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Drylands are vast and face threats from climate change and human activities. Traditional reviews cannot capture interdisciplinary knowledge, but bibliometric analysis provides valuable insights. Our study conducted bibliometric research of scientific production on climate change and land degradation in drylands using remote sensing. We examined 1527 Scopus-indexed publications to identify geographic and thematic hotspots, extracting leading authors, journals, and institutions. China leads in publications, followed by the US, Germany, and Australia. The US has the highest citation count. Collaboration networks involve the US, China, and European countries. There has been an exponential increase in remote sensing of land degradation in drylands (RSLDD) publications since 2011. Key journals include "International Journal of Remote Sensing" and "Remote Sensing of Environment". The analysis highlights the growing interest in the field, driven by Australia, the US, and China. Key areas of study are vegetation dynamics and land use change. Future perspectives for this scientific field involve promoting collaboration and exploring emerging technologies for comprehensive land degradation and desertification research. [ABSTRACT FROM AUTHOR]

The response of dryland vegetation to climate change is particularly sensitive in the context of global climate change. This paper analyzes the characteristics of spatial and temporal dynamics of vegetation cover in the Tarim River Basin, China, and its driving factors in order to investigate the response of vegetation growth to water storage changes in the basin. The Enhanced Vegetation Index (EVI), the GRACE gravity satellite, and meteorological data from 2002 to 2022 are used to decipher the characteristics of the response of water storage changes to vegetation changes, which is of great significance to the realization of regional ecological development and sustainable development. The results of the study show the following: (1) The vegetation in the Tarim River Basin has an overall increasing trend, which is mainly distributed in the Aksu Basin and the Weigangkuche River Basin and is spatially distributed in the form of a ring. (2) Vegetation distribution greatly improved during the 20-year study period, dominated by high-cover vegetation, with a change rate of 200.36%. Additionally, vegetation changes are centered on the watersheds and expand to the surrounding area, with a clear increase in vegetation in the Kumukuri Basin. Areas with a vegetation Hurst index of <0.5 account for 63.27% of the study area, and the areas with a continuous decrease were mainly located in the outer contour area of the Tarim River and Kumu Kuri Basins. (3) There are obvious spatial differences in the correlation between EVI and temperature and precipitation elements. The proportion of areas with positive correlation with temperature within the study area is 64.67%. EVI tends to be consistent with the direction of migration of the center of gravity of the population and GDP, and the areas with positive correlation between vegetation and terrestrial water reserves are mainly distributed in the northern slopes of the Kunlun Mountains, with an area proportion of about 50.513%. The Kumukuli Basin also shows significantly positive correlation. [ABSTRACT FROM AUTHOR]

The coal mining might cause the disturbance to the vegetation and the disturbance impacts might exist the differences for different areas, and few literatures compared and analyzed different disturbed areas based on the location of the mining face, and paid attention to the post mining self-healing effects of vegetation. Here, this paper selected the GaoFen multispectral images during 2017–2021 to study different areas of Shangwan Mine which includes the old mining area more than 5 years after mining, the new working face underground mined in 2018 and 2019, the natural growth control area and the open-pit mining affected area. The spatiotemporal changes of the surface fraction vegetation coverage (FVC) were analyzed in each area and the correlation between vegetation coverage and climatic factors was studied. The results showed that: (1) The overall vegetation coverage showed a moderate decrease trend in fluctuation from 2017 to 2021. The Open-pit mining affected areas showed the largest decline, reaching 68.3%. The FVC in the underground mining areas had a downward trend, but self-healing effect after mining was also observed. (2) The overall FVC in the study area was positively correlated with the number of precipitation days. (3) There were differences in the sensitivity to mining disturbance for different landform in the underground mining areas. (4) Although the FVC in the Old mining areas had recovered to the level of Natural growth control area, but the annual fluctuation was larger, which might mean lower ecological stability. [ABSTRACT FROM AUTHOR]

To explore the response relationship of vegetation dynamic change characteristics, clearness vegetation and precipitation changes in the Hexi Corridor, China. This paper analyzed the NDVI between 2000-2019 using linear regression slope method based on long time series MODIS data. The annual NDVI values of the Hexi Corridor range from 0.15 to 0.22. The vegetation overall showed an upward trend in the last 20 years, with an average slope from 0.0125/10a. But there was significant variability in the trend of inter annual changes, as compared from 2000 to 2009, the rate of exponential increases in vegetation in the Hexi Corridor slowed in 2010-2019, and the average slope of changes in vegetation cover decreased from 0.0139/10a to -0.006 8/10a. The variation trend of NDVI is basically consistent with precipitation. The positive and negative correlation between NDVI and precipitation account for 61.58% and 38.42% respectively. The positive correlation region is mainly located in the central and southwest regions with relatively rich vegetation. The regions with negative correlation are mainly in the northern and northeastern desert areas, where there is no vegetation cover. It shows that the relationship between vegetation index and precipitation in Hexi Corridor is relatively close in areas with abundant vegetation. [ABSTRACT FROM AUTHOR]

Although we are in an era of enormous global commitments to ecological restoration (the UN Decade on ER; the Bonn Challenge), little attention has been paid to the importance of sustained commitment to individual restoration initiatives and few resources have been dedicated to monitoring, especially the long-term and broad-scale evaluations that would allow us to understand how basin-scale restoration can result in complex spatiotemporal patterns. Remote sensing offers a powerful tool for evaluating restoration initiatives focused on water management in arid regions, where changes in vegetation growth can be tracked visually with measures like the generalized difference vegetation index (GDVI). In this paper, we evaluate the Comprehensive Treatment Program of the Shiyang River Basin (CTSRB), a landscape restoration initiative in China's northwest, using a widely available remote sensing tool, showing how it can reveal the causes of fluctuating changes in restoration success. We focus on spatiotemporal variation, studying a time series from 2001 to 2020 using regression, trend, and stability analyses for six different divisions of the study region (the study area as a whole, the irrigated areas, the periphery of the irrigated regions, and the districts of Ba, Quanshan, and Hu) to evaluate the effects of the restoration initiative. The study period was divided into four equal-length phases based on the implementation timeline of the CTSRB, which includes one pre- and post-intervention interval and two stages of the CTSRB. We found that the CTSRB has played a positive role in the restoration of vegetation in the Minqin Basin, especially desert vegetation. However, the positive effects were not obvious in the first CTSRB period, which was characterized by a decline in vegetation growth likely caused by the strategy of "close the pumping-wells, transform the land", which reversed a greening trend caused by the unsustainable irrigation of wasteland prior to the project's initiation. During the second phase of the CTSRB, vegetation in the regions of "transform the land" gradually improved, and the growth of desert vegetation gradually improved and expanded as a result of more water flowing out of agricultural zones. The rate of vegetation recovery slowed down during the final phase of the CTSRB, and some areas even experienced a decline in the GDVI. Overall, our findings show that the CTSRB, by integrating water management and allowing for uninterrupted ecological restoration, drove complex regional changes in the GDVI, including successful restoration of desert vegetation. The spatiotemporal variable we observed underscores the importance of long-term commitment to arid land restoration initiatives and the importance of even longer-term monitoring using tools like remote sensing. [ABSTRACT FROM AUTHOR]

Surface coal mining causes vegetation disturbance while providing an energy source. Thus, much attention is given to monitoring the vegetation of surface coal mining regions. Multitemporal satellite imagery, such as Landsat time-series imagery, is an operational environment monitoring service widely used to access vegetation traits and ensure vegetation surveillance across large areas. However, most of the previous studies have been conducted with change detection models or threshold-based methods that require multiple parameter settings or sample training. In this paper, we tried to analyze the change traits of vegetation in surface coal mining regions using shape-based clustering based on Normalized Difference Vegetation Index (NDVI) time series without multiple parameter settings and sample training. The shape-based clustering used in this paper applied shape-based distance (SBD) to obtain the distance between time series and used Dynamic Time Warping Barycenter Averaging (DBA) to generate cluster centroids. We applied the method to a stack of 19 NDVI images from 2000 to 2018 for a surface coal mining region located in North China. The results showed that the shape-based clustering used in this paper was appropriate for monitoring vegetation change in the region and achieved 79.0% overall accuracy in detecting disturbance-recovery trajectory types. [ABSTRACT FROM AUTHOR]

