Trends In Vegetation Change Research Articles

The Spatio-Temporal Variation of Vegetation and Its Driving Factors during the Recent 20 Years in Beijing

As the most important city in China, Beijing has experienced an economic soar, large-scale population growth and eco-environment changes in the last 20 years. Evaluating climate- and human-induced vegetation changes could reveal the relationship of vegetation-climate-human activities and provide important insights for the coordination of economic growth and environmental protection. Based on a long-term MODIS vegetation index dataset, meteorological data (temperature, precipitation) and impervious surface data, the Theil-Sen regression and the Mann-Kendall method are used to estimate vegetation change trends in this study and the residual analysis is utilized to distinguish the impacts of climate factors and human activities on vegetation restoration and degradation from 2000 to 2019 in Beijing. Our results show that the increasing vegetation areas account for 80.2% of Beijing. The restoration of vegetation is concentrated in the urban core area and mountainous area, while the degradation of vegetation is mainly concentrated in the suburbs. In recent years, the vegetation in most mountainous areas has changed from restoration to significant restoration, indicating that the growth of mountain vegetation has continued to restore. We also found that in the process of urban expansion, vegetation browning occurred in 53.1% of the urban built-up area, while vegetation greening occurred in the remaining area. We concluded that precipitation is the main climatic factor affecting the growth of vegetation in Beijing’s mountainous areas through correlation analysis. Human activities have significantly promoted the vegetation growth in the northern mountainous area thanks to the establishment of environmental protection areas. The negative correlation between vegetation and the impervious surface tends to gradually expand outwards, which is consistent with the trend of urban expansion. The positive correlation region remains stable, but the positive correlation is gradually enhanced. The response of vegetation to urbanization demonstrated a high degree of spatial heterogeneity. These findings indicated that human activities played an increasingly important role in influencing vegetation changes in Beijing.

Impact of water supply on the restoration of the severely damaged riparian plants along the Tarim River in Xinjiang, Northwest China

Restoring degraded ecosystems in drylands is challenging because of low precipitation and high evapotranspiration. Water availability determines the existence and spatiotemporal distribution patterns of key endangered species struggling to survive in the severely damaged desert riparian ecosystem of the lower Tarim River (LTR) in NW China. Ecological water flow restoration measures with high costs and uncertainties have been implemented since 2000. The key task for scientists is to assess the success and drawbacks of this restoration project and propose specific recommendations for stakeholders to optimize the ecological benefits of the project in a cost-effective manner. In this study, Landsat time-series data from 1990 to 2020 were used to extract vegetation index values from river corridors along the LTR and explore the spatiotemporal patterns of desert riparian forest (DRF) vegetation. Based on land use and surface water data, we extracted the interannual spatial distribution range of vegetation and surface water, and selected several sample plots to demonstrate the influence of surface water and groundwater on vegetation change trends. The study results showed the following. 1) From 1990 to 1999, the vegetation slowly degraded; from 2000 to 2012, the extent of vegetation restoration was approximately 1 km from the river; and from 2013 to 2020, the extent of vegetation restoration at the Yingsu and Karday sections increased to 4.5–6.0 km, while that at the Arghan and Yiganbujima sections remained within 1 km from the river. 2) The vegetation growth rate increased substantially and was spatially heterogeneous when the groundwater level reached 4.0–5.0 m and was influenced by surface water divergence; the average vegetation expansion rates per square km for P1–P4 were 0.017 km2/a, 0.011 km2/a, 0.005 km2/a, and 0.002 km2/a, respectively. 3) Flooding by surface water was the main driver of vegetation expansion when groundwater was stable and elevations in surface vegetation area were often accompanied by the spreading of surface water. Surface water was significantly correlated with vegetation cover and normalized difference vegetation index (NDVI) (Water pixels–Vegetation cover [P1: R2 = 0.57, P < 0.001; P2: R2 = 0.45, P < 0.01]; Water pixels–NDVI [P1: R2 = 0.22, P < 0.05; P2: R2 = 0.40, P < 0.01]). The increase in surface vegetation has alleviated the desertification process in the LTR, and the survival of the poplar has been mitigated. The results of this paper can help decision makers to formulate reasonable ecological water conveyance strategies and realize sustainable management of DRF ecosystems in arid zones.

