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Canopy greenness is an indicator of ecosystem primary productivity, which is often limited by temperature and precipitation. Changes in vegetation greening have been reported mostly at global scales. However, we still have a poor understanding of vegetation greening patterns and drivers for major vegetation types, such as the tropical dry forest, one of the most extensive vegetation types in Mexico. Here, we analyze two decades of interannual variation in greenness and its relationship to temperature and precipitation in the northmost neotropical dry forest occurring in Northwestern Mexico. We constructed time-series linear regression models using standardized anomalies with z-scores (i.e., standard deviations away from the long-term mean) for the Normalized Difference Vegetation Index (NDVI) and the climate data from 2001 to 2021. Our best models indicate both temperature and precipitation exert positive effects on vegetation greenness, particularly from a lagged effect perspective, as retained predictors were the accumulated precipitation of two monsoon (summer) seasons and previous year mean temperature. The lowest levels of landscape greenness seem connected to prolonged droughts and extreme frost events. In fact, a switch from negative to positive NDVI anomalies was observed in the years following the February 2011 extreme frost that affected much of North America, including northern Mexico. Notable, under the stricter statistical criterion of −1.7 ≥ z score ≥ 1.7, only climatic variables presented very-extreme anomalies and these were not necessarily linked to a very-extreme response in landscape greenness. However, considering the criterion of −1.3 ≥ z score ≥ 1.3, we identified several extreme NDVI anomalies and their corresponding climatic anomaly. Therefore, more flexible statistical criteria might reveal climate extremes of ecological and social relevance. Our overall findings have implications for forest and climate risk management, particularly as extreme climatic anomalies are expected to continue increasing in light of climate change.

Land degradation is a critical issue at a global level and its progressive increasing greatly reduces soil ecosystem services. In this context, the 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015, defined the Sustainable Development Goals (SDGs) and indicated some targets of particular interest for a territory to be integrated into short- and medium-term national programs. Target 15.3, which aims to end desertification and restore degraded lands, is currently monitored by indicator 15.3.1, measured as the combination of three sub-indicators (trends in land cover change, land productivity and carbon stocks) as suggested by the United Nations Convention to Combat Desertification (UNCCD), the custodian agency for the SDG indicator. In our opinion, this assessment shows some weakness that are generally caused by a lack of information from direct field observations. The greatest limitation regards land productivity dynamics linked to the NDVI trajectory adopted by the UNCCD methodological approach. For this reason, the paper proposes an alternative approach that consists of using annual maximum NDVI value assessments instead of annual mean values for trajectory calculation. To come to these conclusions, the study addresses a reliability assessment by using remote sensing techniques via the Google Earth Engine (GEE) and analysing the NDVI evolution over time at 450 locations spread around the Campania region (southern Italy). To this end, a customised Graphical User Interface (GUI) was built on the GEE platform and a Google Earth time slider tool was applied to visualize land cover changes which occurred at each location over a period of 18 years (2001–2018). The survey was carried out on MODIS and Landsat 7 collections and showed that the new approach had a better performance than the UNCCD approach (90 % vs 62 % of successful reliability tests, up to 96 % considering results from Landsat images). The application of maximum NDVI values to assess productivity dynamics spatially shows, with regard to UNCCD data, more than double the percentages of degraded and stable lands and a drastic reduction in improved areas within the Campania region. Overall, this innovative approach appears to agree more closely with ground truth and the use of finer resolution data is more suitable for investigating land degradation processes within a regional context.

Urban ecosystems are dominated by private lands which poses a significant hurdle to performing field-based assessment of wildlife. An alternative approach is to characterize indices of animal habitat in difficult-to-access areas using data from airborne remote sensing platforms. Characterizing indices of wildlife habitat using remotely sensed data is common in natural systems but has received less attention within urban ecosystems. We tested the utility of using remotely sensed data from high-resolution airborne sensors, including LiDAR, a measure of vertical habitat structure, NDVI, a measure of greenness, image texture, a measure of horizontal habitat structure, and parcel level land-cover data, along with field-based street-tree measurements to predict bird abundance and richness across Greater Los Angeles, California, USA. We surveyed birds and gathered street-tree data on public lands of residential neighborhoods and processed the remote sensing data in 50-m and 300-m circular buffers of bird survey locations to capture data primarily on private, residential land across three winter field seasons (2016–18, 2019/20) at 23 locations along a tree-canopy cover gradient. Data from LiDAR processed as an index for the density of trees summarized in the 50-m and 300-m extents were the strongest univariate predictors of avifaunal abundance and richness explaining 75 % and 74 % of the likelihood in fitted models. NDVI, image texture, land cover, and street-tree density measures were weaker univariate predictors than models fitted with LiDAR data. Models including LiDAR and ground-based street-tree measurements accounted for upwards of 80 % of the variability in avifaunal abundance and richness, particularly for bird species associated with trees and shrubs. We recommend the prioritization of high-resolution remote sensing data, particularly LiDAR, in combination with field-based habitat measures e.g., street trees, to characterize indices of avifaunal habitat on public and private lands of cities, which could help to improve our understanding of the distribution of birds across urban areas.

Greenery is a critical referent for assessing the health and sustainability of urban ecosystems. However, distinct urbanization pathways of cities can be accompanied by complicated encroachment or regeneration of green spaces, thereby leading to certain spatiotemporal diversity in greening signatures at both local and regional scales. Here, this study conducted a long-term (2000–2020) case analysis to evaluate the greening signatures and responsible drivers across the urban–rural gradient of Chinese cities. The major results showed that 1) Most case cities showed general but uneven greening trends. However, the urban–rural gradient shows a Sandwich pattern, i.e., the greening rate is significantly higher in developed and rural areas than in developing regions. 2) Climate change, especially temperature variation, performs as the dominant driver responsible for local greening, followed by anthropogenic-induced landscape alterations. 3) Especially, greening process in urbanized regions was more sensitive to climatic perturbations and less responsive to landscape changes over time, while that in rural regions was relatively stable. In the context of overall greening in Chinese cities, one should be aware of the uneven greening progress along the urban–rural gradient to proactively address the potential challenges of future climate and landscape changes for sustainable urban development.

The impact of environmental change on vegetation dynamics plays a key role in ecosystem evolution, with vegetation demonstrating heightened sensitivity to both climate fluctuations and human activities. However, existing studies have not fully unveiled the emergence of vegetation evolution patterns within the holistic complex systems framework. Based on long-term observational data of the normalized differential vegetation index (NDVI) and meteorological reanalysis data, this study used the mean synthesis and trend analysis methods to explore the spatial distribution and temporal variation of NDVI in the Heihe River Basin (HRB). The results show that a continuous NDVI increase in the HRB from 1982 to 2015, and the growth rate post-2000 being 1.66 times higher than pre-2000, with distinct seasonal periodic fluctuations. Spatially, vegetation thrives predominantly in the upstream and downstream riparian zones of the HRB, displaying significant spatial heterogeneity, and it shows an obvious increasing trend in the middle and lower reaches, especially after 2000. We built a theoretical method using eigen microstates to quantify the dynamic changes in ecosystem vegetation. The first two microstates can account for 64% of the NDVI variations, among which the first microstate captures the overall uptrend and seasonal periodicity of NDVI, while the second microstate highlights the key contributions of soil moisture to vegetation growth. In HRB, soil moisture emerges as the most influential factor for vegetation improvement, surpassing other meteorological variables. Meanwhile, there's a one-month lag in vegetation response to soil moisture, and deeper soil moisture exerts a more pronounced effect on vegetation coverage. This study not only validates the effective applicability of eigen microstates for identifying and attributing ecosystem changes but also provides a new perspective for comprehending the impact of soil moisture on vegetation dynamics.

