Normalized Difference Vegetation Index Trends Research Articles

Spatial and temporal trends of urban green spaces: an assessment using hyper-temporal NDVI datasets

Since the last couple of decades urban areas of developing countries have been experiencing unprecedented growth. However, this growth is not well planned in most of the cities. Even in recent days, there is no significant change in city planning and still, there is a lack of concern regarding environmental issues especially the changing green covers in cities. Identifying the green space dynamics of major cities is crucial considering strategic planning of smart cities, policy intervention and combating the problems associated with degreening. The present study attempts to assess the potentiality of hyper-temporal normalized difference vegetation index (NDVI) and its temporal trend as an indicator of greening or de-greening process in Delhi National Capital Territory (NCT) and its peri-urban areas. Ten days composite hyper-temporal SPOT-VGT (Satellite Pour l'Observation de la Terre Vegetation) NDVI images (1998–2013) have been used for identifying the vegetation trend and further, it was compared with long-term CHIRPS (climate hazards centre infrared precipitation with station data) rainfall data to unveil the root causes behind the variation in vegetation trend specifically the contribution of natural and anthropogenic factor in changing scenario of urban green space. The study shows that hyper-temporal NDVI data with sufficient temporal depth can be highly convenient to explore the trend of vegetation and changes in green space. The spatio-temporal pattern of NDVI trend indicates a significant part of the area including Noida, Gurgaon etc. experienced decreasing vegetation trend which can be explained by large scale conversion of vegetated land into built-up areas due to establishment of new multi-storied buildings. The strong negative correlation (r ≤ − 0.8) between rainfall-NDVI indicates that rainfall is not the major controlling factor of vegetation trend in that area rather anthropogenic activities played a significant role in depletion of vegetation. During 1998–2013, the areas of bounding zone have been largely converted into built-up area and as a consequence, surface vegetation cover of these areas has been depleted. However, human induced greening was also observed in small pockets of Delhi NCT that can be explained by the impact of positive measures like tree plantation and proper maintenance of urban trees along roads, parks, and gardens.

Land surface temperature and vegetation cover changes and their relationships in Taiwan from 2000 to 2020

This study reported decadal trends and acceleration patterns of surface temperatures and vegetation conditions in Taiwan from 2000 to 2020. Time series data on Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), and Land Cover (LC) types were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Seasonal patterns, decadal trends, and acceleration rates for each LU type were evaluated using the natural cubic spline method and multivariate linear regression. Acceleration patterns were then analyzed using the correlations between derived trends and acceleration rates. Results are provided for the entire island, for sub-regions, and for the smallest area elements of 1 km2. Taiwan has had a significant mean increase in decadal trends of NDVI (95%CI: 0.013–0.025 unit/dec), but not of LST (95%CI: −0.018-0.302 °C/dec). Negative correlation between LST and NDVI was stronger in green coverage areas than in built-up areas. On sub-regional level, a larger increase in surface temperature and tremendous vegetation loss were observed at high elevations in the southern regions of the Central Mountain Range. Meanwhile, the central-western lowlands faced moderately warming temperatures and slight greening. At the pixel level, urbanization and agricultural expansion have caused rising surface temperatures, while afforestation contributes cooling. The rise in temperature in accelerating in the different LC types throughout Taiwan. Despite that, the acceleration patterns of the vegetation index have remained stable in most parts of the island. Although the rates of vegetation growth have not yet declined, practitioners and policymakers need to consider a balanced view of green cities to keep pace with the accelerating temperature conditions.

Spatiotemporal differences in climate change impacts on vegetation cover in China from 1982 to 2015.