Exploration of Vegetation Change Trend in the Greater Khingan Mountains Area of China Based on EEMD Method

Vegetation, especially forest ecosystems, plays an important role in the global energy flow and material cycle. The vegetation index (VI) is an important index reflecting the dynamic change in vegetation and directly reflects the response of ecosystem to global climate change. The Greater Khingan Mountains Forest region is located in the northeast of China. It is the largest primeval forest region in China, which is well preserved and less affected by human activities. It is of great significance to study the driving mechanism of forest vegetation change for future ecological prediction and management. In this study, GIMMS NDVI data were used to explore the characteristics of nonlinear temporal and spatial variation of NDVI in the Greater Khingan Mountains and its relationship with climatic factors. Firstly, the EEMD method was used to analyze the characteristics of vegetation change in the study area from 1982 to 2015. Secondly, the relationship between vegetation change and climate was discussed by using precipitation and temperature data. The results showed that the following: (1) from 1982 to 2015, the interannual change in vegetation in the Greater Khingan Mountains presented a trend of slow fluctuation and gradual decrease (SLOPE = −0.1645/10,000, p &lt; 0.01). (2) The spatial distribution of vegetation change had obvious geographical differences, and in the central region, the overall distribution characteristics had an obvious browning trend, and in the northwest and southeast, the distribution characteristics had a green trend. (3) The correlation analysis results of vegetation change and climate factors showed that NDVI change was significantly positively correlated with temperature and precipitation; additionally, NDVI change was more correlated with temperature with a range of 0.8–1 than precipitation. (4) The results of vegetation attribution analysis in four typical areas of the study area showed that the following: the coniferous forest area has good cold tolerance and drought tolerance, the correlation between vegetation change and climate factors (temperature, precipitation) was not the strongest, which was 0.537 and 0.828, respectively. The ecological transition area and the broad-leaved forest area, which was located at the edge of the study area, have relatively fragile ecosystems, showed a strong correlation with precipitation, and the correlation coefficients reached 0.670 and 0.632, respectively. The surface water resources provide favorable conditions for the growth of vegetation, it showed a weak correlation with precipitation, and the correlation coefficient was 0.5349.

Human Activities Accelerated Increase in Vegetation in Northwest China over the Three Decades

Natural ecosystems are changing more quickly because of human activities, the type and intensity of which are directly correlated with vegetation greenness. To effectively determine how human activities affect trends in vegetation under climate change, we must differentiate between various types of human activities. The GTWR model can study the spatiotemporal non-stationary relationship between the NDVI trend and climate change. The GTWR model was incorporated into multiple climate variables and improved residual analysis to quantify the contributions of climate change and human activities on vegetation change trends in the Hexi region during different periods. This study divides human activities into four groups based on land use change: urbanization, agricultural expansion, desertification, and ecological restoration to further investigate their contribution to vegetation greenness change. The results showed that in 56.9% of the significant vegetation greening trends between 1982 and 2015, climate factors contributed only 7.4%, while human factors contributed a significant 22.7%. Since the ecological restoration project implemented in 2000, the expansion intensity of ecological restoration and urbanization increased significantly, followed by agricultural expansion and desertification. For the considerable greening trends in the Hexi region, the ecological restoration project contributed 26.7%, while agricultural expansion and urbanization contributed 17.5% and 4.6%, respectively. This study aims to provide new insights for more accurate simulation and evaluation of the interaction effects of climate change and human socio-economic development on vegetation growth.

Attribution of vegetation fluorescence changes in China based on a decision tree stratification strategy

Vegetation photosynthesis plays a key role in maintaining carbon balance in terrestrial ecosystems. Solar-Induced chlorophyll Fluorescence (SIF) was utilized as a proxy for vegetation photosynthesis in this study, and the Theil-Sen and Mann-Kendall methods were used to examine trends of vegetation change in China. The impacts of climate conditions on vegetation photosynthesis were examined with the correlation analysis and recursive feature elimination (RFE) method. To distinguish the contributions of climate change and human activities on vegetation photosynthesis, land use data and SIF change trends were employed as inputs to a decision tree model. The results revealed that 81.5% of the areas in China exhibited an improving trend of vegetation photosynthesis. Vegetation degradation areas were mainly located in Northern arid and semi-arid region (NASR), Qinghai Tibet Plateau (QTP) and Huang-Huai-Hai Plain (HHHP). Precipitation dominated the effect of vegetation photosynthesis in these regions. The increase of vegetation photosynthesis in the southern regions was positively impacted by solar radiation. The growth of agricultural technology obviously benefited the improvement of vegetation photosynthesis in the Middle-lower Yangtze Plain (MLYP) and Northeast China Plain (NCP). Furthermore, the establishment of ecological conservation programs has helped to restore vegetation in arid and semi-arid areas. The detrimental effects of human activities included overgrazing disturbance and coal mining activities, which resulted in vegetation deterioration in areas of QTP and HHHP. The methodologies and findings of this study can help to comprehend changes in vegetation dynamics and give a theoretical foundation for evaluating regional ecological conservation initiatives.