Recent studies revealed that the greening in China has difficulty crossing the Hu-Line. However, the greening pattern and its driving factors remain unclear. Therefore, we explored NDVI spatio-temporal pattern and their key driving forces between southeastern and northwestern of the Hu-line from 2000 to 2020 using GDM (the geographical detector model) and trend analysis method. The following results were obtained: (1) Overall, NDVI showed a fluctuating upward trend in China over the study period, indicating greening. Although vegetation had improved significantly (48.11%) on the southeastern region of the Hu-line, it had significantly degraded (24.71%) on the northwestern region. The degradation of vegetation cover on unused and construction land was larger than the proportion of improvement. (2) GDM could identify the effect of single-factors on the NDVI variables on both side regions of the Hu-line. P (precipitation), POP (population density), and DEM (Digital Elevation), dominated NDVI’s spatial pattern in China. ET (evapotranspiration), ST (soil types) and POP had the strongest influence on the southeastern region, whereas P was the dominant factor on the northwestern region. In addition, POP, GDP (gross domestic product) and TEM (temperature) dominated the overall trend of NDVI. (3) Seven factors influenced and verged vegetation NDVI spatial distribution and change trends of vegetation were enhanced by their interaction. Overall, China is becoming greener. For various land-use types, natural factors (such as P and DEM) dominated vegetation NDVI pattern. Nevertheless, anthropogenic factors governed NDVI pattern of construction land. Anthropogenic factors mainly controlled NDVI tendency variations. The findings could effectively explain greening on both side regions of the Hu-line, and provide information for decision-making on vegetation restoration and ecological protection.

Drought caused by climate change has significantly increased vegetation vulnerability in Afghanistan during the last decades. This paper investigates vegetation response to drought at multiple scales across the country based on historical data from 1980 to 2020. It explores the multiscale relationships between drought as indicated by the grid-based standardised precipitation evapotranspiration index (SPEI) and vegetation condition as represented by the satellite-derived vegetation anomaly index (VAI). It also examines the links of dominant land cover with drought and their implications for agriculture. We assess the spatiotemporal correlations by integrating TerraClimate grids with timeseries data sourced from NOAA AVHRR (National Oceanic and Atmospheric Administration Advanced Very High-Resolution Radiometer), MODIS (Moderate Resolution Imaging Spectroradiometer) images and ESA CCI (European Space Agency Climate Change Initiative) land cover maps. We evaluated the effect of cumulative drought on predominant land covers. Our results show years 2000–2001 and 2017–2018 as the driest in the last four decades, and vegetation substantially correlated to drought with spatial and temporal variations across Afghanistan. The most sensitive vegetation response months are June to July and the most significant cumulative drought impacts on vegetation are 6 to 8 months. The predominant land covers more prone to negative effects under severe drought are (in order) shrubland, rainfed cropland, grassland, trees, and irrigated cropland. These findings suggest the sensitivity of vegetation to drought that future national scale drought management should be focused on. This study also demonstrates the usefulness of open-access data to researchers and planners in data-poor countries.

Revealing the driving mechanism of climatic change and anthropogenic activities on vegetation variation and identifying the ecologically sensitive zones in Southwest China are critical for formulating regional ecological restoration strategies. Herein, a NDVI-based spatial–temporal model in Southwest China from 2000 to 2021 under the influences of climatic change and anthropogenic activities alone and in combination was determined based on MODIS NDVI and in situ meteorological data using Theil-Sen median analysis, the Mann–Kendall significance test, residual analysis, and relative analysis. The results showed that on the annual scale, the NDVI, predicted NDVI, and residual NDVI significantly increased at rates of 0.0016 yr−1, 0.0008 yr−1, and 0.0008 yr−1 in Southwest China from 2000 to 2021, respectively. Moreover, the NDVI, predicted NDVI, and residual NDVI significantly (P

Over the past two decades (2000–2021), China has experienced a significant increase in vegetation cover. However, the intricate spatial and temporal variations in vegetation cover, arising from the interaction of climate change and human activities, remain enigmatic, particularly across diverse vegetation zones. This research endeavors to unravel the dynamics of vegetation cover using the MOD13A1 Normalized Difference Vegetation Index (NDVI) dataset, delving into China's seven distinct vegetation zones (VZ1-VZ7). This research employs residual analysis, relative analysis, and geo-detector techniques to investigate the drivers of NDVI variation and spatial heterogeneity across China's seven distinct vegetation zones. The findings reveal a compelling upward trajectory in NDVI, predicted NDVI, and residual NDVI across China during the 2000–2021 period. Notably, significant and extremely significant increases are discernible in approximately 53.90%, 50.13%, and 23.29% of China's NDVI, respectively. Among the vegetation zones, VZ3 displays the highest change rates, yet the proportion with a significant increase in VZ1 surpasses other zones. The relative analysis underscores climate change as the predominant catalyst for vegetation enhancement, although an exception is noted in VZ3. Furthermore, the spatial heterogeneity of China's NDVI is primarily influenced by the interactions between land use types and relative humidity, sunshine duration, and precipitation, especially the interaction between relative humidity and land use types. Significant changes in climate can lead to marked alterations in the structure and function of terrestrial ecosystems, while changes in land use patterns at the regional level also alter the type of land cover, which together exert a significant impact on the vegetation NDVI. These results accentuate the importance of formulating tailored ecosystem management strategies tailored to the distinct characteristics of each vegetation zone to ensure the sustainable development of terrestrial ecosystems across China.

Climate projections infer increasing frequency of extreme events in the future which would alter terrestrial hydrologic functioning and ecosystem services. Understanding the responses of vegetation to extreme events can provide prerequisite knowledge to mitigate climate change impacts. The relationships between climate extremes and vegetation phenology and productivity were examined for a transect along the Hu Line across China during 1988–2014. Results show that high frequency and severity of droughts existed in the mid-latitude regions where happened to be mostly occupied by grassland and cropland with relatively low productivity compared to trees. Prolonged growing seasons (4.3 days/decade) resulted mostly from advanced green-up were observed over the transect. No strong impact of extreme droughts on vegetation was observed for any plant functional types, due to the timing of drought occurrences primarily in pre- and post-growing seasons while water supply was likely not so limited during growth indicated by a drought index. Non-woody plants in the mid-to-high latitudes (37-54°N) were identified more sensitive to climate extremes than woody plants in the low latitudes. Meanwhile, grassland in the relatively low-elevation areas (

Water conservation is important for maintaining a well-functioning ecosystem. Using the Soil & Water Assessment Tool (SWAT) model, we simulated the hydrological cycle and evaluated the spatial and temporal variability of water conservation in the Dongjiang River. Spatial autocorrelation analysis and geographically weighted regression (GWR) were used to investigate the effects of landscape patterns and normalized difference vegetation index (NDVI) on water conservation. Four hydrological stations with regular rate, validation period deterministic coefficient R2, and efficiency coefficient ENS (Nash–Sutcliffe) met the adaptation requirements, and the model accurately simulated the hydrological process of the Dongjiang River. The interannual variation of water conservation showed a non-significant upward trend (0.12 mm/10a), and the spatial distribution is given as follows: midstream > downstream > upstream. Regression coefficients of NDVI of high, medium, and low-density forest land in the sub-basin with water conservation decreased from the upper to lower reaches. The negative correlation of LPI, CONTAG, and IJI was concentrated in the upper reaches and southeast of the basin, and that of SHDI was concentrated in the middle and lower reaches; the regression coefficients of LPI and PLAND of forest land decreased from middle reaches to north and south, and ED decreased from southwest to northeast. The positive correlation of grassland was concentrated in the watershed’s upper reaches; the regression coefficient of construction land showed an increasingly negative correlation from the lower to upper reaches. This study provides scientific support for promoting the hydrological ecosystem and comprehensive utilization of water resources in the Dongjiang River basin.

In the circumstance of global change, studying the dynamic changes of vegetation and the factors that influence it holds immense practical significance to monitor the quality of the regional ecological environment and improve the structure and function of ecosystems. Therefore, based on NDVI data, combined with natural and anthropogenic activities, this study focused on the spatiotemporal evolution characteristics and stability of NDVI in the Chengdu-Chongqing region from 2001 to 2020 at multiple spatiotemporal scales, and quantitatively investigates the main influencing factors driving vegetation NDVI spatial differentiation using Theil-Sen Medium slope estimation, Mann-Kendall significance, stability analysis and geographical detector. The findings indicated that: (1) The overall NDVI exhibited favorable levels over the 2001–2020 period; (2) The upward trends observed in the NDVI values indicated an enhancement in the ecological environment of the investigation area; (3) The spatial coefficient of variation for NDVI in the investigation region was computed as 0.048, suggesting a high degree of stability in NDVI values across the region; (4) NDVI is primarily affected by a range of variances in natural phenomena and anthropogenic activities. The collective influence of environmental factors and anthropogenic activities holds a more substantial sway over NDVI. The research results aim to offer guidance for the execution of vegetation restoration initiatives and the establishment of policies for the preservation of the ecological environment in the upstream region of the Yangtze River.