Altay Prefecture, a typical arid region in northwestern China, has experienced the climate transition from warming-drying to warming-wetting since 1980s and has attracted widespread attention. Nonetheless, it is still unclear how climate change has influenced the distribution of vegetation in this region. In this paper, a reaction–diffusion model of the climate–vegetation system is proposed to study the impact of climate change (precipitation, temperature and carbon dioxide concentration) on vegetation patterns in Altay Prefecture. Our results indicate that the tendency of vegetation growth in Altay Prefecture improved gradually from 1985 to 2010. Under the current climate conditions, the increase of precipitation results in the change of vegetation pattern structures, and eventually vegetation coverage tends to be uniform. Moreover, we found that there exists an optimal temperature where the spot vegetation pattern structure remains stable. Furthermore, the increase in carbon dioxide concentration induces vegetation pattern transition. Based on four climate change scenarios of the Coupled Model Intercomparison Project Phase 6 (CMIP6), we used the power law range (PLR) to predict the optimal scenario for the sustainable development of the vegetation ecosystem in Altay Prefecture. [ABSTRACT FROM AUTHOR]

How 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. [ABSTRACT FROM AUTHOR]

Quantitatively analyzing the role of human activity in vegetation change has been a hot topic in the field of ecological research. Taking the Three Gorges Reservoir Region (TGRR) of China as study area, under the support of the geographic information system (GIS) technology and residual trends method (RESTREND), the spatial-temporal characteristics of vegetation change and its human driving forces in the TGRR from 2000 to 2020 were quantitively explored. It was found that, the vegetation cover had shown an overall increasing trend and strong spatial heterogeneity over the past 20 years. The vegetation restoration trend in the north-eastern region of the TGRR were more obvious than that in the south-western region. RESTREND analysis showed that human activity produced an overall positive effect on vegetation change. The areas persistently covered by forested land and farmland contributed the most to the vegetation change (83.3% in total), whereas the contribution of returning farmland to forest was only 3.92%. In the persistent forested land, there was a highly significant positive correlation (R² = 0.84) between the implementation area of the ecological restoration project called "closing hillsides to facilitate afforestation" (CHFA) and the change amount of normalized difference vegetation index (NDVI). The implementation of the project CHFA could proximately explain the change of residual between the observed and estimated NDVI. This paper quantitatively revealed the important role of the CHFA project on vegetation restoration and conducted the first attempt to specify the residual of the RESTREND method. Achievements could provide valuable references for regional study on the driving mechanism behind vegetation cover change. [ABSTRACT FROM AUTHOR]

Since the early 2000s, China has carried out extensive "grain-for-green" and grazing exclusion practices to combat desertification in the desertification-prone region (DPR). However, the environmental and socioeconomic impacts of these practices remain unclear. We quantify and compare the changes in fractional vegetation cover (FVC) with economic and population data in the DPR before and after the implementation of these environmental programmes. Here we show that climatic change and CO2 fertilization are relatively strong drivers of vegetation rehabilitation from 2001-2020 in the DPR, and the declines in the direct incomes of farmers and herders caused by ecological practices exceed the subsidies provided by governments. To minimize economic hardship, enhance food security, and improve the returns on policy investments in the DPR, China needs to adapt its environmental programmes to address the potential impacts of future climate change and create positive synergies to combat desertification and improve the economy in this region. This paper shows that desertification combating practices decline incomes of farmers and herders, and China needs to adapt its ecological programmes to address the impacts of climate change and create positive synergies to combat desertification. [ABSTRACT FROM AUTHOR]

Due to global warming and other climate changes, it is increasingly important to study the response of regional environmental changes and dynamic changes in vegetation to climate change. Based on meteorological data from the last 60 years, this paper calculates the humidity index of western China under a wide range of long time series in different regions and explores the cross-correlation effect between series by offering a comparison with NDVI data, to analyze the cross-correlation between wet and dry changes and changes in vegetation in western China on a spatial scale. The results show that the spatial distribution of the interdecadal humidity index is different between different regions in western China. For example, the semi-arid and the semi-humid zones of the Weihe River region exhibit significant changes, while the Xinjiang and Qinghai–Tibet regions show a trend of constant wetness, on the whole, and the Sichuan and Yunnan–Guizhou regions are relatively humid and the distribution of wetness and dryness is relatively stable. The distribution of high and low values of the humidity index is very obvious and consistent with that of the distribution of desert bare land and precipitation in western China. In common with the distribution in the humidity index, the maximum correlation number between the NDVI and the humidity index in the whole western region is also significantly different in spatial distribution. There is a positive correlation between the NDVI and the humidity index in 99% of the study area. However, the delay in response time of the NDVI to changes in the humidity index in each region is inconsistent. For example, changes in the NDVI lag changes in the humidity index in the Menggan region by generally either 2 months or 5 months, while in the Sichuan region the delay in response time is generally 3 months. The variation and trend in dry and wet areas are closely related to the geographical location, climate zone, and topographic terrain, which may be the reason for the differences in the distribution of vegetation types and the response time to dry and wet changes. There is significant interaction between the humidity index and the vegetation type or precipitation distribution in western China. The positive correlation between the NDVI and the humidity index means that the positive effect is more sensitive, and the response of grassland is the most sensitive in the ecosystem. [ABSTRACT FROM AUTHOR]

Vegetation degeneration has become a serious ecological problem for karst regions in the Anthropocene. According to the deficiency of long serial and high-resolution analysis of karst vegetation, this paper reconstructed the variation of vegetation landscape changes from 1987 to 2020 in a typical karst region of China. Using Landsat time series data, the dynamic changes and driving factors of natural karst vegetation were identified at the landscape scale. On the premise of considering the time-lag effect, the main climatic factors that influence vegetation growth were presented at the interannual timescale. Then, the approach of residual analysis was adopted to distinguish the dominant factors affecting vegetation growth. Results of trend analysis revealed that 21.5% of the forestland showed an overall significant decline in vegetation growth, while only 1.5% showed an increase in vegetation growth during the study period. Precipitation and radiation were the dominant meteorological factors influencing vegetation at the interannual timescale, as opposed to temperature. More than 70% of the natural vegetation growth was dominated by climatic factors. The area percentage of negative human impact has increased gradually since 2009 and reached 18.5% in 2020, indicating the currently serious situation of vegetation protection; fortunately, in recent years, human disturbances on vegetation have been mitigated in karst areas with the promotion of ecological conservation and restoration projects. [ABSTRACT FROM AUTHOR]

The Loess Plateau (LP) of China, which is the pilot region of the "Grain to Green Project" (GGP), has received worldwide attention due to its significant changes in the natural and social environment. Investigation of vegetation variations in response to climate change and human activities is vital for providing support for further ecological restoration planning. This paper aimed to monitor vegetation dynamics of the LP with trend comparisons of various vegetation types, disentangle the effects of climate variations and ecological programs on vegetation variations, and detect the consistency of vegetation variations. More specifically, vegetation dynamics during 1982–2015 were analyzed using the Global Inventory Modelling and Mapping System third-generation Normalized Difference Vegetation Index (GIMMS NDVI3g) data with the application of Breaks for Additive Season and Trend (BFAST) and Hurst Exponent. The results showed that: (1) Vegetation manifested a significant greening trend (0.013 decade−1p < 0.01) in the LP during 1982–2015, and a breakpoint (BP) was detected in 1999, which was the beginning of the GGP. Interannual NDVI after the BP (ABP) showed more than 3.5 times greening rates compared to the NDVI before the BP (BBP). (2) Human activities dominated the vegetation variation (accounted for 59.46% of vegetation variation), among which reforestation and land-use change with steep slopes (i.e., ≥15°) lead to the greening after the GGP implementation. (3) Future trends should be noticed in the Forest Zone and Forest-Grass Zone, where the greening trends tend to slow down or even reverse in the southern LP. The long-term GIMMS NDVI3g time series and multiple geospatial analyses of this study might facilitate a better understanding of the mechanisms of vegetation variations for the assessment of the large restoration programs in fragile ecosystems. [ABSTRACT FROM AUTHOR]

The 16 years of normalized difference vegetation index (NDVI) and precipitation data are used to analyze the time‐lag effects of the growing‐season NDVI response to precipitation at regional scales. This study focuses on the arid and semi‐arid Hulunbuir grassland dominated by perennials in northeast China. The multi‐month time‐lag effects are examined using simple statistical approaches, which can detect the two distinct time‐lags for four subregions with four major land‐cover types. A "positive" time‐lag effect of the growing‐season NDVI response to precipitation is observed at 1‐month (May in the current year) time‐lag and 13‐month (May in the previous year) time‐lag while a "negative" time‐lag effect is observed at 9‐month (September in the previous year) time‐lag. In addition, the prediction results of NDVI based on precipitation indicate that the NDVI prediction model considered the lagged monthly precipitation has good performance. Therefore, revealing the time‐lag effects is very important for accurately predicting the growing‐season NDVI and evaluating the vegetation dynamics. Recommendations for Resource Managers: The multi‐month time‐lag effects of the growing‐season NDVI to precipitation in arid and semi‐arid grasslands in northeastern China is analyzed in this paper. We can detect the two distinct time‐lags for four subregions with four major land‐cover types. The following implications could be realized based on the observations: The "positive" 1‐month (May in the current year) and 13‐month (May in the previous year) time‐lag memory of the growing‐season NDVI response to precipitation can be found.The "negative" 9‐month time‐lag memory of the growing‐season NDVI response to precipitation associated with September (late growing‐season) meteorological condition can be detected. This "negative" effect does not support the green up of the growing‐season in the next year. [ABSTRACT FROM AUTHOR]