The impacts of climate change on vegetation cover in different regions in China are not entirely clear because of the interference of non-climatic factors, such as human activity. This study aims to analyze the spatiotemporal differences in climate impacts qualitatively and quantitatively by applying trend, correlation, and multiple linear regression (MLR) analyses to the data of Normalized Difference Vegetation Index (NDVI) and two climatic factors (air temperature and precipitation) during 1982-2015 in China. The MLR equation linking two climatic variables with NDVI was used to identify the NDVI trend caused by climate change. We demonstrated that the central and eastern regions of China, dominated by deciduous and evergreen broadleaf forests, experienced a rapid increase in NDVI from 1982 to 2015. The response of NDVI to variations in temperature and precipitation exhibited large spatiotemporal differences across China, which was closely related to climatic conditions and vegetation types. Overall, warming, particularly the sharp rise in spring, was the main climatic driving force behind China's NDVI increase, and precipitation also influenced the NDVI increase in temperate grassland and desert regions due to the relatively arid climate, particularly in summer. The contributions of climate change to the total NDVI trend (CC) showed a large spatiotemporal heterogeneity across China. Overall, only 45% of the pixels (with a resolution of 8 km) in the study area showed that the MLR equations between NDVI and two climatic factors were significant at the 0.05 significance level during the growing season (April-October), and the average CC of these pixels was 38%. Among the eight vegetation sub-regions of China, the temperate desert and Qinghai-Tibet Plateau alpine meadow regions generally exhibited relatively larger CCs than other vegetation sub-regions in different seasons. At a national scale, the regional average CC reached 64% during the growing season. These results at multiple scales can help to deeply understand the mechanisms of regional environmental variation and sustainability.

China's deserts greening and response to climate variability and human activities.

Vegetation, which is a good indicator of the impacts of climate variability and human activities, can reflect desert ecosystem dynamics. To reveal the vegetation variations in China’s deserts, trends in the monthly, seasonal, and annual normalized difference vegetation index (NDVI) from 2000 to 2017 were measured both temporally and spatially by the Theil-Sen estimator and Mann-Kendall test. Additionally, correlation coefficients and residual analysis were employed to evaluate the correlations between the NDVI and climatic factors and to distinguish the impacts of climate variability and human activities. The results showed that China’s deserts underwent greening. The annual NDVI showed a significant increasing trend at a rate of 0.0018/yr, with values of 0.094 in 2000 and 0.126 in 2017. Significant increasing trends in NDVI were observed in all four seasons. The NDVI were higher in summer and autumn than in spring and winter. Both the monthly NDVI and its trends showed an inverted U-shaped curve during the year. Spatially, the greening trends were mainly distributed on the southern edge of the Gurbantunggut Desert, in the northwestern part of the Taklimakan Desert, and in the Kubuqi Desert. The correlations between the NDVI and climatic factors at the monthly and seasonal scales were stronger than those at the annual scale. Temperature and precipitation had positive effects on NDVI at the monthly and seasonal scales, but only precipitation had a positive effect at the annual scale. Human activities, especially oasis expansion and sand stabilization measures, were two major causes of large increasing areas of desert greening in China indicated by the NDVI.

Canopy reflectance models illustrate varying NDVI responses to change in high latitude ecosystems.

Multiyear trends in Normalized Difference Vegetation Index (NDVI) have been used as metrics of high latitude ecosystem change based on the assumption that NDVI change is associated with ecological change, generally as changes in green vegetation amount (green leaf area index [LAI] or plant cover). Further, no change in NDVI is often interpreted as no change in these variables. Three canopy reflectance models including linear mixture model, the SAIL (Scattering from Arbitrarily Inclined Leaves) model, and the GeoSail model were used to simulate scenarios representing high latitude landscape NDVI responses to changes in LAI and plant cover. The simulations showed inconsistent NDVI responses. Clear increases in NDVI are generally associated with increases in LAI and plant cover. At higher values of LAI, the change in NDVI per unit change in LAI decreases, with very little change in spruce forest NDVI where crown cover is >50% and at the tundra–taiga ecotone with transitions from shrub tundra to spruce woodland. These lower responses may bias the interpretation of greening/browning trends in boreal forests. Variations in water or snow coverage were shown to produce outsized nonbiological NDVI responses. Inconsistencies in NDVI responses exemplify the need for care in the interpretation of NDVI change as a metric of high latitude ecosystem change, and that landscape characteristics in terms of the type of cover and its characteristics, such as the initial plant cover, must be taken into account in evaluating the significance of any observed NDVI trends.

Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019

Inner Mongolia in China is a typically arid and semi-arid region with vegetation prominently affected by global warming and human activities. Therefore, investigating the past and future vegetation change and its impact mechanism is important for assessing the stability of the ecosystem and the ecological policy formulation. Vegetation changes, sustainability characteristics, and the mechanism of natural and anthropogenic effects in Inner Mongolia during 2000–2019 were examined using moderate resolution imaging spectroradiometer normalized difference vegetation index (NDVI) data. Theil–Sen trend analysis, Mann–Kendall method, and the coefficient of variation method were used to analyze the spatiotemporal variability characteristics and sustained stability of the NDVI. Furthermore, a trend estimation method based on a Seasonal Trend Model (STM), and the Hurst index was used to analyze breakpoints and change trends, and predict the likely future direction of vegetation, respectively. Additionally, the mechanisms of the compound influence of natural and anthropogenic activities on the vegetation dynamics in Inner Mongolia were explored using a Geodetector Model. The results show that the NDVI of Inner Mongolia shows an upward trend with a rate of 0.0028/year (p < 0.05) from 2000 to 2019. Spatially, the NDVI values showed a decreasing trend from the northeast to the southwest, and the interannual variation fluctuated widely, with coefficients of variation greater than 0.15, for which the high-value areas were in the territory of the Alxa League. The areas with increased, decreased, and stable vegetation patterns were approximately equal in size, in which the improved areas were mainly distributed in the northeastern part of Inner Mongolia, the stable and unchanged areas were mostly in the desert, and the degraded areas were mainly in the central-eastern part of Inner Mongolia, it shows a trend of progressive degradation from east to west. Breakpoints in the vegetation dynamics occurred mainly in the northwestern part of Inner Mongolia and the northeastern part of Hulunbuir, most of which occurred during 2011–2014. The future NDVI trend in Inner Mongolia shows an increasing trend in most areas, with only approximately 10% of the areas showing a decreasing trend. Considering the drivers of the NDVI, we observed annual precipitation, soil type, mean annual temperature, and land use type to be the main driving factors in Inner Mongolia. Annual precipitation was the first dominant factor, and when these four dominant factors interacted to influence vegetation change, they all showed interactive enhancement relationships. The results of this study will assist in understanding the influence of natural elements and human activities on vegetation changes and their driving mechanisms, while providing a scientific basis for the rational and effective protection of the ecological environment in Inner Mongolia.

The greening effect characterized by the Normalized Difference Vegetation Index was not coupled with phenological trends and tree growth rates in eight protected mountains of central Mexico

In temperature-limited ecosystems such as high-elevation forests, rising temperatures and increasing carbon dioxide can result in a greening effect and changes in the phenology of the forests. Up to now, phenological metrics of subtropical high-elevation forests remain primarily unquantified, which strongly limits a global comprehension of climate change impacts. We assessed the long-term trend dynamics (greenness and land surface phenology) using the Normalized Difference Vegetation Index (NDVI) and compared them with data from an existing dendrochronological network that came from eight protected mountains in the Trans-Mexican Volcanic Belt in central Mexico during the last 20 yr (2000–2019). We found three main NDVI clusters reflecting a longitudinal geographical pattern (Pacific, Center, and the Gulf of Mexico). A notable greening effect at the canopy level was seen for all mountains (up to 23.8% increase concerning the year 2000). Nevertheless, the NDVI trends were not uniform, and they differed along an elevation gradient with the highest increases at the bottom. Minimum temperature and soil moisture positively impacted the NDVI time series and explained up to 87% of the variance, while maximum temperature negatively affected. Regarding phenological metrics, the start, and end of the growing season were the day of the year 148 ± 20 (late-May) and 9 ± 12 (early-January), respectively, with an average length of 226 ± 33 d, differing from previous studies related to annual tree-ring formation. We did not see a later or earlier start of either the spring or the fall, which partially refutes the hypothesis that the growing season extended despite the increase in temperature. Finally, our investigation revealed that the common greening effect was not parallel with radial growth trends, except for the wettest site from the Gulf of Mexico cluster, and partially related to the forest management strategies. Further modelling of regional phenology coupled with more intensive tree-ring monitoring is needed to explain the factors that mediate the processes of photosynthesis (NDVI) and storage of carbon (tree-ring formation) under a global warming outlook.