The terrestrial water storage anomaly (TWSA) is an important parameter for assessing the land water budget, and it interacts well with terrestrial ecosystems via complex hydrological processes. Recently, the decline in central Asian terrestrial water storage (TWS) has threatened the health of local ecosystems. Therefore, it is of great significance to adopt an efficient approach to explore and identify the nonlinear relationship between two important indicators, i.e., the TWSA and normalized difference vegetation index (NDVI) in the arid central Asian endorheic basins. In this study, we analysed the long-term trends of the TWSA and NDVI, and identified the lag month (1 month) as the optimal moving window of the nonlinear Granger causality test embedded in random forest to detect the nonlinear NDVI change response of NDVI changes in vegetation to the TWSA from 2003 to 2015. There are decreasing trends in TWSAs over approximately 81.7% of the study area and the NDVI generally decreased resulting in approximately 36% vegetation browning in the study area. The nonlinear Granger unidirectional causes of the TWSA were responsible for 97.9% of the NDVI variation in the study area considering the optimal response time for moving windows. The causes of vegetation browning in the central Asian Aral Sea basin and vegetation greening in the basins of Northwest China could be mostly explained by the changes in TWS. Our findings contribute to understanding the nonlinear causal linkages between vegetation and the TWSA in endorheic basins, and these findings provide insights for obtaining terrestrial water consumption patterns and water resource management under the joint influence of climate change and human activities.

Land surface phenology (LSP), an important branch of vegetation phenology, which is sensitive to climate change and play important role in ecosystem. As a typical subtropical area, in spite of its complex vegetation structure, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) LSP was not focused fully. Hence, In this study, a long time series LSP data from 2001 to 2020 was obtained based on MODIS normalized difference vegetation index (NDVI) using classical Savitzky-Golay filter. Subsequently, phenology spatial pattern and spatiotemporal trends and their correlation with climate change were analyzed for different vegetation types in the Guangdong-Hong Kong-Macao Greater Bay Area. The results revealed an obvious spatial heterogeneity and significant annual variation trend in vegetation phenology. The start of growing season (SOS) and the end of growing season (EOS) appeared delayed trend and the length of growing season (LOS) showed a prolonged trend, with average slope values of 0.55, 0.52 and 0.81 days per year, respectively. In general, preseason precipitation and solar radiation are the main climatic factors regulating vegetation phenology in the Greater Bay Area, and the enhancement of preseason precipitation and solar radiation is the main reason for the delay of vegetation withering and yellowing period and the extension of growing season. Ecosystems of the Greater Bay Area were mainly depending on preseason precipitation and solar radiation to regulate the leaf senescence and control plant growth duration. The present study can enhance performance of vegetation phenology models and provide useful information on ecological protection in subtropical areas.

Tunnel construction in karst areas greatly impacts the local hydrological conditions. The drawdown of groundwater levels and the loss of soil water storage due to tunneling limit the available water resources on the karst land surface, and this may affect the function of plants. Here, we evaluated the responses of the radial growth rates of two major tree species (Masson pine and Camphor laurel) to a tunneling induced water stress event in a typical karst trough valley. Meanwhile, to discuss the overall influence of tunnel excavation on plant growth in all vegetation cover types, the dynamics of vegetation productivity around the tunnel were evaluated using remote sensing indices, the Normalized Difference Vegetation Index (NDVI), and Net Primary Productivity (NPP). Furthermore, we estimated the spatial–temporal variations of NDVI and NPP in other tunneling regions of Southwest China. The results showed that the soil moisture contents (SMC) around the Zhongliang Mountain Railway Line 6 (ZMRL6) tunnel had dramatically declined −5.06%∼−10.53% within the areas

Vegetation is among the key elements of ecosystems, and the normalized difference vegetation index (NDVI) is among the most frequently used tools for studying changes in regional vegetation dynamics. Studying these changes and their drivers is essential for understanding the interactions between vegetation and ecosystems; therefore, here, we analyzed the spatial and temporal patterns of NDVI in Jilin Province and their influencing factors. The correlation between NDVI and climatic factors was analyzed using Pearson’s correlation analysis. Additionally, we explored the correlation between NDVI and human activity using geographically weighted regression analysis. The results were as follows: (1) From 1998 to 2020, although the temporal trends of NDVI for different vegetation types in Jilin Province showed some differences in general, they all showed an increasing trend. (2) The spatial trends of NDVI in Jilin Province ranged from − 0.031 to 0.046. The NDVI values exhibited a trend toward growth in most regions. (3) Climate change factors are more important than human activity factors for improving vegetation. (4) Among the climatic factors, the negative correlation between temperature and NDVI was stronger than the positive correlation, while the positive and negative correlations between precipitation and NDVI were equally strong. (5) Human activity factors such as GDP and population have mostly negative effects on regional NDVI, whereas land-use type shifts have mostly positive effects. The results of this study contribute to a deeper understanding of the drivers of vegetation change. Additionally, some discussions can provide theoretical references for ecological management and sustainable development in Jilin Province.

Plant diversity is very important for maintaining the structure and function of ecosystems. Affected by climate change, it is increasingly sensitive to abiotic stresses, especially in ecologically fragile arid areas. Moreover, human activities have accelerated this process, resulting in a reduction in plant diversity. Revealing the spatial pattern and effecting mechanisms of plant diversity at the watershed level is beneficial to biodiversity conservation. Here, we analyze changes in plant diversity and its influencing factors in the desert riparian forest in the lower reaches of the Kashgar River using niche theory, interspecific association analysis, geographic information system (GIS) mapping, redundancy analysis (RDA), and structural equation modelling (SEM). We show that the niche breadth of plants varies from 0.174 to 0.451, and niche overlap ranges from 0.059 to 0.995, and Populus euphratica and Tamarix chinensis are the dominant species for improving the desert environment and maintaining the stable and ecological balance of communities. Plant diversity has spatial heterogeneity that woody plants are higher than herbaceous plants, which decreases with increasing groundwater depth and salt content. Groundwater depth was the key factor to stress plant diversity. In fact, it works by influencing soil moisture and salt content. LULC change significantly reduced plant diversity (P

Anthropogenic activities and climate change play crucial roles in the productivity and sustainability of global forests. Although the impacts of anthropogenic activities and climate change on forests in specific regions have been reported in previous studies, their impact on the global forests along the elevation gradient remains poorly understood. Thus, here, we aimed to investigate anthropogenic activities and climate change effects on global forests by focusing on the drivers on the altitude gradient. To this end, we used spaceborne remote sensing and statistical methods to elucidate the forest areas experiencing significant change and the main factors governing the changes. Our analysis demonstrated that: (1) With respect to the altitude distribution of forest greening/browning, the higher the altitude is, the smaller is the indicated area of forest, and the smaller is the area of forest, the more significant is the greening/browning. In addition, although the area with significant forest greening was generally larger than that of browning, this trend was not consistent through the months or altitude gradients. (2) Except for the high-altitude areas in August and September (>2400 m), the impact of human global forest governance activities on the greening of forests in most altitude areas and months was greater than that of climate change. However, at most altitudinal gradients, the area of forest with significant browning caused by anthropogenic activities was smaller than that caused by climate change. This was because the scope of the impact of climate change was usually larger than that of anthropogenic activities. (3) Although temperature was the main factor responsible for significant monthly forest greening, it was not always the most crucial factor at specific altitudes. Temperature and sunshine duration were the most influential factors causing significant forest greening for the most months of the year in five terrestrial ecoregions. Sunshine duration, as the largest influencing factor causing significant forest browning, had the longest monthly impact in five terrestrial ecoregions. The findings of our study can provide a reference for better global forest management and sustainability.