China has 14 large coal bases, twelve of which are located in the northern region. After years of coal mining, the ecological vegetation of the coal bases has changed. This paper explores the spatial and temporal variation of fractional vegetation cover (FVC) in coal bases in northern China and identifies the trends and influencing factors of FVC using trend analysis, correlation analysis, statistical analysis, and dynamic coefficient method. The results show: Datong River basin of Qilian Mountain coal base is located in the alpine tundra, the FVC increased from 63.4% in 1998 to 69.77% in 2019, regionally FVC is mainly influenced by precipitation and temperature, and the mine area and its surroundings were mainly influenced by coal mining. The Chaibeiyuan coal base is located in the Gobi-desert region, and the FVC increased from 6.04% in 1998 to 9.78% in 2019, which is mainly influenced by temperature and precipitation, and the influence of coal mining is smaller and shorter. The Wulanmulun River basin of Shendong coal base is located in the transition zone of Loess Plateau and desert, and the FVC increased from 36.5% in 1996 to 51.45% in 2019, and the FVC mainly affected by coal mining, ecological protection measures, precipitation, and runoff. The Yanhe Spring area of Jindong coal base belongs to the northern karst area, and the FVC increases overall, from 50.0% in 1996 to 69.29% in 2021, which is jointly influenced by precipitation, coal seam burial conditions, ecological protection measures and the production and living of the densely populated area. [ABSTRACT FROM AUTHOR]

The middle reaches of the Yellow River (MRYR) are a key area for carrying out China's vegetation restoration project. However, the impact of vegetation variation on runoff in the MRYR is still unclear. For quantitatively evaluating the contribution rate of vegetation variation to runoff in the MRYR, this paper quantified the relationship between Normalized Difference Vegetation Index (NDVI) and Budyko parameters (w). Then, we used multiple linear regression to quantitatively calculate the contribution rate of different factors on vegetation variation. Finally, an adjusted Budyko formula was constructed to quantitatively calculate the influence of vegetation variation on runoff. The results showed that there is a linear relationship between NDVI and Budyko parameters (w) (p < 0.05); the fitting parameter and constant term were 12.327 and −0.992, respectively. Vegetation change accounted for 33.37% in the MRYR. The contribution of climatic and non-climatic factors on vegetation change is about 1:99. The contribution of precipitation, potential evaporation, anthropogenic activities on the runoff variation in the MRYR are 23.07%, 13.85% and 29.71%, respectively. [ABSTRACT FROM AUTHOR]

The increased growth of vegetation has the potential to slow global climate warming. Therefore, analyzing and predicting the response assessment of Chinese vegetation to climate change is of great significance to studies of global warming. In this paper, we examine the spatiotemporal dynamics of vegetation leaf area index (LAI) values in China from 1981 to 2017 and their correlations with meteorological (hydrothermal) factors based on trend analysis and correlation analysis. We further construct an LAI prediction model based on hydrothermal conditions. The climate data obtained under different scenarios in the CMIP5 and CMIP6 climate models were used to predict the dynamic change trend of vegetation LAI from 2021 to 2100. The results show that most areas of China (72.82%) showed an improving trend in vegetation LAI from 1981 to 2017, during which the annual average LAI value increased at a rate of 0.0029 year−1. Vegetation LAI in China was significantly correlated with climatic factors (temperature, precipitation, and evapotranspiration), and the LAI prediction model constructed based on hydrothermal conditions had a high accuracy (Pearson's Cor value is 0.9729). From 2021 to 2100, approximately 2/3 of China's vegetation LAI area showed an improvement trend, and the impact of climate change on vegetation LAI predictions under the high emission scenario was greater than that under the low emission scenario. This research can provide a basis for studies on the climatic drivers of vegetation change and the global vegetation dynamic model. [ABSTRACT FROM AUTHOR]

This study aims to understand the factors and mechanisms influencing the spatio-temporal changes of fractional vegetation cover (FVC) in the northern slopes of the Tianshan Mountains. The MOD13Q1 product data between June and September (peak of plants growing) during the 2001–2020 period was incorporated into the pixel dichotomy model to calculate the vegetation cover changes. Then, the principal component analysis method was used to identify the primary driving factors affecting the change in vegetation cover from the natural, human, and economic perspectives. Finally, the partial correlation coefficients of FVC with temperature and precipitation were further calculated based on the pixel scale. The findings indicate that (1) FVC in the northern slopes of the Tianshan Mountains ranged from 0.37 to 0.47 during the 2001–2020 period, with an obvious inter-annual variation and an overall upward trend of about 0.4484/10 a. Although the vegetation cover had some changes over time, it was generally stable, and the area of strong variation only accounted for 0.58% of the total. (2) The five grades of vegetation cover were distributed spatially similarly, but the area-weighted gravity center for each vegetation class shifted significantly. The FVC under different land use/land cover types and elevations was obviously different, and as elevation increased, vegetation coverage presented a trend of a "∩"-shape change. (3) According to the results of principal component analysis, human activities, economic growth, and natural climate were the main driving factors that caused the changes in vegetation cover, and the cumulative contribution of the three reached 89.278%. In addition, when it came to climatic factors, precipitation had a greater driving force on the vegetation cover change, followed by temperature and sunshine hours. (4) Overall, precipitation and temperature were correlated positively with FVC, with the average correlation coefficient values of 0.089 and 0.135, respectively. Locally, the correlations vary greatly under different LULC and altitudes. This research can provide some scientific basis and reference for the vegetation evolution pattern and ecological civilization construction in the region. [ABSTRACT FROM AUTHOR]

The water diversion project in Central Yunnan Province (WDP-YN) is the largest water diversion project under construction in China. However, the ecological effects of this water diversion project are still unclear. This study utilized Sentinel-2 remote sensing data to estimate fractional vegetation cover (FVC), maps spatiotemporal variations of FVC in construction areas from 2017 to 2022, and evaluates the impact of the WDP-YN on regional vegetation coverage using buffer analysis and vegetation type transition matrix methods. The study led to the following findings: (1) From 2017 to 2022, FVC within 10 km of the tunnel construction route showed a slightly downward trend or remained relatively stable with no significant changes in the spatial pattern of FVC. (2) Before and after the construction of WDP-YN, over 60% of the area within 10 km of the tunnel construction route showed no change in FVC. On Construction Route Section I (CRS-I), vegetation improved and/or degraded within 12.90% (14.10%) of the area and the regions with degraded FVC concentrated in the northern CRS-I. For Construction Route Section II (CRS-II), 11.96% and 27.51% of the regions were dominated by improved and/or degraded FVC. Vegetation changes near Groundwater Monitoring Point a (GMPa) were relatively stable. (3) The WDP-YN degraded vegetation within 2 km of both sides of CRS-I, slowing down the increase in FVC, while the WDP-YN improved vegetation within 2–6 km of both sides of CRS-II, the closer the distance to CRS-II, the faster the increase in FVC and the decrease in FVC slowed down within 0–2 km of both sides of CRS-II. This study sheds light on the impacts of water diversion infrastructure on vegetation coverage and provides practical guidance and reference for eco-environment protection and ecological restoration given water conservancy projects in China and other regions of the world. [ABSTRACT FROM AUTHOR]