Vegetation cover change and its response to climate change on the Loess Plateau, Northwest China based on ICEEMDAN method

The long-term series of geographic data and remote sensing data contain noise and perio-dic fluctuation. We used the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to decompose the data of the normalized difference vegetation index (NDVI), precipitation, and temperature from 1982 to 2015 on per-pixels in the Loess Pla-teau to obtain residuals. Using the residual with less noise and periodic fluctuations, we examined the changes of NDVI and the relationship between NDVI and climatic factors. The results showed that the spatial change trend of NDVI was mainly increasing from 1982 to 2015 in the Loess Plateau. The significance of the change trend of residual NDVI (95.9%) was greater than the original NDVI (72.3%), with spatial variations. Temperature and precipitation could largely explain the changes in vegetation coverage. The proportions of areas with extremely significant positive and negative correlations between temperature and NDVI on the Loess Plateau were 83.7% and 13.9%, respectively, while that between precipitation and NDVI were 54.4% and 37.2%, respectively. There were obvious spatial variations in the responses of vegetation to climate change on the Loess Plateau. Different climatic factors had different effects on different types of vegetation. In general, temperature had stronger correlation with different vegetation than precipitation. Therefore, temperature was the main driving factor for the changes of vegetation cover in the Loess Plateau.

Spatiotemporal Peatland Productivity and Climate Relationships Across the Western South American Altiplano

AbstractThe South American Altiplano is one of the largest semiarid high‐altitude plateaus in the world. Within the Altiplano, peatlands known as “bofedales” are important components of regional hydrology and provide key water resources and ecosystem services to Andean communities. Warming temperatures, changes in hydroclimate, and shifting atmospheric circulation patterns all affect peatland dynamics and hydrology. It is therefore urgent to better understand the relationships between climate variability and the spatiotemporal variations in peatland productivity across the Altiplano. Here, we explore climate influences on peatland vegetation using 31 years of Landsat data. We focus specifically on the bofedal network in the western Altiplano, the driest sector of the plateau, and use the satellite‐derived Normalized Difference Vegetation Index (NDVI) as an indicator of productivity. We develop temporally and spatially continuous NDVI products at multiple scales in order to evaluate relationships with climate variables over the past three decades. We demonstrate that cumulative precipitation and snow persistence over the prior 2 years are strongly associated with growing season productivity. A step change in peatland productivity between 2013–2015 drives an increasing trend in NDVI and is likely a response to consecutive years of anomalously high snow accumulation and rainfall. Early summer minimum temperatures emerge as a secondary influence on productivity. Understanding large‐scale productivity dynamics and characterizing the response of bofedales to climate variability over the last three decades provides a baseline to monitor the responses of Andean peatlands to climate change.