Climate change and human activities are two major drivers of grasslands degradation. Understanding the vulnerability of grasslands to both drives is of great importance for grassland conservation. This research established a vulnerability assessment model with historical and future the Normalized Difference Vegetation Index (NDVI), which was predicted by an optimized spatiotemporal NDVI prediction model, and then examined the vulnerability of grasslands under climate change and human activities in Gannan Prefecture on the north-eastern Qinghai-Tibet Plateau. Our results show that Gannan grasslands would show a vulnerability pattern of higher in the west and lower in the east under climate change and human activities. More than 46 % and 17 % of the region will become highly and medium vulnerable areas in the future, mainly concentrated in Maqu, Luqu and Xiahe counties in the west, southwest and northwest of Gannan. Specifically, the vulnerability is the lowest under the future climate scenario of moderate carbon emissions (i.e. RCP 4.5). Land use types such as forest land, unutilized land and cultivated land conversion to grassland could partially offset the vulnerability mainly caused climate change, while the conversion of grassland to unutilized land, forest land and cultivated land would increase the vulnerability of grassland. Our results would help to deepen the understanding of the patterns and main drivers of Gannan grasslands vulnerability under the impacts of climate change and human activities, and provide theoretical basis for the development of corresponding grassland management policies.

Understanding the relationship between urbanization and vegetation is essential for sustainable urban development. Over the last few decades, China has experienced rapid and dramatic urbanization along with impressive land transformations. However, the dynamic relationship between urban expansion and vegetation cover lacks enough understanding. We proposed an urbanization-vegetation cover coordination index (UVCI) by combining the physical boundaries of cities at 10-year intervals and the 250 m vegetation index, and measured the conflict or coordination relationships between urbanization and vegetation cover for 338 cities in China from 1990 to 2018. The results show that China has experienced a process of rapid, large-scale, but uneven urban expansion over the past 30 years, with urban areas expanding by approximately 556 %. Urbanization has made China’s urban areas less green, yet vegetation cover changes vary between newly developed and older urban areas. There is a substantial tendency for vegetation degradation in the newly urbanized areas, whereas vegetation cover has generally improved in the older urban areas. The vegetation in non-urban areas has been showing a noticeable greening trend after 2010, with NDVI maintaining an annual growth rate of 0.89 %. The relationship between urbanization and urban vegetation cover in China is becoming increasingly coordinated. The proportion of conflict cities decreased from 93 % in 1990–2000 to 15 % in 2010–2018. The spatial heterogeneity of the UVCI is apparent, with northern Chinese cities having achieved a largely coordinated urban-vegetation relationship, while most southern cities still face challenges. The methodology of this study can be used as a reference for subsequent research, and the findings can help shape government policies that will support green and sustainable urban development in China.

Dam construction is economically and ecologically important for power generation and water capture. However, the effects of cascade dams with various regulation types (i.e., daily, weekly, seasonal, yearly) on vegetation coverage are still need to be clarified. In the current study, we investigated the effects of dam construction on vegetation coverage and predicted the dynamic trends of vegetation at the sub-catchment (i.e., the catchment removing the overlapping catchment of the upstream reservoir) and buffer scale of each reservoir. MODIS Terra normalized difference vegetation index (NDVI) composites (MOD13Q1, 2000–2019) were used to study spatial and temporal changes in riparian vegetation coverage. At the sub-catchment scale, the long-term mean NDVI increased significantly from upstream to downstream from 2000 to 2019, and the NDVI mutation point (i.e., turning point) was consistent with the construction year of the corresponding dam. At the temporal scale, the vegetation trend predictions indicated a greater degree of degradation (23 %–61 %) than in the past (2000–2019; 3 %–32 %). Our findings also showed that reservoir vegetation degradation was associated with regulation type, i.e., daily reservoirs experienced a higher degree of vegetation degradation. Each cascade reservoir exhibited higher past vegetation degradation within the 0–1 km buffer zone compared to the sub-catchment. However, future degradation was predicted to be much higher in the sub-catchment than in the 0–1 km buffer zone, indicating the potential impact of damming on vegetation from adjacent areas (i.e., 0–1 km buffer) with gradual expansion to more distant areas (i.e., sub-catchment) over time. A potential cascade effect of upstream dams on sub-catchment vegetation of downstream dams was also observed. This study provides a reference for watershed management and ecological civilization construction. Relevant laws and policies should be established in areas surrounding reservoirs to help mitigate dam-induced degradation of vegetation communities, especially in reservoirs with short retention times.

Inner Mongolia is one of the most sensitive regions to global change with vegetation significantly affected by global warming and human activities. Therefore, exploring the dynamic change of vegetation and its impact mechanism is of great significance for guiding the protection ecosystem and policy formulation. Based on the 1982–2015 growing season and the interannual normalized difference vegetation index (NDVI), simultaneous temperature and precipitation data sets, we extracted the characteristic components of the NDVI and climatic factors by the Variational Mode Decomposition (VMD) methods. And the periodic characteristics of the NDVI in Inner Mongolia and its relationship with climate over a period of 34 years were analyzed. The results show that: 1) The VMD algorithm overcomes the “mode aliasing” problem of Empirical Mode Decomposition (EMD) and can more accurately extract the periodic information of long time series vegetation NDVI dataset containing complex frequencies.; 2) the NDVI in the growing season of Inner Mongolia has main change cycles of one growing season (7 months) and half a growing season, and temperature and NDVI have the same double-cycle relationship, whereas precipitation has a periodic change of one growing season; 3) the NDVI cycle change of Inner Mongolia vegetation from 1982 to 2015 shows multiple time scales, showing 16.95a, 6.8a, 4.85a, 4.25a, 3.4a and 2.83a change cycles; 4) vegetation NDVI in Inner Mongolia from 1982 to 2015 has a good correlation with climate factors, and the partial correlation coefficients are different in Inner Mongolia and three sampling areas. NDVI and precipitation have relatively good periodic consistency, and there is a significant positive correlation between vegetation NDVI and temperature and precipitation before and after removing the interference of periodic variables. In general, temperature and precipitation jointly affect the NDVI cyclical changes of Inner Mongolia. The VMD algorithm has significant effects on determining the cyclic dynamic changes of NDVI and climatic factors.

• We analyzed two decades of anomalies of NDVI, precipitation and temperature. • Temperature and precipitation exert positive effects on vegetation greenness. • Lower landscape greenness seems related to droughts and extreme frost events. • Landscape greenness increased steadily after the 2011 extreme frost disturbance. Canopy greenness is an indicator of ecosystem primary productivity, which is often limited by temperature and precipitation. Changes in vegetation greening have been reported mostly at global scales. However, we still have a poor understanding of vegetation greening patterns and drivers for major vegetation types, such as the tropical dry forest, one of the most extensive vegetation types in Mexico. Here, we analyze two decades of interannual variation in greenness and its relationship to temperature and precipitation in the northmost neotropical dry forest occurring in Northwestern Mexico. We constructed time-series linear regression models using standardized anomalies with z-scores (i.e., standard deviations away from the long-term mean) for the Normalized Difference Vegetation Index (NDVI) and the climate data from 2001 to 2021. Our best models indicate both temperature and precipitation exert positive effects on vegetation greenness, particularly from a lagged effect perspective, as retained predictors were the accumulated precipitation of two monsoon (summer) seasons and previous year mean temperature. The lowest levels of landscape greenness seem connected to prolonged droughts and extreme frost events. In fact, a switch from negative to positive NDVI anomalies was observed in the years following the February 2011 extreme frost that affected much of North America, including northern Mexico. Notable, under the stricter statistical criterion of −1.7 ≥ z score ≥ 1.7, only climatic variables presented very-extreme anomalies and these were not necessarily linked to a very-extreme response in landscape greenness. However, considering the criterion of −1.3 ≥ z score ≥ 1.3, we identified several extreme NDVI anomalies and their corresponding climatic anomaly. Therefore, more flexible statistical criteria might reveal climate extremes of ecological and social relevance. Our overall findings have implications for forest and climate risk management, particularly as extreme climatic anomalies are expected to continue increasing in light of climate change. [ABSTRACT FROM AUTHOR]