Global climate change is considered one of the greatest environmental threats in the world. It is expected to significantly change the global hydrological cycle. The two main water cycle components, potential evapotranspiration (PET) and precipitation (P), are closely related to vegetation dynamics. In this study, the partial correlation analysis method was used to analyzed the relationship between Normalized Difference Vegetation Index (NDVI) and climate factors (PET and P) based on grid cells. PET was calculated by FAO-56 Penman-Monteith method. Moreover, we also investigated the NDVI and climate factors in different vegetation cover types. The results showed that grassland, forest and cropland in China were positively correlated with PET and P. The time scales of the maximum partial correlation coefficients between NDVI and PET of grassland vegetation were mostly longer than 5-6 months. These time scales were longer than the time scales related to P. The partial correlation coefficients between NDVI and PET, P of forest vegetation were higher in northern China, whereas the spatial distribution of related time scales was the opposite. The partial correlation coefficients between NDVI and PET, P of forest vegetation were higher in northern China. However, the spatial distribution of related time scales was the opposite. The correlations between NDVI and PET, P of cropland vegetation and the time scales related to PET had clear spatial heterogeneity. The time scale of the correlation between NDVI and P for cropland in the northern China was about 2 months. P had a strong influence on the growth of various types of vegetation in the study area, and grassland vegetation was affected by P over the shortest time scale. We compare and analyze the results of this study with other related studies. These results provide a reference for exploring the dynamic changes in different vegetation types and factors impacting them. [ABSTRACT FROM AUTHOR]

Recent research shows that China is experiencing significant greening, with afforestation initiatives being the main cause. Quantitative calculation of vegetation change influencing factors and evaluation of the contribution of afforestation to vegetation greening in China are critical to coping with climate change and improving the implementation and efficacy of forestry projects. We investigated the temporal and spatial dynamics of the Normalized Difference Vegetation Index (NDVI) from 1982 to 2020, and quantified the contribution of afforestation initiatives, a typical human activity, to the dynamic changes of vegetation. The results showed that NDVI in China has primarily increased in the last 39 years. 57% of the pixels increased, 27% were stable and unchanged, and 16% decreased. Climate change was responsible for 72.34% of vegetation restoration, while human activities were responsible for 27.36% of vegetation restoration, according to residual analysis. In the future, only 14% of the regions showed continuous growth of the NDVI, while the remaining regions showed obvious antipersistence (59% will go from increasing to decreasing, and 22% will go from decreasing to increasing). The contribution of climate factors to vegetation change will decrease in the future, and human activities will become more complex. Except for Huaihe River and Taihu Lake (SPHRTL), other forestry projects showed an increasing trend of NDVI after the implementation of ecological engineering. However, due to differences in climate conditions and ecological engineering implementation, there are differences in the benefits of forestry projects. Some forestry project areas still have obvious vegetation degradation, and appropriate forestry management is necessary. This work provides a quantitative analysis of vegetation change and its driving factors in China, which will help to cope with future climate change and provide a reference for the implementation and management of ecological projects. [ABSTRACT FROM AUTHOR]

Hydrology is an important environmental factor for the evolution of wetland landscape patterns. In the past 20 years, Poyang Lake, the largest freshwater lake in China, has experienced significant inundation shrinkage and water level decrease, posing a significant threat to the local vegetation community. To explore the potential relationship between aquatic vegetation and hydrological processes in the recent hydrological situation, in this study, the landscape patterns of aquatic vegetation communities in Poyang Lake were studied using time-series Landsat remote sensing images and a support vector machine classifier. The stepwise regression analysis method was adopted to analyze the relationship between the vegetation area and hydrological factors. The results indicated that the area of submerged and emergent vegetation in the entire lake decreased significantly from 2001 to 2017, whereas the area of moist vegetation showed a remarkably increasing trend. The average distribution elevation of the submerged vegetation increased by 0.06 m per year. The corresponding landscape patterns showed that the degree of fragmentation of aquatic vegetation communities in Poyang Lake increased. Several hydrological factors were selected to quantify the potential impact of water level fluctuations. The correlation analysis results indicated that hydrological conditions during the rising- and high-water periods may be the key factors affecting the area of aquatic vegetation. This study systematically investigated the evolution of vegetation communities in Poyang Lake wetlands over the past two decades, which contributes to the protection and management of this unique ecosystem. [ABSTRACT FROM AUTHOR]

Human activities and global environmental changes have transformed terrestrial ecosystems, notably increasing vegetation greenness in China. However, this greening is less effective across the Hu Huanyong Line (Hu Line). This study analyzes dynamic changes and driving factors of nine vegetation net primary productivities (NPPs) in regions divided by the Hu Line using remote sensing data, trend analysis, and the Geodetector model. Findings reveal that from 2001 to 2022, 38.22% of regional vegetation NPP in China increased, especially in the Loess Plateau, Sichuan Basin, and Northeast Plains, while 2.39% decreased, primarily in the southeastern region and southern Tibet. Grasslands contributed 39.71% to NPP north of the Hu Line, and cultivated vegetation contributed 50.58% south. The driving explanatory power of factors on vegetation NPP on the north side of the Hu Line is generally greater than that on the south side. Natural factors primarily drive NPP changes, with human activities having less impact. Combined factors, particularly climate and elevation, significantly enhance the driving explanatory power (q, 0–1). The joint effects of elevation and precipitation on grassland NPP dynamics (q = 0.602) are notable. GDP's influence on broadleaf forests north of the Hu Line (q = 0.404) is significant. Grasslands respond strongly to land use changes and population density, with a combined effect of q = 0.535. Shrubs, alpine vegetation, and meadows show minimal response to individual factors (q < 0.2). These findings offer insights for devising ecological protection measures tailored to local conditions. [ABSTRACT FROM AUTHOR]

The Lower Yangtze Valley, a key region in understanding early Neolithic human activities, presents an unsolved question regarding the early occurrence of such impact before 8.0 cal ka BP. The elusive details around the specific time and the environmental background underscore the need for more investigation. In addition, regional vegetation is obscurely known in this region mainly attributed to variable pollen sources resulting from highly unstable deposition environment in this widespread marine-flood interaction plains. This paper reconstructs the vegetation-environmental history in the Lower Yangtze Valley during 10.1–7.2 cal ka BP and identifies regional forests and potential signals of human activities through analyses of AMS 14C dates, pollen data, and grain size. The results show that during the period from 10,100 to 8270 cal a BP, a stable deciduous oak forest as the regional forest dominated the region in a monotonous vegetation pattern. After 8270 cal a BP, deciduous oak trees receded, while evergreen oak trees and other woody types expanded, resulting in the regional vegetation transitioning to a regional mixed evergreen-deciduous broad-leaved forest up to 7230 cal a BP. Moreover, early indications of human activity were detected by high abundance of Poaceae including rice (Oryza sativa) type. Together with a previously reported rice phytolith record from neighboring areas, this research provides compelling pollen evidence suggesting that human activities in the Lower Yangtze Valley likely at least dated back to 8200 cal a BP, around 2000 years earlier than current archeological records. • Pollen record reveals vegetation history during 10.0–7.3 cal. ka BP. • Regional deciduous oak forests flourished during the early Holocene. • Cool and dry during the early Holocene while warm and humid after 8.2 cal. ka BP. • Neolithic human impact probably dated back to 8.2 cal. ka BP in the Lower Yangtze. [ABSTRACT FROM AUTHOR]

In this paper, we investigate Holocene precipitation changes in northern China to improve the understanding of the East Asian summer monsoon (EASM) and provide an environmental context for the development of Chinese civilisation. Our study is based on an analysis of 260 pollen samples from Holocene lacustrine sediments at Ningjinpo in northern China, and employs the biomisation method to reconstruct regional vegetation types and vegetation changes, and to decipher their driving factors, by incorporating regional climatic data and information on human activities. Results indicate that, in the early Holocene (10,000–8100 cal yr B.P.), regional climate was dry, with annual precipitation of 470 mm. In the middle Holocene (8100–3000 cal yr B.P.), a rapid increase was observed in precipitation, reaching 580 mm. During the late Holocene (2600–600 cal yr B.P.), climate became drier again, with annual precipitation decreasing to 480 mm. Between 10,000–600 cal yr B.P., vegetation exhibited distinct successional patterns dominantly influenced by climate, transitioning from meadow-steppe to forest, and then back to meadow-steppe. After 600 cal yr B.P., vegetation succession decoupled from climate change, with human activities emerging as the primary driving force, as indicated by the substantial increase in anthropogenic pollen and charcoal concentrations. In the future, under the combined influence of global warming and intensified human activity, the implementation of scientific environmental planning measures will become increasingly important for the restoration and sustainable development of regional vegetation ecosystems. • Unambiguous high-resolution precipitation record from the North China Plain. • Human activities variedly impact vegetation evolution since 2600 cal yr B.P. • A shift from natural to anthropogenic vegetation change happened at 600 cal yr B.P. [ABSTRACT FROM AUTHOR]