Warming as a Driver of Vegetation Loss in the Sonoran Desert of California

AbstractDryland ecosystems cover large regions of the Earth and have important impacts on global biogeochemistry and the carbon cycle. The plant species that occupy dryland environments have traits that enable them to withstand harsh environmental conditions, and some researchers have hypothesized that dryland vegetation may be comparatively resilient to changing climate, while others have pointed out that dryland vegetation often operates close to the physiological limits of many species, implying a possible vulnerability to warming. Here we use the Landsat archive to analyze vegetation dynamics for part of the Sonoran Desert and adjacent mountains in southern California. We show that large decreases in vegetation cover occurred over the last 34 years (1984–2017), especially across the xeric portions of our study region, where we observed a normalized difference vegetation index (NDVI) decline of 1.1 ± 0.3% yr−1. Changes in precipitation explain most of the year‐to‐year variation but are unable to fully explain the observed long‐term decline in NDVI. Statistical models that combined summer temperature and mean annual precipitation explained more of the spatial and temporal structure of NDVI trends and implicate climate warming as an important driver of declining vegetation cover. The impact of warming contributed to a change in the precipitation‐vegetation relationship through time for this desert region, indicating a structural change in ecosystem function during the study period. These results suggest that recent climate change has already had significant impact on these drylands and highlight the potential for future warming to increase risks for dryland ecosystems in other regions.

Article Title Variations in Growing Season NDVI and Its Sensitivity to Climate Change Responses to Green Development in Mountainous Areas

The Normalized Difference Vegetation Index (NDVI) is sensitive to changes in surface vegetation cover. Research into how climate change impacts surface vegetation cover is essential to manage ecological systems and promote green development. The Western Henan Mountains, located in the transitional zone between the northern subtropical and warm temperate zones of China, is an ideal location to study the impacts of climate change on surface vegetation cover. Combining a digital elevation model (DEM) with temperature and precipitation data; and MODIS-NDVI imagery (2000∼2017) for the Western Henan Mountains, this study explores variations in the growing season NDVI and its response to climate change. Results show that there are significant changes with fluctuations in NDVI values from 2000 to 2017. NDVI increased at a growth rate of 0.027 per decade (p < 0.05) overall, indicating vegetation conditions have gradually improved. Although the NDVI value showed an overall increasing trend, 19.12% of the areas showed a decreasing trend, interspersing and intersecting spatially, showing significant spatial differences. NDVI increased initially, but then decreased as a function of elevation, which was shown to be proportional to slope and independent of aspect. Variables including elevation and slope gradient are shown to provide high explanation of NDVI variability, whilst temperature is shown to have a more significant impact on NDVI than precipitation. However, vegetation responses to temperature and precipitation covaried with both slope and aspect. Positive NDVI trends were strongest at low elevations (i.e., <1,100 masl), which we attribute to vegetation restoration activities. Lower NDVI values characterized gentle slopes (<5°), whilst higher values were, in contrast, associated with steeper slopes (5∼10°). This study highlights the complex mechanisms and their relations governing vegetation response to climate change and should form an instructive basis for both future modeling studies investigating the response of vegetation to future global warming.

Recent greening of grasslands in northern China driven by increasing precipitation

Abstract Aims Recent warmer and wetter climate in northern China remains a hot topic in recent years, yet its effect on vegetation growth has not been fully understood. This study investigated the temporal change of vegetation cover and its correlations with climatic variables from 1982 to 2018 for grasslands in northern China. Our aim is to clarify whether the warmer and wetter climate in recent years drives the greening of the vegetation in this region. Methods We investigated the temporal dynamic of vegetation normalized difference vegetation index (NDVI) and its driving forces based on long time-series data. Piecewise regression was used to examine whether there was a turning point of the trend of NDVI and climatic variables. Pearson correlation analyses were conducted to quantify the relationship between NDVI and climatic factors. Stepwise multivariable regression was used to quantify the contributions of climate variables to the temporal variations in NDVI. Important Findings We found a turning point of NDVI trend in 2008, with GIMMS NDVI indicating a slight increase of 0.00022 yr−1 during 1982–2008 to an increase of 0.002 yr−1 for GIMMS NDVI during 2008–2015 and 0.0018 yr−1 for MODIS NDVI during 2008–2018. Precipitation was the predominant driver, and air temperature and vapor pressure deficit exerted a minor impact on the temporal dynamics of NDVI. Overall, our results suggest a turning point of NDVI trend, and that recent warmer and wetter climate has caused vegetation greening, which provides insights for better predicting the vegetation cover in this region under changing climate.