Vegetation EVI changes and response to natural factors and human activities based on geographically and temporally weighted regression

The research on vegetation changes plays a crucial role in the assessment of ecosystem health, monitoring environmental changes, providing early warnings for natural disasters, and supporting decision-making for sustainable development. However, understanding the nonstationary characteristics of drivers affecting vegetation change remains challenging. This study used Enhanced Vegetation Index (EVI) data obtained through Google Earth Engine (GEE), Theil-Sen, and Mann-Kendall methods to analyze the spatial-temporal patterns and trends of vegetation changes in Sichuan, western China from 2000 to 2020. The Geographical and Temporal Weighted Regression (GTWR) method was applied to deal with spatial and temporal nonstationarity simultaneously. Results showed that vegetation cover in Sichuan was good overall, with medium and high vegetation covering more than 78% of the area. About 72.75% of the area showed an increasing trend in vegetation cover, and areas with extremely significant and significant EVI growth (p < 0.01 and 0.01 ≤ p < 0.05) accounted for 23.94% of the total area. The areas with significant increases in vegetation EVI were mainly distributed in northeast, east, southeast, central, and southwest in Sichuan, while the areas with significant decreases were mainly distributed in the central Sichuan plain urban agglomeration and western Sichuan plateau. GTWR addressed the nonstationary effect of the temporal dimension on the drivers of natural and human activities, with a fitted R2 of 0.846. The study identified climate, terrain, and human activities as the primary driving factors behind vegetation EVI fluctuations. Annual average temperature and precipitation, human activities, and slope had a positive impact on vegetation EVI changes, while solar radiation and aspect had a negative inhibitory effect. The effects of climate, terrain, and human activities on EVI changes exhibited significant spatial heterogeneity and clustering, resulting in either positive promotion or negative inhibition. This study provides an additional methodology to solve the nonstationary problem of vegetation change trends and their response mechanisms. The revealed changes in vegetation EVI and the spatiotemporal heterogeneity characteristics of their driving factors are important for fragile ecosystems to adapt to and mitigate the effects of natural changes and human activities. Revealing the variations in vegetation EVI and their underlying drivers can showcase diverse characteristics across regions and time periods, the presence of spatiotemporal heterogeneity holds great significance in comprehending the adaptive strategies employed by fragile ecosystems to mitigate the effects of natural fluctuations and human-induced activities.

Half a century of herb layer changes in Quercus-dominated forests of the Western Carpathians

Species composition of European temperate Quercus forests have undergone dramatic changes in the past century. Observed changes have largely been attributed to anthropogenic environmental changes that drove the processes of thermophilization, eutrophication, or biotic homogenization. However, ecological processes behind these changes can be difficult to explain considering the variability among regions: their history, management, landscape patterns, site properties, and vegetation types. To reveal vegetation change trends and their drivers in the subcontinental Western Carpathians, we analysed vegetation and environmental data from 158 quasi-permanent plots established between 1961 and 1980 in Quercus petraea- dominated forests on the andesite bedrock of the Slovenské stredohorie Mountains in Slovakia (Central Europe), and we resurveyed them again between 2005 and 2020.Among alpha and gamma diversity measures, we found only slight species richness decreases. A significant shift in species composition associated with very high species turnover (64%) and a decrease in herb layer cover (by 24%) resulted in higher spatial heterogeneity of herb understoreys in the resurvey. Winning species were more thermophilous, eutrophic, and mesic/oceanic plants of lower latitudes, replacing original species of open Quercus forests. Increased temperatures was the most significant environmental driver. Climate warming corresponded not only to thermophilization, but, after interaction with management, it was also related to eutrophication of the herb layer. Eutrophication was identified as the main trend of the species composition changes (mean EIV for nutrients of winners was 5.9 compared to 4.1 of losers) even though the effect of anthropic nitrogen depositions was negligible. Successive undergrowing of Quercus canopies by more shade-casting trees (Acer campestre, Carpinus betulus, and Fagus sylvatica) in the period of decreasing management intensity was another major driver. Their increase by 8.6% in the resurveyed plots contributed to decreased species richness and they supported shade-tolerant eutrophic understorey species typical of mesic Carpinus betulus and Fagus sylvatica forests.We concluded that Quercus forests in the Western Carpathians underwent thermophilization and eutrophication, similar to the forests in densely inhabited Western Europe; however, their plant understoreys were not homogenized, and eutrophication was not driven by nitrogen depositions. Eutrophication should be recognised as the next important negative impact of global warming on ecosystems. Dramatic changes in species composition and loss of original characteristic species can only be partly halted by reducing shade-casting trees from lower tree layers in managed stands.