The newly developed offline land ecosystem model Terrestrial Ecosystem Model in R (TEMIR) version 1.0 is described here. This version of the model simulates plant ecophysiological (e.g., photosynthetic and stomatal) responses to varying meteorological conditions and concentrations of CO 2 and ground-level ozone (O 3) based on prescribed meteorological and atmospheric chemical inputs from various sources. Driven by the same meteorological data used in the GEOS-Chem chemical transport model, this allows asynchronously coupled experiments with GEOS-Chem simulations with unique coherency for investigating biosphere–atmosphere chemical interactions. TEMIR agrees well with FLUXNET site-level gross primary productivity (GPP) in terms of both the diurnal and monthly cycles (correlation coefficients R2>0.85 and R2>0.8 , respectively) for most plant functional types (PFTs). Grass and shrub PFTs have larger biases due to generic model representations. The model performs best when driven by local site-level meteorology rather than reanalyzed gridded meteorology. Simulation using gridded meteorology agrees well for annual GPP in seasonality and spatial distribution with a global average of 134 Pg C yr -1. Application of Monin–Obukhov similarity theory to infer canopy conditions from gridded meteorology does not improve model performance, predicting an increase of + 7 % in global GPP. Present-day O 3 concentrations simulated by GEOS-Chem and an O 3 damage scheme at high sensitivity show a 2 % reduction in global GPP with prominent reductions of up to 15 % in eastern China and the eastern USA. Regional correlations are generally unchanged when O 3 is present and biases are reduced, especially for regions with high O 3 damage. An increase in atmospheric CO 2 concentration of 20 ppmv from the level in 2000 to the level in 2010 modestly decreases O 3 damage due to reduced stomatal uptake, consistent with ecophysiological understanding. Our work showcases the utility of this version of TEMIR for evaluating biogeophysical responses of vegetation to changes in atmospheric composition and meteorological conditions. [ABSTRACT FROM AUTHOR]

The subtropical forests of China play a pivotal role in the global carbon cycle and in regulating the global climate. Quantifying the individual and combined effects of forest cover change (FCC), vegetation structural change (e.g. leaf area index (LAI)), CO2 fertilisation, and climate change (CC) on the annual gross primary productivity (GPP) dynamics of different subtropical forest types are essential for mitigating carbon emissions and predicting future climate changes, but these impacts remain unclear. In this study, we used a processed-based model to comprehensively investigate the impacts of these factors on GPP variations with a series of model experiments in China's subtropical forests from 2001 to 2018. Simulated GPP showed a significant increasing trend (20.67 gCm-2yr-1 , p<0.001) under the interaction effects of FCC, LAI change, rising CO2 , and CC. The CO2 fertilisation (6.84 gCm-2yr-1 , p<0.001) and LAI change (3.79 gCm-2yr-1 , p=0.004) were the two dominant drivers of total subtropical forest GPP increase, followed by the effects of FCC (0.52 gCm-2yr-1 , p<0.001) and CC (0.92 gCm-2yr-1 , p=0.080). We observed different responses to drivers depending on forest types. The evergreen broad-leaved forests showed the maximum carbon sequestration rate due to the positive effects of all drivers. Both the FCC (0.19 gCm-2yr-1 , p<0.05) and CC (1.22 gCm-2yr-1 , p<0.05) significantly decreased evergreen needle-leaved forest GPP, while their negative effects were almost offset by the positive impact of LAI changes. Our results indicated that LAI outweighed FCC in promoting GPP, which is an essential driver that needs to be accounted for in studies and ecological and management programmes. Overall, our study offers a novel perspective on different drivers of subtropical forest GPP changes and provides valuable information for policy makers to better manage subtropical forests to mitigate climate change risks. [ABSTRACT FROM AUTHOR]

Vegetation to some extent can reflect the overall state of the ecological environment, and increasing vegetation cover can improve the quality of the ecological environment. The southwest karst region of China is a typical ecologically fragile area and an important ecological barrier. Against the backdrop of intensified human activities, the vegetation dynamics in the karst region have attracted widespread attention. Analyzing vegetation cover changes and their responses to human activities is important for regional ecological development. This study is based on SPOT-VGT NDVI data from 1998 to 2020 and employs trend analysis, "baseline" determination, multiple regression, residual analysis, and principal component analysis to investigate the characteristics of vegetation cover changes in the southwest karst region of China. Additionally, it quantifies the specific impacts of various human activities on these changes. The research findings indicate that (1) over the past 23 years, the inter-annual variability and seasonal differences in the NDVI of the southwest karst region have been significant, exhibiting an overall increasing trend. Spatially, the vegetation in this region demonstrates a decreasing trend from south to north and southwest to northeast. (2) Over time, the areas where vegetation has improved gradually expand, and the effectiveness of vegetation restoration increases with time. (3) Overall, human activities influence vegetation growth in the southwest karst landscape region of China. (4) The socioeconomic factors affecting vegetation cover changes in the southwest karst region of China primarily include economic development level, population and labor migration, urbanization development, and vegetation construction. These results provide a scientific basis for formulating reasonable ecological environment protection strategies by humans. [ABSTRACT FROM AUTHOR]

Climate is one of the key factors driving changes in vegetation, and the response of the vegetation to climate often occurs with a time delay. However, research on the cumulative lagged response of the vegetation to meteorological factors in large-scale regions is limited. Therefore, this study first evaluated the performance of the Gross Primary Productivity (GPP) products provided by Moderate Resolution Imaging Spectroradiometer (MODIS) and Penman–Monteith–Leuning (PML) over the past 20 years in China and then determined the lagged relationships between the GPP and major meteorological factors in different regions and land-use types in China based on a partial correlation analysis. The results indicate that (1) GPP_PML outperforms GPP_MODIS products in the regional context of China; (2) China's regional GPP has shown a fluctuating upward trend over the past 20 years, with a stepwise increase in the multi-year average from the northwest inland to the southeast coastal regions, and a higher contribution from the southern regions than the northern ones; (3) unlike the recent upward trend in regional temperatures, both precipitation and radiation have decreased, with these two factors showing completely opposite multi-year trends in most regions; and (4) the proportion of regions with lagged effects of the GPP on meteorological factors is higher than those with cumulative effects in China. Among these, GPP exhibits a higher proportion of a 3-month lagged response to precipitation, which is particularly pronounced at altitudes between 500 and 2500 m and above 5500 m. the proportion of the areas with no lag cumulative response to temperature and radiation with GPP in China is the highest due to the influence of more barren land and grassland in the northwest interior. Simultaneously, grassland and barren land have a higher proportion of the non-lagged cumulative responses to temperature and precipitation. This study contributes to our understanding of vegetation dynamics in the context of global climate change and provides a theoretical foundation for regional ecological conservation and high-quality coordinated development. [ABSTRACT FROM AUTHOR]

The clarification of the impact of human activities on vegetation in mining areas contributes to the harmonization of mining and environmental protection. This study utilized Geographically and Temporally Weighted Regression (GTWR) to establish a quantitative relationship among the Normalized Difference Vegetation Index (NDVI), temperature, precipitation, and Digital Elevation Model (DEM). Furthermore, residual analysis was performed to remove the impact of natural factors and separately assess the impact of human activities on vegetation restoration. The experiment was carried out in Shangwan Mine, China, and following results were obtained: (1) During the period of 2000 to 2020, intensified huan activities corresponded to positive vegetation changes (NDVI-HA) that exhibited an upward trend over time. (2) The spatial heterogeneity of vegetation restoration was attributed to the DEM. It is negatively correlated with NDVI in natural conditions, while under the environment of mining activities, there is a positive correlation between NDVI-HA and DEM. (3) The contribution of human activities to vegetation restoration in mining areas has been steadily increasing, surpassing the influences of temperature and precipitation since 2010. The results of this study can provide important references for the assessment of vegetation restoration to some extent in mining areas. [ABSTRACT FROM AUTHOR]

Reconstructing Holocene vegetation history and human impact on vegetation is critical for understanding past interactions between humans and nature. This study concentrates on the lower West River area in Southern China, offering high-resolution reconstructions of vegetation changes over the last 9000 years. Our findings reveal that during the Holocene Climatic Optimum (9–5 ka BP), the area was predominantly covered by dense evergreen broad-leaved forests. Around 5.1 ka BP, the emergence of marsh forests, as evidenced by abundant pollen of Glyptostrobus and in situ buried woods and roots, indicated a transition towards a wetland ecosystem. Notably, a thriving wetland environment and high production of peat deposits dating from 4.3 ka to 2.5 ka BP reflect the continuous drop of lake levels and the formation of swamps driven by a climatic pattern marked by decreased precipitation, potentially linked to a reduction in summer monsoon intensity during the latter part of the Holocene. This period of wetland expansion also coincides with the earliest evidence of rice cultivation in Southern China, suggesting a relationship between agricultural development and regional landscape changes. Additionally, millennial-scale paleo-megafloods are not only identified by lithological features but also by pollen taxa and principal component analysis. Despite the temporary disruptions of the marsh forest, it demonstrated resilience, quickly recovering within decades. By approximately 2.5 ka BP, a significant increase in rice-type Poaceae pollen and Pteridophyte spores, alongside the sharp decline of Glyptostrobus and a decrease in arboreal taxa, were observed. This shift in pollen spectra, coupled with a PC1 curve of principal component analysis, points to intensified human activity as a primary driver behind the collapse of the local wetland ecosystem. These findings align with regional evidence from the Pearl River Delta and coastal Southern China, highlighting extensive human intervention in natural forests and the onset of large-scale agriculture post-2.5 ka BP. [ABSTRACT FROM AUTHOR]