Assessment of Vegetation Dynamics and Ecosystem Resilience in the Context of Climate Change and Drought in the Horn of Africa

Understanding the response of vegetation and ecosystem resilience to climate variability and drought conditions is essential for ecosystem planning and management. In this study, we assessed the vegetation changes and ecosystem resilience in the Horn of Africa (HOA) since 2000 and detected their drivers based mainly on analysis of the Moderate Resolution Imaging Spectroradiometer (MODIS) products. We found that the annual and seasonal trends of NDVI (Normalized Difference Vegetation Index) generally increased during the last two decades over the Horn of Africa particularly in western parts of Ethiopia and Kenya. The weakest annual and seasonal NDVI trends were observed over the grassland cover and tropical arid agroecological zones. The NDVI variation negatively correlated with Land Surface Temperature (LST) and positively correlated with precipitation at a significant level (p < 0.05) account for 683,197 km2 and 533,385 km2 area, respectively. The ecosystem Water Use Efficiency (eWUE) showed overall increasing trends with larger values for the grassland biome. The precipitation had the most significant effect on eWUE variation compared to LST and annual SPEI (Standardized Evapotranspiration Index). There were about 54.9% of HOA resilient to drought disturbance, whereas 32.6% was completely not-resilient. The ecosystems in the humid agroecological zones, the cropland, and wetland were slightly not-resilient to severe drought conditions in the region. This study provides useful information for policy makers regarding ecosystem and dryland management in the context of climate change at both national and regional levels.

Unexpected greening in a boreal permafrost peatland undergoing forest loss is partially attributable to tree species turnover.

Time series of vegetation indices derived from satellite imagery are useful in measuring vegetation response to climate warming in remote northern regions. These indices show that productivity is generally declining in the boreal forest, but it is unclear which components of boreal vegetation are driving these trends. We aimed to compare trends in the normalized difference vegetation index (NDVI) to forest growth and demographic data taken from a 10ha mapped plot located in a spruce-dominated boreal peatland. We used microcores to quantify recent growth trends and tree census data to characterize mortality and recruitment rates of the three dominant tree species. We then compared spatial patterns in growth and demography to patterns in Landsat-derived maximum NDVI trends (1984-2019) in 78 pixels that fell within the plot. We found that NDVI trends were predominantly positive (i.e., "greening") in spite of the ongoing loss of black spruce (the dominant species; 80% of stems) from the plot. The magnitude of these trends correlated positively with black spruce growth trends, but was also governed to a large extent by tree mortality and recruitment. Greening trends were weaker (lower slope) in areas with high larch mortality, and high turnover of spruce and birch, but stronger (higher slope) in areas with high larch recruitment. Larch dominance is currently low (~11% of stems), but it is increasing in abundance as permafrost thaw progresses and will likely have a substantial influence on future NDVI trends. Our results emphasize that NDVI trends in boreal peatlands can be positive even when the forest as a whole is in decline, and that the magnitude of trends can be strongly influenced by the demographics of uncommon species.

Indication of long-term changes in the vegetation of abandoned agricultural lands for the forest-steppe zone using NDVI time series

The paper presents results of the analysis of long-term changes in the vegetation cover of abandoned agricultural lands in the forest-steppe zone of the Central Chernozem Region using time series of the Normalized Difference Vegetation Index (NDVI), which are measured using MOD13Q1 data. The vegetation index dynamics linked with the proportion of forest communities formed on the abandoned agricultural lands is investigated. The index values for the period of mid-August are the most informative for analyzing the share of forest communities growing on the abandoned agricultural land. Abandoned agricultural lands with coniferous forests have a higher correlation with NDVI than fallows with deciduous species. In the period 2000-2018, for all types of abandoned arable lands, the presence of a positive statistically significant trend component of the vegetation index long-term series is established. Using a slope angle coefficient of the NDVI trend line, a spatio-temporal analysis of the rate of formation of forest stands in the forest-steppe fallows at the beginning of the XXI century was carried out. Features of this process are studied.