Spatial heterogeneity as well as landscape services’ provision are a function of spatio-temporal scales, therefore, pattern–process relationships must be assessed at the multiple scales. In this context, this research aims at: (1) analyzing at the regional scale how pollination service can be affected by landscape heterogeneity, using two landscape indicators useful to quantify the multiscale landscape composition and landscape configuration simultaneously; and (2) assessing the effect that the infection of Xylella fastidiosa has exerted on the pollination services. The multi-scale spatial assessment has been focused on two land-covers: forests and olive groves that can act as source of pollination services. The multi-temporal analysis, based on the annual NDVI, has been used to assess the functionality of olive groves before and at the beginning of the infection of Xylella fastidiosa, and currently. The results have shown that in 2012 the most representative cluster (C1) (73.6% for forests and 63% for olive groves) is in the lower left part of the multiscale metric space, meaning that both land-covers show a fragmented spatial configuration at small spatial scales and tend to be aggregated at large scales. The multi-temporal analysis has allowed to show the evident change in the landscape functioning in the provinces interested by the infection of Xylella fastidiosa (Lecce, Brindisi and Taranto) from 2013 to 2021, highlighting that the stability of the landscape has resulted completely changed for the loss of permanent land-covers (olive groves). In this study the spatio-temporal analyses have helped in giving a more complete indication in the assessment of landscape services where different factors can play a crucial role. The analysis of spatial patterns along a continuum of scale has been implemented by the analysis of multi-temporal dynamics to consider the effect of Xylella fastidiosa infection on pollination. The temporal behavior of NDVI has resulted completely changed in the provinces interested by this infection, meaning that recovering policies need to be undertaken to regenerate the landscape. When studying landscape services, some considerations must be considered in choosing the suitable spatial and temporal scale for its assessment. One of the recent drivers of change, represented by the infection of Xylella fastidiosa, and the planning of landscape functionality recovery interest higher spatial scales and will affect the scale at which landscape services, included pollination, are delivered.

Vegetation, being a core component of ecosystems, is known to be influenced by natural and anthropogenic factors. Studying vegetation cover dynamics variation and its drivers is critical to understanding the interactions between vegetation and ecosystems. The Loess Plateau (LP) is located in a semi-arid and semi-humid region with severe soil erosion and fragile ecology. This paper used the annual maximum Normalized Difference Vegetation Index (NDVIymax) and growing season mean NDVI (NDVIgsmean) as the vegetation cover indicator. The vegetation cover variation of LP from 2000 to 2020 was analyzed using Sen's slope and Mann-Kendall test. Then the influence of natural and anthropogenic factors on the driving mechanisms of spatial vegetation differentiation was explored by the Geodetector model. The results revealed that the growth trends of NDVIymax and NDVIgsmean were 0.075/10 years and 0.038/10 years, respectively, and the areas with improved vegetation cover accounted for 92.67 % and 88.58 % of the total area. The vegetation cover of the southeastern and northern parts of the LP decreased significantly; however, the central, southwestern, and northeastern parts exhibited a remarkable improvement. Precipitation, vegetation type, soil type, temperature, and land use type were the key driving forces, ranked differently on NDVIymax and NDVIgsmean. However, precipitation was the most critical factor both on NDVIymax and NDVIgsmean. The interaction detection showed non-linear and mutual enhancement, with no independent factor. The findings of our study can assist in identifying the vegetation cover status of the LP, as well as the driving forces, which can provide theoretical support for the formulation of environmental conservation policies.

Yellow River Basin (YRB), a climate-sensitive and ecologically compromised area in China, is increasingly affected by extreme climate events (especially droughts) resulting from climate change and frequent human activity. Vegetation responds asymmetrically to drought with cumulative and time-lag effects, whereas response across various climatic zones and diverse vegetation types in the YRB remains unclear. To address this deficiency, we examined the spatiotemporal patterns of accumulated and lagged drought effects on vegetation dynamics for the period 1982 to 2015. The examination was based on the long-term Normalized Difference Vegetation Index (NDVI) and multiscale dataset of the Standardized Precipitation Evapotranspiration Index (SPEI). Cumulative (time-lag) effects were determined via the maximum correlation between the NDVI and the one- to 12-month timescale SPEI (one-month timescale SPEI), as well as the corresponding months of optimal response to drought. The main findings were as follows: (1) Accumulated and lagged drought significantly affected approximately 50% and 60% of the vegetated area in the YRB, respectively, with the strongest effects and the corresponding optimal months varying across climatic zones and vegetation types. (2) In general, vegetation in the arid zone tended to be more sensitive and resistant to drought, as evidenced by the occurrence of lagged and accumulated drought effects mostly in the short-term (one–three months) and medium-term (six–eight months), respectively. This finding may be related to the vegetation’s strategy for coping with water deficits. (3) The biome-level effects of drought on grassland and cultivated vegetation were stronger than those on forests, which may be associated with differences in the functional characteristics of root systems. (4) Annual water availability significantly affected the spatiotemporal patterns by which the NDVI responded to droughts of multiple timescales, with the correlation coefficients and corresponding months of response decreasing with increasing average annual SPEI. These results indicate that vegetated areas with low water availability were more susceptible to cumulative (time-lag) droughts. (5) Independent of the climate zone or vegetation type, drought cumulatively affected vegetation more than time-lag effects. The study improves the knowledge of climate–vegetation relationships in the YRB and provides theoretical support for addressing drought risk in a changing climate.

Temperate desert vegetation (TDV) is subjected to long-term seasonal or intermittent water stress, and precipitation is the main water source for desert vegetation. However, due to the limitations of existing research methods, data resources and spatiotemporal differences, universal conclusions and benchmark references at large spatial scales under the background of climate warming have been lacking in recent decades. In a systematic review, we evaluated primary studies and comprehensively analyzed the pooled correlation (pooled r (Pr)) between precipitation and TDV (P-TDV) in the Northern Hemisphere (NH) over the past 40 years by using a meta-analysis method to explore the monthly scale response pattern and heterogeneity pattern and to reveal the key TDV growth period under drought stress at a large spatial scale. The results showed the following. (1) In the mean state, the correlation between precipitation and TDV was at a moderate level in the NH over the last 40 years (Pr = 0.31). The spring Pr value was lowest, and the results were nonsignificant (p > 0.05). The highest Pr value was 0.40, and this value appeared in summer, followed by a value of 0.22 in autumn; the strongest relationship was found in June (Pr = 0.56). (2) The Pr values of TDV and precipitation with 1- and 2-month lag times were 0.34 and 0.31, respectively; these values were higher than those derived using lag times longer than 2 months. The statistical results were all significant and reflected a moderate correlation level. The above conclusions showed that TDV growth has the strongest demand for water in June and that spring drought conditions may have been one of the main stress factors affecting the growth of desert vegetation at the intra-annual scale in the northern temperate zone over the past 40 years.

Anticipating and mitigating the effects climate change will have on wildlife populations requires an improved understanding of the ways in which those populations are currently adapted to climate and how they are affected by variation in weather conditions. We used over 70,000 greater sage-grouse (Centrocercus urophasianus) wings, derived from hunter harvest in three western states, to characterize spatiotemporal variation in sage-grouse productivity throughout the North American Great Basin during 1993 – 2020. We then tested the hypothesis that previously-identified associations between precipitation and sage-grouse productivity are mediated by the availability of mesic habitats, which provide the diet resources required by broods during typical late-summer seasonal drought. We used random forest regression to model sage-grouse productivity as a function of mesic habitat availability (defined as those areas with maximum Normalized Difference Vegetation Index (NDVI) ≥0.3) during the late brood-rearing period, the more general effect of annual precipitation, and landcover composition. We also evaluated potential acute direct effects of exposure to inclement weather on sage-grouse productivity. Finally, we examined which weather and topographic variables best predict mesic habitat availability. We found the predicted positive relationship between mesic habitat availability and sage-grouse productivity, but annual precipitation explained additional variation in productivity even after accounting for mesic habitat availability. Hence, precipitation and drought may drive sage-grouse productivity via more than one mechanism acting on multiple demographic rates. Productivity was also limited by exotic annual grass invasion and conifer encroachment. Mesic habitat availability was a function of topographic relief, mean elevation, annual mean snow water equivalent, and winter temperatures, indicating that snowpack recharges the late summer mesic resources that support sage-grouse productivity. Management actions focused on maintaining and restoring mesic resources and drought resilient habitats, limiting the spread of exotic annual grasses, and reversing conifer encroachment should support future sage-grouse recruitment and help mitigate the effects of climate change.