Response Time of Vegetation to Drought in Weihe River Basin, China

Frequent droughts may have negative influences on the ecosystem (i.e., terrestrial vegetation) under a warming climate condition. In this study, the linear regression method was first used to analyze trends in vegetation change (normalized difference vegetation index (NDVI)) and drought indices (Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)). The Pearson Correlation analysis was then used to quantify drought impacts on terrestrial vegetation in the Weihe River Basin (WRB); in particular, the response time of vegetation to multiple time scales of drought (RTVD) in the WRB was also investigated. The trend analysis results indicated that 89.77% of the area of the basin showed a significant increasing trend in NDVI from 2000 to 2019. There were also significant variations in NDVI during the year, with the highest rate in June (0.01) and the lowest rate in January (0.002). From 2000 to 2019, SPI and SPEI at different time scales in the WRB showed an overall increasing trend, which indicated that the drought was alleviated. The results of correlation analysis showed that the response time of vegetation to drought in the WRB from 2000 to 2019 was significantly spatially heterogeneous. For NDVI to SPEI, the response time of 12 months was widely distributed in the north; however, the response time of 24 months was mainly distributed in the middle basin. The response time of NDVI to SPI was short and was mainly concentrated at 3 and 6 months; in detail, the response time of 3 months was mainly distributed in the east, while a response time of 6 months was widely distributed in the west. In autumn and winter, the response time of NDVI to SPEI was longer (12 and 24 months), while the response time of NDVI to SPI was shorter (3 months). From the maximum correlation coefficient, the response of grassland to drought (SPEI and SPI) at different time scales (i.e., 6, 12, and 24 months) was higher than that of cultivated land, forestland, and artificial surface. The results may help improve our understanding of the impacts of climatic changes on vegetation cover.

Nonlinear trends of vegetation changes in different geomorphologic zones and land use types of the Yangtze River basin, China

AbstractHow land use changes and geomorphologic zones impact on vegetation nonlinear trends remains unclear in economically developed areas with complicated terrain. This paper explores the nonlinear trends of vegetation changes with normalized vegetation index (NDVI) in the Yangtze River basin, China, and further analyzes the effect of geomorphologic zones and land use changes on the nonlinear trend. The results show that: (1) Although monotonic increasing is the main trend type of vegetation NDVI (32.46%), reversal trends from decreasing to increasing and from increasing to decreasing account for 11.87% and 24.95%, respectively. (2) The vegetation change is mainly monotonically increasing in low altitude and relief zones, while that is mainly a reversal trend in high altitude and relief zones, indicating an increased risk of vegetation degradation with altitude and relief increasing. (3) The trends in most land use types are mainly monotonically increasing, but those in urban, forest, and grassland are mainly from increasing to decreasing, with area percentages as high as 32.29%, 27.25%, and 35.97%, indicating degradation risk. (4) The conversion of cropland and wetland to forestland has greatly promoted the vegetation restoration. However, a risk of vegetation degradation exists in land conversions between grassland and other land use types, especially the afforestation of grassland. Over all, considering the effects of both geomorphic zones and land use changes can help us better explore the driving of the nonlinear trends of vegetation changes and understand the process of vegetation dynamics.