Green vegetation is one of the main objects of ecological environment restoration and protection, objectively reflecting the quality of regional ecological environments. Studying its spatial distribution characteristics is of great significance to the formulation of ecological environment restoration policies. Based on data on urban green vegetation in China from 2000 to 2022, this study attempts to analyze the destruction and protection patterns of urban green vegetation in China from the perspectives of total changes in green vegetation contraction and growth and spatial evolution characteristics and trends, and it explores the driving factors affecting the change in green vegetation area. The results show the following: (1) Green vegetation growth and contraction occurred alternately in China from 2000 to 2022. Vegetation contraction showed a "point–line–plane" evolution pattern, forming a contraction stage of point-like aggregation, linear series, and planar spread. Vegetation growth has always presented a frontal pattern. (2) The growth and contraction of green vegetation in China showed a north–south differentiation phenomenon. The vegetation contraction phenomenon spread in the Central Plains urban agglomeration and its surrounding areas and showed an expanding trend. The growth trend is obviously moving northward, mainly concentrated in Inner Mongolia, Ningxia, Gansu, Xinjiang, and other northern provinces, which also coincides with the key ecological restoration policies in northern China in recent years. (3) City scale, economic level, population scale, agro-industrial structure, and water resources content have significant effects on the spatial distribution of green vegetation. [ABSTRACT FROM AUTHOR]

Exploring the evolution of vegetation cover and its drivers in the Ferghana Basin helps to understand the current ecological status of the Ferghana Basin and to analyze the vegetation changes and drivers, with a view to providing a scientific basis for regional ecological and environmental management and planning. Based on GIMMS NDVI3g and meteorological data, the spatial and temporal evolution characteristics of NDVI were analyzed from multiple perspectives with the help of linear trend and Mann–Kendall (MK) test methods using arcgis and the R language spatial analysis module, combined with partial correlation coefficients and residual analysis methods to analyze the impacts of climate change and human activities on the regional vegetation cover of the Ferghana Basin from 1982 to 2015. NDVI driving forces. The results showed the following: (1) The growing season of vegetation NDVI in the Ferghana Basin showed an increasing trend in the 34-year period, with an increase rate of 0.0044/10a, and the spatial distribution was significantly different, which was high in the central part of the country and low in the northern and southern parts of the country. (2) Temperature and precipitation simultaneously co-influenced the vegetation NDVI growth season, with most of the temperature and precipitation contributing in the spring, most of the temperature in the summer being negatively phased and the precipitation positively correlated, and most of the temperature and precipitation in the fall inhibiting vegetation NDVI growth. (3) The combined effect of climate change and human activities was the main reason for the overall rapid increase and great spatial variations in vegetation NDVI in China, and the spatial distribution of drivers, namely human activities and climate change, contributed 44.6% to vegetation NDVI in the growing season. The contribution of climate change and human activities to vegetation NDVI in the Ferghana Basin was 62.32% and 93.29%, respectively. The study suggests that more attention should be paid to the role of human activities and climate change in vegetation restoration to inform ecosystem management and green development. [ABSTRACT FROM AUTHOR]

A national distribution of secondary forest age (SFA) is essential for understanding the forest ecosystem and carbon stock in China. While past studies have mainly used various change detection algorithms to detect forest disturbance, which cannot adequately characterize the entire forest landscape. This study developed a data-driven approach for improving performances of the Vegetation Change Tracker (VCT) and Continuous Change Detection and Classification (CCDC) algorithms for detecting the establishment of forest stands. An ensemble method for mapping national-scale SFA by determining the establishment time of secondary forest stands using change detection algorithms and dense Landsat time series was proposed. A dataset of national secondary forest age for China (SFAC) for 1 to 34 and with a 30-m spatial resolution was produced from the optimal ensemble model. This dataset provides national, continuous spatial SFA information and can improve understanding of secondary forests and the estimation of forest carbon storage in China. [ABSTRACT FROM AUTHOR]

It is essential to monitor the dynamics of vegetation at different scales in space and time to promote the sustainable development of terrestrial ecosystems. We used the Google Earth Engine (GEE) cloud platform to perform a comprehensive analysis of the changes in normalized difference vegetation index (NDVI) Mann-Kendall (MK) + Sen trend in the hinterland region of the Maowusu sandland in China over the last two decades. We performed bias-correlation studies using soil and climate data. Furthermore, we performed a partial Mantel test to analyze the spatial and temporal fluctuations of vegetation health-related indices. Additionally, we developed a logistic dual model of the phenology index using the Lenvenberg–Marquardt technique. The objective was to uncover the factors contributing to the regional shifts in vegetation dynamics. We provide a comprehensive analytic method designed to monitor vegetation over some time and forecast its future recovery. The findings indicate that over the past 20 years, more than 90% of the regional NDVI in the study area has exhibited a consistent and significant upward trend. This trend is primarily influenced by the adverse impact of temperature and the beneficial impact of precipitation. Additionally, long-term phenological indicators in the study area reveal that the vegetation's growth cycle commences on the 125th day of the year and concludes on the 267th day of the year. This suggests that the shorter duration of the vegetation's growth season may be attributed to the local climate and unfavorable groundwater depth conditions. levated temperatures throughout the next spring and autumn seasons would significantly affect the wellbeing of plants, with soil moisture being a crucial determinant of plant development in the examined region. This study presents a wide range of analytical tools for monitoring vegetation over a long period and predicting its future recovery. It considers factors such as vegetation health, phenology, and climatic influences. The study establishes a solid scientific foundation for understanding the reasons behind regional vegetation changes in the future. [ABSTRACT FROM AUTHOR]

The response of vegetation phenology to urbanization has become a growing concern. As impervious surfaces change as urbanization advances, the variation in vegetation phenology at the dynamic urbanization level was analyzed to significantly quantify the impact of urbanization processes on vegetation phenology. Based on the MOD13Q1 vegetation index product from 2001 to 2020, vegetation phenology parameters, including the start of the growing season (SOS), the end of the growing season (EOS), and the length of the growing season (GSL), were extracted, and the spatial–temporal variation in vegetation phenology, as well as its response to urbanization, was comprehensively analyzed. The results reveal that (1) from 2001 to 2020, the average rates of change for the SOS, EOS, and GSL were 0.41, 0.16, and 0.57 days, respectively. (2) The vegetation phenology changes showed significant spatial–temporal differences at the urbanization level. With each 10% increase in the urbanization level, the SOS and EOS were advanced and delayed by 0.38 and 0.34 days, respectively. (3) The urban thermal environment was a major factor in the impact of urbanization on the SOS and EOS. Overall, this study elucidated the dynamic reflection of urbanization in phenology and revealed the complex effects of urbanization on vegetation phenology, thus helping policymakers to develop effective strategies to improve urban ecological management. [ABSTRACT FROM AUTHOR]

Rapid global changes are altering regional hydrothermal conditions, especially in ecologically vulnerable areas such as coastal regions, subsequently influencing the dynamics of vegetation growth. However, there is limited research investigating the response of vegetation in these regions to extreme climates and the associated time lag-accumulation relationships. This study utilized a combined approach of gradual and abrupt analysis to examine the spatiotemporal patterns of vegetation dynamics in the coastal provinces of China from 2000 to 2019. Additionally, we evaluated the time lag-accumulation response of vegetation to extreme climate events. The results showed that (1) extreme high temperatures and extreme precipitation had increased over the past two decades, with greater warming observed in high latitudes and concentrated precipitation increases in water-rich southern regions; (2) both gradual and abrupt analyses indicate significant vegetation improvement in coastal provinces; (3) significant lag-accumulation relationships were observed between vegetation and extreme climate in the coastal regions of China, and the time-accumulation effects were stronger than the time lag effects. The accumulation time of extreme temperatures was typically less than one month, and the accumulation time of extreme precipitation was 2–3 months. These findings are important for predicting the growth trend of coastal vegetation, understanding environmental changes, and anticipating ecosystem evolution. [ABSTRACT FROM AUTHOR]