The development of near-Earth remote sensing platforms, such as unmanned aerial vehicles (UAV), have provided new opportunities for wetland plant diversity monitoring. Most previous studies on the relationship between spectral and species diversity have focused on the development and use of spectral indices. However, commonly used species diversity indices may not be the best choice for spectral-species diversity research. In this paper, high spatial resolution multispectral images of freshwater marsh were obtained based on UAV in Sanjiang National Nature Reserve, Northeast China, and the commonly used species diversity indices (Richness, Shannon, and Gini-Simpson) and Hill numbers were calculated based on 135 quadrats information obtained from field surveys. The mean value of NDVI (NDVIMEAN), its standard deviation (NDVISD) and coefficient of variation (NDVICV) of each quadrat were calculated based on multispectral imagery as a proxy for the spectral indices of the quadrats. The univariate and multivariate linear models were employed to test the predictive ability of NDVI-related indices for commonly used species diversity indices and Hill numbers. The results showed that the predictive ability of NDVIMEAN for species diversity indices was limited, and the combined use of NDVIMEAN and NDVISD significantly improved the predictive ability of species diversity. The predictive ability of NDVI-related indices to Hill numbers is better than that of commonly used species diversity indices. Commonly used species diversity indices can only represent one or several “point” of the community species diversity, the Hill numbers provide a continuous measure of community species diversity, which can balance the inconsistency between the abundance and coverage of species in the community. Previous spectral-species diversity studies might not have shown the real predictive ability of species diversity based on NDVI-related indices. Our study provides innovative ideas for the selection of species diversity indices in future spectral-species diversity studies.

The Qilian Mountains (QLMs) has functioned as an ecological barrier area; water connotation area; priority area for biodiversity conservation; practice area; bearing area; and demonstration area for the construction of ecological civilization in China; and its ecological function and demonstration role cannot be ignored. To investigate the response of vegetation to climate change and human activities, the GIMMS NDVI3g, MODIS NDVI, meteorological data, and nighttime light data were analyzed using Theil-Sen trend analysis, Mann-Kendall significance test, Hurst index, and correlation analysis to explore the spatial and temporal characteristics and trends of vegetation in the QLMs from 1982 to 2020. The results show that from 1982 to 2020, the NDVI in the QLMs showed an increasing trend in the interannual, growing season (0.0011/a), and through the four seasons—spring (0.0003/a), summer (0.0015/a), autumn (0.0007/a), and winter; and demonstrated a high spatial pattern in the southeast and low spatial pattern in the northwest. The NDVI showed a gradual increase in the improved area (46.43% → 79.32%), a gradual decrease in the unchanged area (50.81% → 18.23%), and a stable degraded area (2.76% → 2.45%), but the distribution of the degraded area shifted from the northern part of Daxue Mountain to the Qinghai Lake and Datong River basin, and the intersection of Qinghai South Mountain and Laji Mountain. Additionally, the vegetation cover was not persistent, and the area of unclear future was more than half and distributed in the central and western parts of the QLMs, with obvious anti-sustainability characteristics. Temperature was the dominant climatic factor for vegetation growth in the QLMs, and from the spatial distribution, the area controlled by temperature (63.81%) was significantly higher than that controlled by precipitation (29.95%). On a temporal scale, NDVI during spring, autumn, and winter was positively correlated with the temperature and negatively correlated with precipitation; and vegetation growth was influenced by temperature for a longer period of time during the year. NDVI has an obvious lagging effect on climate change, with a maximum response to temperature and precipitation lagging by approximately 2–3 months and 0–1 month, respectively. Human activities have both promoting and inhibiting effects on vegetation. In human activity areas, NDVI shows an increasing trend, and the ecological environment is improving annually.

Achieving zero land degradation neutrality in Aeolian Desertified Land (ADL) is a challenging task that requires continuous scientific understanding and technical efforts to effectively manage natural resources for economic development while meeting environmental needs. This study used Landsat collection and annual average climate data from the growing season to investigate the efficacy of established land management policies to restore degraded land and reverse the aeolian desertification process in the Shiyang river basin between 1974 and 2020. The time-series Normalized Difference Vegetation Index (NDVI) results revealed that greening and browning trend patterns depend on climatic conditions in rangelands and water allocation management systems in farmlands. Thanks to land rehabilitation and watershed management projects, farmland has been extended to 3.1%, shifting sand area has been reduced to 9.2%, and water in Qingtu lake area re-emergedfrom 0 to 17.05 Km2 in the early 2010 s. Built-up areas also extended to 87.05 km2 between 2010 and 2020 due to extensive construction of solar power panels in the area. The severity of desertification is profoundly linked to climate and land-use management systems in the Shiyang river basin. Moreover, the ADL area declined from 14.65% to 10.74% of the entire river basin, where intensity has steadily reversed in several basin parts, mainly in the middle reach. Around 2248 km2 of land were entirely rehabilitated, 2298 km2 remained steady, 1706 km2 exhibited a reverse in desertification intensity, 199 km2 degraded, and 277 km2 of land were newly turned into ADL between 1974 and 2020, with a large portion of degraded land occurred in the lower reach of the Basin. These findings highlight land management contribution, rational water allocation, and conservation measures to reverse desertification and maintain a sustainable ecosystem despite climate change impacts and water resources shortage in the Shiyang river basin. This research will help decision-makers set up novel land management programs for better eco-environment development in the future.

Groundwater depth (GWD) is an important factor to sustain the ecological integrity of some ecosystems and is often used as an indicator of environmental quality in dry areas. Single-scale data gained from quadrat surveys is always used to establish a relationship with GWD to determine the optimum GWD. However, the randomness and uncertainty in single-scale data may result in insufficient reliability of results. To overcome this shortage, multiple growth indicators of poplar trees (Populus euphratica) in Hetao Irrigation District, including average crown width (ACW), tree height, diameter at breast height (DBH), mean ring spacing (MRC), and normalized difference vegetation index (NDVI), were acquired by field sampling and remote sensing. These indicators were used to establish relationships with the GWD by considering spatial and temporal variation to identify the optimum GWD. The cloud model was introduced and its three digital features derived from optimum groundwater depth data (expectation: Ex, entropy: En, and super-entropy: He) were calculated to construct the reverse cloud models W (Ex, En, He) for describing ecological GWD to determine the optimum ecological GWD in semi-arid areas. The results show that the optimum GWD range was 1.60–2.20 m. The cloud models obtained on spatial and temporal scales were WS (2.01, 0.07, 0.04) and WT (1.78, 0.10, 0.02), respectively. The resulting comprehensive cloud model WC (1.87, 0.14, 0.03) exhibited better variability, so 1.87 m was taken as the optimum GWD for poplars. This method can determine the regional ecological groundwater level more accurately and effectively, and provide evaluation indicators for the management of regional groundwater.

Within the context of global climate changes, rainstorms have increasingly become one of the important ecological interferences limiting plant growth and population turnover. However, due to the significant spatial and temporal variability of rainstorms, determining their quantitative relationships with plant growth at some longer temporal and larger spatial scales is challenging. In this study, the relationships between rainstorm changes and vegetation activities are explored by analysing the spatial and temporal correlations between the normalized difference vegetation index (NDVI) and rainstorm frequency on the coast of China during the period of 1982–2015. Our results show that: (1) Both rainstorm frequency and vegetation activities tend to increase gradually over the study period. The impact of rainstorms on vegetation in the northern part of the study area is greater than that in the southern part. (2) Overall, rainstorms have a significant (p

Attributing vegetation change in regions with complex ecosystems is fundamental for environmental management. The Manas River Basin in northwest China has a complex mountain-oasis-desert ecosystem sensitive to environmental change because of the arid and cold climate. This study investigated the spatiotemporal variations in vegetation coverage, and further analyzed the attributions in the Manas River basin. In particular, we separated the contributions of climate change and CO2 fertilization from human activities for each subregion and the whole basin. From 1982 to 2015, the mean annual NDVI values varied with ecosystems, i.e., high in the oasis ecosystem and low in the mountain and desert ecosystems. NDVI significantly increased at a rate of 0.021/10a for the whole study period and for the whole basin, but had spatial and temporal variations. The increase rates of NDVI were larger in the oasis ecosystem than other ecosystems, and they were larger for the period 1982–1997 (0.03/10a) relative to 1998–2015 (0.02/10a). Compared with the period 1982–1997, the vegetation for 1998–2015 was less correlated with climate and more strongly perturbed by human activities. Quantification showed that the anthropogenic contributions of vegetation change were 87% and 88% for the whole basin and the oasis ecosystem, respectively; however, the vegetation change in the mountain and desert ecosystems was overwhelmingly dominated by climate change and CO2 fertilization. The heterogeneity in changes and driving forces of vegetation highlights that the environmental management should be differentiated over space and time for those regions with complex ecosystems.