Spatial distribution and temporal change of vegetation restoration after the eruption of Mount St. Helens: from 1984 to 2019

Approximately ten terrestrial volcanic eruptions occur yearly, significantly impacting the surrounding area. Mount St. Helens has been one of the most popular active volcanoes in recent years. This study analysed the vegetation cover status from 1984 to 2019. Remote Sensing and GIS technology were mainly used in this study to perform detailed research on the rates and trends of vegetation change. The results show that the vegetation in the study area has continued to recover in general, but some areas are consistently devoid of vegetation growth. After analysing the spatial and temporal perspectives, the vegetation recovery rate was relatively slow in the eastern part of the study area. This vegetation recovery status may be related to factors such as volcanic eruption, deep slope, lack of seeds, and massive wildfires. The area in the north-western part of the eruption area gained rapid growth in the first ten years, which may be caused by plant measures.

Plant functional type aboveground biomass change within Alaska and northwest Canada mapped using a 35-year satellite time series from 1985 to 2020

Changes in vegetation distribution are underway in Arctic and boreal regions due to climate warming and associated fire disturbance. These changes have wide ranging downstream impacts—affecting wildlife habitat, nutrient cycling, climate feedbacks and fire regimes. It is thus critical to understand where these changes are occurring and what types of vegetation are affected, and to quantify the magnitude of the changes. In this study, we mapped live aboveground biomass for five common plant functional types (PFTs; deciduous shrubs, evergreen shrubs, forbs, graminoids and lichens) within Alaska and northwest Canada, every five years from 1985 to 2020. We employed a multi-scale approach, scaling from field harvest data and unmanned aerial vehicle-based biomass predictions to produce wall-to-wall maps based on climatological, topographic, phenological and Landsat spectral predictors. We found deciduous shrub and graminoid biomass were predicted best among PFTs. Our time-series analyses show increases in deciduous (37%) and evergreen shrub (7%) biomass, and decreases in graminoid (14%) and lichen (13%) biomass over a study area of approximately 500 000 km2. Fire was an important driver of recent changes in the study area, with the largest changes in biomass associated with historic fire perimeters. Decreases in lichen and graminoid biomass often corresponded with increasing shrub biomass. These findings illustrate the driving trends in vegetation change within the Arctic/boreal region. Understanding these changes and the impacts they in turn will have on Arctic and boreal ecosystems will be critical to understanding the trajectory of climate change in the region.

Ecological Policies Dominated the Ecological Restoration over the Core Regions of Kubuqi Desert in Recent Decades

Climate change and human activities significantly affected environmental changes in drylands. However, the relative roles remain unclear regarding these factors’ effects on environment changes in drylands. Herein, we analyzed vegetation change trends using remote-sensing datasets to determine the interactions of vegetation, climate, and anthropogenic activities in an arid region of China, Kubuqi Desert. Our study showed that 67.64% of the pixels of fractional vegetation coverage (FVC) increased in 2020 in comparison with those of 1986. The FVC exhibited a significant greening trend (0.0011/yr, p &lt; 0.05) in 1986–2020 as a whole. This greening trend revealed two distinct periods separated by a turning point in 2001. There was no clear trend of FVC before 2001, and then there was a dramatically greening trend since 2001 in most regions of the study area. The increasing rate (0.0036/yr) in the later period was three times higher than the entire period. The accelerated increasing trend was due to the variable compound effects of climate and human activities. The correlation between FVC and precipitation was mainly positive, which outweighs the significantly negative correlation between vegetation and temperature. However, both climatic factors cannot well explain the trends of vegetation dynamics, implying a possible role for human activities. Generally, climate change and anthropogenic activities contributed 42.15% and 57.85% to the overall vegetation variations in 1986–2020. Specifically, the relative role of the two factors was vastly different in two distinct periods. Climate change led the dominant roles (58.68%) in the vegetation variations in 1986–2001, while anthropogenic activities dominated (86.79%) in driving vegetation recovery in the period after 2001. Due to the massive ecological conservation programs such as the Grain for Green Project launched in 2001, substantial deserts have been transformed into grasslands and forests. This analysis highlights the ecological policies largely responsible for vegetation restoration and provides references for ecological protection and sustainable development in eco-fragile ecosystems.