Coal mining and ecological restoration activities significantly affect land surfaces, particularly vegetation. Long-term quantitative analyses of vegetation disturbance and restoration are crucial for effective mining management and ecological environmental supervision. In this study, using the Google Earth Engine and all available Landsat images from 1987 to 2020, we employed the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm and Support Vector Machine (SVM) to conduct a comprehensive analysis of the year, intensity, duration, and pattern of vegetation disturbance and restoration in the Heidaigou and Haerwusu open-pit coal mines (H-HOCMs) in the Jungar Coalfield of China. Our findings indicate that the overall accuracy for extractions of disturbance and restoration events in the H-HOCMs area is 83% and 84.5%, respectively, with kappa coefficients of 0.82 for both. Mining in Heidaigou has continued since its beginning in the 1990s, advancing toward the south and then eastward directions, and mining in the Haerwusu has advanced from west to east since 2010. The disturbance magnitude of the vegetation greenness in the mining area is relatively low, with a duration of about 4–5 years, and the restoration magnitude and duration vary considerably. The trajectory types show that vegetation restoration (R, 44%) occupies the largest area, followed by disturbance (D, 31%), restoration–disturbance (RD, 16%), disturbance–restoration (DR, 8%), restoration–disturbance–restoration (RDR), and no change (NC). The LandTrendr algorithm effectively detected changes in vegetation disturbance and restoration in H-HOCMs. Vegetation disturbance and restoration occurred in the study area, with a cumulative disturbance-to-restoration ratio of 61.79% since 1988. Significant restoration occurred primarily in the external dumps and continued ecological recovery occurred in the surrounding area. [ABSTRACT FROM AUTHOR]

The Southwest Alpine Canyon Area (SACA), a well-known ecological vulnerability region, plays a very important role in China. Identifying the driving force of the spatial heterogeneity of vegetation and the response of interannual vegetation changes to climate change and human activities would be helpful for ecosystem management. Based on the NDVI dataset, the study analyzed the trend of NDVI change from 2000 to 2019 using the Theil–Sen trend analysis and the Mann–Kendal significance test, detected the driving forces of the spatial heterogeneity of NDVI by the means of the geographical detector, and analyzed the relative contribution of climate change and human activities to interannual NDVI changes using residual analysis model. The results showed that, in terms of the spatial distribution, the pattern of NDVI showed that it is higher in the southeast and lower in the northwest region of the SACA. Elevation was the dominant factor influencing the spatial heterogeneity of NDVI, with the explanatory power of 64%, much larger than other factors, and vegetation type, temperature, precipitation, land use type, and soil type were the main factors. In addition, the explanatory power of the dual factor interaction was higher than that of the single factor effect, which showed two kinds of interaction relationships: bivariate enhancement and nonlinear enhancement. In terms of the temporal variation, 85.59% of the study area showed an increasing trend, and only 14.41% of the area showed a decreasing trend. The main factor affecting NDVI changes was human activities, and climate change was the secondary factor, with relative contributions of 71.35% and 28.65%, respectively. The study will promote a better understanding of the complex mechanisms of vegetation changes and provide scientific recommendations for the prevention of vegetation degradation and vegetation restoration in the SACA. [ABSTRACT FROM AUTHOR]

Under the combined impact of climatic, socioeconomic, and environmental factors, the vegetation NPP change process and its responses to drive factors in the sub-regions of Mainland China are not clear. This study analyzes the changing pattern of vegetation NPP in China from 2000 to 2022 from the perspective of zoning and clarifies its response mechanism to climate-human interaction based on the gravity center model, third-order partial correlation coefficient and geographical detector. The results showed that: (1) There was an overall decreasing trend of vegetation NPP in China from the southeast to the northwest; (2) The vegetation NPP gravity center in Northeast, Northwest, and North China migrated southwards, while that of Southwest, Central South, and East China showed northward migration.;(3) Human activities played a dominant role in zones with increasing vegetation NPP from 2000 to 2010, while climate change greatly contributed to the increase in vegetation NPP during 2011–2022; (4) Human activities, such as deforestation and overgrazing, in Northeast and North China should be reduced to prevent vegetation ecosystem degradation, and the negative impact of human activities should be reduced to maintain the growth of vegetation NPP. This study was conducted to support decision-making for the precise restoration of ecosystems. [ABSTRACT FROM AUTHOR]

Vegetation changes have a significant impact on the runoff and sediment process of rivers in arid areas. Since 2000, the middle reaches of the Yellow River Basin have experienced rapid vegetation restoration, and the sediment transport in this area has also shown a significant decreasing trend. In this paper, we selected 10 rivers from north to south located in the middle reaches of the Yellow River as the research area, and analyzed the temporal and spatial characteristics of vegetation restoration and changes in runoff and sediment, as well as the effects of vegetation restoration on runoff and sediment process. The results show that sediment transport modulus in the middle reaches of the Yellow River are very significantly decreasing (p < 0.001), and the rate of change of the northern rivers is higher than that of the southern rivers. During the periods of 1956–1979 and 1980–1999, the sediment transport modulus showed a very significant decreasing trend from north to south (p < 0.001), but the trend of significantly decreasing the sediment transport modulus from north to south from 2000 to 2020 no longer existed (p = 0.761), after 2000, the middle reaches of the Yellow River underwent a rapid process of vegetation restoration, vegetation restoration significantly reduced the spatial heterogeneity of river sediment transport. Partial correlation analysis and sensitivity analysis show that, there was no significant correlation between vegetation restoration and runoff reduction (R2 = 0.03, p = 0.63), but there was a significant correlation between vegetation restoration and reduced sediment transport modulus (R2 = 0.49, p < 0.05), and a significant correlation between vegetation restoration and decreased sediment concentration (R2 = 0.66, p < 0.01). During the periods of 1981–1999 and 2000 - 2020, the relationship between precipitation and sediment transport modulus was positively correlated. In the 1981–1999 period, NDVI showed a weak negative correlation with sediment transport modulus but in the 2000 - 2020 period, NDVI and sediment transport modulus showed a significant negative correlation. This study provides valuable insights into the natural response of river sediment transport to vegetation restoration, and the findings are critical for soil and water conservation planning in arid area. [ABSTRACT FROM AUTHOR]

The Tarim River is the largest inland river in China, which plays a crucial role in maintaining regional ecological security and carbon cycle/dynamic. However, the "green corridor" in the Taitema Lake region at the lower reaches of the Tarim River has unclear environmental changes and future dynamics due to the influence of the ecological water conveyance. Hence, protecting the "green corridor" at the lower reaches of the Tarim River in China is strategically important not only ecologically but also socially and economically. In this paper, the temporal and spatial features of the fractional vegetation coverage (FVC) dynamics in the Taitema Lake region at the lower reaches of the Tarim River in 2000–2018 are analyzed and calculated using Landsat TM/OLI remote sensing images and MODIS data products. Additionally, the future trend of FVC dynamics in the study region are predicted using trend analysis and the pixel-based Hurst index. The results show that FVC in the Taitema Lake region exhibit a positive development after the implementation of ecological water conveyance. Specifically, from 2000 to 2018, the areas of low, medium, and high FVC expanded from 1.28 km2 to 179.87 km2, resulting in an increase of 140.52%. Spatially, the regions around the lake entrance channel of the Tarim River saw a significant increase in FVC of 9.71%. The middle part of the study region, accounting for only 1.96% of the area, displayed relatively high and high fluctuations in FVC. In the future, the regions at the middle part of the lake and around the lake entrance channel of the Tarim River, accounting for 11.33% of the area, will likely show an increasing trend in FVC. The regions with either extremely low or low FVC are predicted to decrease to 14.16% of the overall area. Because the positive effects of ecological water conveyance were more significant on FVC in the study region than the influences of either temperature or precipitation, ecological water conveyance should remain the primary means of ecological restoration for Taitema Lake. [ABSTRACT FROM AUTHOR]

