuzzy logic) for each class. The land use land cover distribution in the area for forest dominated by Acacia Senegal is (20%) and for residential area is (1.50%) for the two methods of classification. While for bare and farm land, grass and bush land and mixed woodland classes are (6.69% and 1.63%), (18.62% and 15.16%) and (53% and 61%) for pixel based and object based methods, respectively. The overall accuracy and Kappa statistic of the classification produced by the pixel-based and object-based were (72.92%, and 54.17%) and (0.6259 and 0.3810), respectively. The pixel based approach performed slightly better than the object-based approach in land use land cover classification in the semi-arid land in gum Arabic belt.

MODIS normalized difference vegetation index (NDVI) and vegetation phenology dynamics in the Inner Mongolia grassland

Abstract. The Inner Mongolia grassland, one of the most important grazing regions in China, has long been threatened by land degradation and desertification, mainly due to overgrazing. To understand vegetation responses over the last decade, this study evaluated trends in vegetation cover and phenology dynamics in the Inner Mongolia grassland by applying a normalized difference vegetation index (NDVI) time series obtained by the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) during 2002–2014. The results showed that the cumulative annual NDVI increased to over 77.10 % in the permanent grassland region (2002–2014). The mean value of the total change showed that the start of season (SOS) date and the peak vegetation productivity date of the season (POS) had advanced by 5.79 and 2.43 days, respectively. The end of season (EOS) was delayed by 5.07 days. These changes lengthened the season by 10.86 days. Our results also confirmed that grassland changes are closely related to spring precipitation and increasing temperature at the early growing period because of global warming. Overall, productivity in the Inner Mongolia Autonomous Region tends to increase, but in some grassland areas with grazing, land degradation is ongoing.

Long-term trends of dust events over Tibetan Plateau during 1961–2010

In this study we analyzed dust events records of surface meteorological stations on Tibetan Plateau (TP) during 1961–2010 and provided the spatial and temporal distribution of dust events. The occurrence of dust events has significantly decreased since the 1970s. We defined the Tibetan Plateau Dust Index (TPDI) for the most dust active periods, spring and winter, to characterize the large scale variability of dust events over TP. Mann–Kendall test suggested the decreasing trend was possibly an abrupt change in the 1990s. The decline of surface wind speeds could partly explain the decrease of dust events over TP. TPDI is positively correlated to the surface winds, with correlation coefficients of 0.42 for spring and 0.46 for winter, respectively. The averaged number days with strong winds (wind speed greater than 6.5 ms-1) for the 4 selected stations, which were chosen to define TPDI, are significantly correlated with TPDI for both spring (correlation coefficient = 0.69) and winter (correlation coefficient = 0.76) and also showed a deceasing trend. The upward trend of vegetation cover was indicated by the normalized difference vegetation index (NDVI), which can be attributed as another factor driving the decrease of dust events over TP. TPDI is negatively correlated to the NDVI, with correlation coefficients of −0.48 for spring and −0.29 for winter. Additionally, analysis of geopotential height fields and wind fields indicate an enhanced ridge in the north of TP and weakened westerly jet in the low-frequency years of dust events, which also drive the decline of dust events over TP.

Erratum to: Human factors explain the majority of MODIS-derived trends in vegetation cover in Israel: a densely populated country in the eastern Mediterranean

Land cover and land use changes can result from climatic variability and climate changes, as well as from direct and indirect human drivers, such as growth in population and consumption. In this study, we aimed to examine whether major factors driving landscape changes (expressed in vegetation cover) in Israel, a densely populated country in the eastern Mediterranean Basin, are related to physical drivers or to human causes. To this end, we calculated statistical trends in the Normalized Difference Vegetation Index (NDVI—a spectral index representing vegetation cover) from a 14-year MODIS time series, between 2000 and 2014, to identify areas where vegetation cover has either increased or decreased. We chose 125 study areas where statistically significant changes in NDVI were found and used time series of monthly rainfall, Landsat images, Google Earth images and environmental GIS layers to identify the type and cause of landscape changes. The two most common general classes driving land cover changes were agricultural (56 of 125; expansion of agricultural areas or change in agricultural crops) and urban (28 of 125; urban expansion or urban greening). Other important drivers of landscape changes included forestry, woody encroachment, wildfire dynamics and water management. Climate variability was found to explain landscape changes in only 3 of the 125 study areas, all located in the transition zone between the desert and the Mediterranean climate regions of Israel, where a decrease in rainfall led to a decrease in NDVI values. NDVI as an indicator of landscape changes is not effective to detect changes in non-photosynthetic vegetation or to monitor changes in forests where leaf area index values are high. However, we show here that even in a highly heterogeneous and densely populated country, MODIS-derived time series of NDVI are informative to identify landscape change processes.