Object-based change detection for vegetation disturbance and recovery using Landsat time series

ABSTRACT Accurate characterization of historical trends in vegetation change at the landscape scale is necessary for resource management and ecological assessment. Vegetation disturbance and recovery are coherent spatial and temporal processes. Pixel-based change detection methods often struggle to provide reliable estimates of change events because they neglect spatial contextual information and are affected by salt-and-pepper noise. To address such problems, we propose a new approach, “object-based change detection of trends in disturbance and recovery” (Object-LT), which introduces object-based image analysis (OBIA) into the current framework of LandTrendr algorithm. We then applied this approach to detect vegetation changes during 2000–2020 in the ecologically fragile region of Guyuan, Ning Xia, China. Accuracy assessment indicated that Object-LT could accurately identify disturbance and recovery trends in vegetation with overall accuracies of 90.05% and 87.50%, respectively. Compare with pixel-based LandTrendr algorithm, Object-LT significantly improved user’s accuracy and removed salt-and-pepper noise. Spatial–temporal maps of vegetation change showed that the recovery area was 571.27 km2 while the disturbed area was 297.65 km2, accounting for 5.44% and 2.83% of the study area, respectively. This indicates a general vegetation recovery trend in the study area. Object-LT allowed for an accurate and comprehensive characterization of vegetation change over large areas, which contributes to a better understanding of change processes of vegetation landscape over time.

Response of NDVI and SIF to Meteorological Drought in the Yellow River Basin from 2001 to 2020

Understanding the response of vegetation to drought is of great significance to the biodiversity protection of terrestrial ecosystem. Based on the MOD13A2 NDVI, GOSIF, and SPEI data of the Yellow River Basin from 2001 to 2020, this paper used the methods of Theil–Sen median trend analysis, Mann–Kendall significance test, and Pearson correlation analysis to analyze whether the vegetation change trends monitored by MODIS and GOSIF are consistent and their sensitivity to meteorological drought. The results showed that NDVI and SIF increased significantly (p &lt; 0.001) at the rate of 0.496 × 10−2 and 0.345 × 10−2, respectively. The significant improvement area of SIF (66.49%, p &lt; 0.05) is higher than NDVI (50.7%, p &lt; 0.05), and the spatial distribution trend of vegetation growth monitored by NDVI and SIF is consistent. The negative value of SPEI-12 accounts for 65.83%, with obvious periodic changes. The significant positive correlation areas of SIF-SPEI in spring, summer, and autumn (R &gt; 0, p &lt; 0.05) were 7.00%, 28.49%, and 2.28% respectively, which were higher than the significant positive correlation areas of NDVI-SPEI (spring: 1.79%; summer: 20.72%; autumn: 1.13%). SIF responded more strongly to SPEI in summer, and farmland SIF was significantly correlated with SPEI (0.3424, p &lt; 0.01). The results indicate that SIF is more responsive to drought than NDVI. Analyzing the response of vegetation to meteorological drought can provide constructive reference for ecological protection.

Monitoring vegetation phenology using MODIS NDVI 250m in the City of Tshwane, South Africa

The unprecedented influx of people into urban areas has led to the horizontal and vertical growth of urban environments. One of the notable impacts of urbanisation is the encroachment of urban-like environments into non-urban areas. This is common in both developed and developing countries, and South Africa’s City of Tshwane, the administrative capital of the country, has been affected by urbanisation because of migration. One of the parameters or proxies used to quantify urban growth is vegetation cover. There is a consensus that with the increase in the population of urban dwellers, vegetation cover will decrease. To assess and monitor vegetation cover, the Normalised Difference Vegetation Index (NDVI) is commonly used. In this study, MODIS NDVI data with a 250m spatial resolution was used to assess the impact of urban growth on vegetation. A time series analysis of the MODIS NDVI with a spatial resolution of 250m was used to establish the patterns of vegetation cover. Trends in vegetation change were determined in newly developed residential areas, informal settlements, and various vegetated areas. Sen's slope estimator and Mann-Kendall’s statisticwere used to analyse the spatial trends and variations in trends among different land cover classes. The slope of the trends differs significantly but there is a general decline in vegetation cover. The temporal profiles revealed the high and low NDVI values, respectively showing greening (high vegetation) and browning (low vegetation) trends from 2000 to 2016. It is concluded that urban growth has an impact on vegetation cover. However, this does not disturb the seasonal changes in vegetation where high NDVI values prevail in summer and low values in winter.

Vegetation Responses to Climate Change and Anthropogenic Activity in China, 1982 to 2018.