In recent years, grassland ecosystems have faced increasingly severe desertification, which has caused continuous changes in the vegetation composition in grassland ecosystems. Therefore, effective research on grassland plant taxa is crucial to exploring the process of grassland desertification. This study proposed a solution by constructing a UAV hyperspectral remote sensing system to collect the hyperspectral data of various species in desert grasslands. This approach overcomes the limitations of traditional grassland survey methods such as a low efficiency and insufficient spatial resolution. A streamlined 2D-CNN model with different feature enhancement modules was constructed, and an improved depth-separable convolution approach was used to classify the desert grassland plants. The model was compared with existing hyperspectral classification models, such as ResNet34 and DenseNet121, under the preprocessing condition of data downscaling by combining the variance and F-norm2. The results showed that the model outperformed the other models in terms of the overall classification accuracy, kappa coefficient, and memory occupied, achieving 99.216%, 98.735%, and 16.3 MB, respectively. This model could effectively classify desert grassland species. This method provides a new approach for monitoring grassland ecosystem degradation. [ABSTRACT FROM AUTHOR]

How to distinguish the relative role of climate change and human activities in vegetation dynamics has attracted increasing attention. However, most of the current studies concentrate on arid and semiarid regions, while the relative contributions of climate change and human activities to vegetation changes remain unclear in warm-humid regions. Based on the normalized difference vegetation index (NDVI) and climatic variables (temperature, precipitation, radiation) during 2001–2020, this study used the Theil–Sen median trend analysis, partial correlation analysis, and residual trend analysis to analyze the spatiotemporal pattern of vegetation trends, the response of vegetation to climate variations, and the climatic and anthropogenic contributions to vegetation dynamics in the warm and humid Guangdong Province of south China. Results showed that the NDVI in most areas exhibited an increasing trend. Changes in climatic variables displayed different spatial variations which, however, were not significant in most areas. Vegetation responded diversely to climate change with temperature as the most important climatic factor for vegetation improvement in most areas, while precipitation was the dominant climatic factor in the southern edge region and radiation was the dominant climatic factor in the central and western regions. Vegetation in most areas was influenced by both climate change and human activities, but the contribution rate of human activities was commonly much higher than climate change. The findings of this study are expected to enhance our understanding of the relative climatic and anthropogenic contributions to vegetation changes in warm-humid regions and provide a scientific basis for future ecological policies and ecosystem management in highly urbanized regions. [ABSTRACT FROM AUTHOR]

Desert farmland provides food for desert areas, but water is the main limiting factor of this region, thus desert farmland has an extremely fragile ecological environment. This study investigated the temporal and spatial variations of vegetation NDVI (Normalized Difference Vegetation Index) in the Ulan Buh Desert, China, from 1990 to 2022, using long-term Landsat satellite data obtained from the Google Earth Engine platform and local statistical data. The results showed that from 1990 to 2022, the NDVI exhibited relatively small fluctuations and a steady increase. Furthermore, the study analyzed the impact of climate factors, namely precipitation and temperature, on NDVI, and collected the groundwater lever changes under irrigation and farmland development. The results demonstrated a positive correlation between NDVI and both precipitation and temperature from 1990 to 2006. The study area experienced an overall trend of increasing humidity. Specifically, from 1990 to 2006, significant positive correlations with precipitation and temperature were observed in 4.4% and 5.5% of the region, respectively. From 2007 to 2022, significant positive correlations were observed in 5.4% and 72.8% of the region for precipitation and temperature, respectively. These findings suggest that temperature has become increasingly influential on vegetation NDVI, while the impact of precipitation remains relatively stable. Moreover, the study assessed the impact of human activities on vegetation NDVI. The results revealed that from 1990 to 2006, human activities contributed to 43.1% of the promotion of local vegetation NDVI, which increased to 90.9% from 2007 to 2022. This study provides valuable insights into the dynamics of vegetation in the Ulan Buh Desert and its response to climatic changes and human activities. The findings highlight the significance of climate conditions and human interventions in shaping the vegetation dynamics in the region, offering essential information for ecological restoration and conservation efforts. [ABSTRACT FROM AUTHOR]

Net primary productivity (NPP) can indirectly reflect vegetation's capacity for CO 2 fixation, but its spatiotemporal dynamics are subject to alterations to some extent due to the influences of climate change and human activities. In this study, NPP is used as an indicator to investigate vegetarian carbon ability changes in the vital ecosystems of the Yangtze River Basin (YRB) in China. We also explored the NPP responses to climate change and human activities. We conducted a comprehensive analysis of the temporal dynamics and spatial variations in NPP within the YRB ecosystems from 2003 to 2020. Furthermore, we employed residual analysis to quantitatively assess the contributions of climate factors and human activities to NPP changes. The research findings are as follows: (1) Over the 18-year period, the average NPP within the basin amounted to 543.95 gC/m 2 , displaying a noticeable fluctuating upward trend with a growth rate of approximately 3.1 gC/m 2 ; (2) The areas exhibiting an increasing trend in NPP account for 82.55% of the total study area. Regions with relatively high stability in the basin covered 62.36% of the total area, while areas with low stability accounted for 2.22%, mainly situated in the Hengduan Mountains of the western Sichuan Plateau; (3) NPP improvement was jointly driven by human activities and climate change, with human activities contributing more significantly to NPP growth. Specifically, the contributions were 65.39% in total, with human activities contributing 59.28% and climate change contributing 40.01%. This study provides an objective assessment of the contributions of human activities and climate change to vegetation productivity, offering crucial insights for future ecosystem development and environmental planning. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is a key indicator of vegetation structure and function, and its products have a wide range of applications in vegetation condition assessment and usually act as important input parameters for ecosystem modeling. Grassland plays an important role in regional climate change and the global carbon cycle and numerous studies have focused on the product-based analysis of grassland vegetation changes. However, the performance of various LAI products and their discrepancies across different grassland types in drylands remain unclear. Therefore, it is critical to assess these products prior to application. We evaluated the accuracy of four commonly used LAI products (GEOV2, GLASS, GLOBMAP, and MODIS) using LAI reference maps based on both bridging and cross-validation approaches. Under different grassland types, the GLASS LAI performed better in meadow steppe (R2 = 0.26, RMSE = 0.41 m2/m2) and typical steppe (R2 = 0.32, RMSE = 0.38 m2/m2); the GEOV2 LAI performed better in desert steppe (R2 = 0.39, RMSE = 0.30 m2/m2). When we assessed their spatial and temporal discrepancies during the period from 2010 to 2019, the four LAI products overall showed a high spatial and temporal consistency across the region. Compared with GLASS LAI, the most consistent to least consistent correlations can be ordered by GEOV2 LAI (R2 = 0.94), MODIS LAI (R2 = 0.92), and GLOBMAP LAI (R2 = 0.87). The largest differences in LAI throughout the year occurred in July for all grassland types. Limited by the location and number of sample plots, we mainly focused on spatial and temporal variations. The spatial heterogeneity of land surface is pervasive, especially in vast grassland areas with rich grassland types, and the results of this study can provide a basis for the application of the product in different grassland types. Furthermore, it is essential to develop highly accurate and reliable satellite-based LAI products focused on grassland from the regional to the global scale according to these popular approaches, which is the next step in our work plan. [ABSTRACT FROM AUTHOR]

As a vital ecological barrier in China, Yellow River Basin (YRB) is strategically significant for China's national development and modernization. However, YRB has fragile ecosystems, and is sensitive to climatic change. Extreme climate events (e.g., heavy precipitation, heatwaves, and extreme hot and cold) occur frequently in this basin, but the implications (positive and negative effects) of these events on vegetation dynamics remains insufficiently understood. Combing with net primary productivity (NPP), the normalized difference vegetation index (NDVI) and extreme climate indexes, we explored the spatio–temporal characteristics of plants' growth and extreme climate, together with the reaction of plants' growth to extreme climate in the Yellow River Basin. This study demonstrated that annual NPP and NDVI of cropland, forest, and grassland in the study region all revealed a climbing tendency. The multi-year monthly averaged NPP and NDVI were characterized by a typical unimodal distribution, with the maximum values of NPP (66.18 gC·m−2) and NDVI (0.54) occurring in July and August, respectively. Spatially, multi–year averaged of vegetation indicators decreased from southeast to northwest. During the study period, carbon flux (NPP) and vegetation index (NDVI) both exhibited improvement in most of the YRB. The extreme precipitation indexes and extreme high temperature indexes indicated an increasing tendency; however, the extreme low temperature indexes reduced over time. NPP and NDVI were negatively associated with extreme low temperature indexes and positively correlated with extreme high temperature indexes, and extreme precipitation indicators other than consecutive dry days. Time lag cross–correlation analysis displayed that the influences of extreme temperature indexes on vegetation indexes (NPP and NDVI) were delayed by approximately six months, while the effects of extreme precipitation indexes were immediate. The study outcomes contribute to our comprehension of plants' growth, and also their reaction to extreme climates, and offer essential support for evidence–based ecological management practices in the Yellow River Basin. [ABSTRACT FROM AUTHOR]

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 < 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]