To understand the spatiotemporal evolution and driving mechanisms of vegetation in the Yellow River Basin, as well as to promote regional ecological construction, we used MODIS NDVI data as the vegetation growth indicator, and the methods of Theil-Sen slope statistics, Mann-Kendall, GeoDetector, correlation analysis, and residual trend analysis. The results show that the spatial distribution of NDVI grows stepwise from northwest to southeast, leached soil with high organic matter content and weak water erosion can make vegetation grow better. The growing season average NDVI is a fluctuating upward trend of 0.005/a during 2000–2020, areas with obviously improved vegetation mainly distributed in the Qinling Mountains, the Northern Shaanxi Plateau, and the Lvliang Mountains in the basin. The partial correlation coefficients of NDVI with precipitation and temperature are 0.57 and 0.53, respectively, and the impact of precipitation on vegetation is higher than temperature. The areas where human activities increase vegetation rapidly are mainly distributed in the northern Shaanxi Plateau, the Lvliang Mountains, and southern Ningxia. The areas where human activities decrease vegetation growth are mainly distributed in cities with strong human activities such as Yinchuan, Baotou, Xi'an, Luoyang, Zhengzhou, and Taiyuan. Human activities and climate change contribute 66% and 34% of the vegetation change respectively in the YRB.

Critical ecological restoration and reconstruction information can be presented by distinguishing the vegetation dynamics due to human activities and climate changes and by determining the main driving factors. In this study, the long-term (1982–2020) satellite-derived Normalized Difference Vegetation Index (NDVI) data sets and climatic factors were employed to analyze the spatiotemporal patterns of the vegetation dynamics in Mu Us Sandy Land (MUSL). In addition, this study investigated their links to variations of temperatures (T) and precipitations (P), and the contribution of human activities and climate change to the vegetation dynamics using the residual analysis were investigated. The results revealed a statistically significant overall greening trend in NDVI changes in 1982–2020 at the MUSL regional scale. To be more specific, this greening trend underwent two distinct periods separated by the breakpoint in 2006. The NDVI first slowly increased before 2006, and then the greening trend rose dramatically after 2006. At the pixel scale, pixels exhibiting greening tendencies exceeded browning trends observably. The correlation between the NDVI and P and T was largely positive, indicating that the P and the T could well explain the dynamic vegetation changes. However, the climatic driving factors for the vegetation growth in the MUSL varied at different time scales. On the monthly scale, T was the main climatic driving factor for the vegetation growth, while P dominated at the annual scale. In addition, the residual analysis indicated that human activities accounted for 56.44% of the NDVI change, while climate change accounted for 43.56%, demonstrating that human activities acted as the dominant drive for vegetation changes. As opposed to the above, degradation was attributed to human activities (52.23%) and climate change (47.77%), respectively. These results are expected to deepen the insights into the driving factors of vegetation change and provide theoretical guidance for ecological management and sustainable development in the MUSL.

Convenient and reliable large-scale crop yield prediction is needed when formulating administrative plans and ensuring food security, especially under changing climate and international conditions. In this study, we explored Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices- and phenology-based yield prediction generalization model taking the US Corn Belt as an example. We calculated the normalized difference vegetation index (NDVI) and 2-band enhanced vegetation index (EVI2) time series, which were adjusted using greenup dates derived from the Land Cover Dynamics product MCD12Q2. Based on the adjusted VI (NDVI, EVI2) time series, the VI change rate (dVI) time series was calculated, which represents crop growth rate. The first step was to cluster the adjusted VI and dVI time series, called ‘greenup groups’, according to corresponding greenup dates with a five-day interval. Then in different greenup groups, we constructed empirical univariate models with VI having the maximum correlation with crop yield, and multivariate models with VI and dVI, which were also used to construct the generalized model. After clustering, the days with maximum VI correlation gradually decreased as greenup days increasing, and the univariate VI model and multivariate VI and dVI model performances in different groups improved. The generalized models with specific VI and dVI variables in each group predicted yields of corn and soybean with R2 values mainly ranging from 0.55 to 0.75 and 0.55 to 0.70, while RMSE mainly ranging from 1000 to 1500 kg/ha and 300 to 400 kg/ha for both NDVI and EVI2 from 2008 to 2018 with leave-one-year-out cross-validation for all groups. The model using MODIS data was convenient and scalable with limited data requirements and date-determined variables after greenup, and offered a generalized method to predict crop yields at a large scale before harvest with good performance.

• There are few examples of field survey data being utilized to test the effectiveness of UN SDG indicator 15.3.1. • The annual mean of NDVI shows some weakness in detecting land productivity dynamics. • Remote sensing coupled with a cloud-based computing platform may improve knowledge of the land productivity index. • The trend of maximum NDVI values appears more suitable for the assessing of land degradation processes. • Analysis of finer resolution satellite datasets is the best solution for regional/local contexts. Land degradation is a critical issue at a global level and its progressive increasing greatly reduces soil ecosystem services. In this context, the 2030 Agenda for Sustainable Development, adopted by all United Nations Member States in 2015, defined the Sustainable Development Goals (SDGs) and indicated some targets of particular interest for a territory to be integrated into short- and medium-term national programs. Target 15.3, which aims to end desertification and restore degraded lands, is currently monitored by indicator 15.3.1, measured as the combination of three sub-indicators (trends in land cover change, land productivity and carbon stocks) as suggested by the United Nations Convention to Combat Desertification (UNCCD), the custodian agency for the SDG indicator. In our opinion, this assessment shows some weakness that are generally caused by a lack of information from direct field observations. The greatest limitation regards land productivity dynamics linked to the NDVI trajectory adopted by the UNCCD methodological approach. For this reason, the paper proposes an alternative approach that consists of using annual maximum NDVI value assessments instead of annual mean values for trajectory calculation. To come to these conclusions, the study addresses a reliability assessment by using remote sensing techniques via the Google Earth Engine (GEE) and analysing the NDVI evolution over time at 450 locations spread around the Campania region (southern Italy). To this end, a customised Graphical User Interface (GUI) was built on the GEE platform and a Google Earth time slider tool was applied to visualize land cover changes which occurred at each location over a period of 18 years (2001–2018). The survey was carried out on MODIS and Landsat 7 collections and showed that the new approach had a better performance than the UNCCD approach (90 % vs 62 % of successful reliability tests, up to 96 % considering results from Landsat images). The application of maximum NDVI values to assess productivity dynamics spatially shows, with regard to UNCCD data, more than double the percentages of degraded and stable lands and a drastic reduction in improved areas within the Campania region. Overall, this innovative approach appears to agree more closely with ground truth and the use of finer resolution data is more suitable for investigating land degradation processes within a regional context. [ABSTRACT FROM AUTHOR]

Forests have become increasingly stressed by climate change, including in Romania over recent decades, but their response to climate dynamics has not yet been analysed in this country. This study aims to investigate, for the first time, recent ecological changes in forests across Romania, in relation to climate dynamics that affected the country from 1987 to 2018. To this end, countrywide remote sensing (Landsat) data were processed for forest boundaries over the 32 years, in order to compute annual (summer season data) Normalized Difference Vegetation Index (NDVI) datasets, which were subsequently investigated as trends using the Mann-Kendall test and Sen's slope estimator. Simultaneously, various climatic data (temperature, precipitation and reference evapotranspiration) were processed through interpolation techniques and via the same two statistical tools, and were subsequently used for exploring the impact of climate change on Romanian forestlands after 1987. The results highlighted general greening (increasing NDVI) trends of forests nationally (65% of all NDVI changes, which total over 30.000 km2 across Romania), which were dominated by widespread positive NDVI trends detected in the Carpathians region of Romania. This general ecological dynamic suggests a possible enhancement in vegetation productivity in the country’s high-altitude areas. Contrasting browning (decreasing NDVI) trends were found for 35% of Romanian forestlands marked by NDVI changes, especially apparent in the Extra-Carpathians (lowland) region, which indicates that in these cases forests were degraded or devitalized. However, the statistical significance of both greening and browning trends is limited across the country. The analysis of climatic trends and of correlations between annual NDVI and climate data indicated that recent warming throughout Carpathians may be an important driving force of forest greening in temperature-limited mountain regions. This finding is supported by an at least moderate intensity of air temperature – NDVI relationships (r correlation coefficient values of ∼50%, the highest of all eco-climatic relationships analysed), generally detected throughout mountain environments. At the same time, it seems that evapotranspiration increase accounted at least in part for forest browning in lowland areas, while the impact of precipitation in forest ecological dynamics remains unclear. All these findings can be useful for a better forest management under the future climate change conditions in Romania.