Climate change and human activities significantly affect vegetation growth in terrestrial ecosystems. Here, data reconstruction was performed to obtain a time series of the normalized difference vegetation index (NDVI) for China (1982–2018) based on Savitzky–Golay filtered GIMMS NDVI3g and MOD13A2 datasets. Combining surface temperature and precipitation observations from more than 2000 meteorological stations in China, Theil–Sen trend analysis, Mann–Kendall significance tests, Pearson correlation analysis, and residual trend analysis were used to quantitatively analyze the long-term trends of vegetation changes and their sources of uncertainty. Significant spatial and temporal heterogeneity was observed in vegetation changes in the study area. From 1982 to 2018, the vegetation showed a gradually increasing trend, at a rate of 0.5%·10 a−1, significantly improving (37.15%, p < 0.05) more than the significant degradation (7.46%, p < 0.05). Broadleaf (0.66) and coniferous forests (0.62) had higher NDVI, and farmland had the fastest rate of increase (1.02%/10 a−1). Temperature significantly affected the vegetation growth in spring (R > 0; p < 0.05); however, the increase in summer temperatures significantly inhibited (R < 0; p < 0.05) the growth in North China (RNDVI-tem = −0.379) and the Qinghai–Tibetan Plateau (RNDVI-tem = −0.051). Climate change has highly promoted the growth of vegetation in the plain region of the Changjiang (Yangtze) River (3.24%), Northwest China (1.07%). Affected by human activities only, 49.89% of the vegetation showed an increasing trend, of which 22.91% increased significantly (p < 0.05) and 9.97% decreased significantly (p < 0.05). Emergency mitigation actions are required in Northeast China, Xinjiang, Northwest China, and the Qinghai–Tibetan Plateau. Therefore, monitoring vegetation changes is important for ecological environment construction and promoting regional ecological protection.

Spatial Pattern and Dynamic Change of Vegetation Greenness From 2001 to 2020 in Tibet, China.

Due to the cold climate and dramatically undulating altitude, the identification of dynamic vegetation trends and main drivers is essential to maintain the ecological balance in Tibet. The normalized difference vegetation index (NDVI), as the most commonly used greenness index, can effectively evaluate vegetation health and spatial patterns. MODIS-NDVI (Moderate-resolution Imaging Spectroradiometer-NDVI) data for Tibet from 2001 to 2020 were obtained and preprocessed on the Google Earth Engine (GEE) cloud platform. The Theil–Sen median method and Mann–Kendall test method were employed to investigate dynamic NDVI changes, and the Hurst exponent was used to predict future vegetation trends. In addition, the main drivers of NDVI changes were analyzed. The results indicated that (1) the vegetation NDVI in Tibet significantly increased from 2001 to 2020, and the annual average NDVI value fluctuated between 0.31 and 0.34 at an increase rate of 0.0007 year−1; (2) the vegetation improvement area accounted for the largest share of the study area at 56.6%, followed by stable unchanged and degraded areas, with proportions of 27.5 and 15.9%, respectively. The overall variation coefficient of the NDVI in Tibet was low, with a mean value of 0.13; (3) The mean value of the Hurst exponent was 0.53, and the area of continuously improving regions accounted for 41.2% of the study area, indicating that the vegetation change trend was continuous in most areas; (4) The NDVI in Tibet indicated a high degree of spatial agglomeration. However, there existed obvious differences in the spatial distribution of NDVI aggregation areas, and the aggregation types mainly included the high-high and low-low types; and (5) Precipitation and population growth significantly contributed to vegetation cover improvement in western Tibet. In addition, the use of the GEE to obtain remote sensing data combined with time-series data analysis provides the potential to quickly obtain large-scale vegetation change trends.

Vegetation Browning Trends in Spring and Autumn over Xinjiang, China, during the Warming Hiatus

Satellite-derived vegetation records (GIMMS3g-NDVI) report that climate warming promotes vegetation greening trends; however, the climate impacts on vegetation growth during the global warming hiatus period (1998–2012) remain unclear. In this study, we focused on the vegetation change trend in Xinjiang in spring and autumn before and during the recent warming hiatus period, and their climate-driving mechanisms, which have not been examined in previous studies. Based on satellite records, our results indicated that the summer normalized difference vegetation index (NDVI) in Xinjiang experienced a greening trend, while a browning trend existed in spring and autumn during this period. The autumn NDVI browning trend in Xinjiang was larger than that in spring; however, the spring NDVI displayed a higher correlation with climatic factors than did the autumn NDVI. During the warming hiatus, spring climatic factors were the main controlling factors of spring NDVI, and spring vapor pressure deficit (VPD) had the highest positive correlation with spring NDVI, followed by spring temperature. The larger increase in air temperature in spring than in autumn resulted in increased VPD differences in spring and autumn. In autumn, summer climatic factors (e.g., VPD, WS, RH, and precipitation) were significantly correlated with the autumn NDVI during the warming hiatus. However, the autumn temperature was weakly correlated with the autumn NDVI. Our results have significant implications for understanding the response of vegetation growth to recent and future climatic conditions.