NDVI-Based Analysis on the Influence of Climate Change and Human Activities on Vegetation Restoration in the Shaanxi-Gansu-Ningxia Region, Central China

In recent decades, climate change has affected vegetation growth in terrestrial ecosystems. We investigated spatial and temporal patterns of vegetation cover on the Loess Plateau’s Shaanxi-Gansu-Ningxia region in central China using MODIS-NDVI data for 2000–2014. We examined the roles of regional climate change and human activities in vegetation restoration, particularly from 1999 when conversion of sloping farmland to forestland or grassland began under the national Grain-for-Green program. Our results indicated a general upward trend in average NDVI values in the study area. The region’s annual growth rate greatly exceeded those of the Three-North Shelter Forest, the upper reaches of the Yellow River, the Qinling–Daba Mountains, and the Three-River Headwater region. The green vegetation zone has been annually extending from the southeast toward the northwest, with about 97.4% of the region evidencing an upward trend in vegetation cover. The NDVI trend and fluctuation characteristics indicate the occurrence of vegetation restoration in the study region, with gradual vegetation stabilization associated with 15 years of ecological engineering projects. Under favorable climatic conditions, increasing local vegetation cover is primarily attributable to ecosystem reconstruction projects. However, our findings indicate a growing risk of vegetation degradation in the northern part of Shaanxi Province as a result of energy production facilities and chemical industry infrastructure, and increasing exploitation of mineral resources.

Human factors explain the majority of MODIS-derived trends in vegetation cover in Israel: a densely populated country in the eastern Mediterranean

Human factors explain the majority of MODIS-derived trends in vegetation cover in Israel: a densely populated country in the eastern Mediterranean

Linear and segmented linear trend detection for vegetation cover using GIMMS normalized difference vegetation index data in semiarid regions of Nigeria

Quantitative analysis of trends in vegetation cover, especially in Kogi state, Nigeria, where agriculture plays a major role in the region’s economy, is very important for detecting long-term changes in the phenological behavior of vegetation over time. This study employs the use of normalized difference vegetation index (NDVI) [global inventory modeling and mapping studies 3g (GIMMS)] data from 1983 to 2011 with detailed methodological and statistical approach for analyzing trends within the NDVI time series for four selected locations in Kogi state. Based on the results of a comprehensive study of seasonalities in the time series, the original signals are decomposed. Different linear regression models are applied and compared. In order to detect structural changes over time a detailed breakpoint analysis is performed. The quality of linear modeling is evaluated by means of statistical analyses of the residuals. Standard deviations of the regressions are between 0.015 and 0.021 with R2 of 0.22–0.64. Segmented linear regression modeling is performed for improvement and a decreasing standard deviation of 33%–40% (0.01–0.013) and R2 up to 0.82 are obtained. The approach used in this study demonstrates the added value of long-term time series analyses of vegetation cover for the assessment of agricultural and rural development in the Guinea savannah region of Kogi state, Nigeria.

Vegetation dynamics and responses to recent climate change in Xinjiang using leaf area index as an indicator

There is a strong signal showing that the climate in Xinjiang, China has changed from warm-dry to warm-wet since the early 1980s, leading to an increase in vegetation cover. Based on a regression analysis and Hurst index method, this study investigated the spatial–temporal characteristics and interrelationships of the vegetation dynamics and climate variability in Xinjiang Province using the leaf area index (LAI) and a gridded meteorological dataset for the period 1982–2012. Further analysis focused on the discrimination between climatic change and human-induced effects on the vegetation dynamics, and several conclusions were drawn. (1) Vegetation dynamics differ in mountain and plains regions, with a significant increasing trend of vegetation cover in oases and decreasing trend of vegetation growth in the Tienshan and Altay Mountain. The Hurst exponent results indicated that the vegetation dynamic trend was consistent, with a sustainable area percentage of 51.18%, unsustainable area percentage of 4.04%, and stable and non-vegetated area ratio of 44.78%. (2) The warm-dry to warm-wet climatic pattern in Xinjiang Province since the 1980s mainly appeared in the western part of the Tienshan region and North Xinjiang. Temperatures increased in all seasons over the majority of Xinjiang, and precipitation showed a significant increasing trend in the mountainous regions in spring, summer and autumn, whereas the rate of precipitation change was higher in the plains region in winter compared with that in other seasons. (3) A correlation occurs between the climate variables (precipitation and temperature) and mean LAI, and this correlation varies at the seasonal and regional scales, with coniferous forest, meadow and grassland more correlated with precipitation in spring and summer and not correlated with temperature, which indicated that precipitation was the dominant factor affecting the growth of mountain vegetation. The mean LAI of vegetation in the plains exhibited significant correlation with precipitation in winter and temperature in spring and summer. (4) A residual analysis showed a human-induced change that was superimposed on the climate trend and exhibited two effects: vegetation regeneration in oases throughout Xinjiang and desertification in the meadow located in the mountainous area of the western Tienshan Mountains and Altay Mountains. (5) Grassland is the most sensitive vegetation type to short-term climatic fluctuations and is the land-use type that has been most severely degraded by human activity; thus, local governments should take full advantage of this climatic warm-wet shift and focus on protecting vegetation to improve this fragile arid environment.