Both climate and socioeconomic drivers contribute to vegetation greening of the Loess Plateau

Abstract Revegetation of the degraded ecosystems of the Loess Plateau (LP) of China is mainly attributed to Grain for Green (GFG) project introduced in 1999. This research aims to investigate the individual and collective impacts of climate and socioeconomic factors on variations and trends in satellite-based normalized difference vegetation index (NDVI) of the LP at the county level for the period of 2000 to 2018, and to identify the dominant driving factors for each county. The contributions of six driving factors (precipitation, temperature, solar radiation, gross domestic product, population, and livestock) are determined by applying the partial derivative equation method. Our results reveal increasing NDVI trends in 284 out of 294 counties of the LP, which are mostly dominated by the combined effect of climatic and socioeconomic factors covering 62% area of the LP. Among the six influencing factors, precipitation is the leading contributor factor to NDVI restoration in 152 counties. Our investigation found that climate variations, balanced implementation of environmental protection policies, and the economic incentives given to the local communities under the GFG program have made a remarkable contribution to vegetation greening in the LP. This research provides a theoretical reference for each county to facilitate policy-makers and local administrations to review and enhance the sustainable management of green infrastructure to solve evolving ecological challenges.

Spatial–Temporal Characteristics of Precipitation and Its Relationship with Land Use/Cover Change on the Qinghai-Tibet Plateau, China

The Qinghai-Tibet Plateau (QTP) is an area sensitive to global climate change, and land use/land cover change (LUCC) plays a vital role in regulating climate system at different temporal and spatial scales. In this study, we analyzed the temporal and spatial trend of precipitation and the characteristics of LUCC on the QTP. Meanwhile, we also used the normalized difference vegetation index (NDVI) as an indicator of LUCC to discuss the relationship between LUCC and precipitation. The results show the following: (1) Annual precipitation showed a fluctuant upward trend at a rate of 11.5 mm/decade in this area from 1967 to 2016; three periods (i.e., 22 years, 12 years, and 2 years) of oscillations in annual precipitation were observed, in which expectant 22 years is the main oscillation period. It was predicted that QTP will still be in the stage of increasing precipitation. (2) The LUCC of the plateau changed apparently from 1980 to 2018. The area of grassland decreased by 9.47%, and the area of unused land increased by 7.25%. From the perspective of spatial distribution, the transfer of grassland to unused land occurred in the western part of the QTP, while the reverse transfer was mainly distributed in the northwestern part of the QTP. (3) NDVI in the northern and southwestern parts of the QTP is positively correlated with precipitation, while negative correlations are mainly distributed in the southeast of the QTP, including parts of Sichuan and Yunnan Province. Our results show that precipitation in the QTP has shown a fluctuating growth trend in recent years, and precipitation and NDVI are mainly positively correlated. Furthermore, we hope that this work can provide a theoretical basis for predicting regional hydrology, climate change, and LUCC research.

Distinct vegetation response to drying and wetting trends across an aridity threshold