Long Term Observation of Fractional Vegetation Cover in Qingyang of Gansu Province and Its Response to Climate Change

Vegetation is seen as a sensitive indicator of global change because of its crucial role in connecting the atmosphere, soil, and water. Fractional vegetation cover (FVC), in turn, is an important indicator of vegetation status. Qingyang is a typically ecologically sensitive region, with a range of changes in vegetation in the last decade as a result of climatic and non-climatic factors. However, the exact impact of climate change and human activities remains unclear. Satellite observations can help to clarify that impact, allowing us to assess trends in vegetation change in the last two decades (2000–2019). In this study, daily and composite time series vegetation variations were derived from moderate resolution imaging spectroradiometer (MODIS) data and the impact of climate and human activity factors was examined for different administrative districts. By deploying multiple regression models, the research revealed that human activity has contributed 46% to the FVC variation, while the remaining 54% was led by climate factors. In areas where FVC was increasing, human activity contributed 55.89% while climate factors contributed 44.11%. In areas where FVC was decreasing, human activity and climate factors contributed 24.58% and 75.42%, respectively. The study also looks at the impacts of El Nino/IOD events in FVC dynamics in the study site. The FVC inversion result from MODIS proved capable of capturing long-term and seasonal vegetation patterns and thus provide a valuable archive for decadal-scale vegetation dynamics in the study area. Moreover, the improvement in FVC was a dual effect of climatic and human activities, while the latter owns a higher contribution especially for the implementation of ecological construction projects.

Assessing the Spatiotemporal Evolution of Anthropogenic Impacts on Remotely Sensed Vegetation Dynamics in Xinjiang, China

The dynamics of the ecosystem represented by vegetation under the influence of human activities have become an important issue in the study of the regional ecological environment. Xinjiang is one of the most ecologically fragile areas in the world, and vegetation changes have received extensive attention. Xinjiang is one of the most ecologically fragile areas in the world, and vegetation changes have received extensive attention. However, the spatiotemporal patterns and evolutionary trends of anthropogenic impacts on vegetation dynamics in Xinjiang are still unclear. In this study, the anthropogenic impacts on vegetation dynamics were quantitatively assessed by combining the improved normalized difference vegetation index (NDVI) prediction model and the residual analysis method in Xinjiang, China. The human driving factors were analyzed with the support of a stepwise multiple regression model for vegetation changes at the county scale. Based on trend analysis and the Hurst exponent, the spatiotemporal characteristics and evolutionary trends of the impact of human activities on vegetation change were discussed. The results show that (1) the NDVI values in Xinjiang showed a gradually increasing trend at a rate of 0.005/10 years from 1982 to 2018, and the vegetation dynamics mainly showed significant improvements (57.09% of the vegetated areas), especially for crops. (2) The anthropogenic effects of vegetation changes in Xinjiang mainly included positive impact increases (43.22% of the vegetated areas) from 2000 to 2018. Human activities promoted the increase in the NDVI of various vegetation types. Both the positive and negative impacts of human activities increased over the study period, and the growth rate of the positive influence (0.08%/10 years) was higher than that of the negative influence (0.04%/10 years). (3) The cultivated area, GDP of primary industry, and population are the main anthropogenic factors causing the increase in NDVI, which dominate the vegetation greening in 30.34%, 29.22%, and 28.09% of the counties in Xinjiang, respectively. The animal husbandry population, agricultural population, and livestock number are the main anthropogenic factors causing the decrease in NDVI, which dominate the vegetation degradation in 23.60%, 21.35%, and 17.98% of the counties in Xinjiang, respectively. (4) The evolutionary trend of the anthropogenic impact on vegetation dynamics in Xinjiang will be dominated by anti-persistence (53.84% of the vegetated areas), thereby mainly showing that the positive impacts continued to increase (22.56% of the vegetated areas), especially for crops, shrubs, grasslands, and alpine vegetation. Our results are helpful in understanding the characteristics and evolutionary trends of vegetation changes in arid areas caused by human activities and are of significance as a reference for policymakers to appropriately adjust policy guidance in a timely manner to promote the protection and sustainable development of fragile ecosystems.