Aridity regulates the terrestrial ecosystem productivity in water-limited regions. The aridity index (AI) is often defined as the ratio of annual precipitation to annual potential evapotranspiration. However, how the drying and wetting influence the vegetation response and its characteristic along aridity gradient remains unclear. Here, we examined trends of the AI and normalized difference vegetation index (NDVI) in the drylands of East Asia from 1982 to 2015, which denoted the drying and wetting trends and vegetation response, respectively. The results show that the variability in land area and AI from 1982–2015 was lower in the whole dryland than its subtypes including hyper-arid, arid, semi-arid, and dry sub-humid regions. Drying and wetting trends were observed in each AI interval along a spatial aridity gradient. Wetting trends are prevalent owing to their area and magnitude were twice those of drying trends. Spatial variation of aridity shaped the pattern of trends in vegetation response to drying and wetting trends; drier regions had smaller and narrower ranges of variation in NDVI trends relative to wetter regions. A shift in AI trends and NDVI trends along the spatial aridity gradient occurred at 0.4 of AI. Distinct patterns of vegetation response to aridity change were observed across the aridity threshold, and the transition region was identified in the studied drylands. The results suggest that changes in the subtypes might be masked by the entire drylands and then leading to failure in recognizing the transformation of the subtypes. This implies that terrestrial carbon storage variability prediction should consider the spatial aridity changes to avoid the uncertainties due to the divergent vegetation response to AI trends at different aridity levels.

Vegetation Change and Its Response to Climate Change in Yunnan Province, China

The impact of global climate change on vegetation has become increasingly prominent over the past several decades. Understanding vegetation change and its response to climate can provide fundamental information for environmental resource management. In recent years, the arid climate and fragile ecosystem have led to great changes in vegetation in Yunnan Province, so it is very important to further study the relationship between vegetation and climate. In this study, we explored the temporal changes of normalized difference vegetation index (NDVI) in different seasons based on MOD13Q1 NDVI by the maximum value composite and then analyzed spatial distribution characteristics of vegetation using Sen’s tendency estimation, Mann–Kendall significance test, and coefficient of variation model (CV) combined with terrain factors. Finally, the concurrent and lagged effects of NDVI on climate factors in different seasons and months were discussed using the Pearson correlation coefficient. The results indicate that (1) the temporal variation of the NDVI showed that the NDVI values of different vegetation types increased at different rates, especially in growing season, spring, and autumn; (2) for spatial patterns, the NDVI, CV, and NDVI trends had strong spatial heterogeneity owning to the influence of altitudes, slopes, and aspects; and (3) the concurrent effect of vegetation on climate change indicates that the positive effect of temperature on NDVI was mainly in growing season and autumn, whereas spring NDVI was mainly influenced by precipitation. In addition, the lag effect analysis results revealed that spring precipitation has a definite inhibition effect on summer and autumn vegetation, but spring and summer temperature can promote the growth of vegetation. Meanwhile, the precipitation in the late growing season has a lag effect of 1-2 months on vegetation growth, and air temperature has a lag effect of 1 month in the middle of the growing season. Based on the above results, this study provided valuable information for ecosystem degradation and ecological environment protection in the Yunnan Province.

Time-lag effects of NDVI responses to climate change in the Yamzhog Yumco Basin, South Tibet

Vegetation dynamics and their response to climate change is critical for determining the mechanisms of climate-derived variations in terrestrial ecosystems. Here, the Normalized Difference Vegetation Index (NDVI) and monthly temperature and precipitation data were employed to examine the spatiotemporal patterns in vegetation dynamics and investigate the time-lag effects of vegetation responses to climate variables in the Yamzhog Yumco Basin, South Tibet, during 2000–2018. The results reveal that the annual average growing season NDVI in the basin was 0.28, with lower values in the mountainous regions and higher values broadly distributed in the lake and river zones. An increasing NDVI trend was observed in nearly 62% of the total vegetation area, with significant changes observed in the western and southern boundaries of the basin. The vegetation areas in the eastern part of basin exhibited a decreasing NDVI trend. The growing season NDVI was positively correlated with temperature and precipitation, with temperature being the primary driving factor for vegetation growth across the entire basin. The cumulative temperature and precipitation of the current and previous months had the strongest impact on NDVI variations. The correlation coefficients between the NDVI, and temperature and precipitation were greater than 0.6 in approximately 72% and 60% of the vegetation area, respectively, with Lag 0–1 accounting for most of these larger correlation coefficients. Future development targets for grassland conservation projects, such as the reasonable livestock capacity, should consider the time-lag effects on vegetation dynamics.