A deep understanding of the response characteristics of vegetation to drought is important for ecosystem and water resource management. In this study, we investigate spatiotemporal patterns of vegetation responses to droughts and their causes in the Qinling Mountains (QMs). The study showed that (1) there was a significant positive correlation (p

The study of the feedback relationship between vegetation and soil water is of great significance for improving the understanding of hydrological process, soil and water conservation and water resources utilization. However, the mutual feedback between vegetation and soil moisture had remained unresolved. To this end, the feedback relationships between NDVI and soil moisture (SM) over the Loess Plateau (LP) were studied by using the Granger causality method. Three layers of soil moisture data were used, with depths of 0–10 cm (SM1), 10–40 cm (SM2) and 40–100 cm (SM3), respectively. The spatial distribution characteristics of causality between NDVI and SM1/ SM2/SM3 were further analyzed. Results indicated that (1) NDVI and SM1 were positively correlated in the whole region of the LP, while NDVI and SM2/SM3 were negatively correlated in the southeast of the region; (2) compared with SM2 and SM3, SM1 and NDVI had a more significant mutual feedback relationship; (3) after the program of “Grain for Green” on the LP, the vegetation increased, the feedback relationship between NDVI and SM1 was further enhanced, and the mutual feedback relationship was the most obvious in the key areas of revegetation. In general, this study sheds new insights into the feedback between vegetation and soil moisture, which can help guide the adjustment of local agricultural structure and ecological restoration.

The Three-River Headwaters Region (TRHR) is an area of crucial significance for the ecological security of China. Owing to dramatic climate change and human activities driven by a series of ecological restoration projects, the ecological environment and vegetation have changed drastically in recent years. However, our knowledge of the current state and driving force of the change in the entire region is limited. This study mapped the spatiotemporal changes in vegetation coverage and quantitatively identified the contributions of climate change and anthropogenic factors to such variations from 2000 to 2018 using 133 MODIS NDVI images at 250 m resolution. The results show that over the past 19 years, the average NDVI demonstrated a fluctuating but upward trend, with a rate of increase of 0.0013 yr−1. Areas with increased vegetation coverage accounted for 54.93% of the entire area, and among them, 18.52% showed a significant greening trend. Based on the Hurst exponent, areas with sustained increasing trends were primarily concentrated in the northeastern, western, and southwestern parts of the Yellow River region, and the western Yangtze River region. The average contributions of the precipitation, temperature, and sunshine duration to NDVI were 0.069, 0.098, and 0.059%·yr−1, respectively. Sunshine duration had the greatest influence on the inter-annual change of NDVI during the entire study period, while the temperature was the main climatic factor from 2000 to 2010. The average contributions of climate change and anthropogenic factors to inter-annual variations of NDVI were 0.226 and –0.096%·yr−1, respectively. The general climate conditions are beneficial to the vegetation recovery, whereas human activities negatively impact the vegetation growth. However, the ecological afforestation projects positively influenced the east Yellow River, Lancang River, and west Yangtze River regions.

Vegetation is a key component of the terrestrial ecosystems, its dynamics serve as an indicator of vegetation health and ecosystem stability. However, the vegetation dynamics and its responses to climatic change and human activities remain unclear in southwestern China. The normalized difference vegetation index (NDVI) is widely used as a proxy of vegetation production. Here, a breakpoint method was used to better understand the vegetation trend dynamics based on the third generation Global Inventory Monitoring and Modeling System (GIMMS3g) NDVI in southwestern China. Meanwhile, a partial correlation analysis and residual trend (RESTREND) analysis were employed to detect the climatic and anthropogenic drivers behind vegetation dynamics. We found that the trend showed high heterogeneity in space. The annual mean NDVI generally increased at a rate of 0.0011 year−1 from 1982 to 2015, but much of the study area showed an opposite trend before and after the breakpoint. In the western part, the vegetation degraded with a reversal of trend from increase to decrease, where the vegetation types were mainly evergreen needle-leaved and grassland. In contrast, the vegetation increased significantly in the eastern part, showing greening trend regardless of browning or greening trend before the breakpoint. Temperature had a positive correlation with vegetation in the eastern part of the study area, while also had some negative correlations with vegetation in western mountainous areas where surface soils are prone to droughts. Solar radiation had a positive correlation with vegetation in the western part of the study area. Overall, temperature was the dominant climatic factor, and intensified human activities of afforestation and agriculture activities leaded to the strong greening trend. Our findings improved the understanding of long-term changes in vegetation and the roles of climate and anthropogenic activities over southwestern China.

With the intensification of extreme weather events in recent years, the drought, flood and fire caused by super El Niño - Southern Oscillation (ENSO) led to lot’s of vegetation destruction in multiple tropical regions. However, little is known about the influence of ENSO on tropical vegetation of long time series (especially after 2015) and the influence’s internal mechanism. Based on generating the long-term vegetation data (combined multiple satellite sensors data), a new index (Vegetation - ENSO response index, VERI) was created to evaluate the influence of ENSO on vegetation more accurately and comprehensively, which considering several factors including the correlation between ENSO index and NDVI anomaly, the change amplitude of NDVI in ENSO events and the duration time and lag time of the influence of ENSO on vegetation. The analysis indicated that tropical vegetation exhibited a fluctuating increase state with an overall increasing range of 4.46%, and the greening trends were the most significant and dramatical in the Indian Peninsula (0.0031/y), the coast of the Gulf of Guinea (0.0026/y), and the Amazon River estuary (0.0025/y). ENSO can lead to significant enhancement or degradation of vegetation in at least 10% of tropical regions, especially in East Africa (|VERI| =0.16), Southern Africa (|VERI|=0.18) and Indonesia (|VERI|=0.14). Among them, shrubs are the most widely affected areas (32.08%) due to limited water resources. ENSO's greening effect is mainly accomplished by increasing precipitation (warm/r = 0.166, cold/r = 0.249) and its degradation effect is more closely related to temperature increase (warm/r = −0.374, cold/r = −0.101). The change of VERI in different periods indicated that the ENSO's influence on vegetation has changed after 2000, which meant the improving effect of cold events on tropical vegetation was weakened, while the inhibiting effect was enhanced. These findings can help us better understand and respond to the effects of ENSO on tropical vegetation in the context of global change.

As an important part of the terrestrial ecosystem, vegetation is commonly used as an indicator to evaluate climate change and ecological restoration efforts. In China’s farming-pastoral areas, the Mu Us Desert is a typical area utilizing desertification control methods where vegetation cover has significantly changed in recent years. It is important to assess how climate change and afforestation affect vegetation activity dynamic in this region. This study aims to investigate the relationship between vegetation and climate change by using the normalized difference vegetation index (NDVI), and to find the cause of vegetation change by performing multiple linear regression analysis in the Mu Us Desert (including 11 counties) during the period from 2000 to 2019. The results showed that NDVI of the growing season (May to October) across the whole region increased by 0.0058 per year during the 20-year study period. Correlation analysis between the NDVI and climatic factors indicated that precipitation, temperature and relative humidity had a significant positive correlation, and mean wind speed and sunshine hours had a significant negative correlation across the study area (except for mean wind speed in Shenmu and Yuyang). Stepwise multiple linear regression analysis indicated that the relative contribution by climatic factors and afforestation to NDVI change was 41.9% and 58.1% across the study area, respectively. Findings from our study may help to provide scientific support for decision making concerning ecological restoration management and policy efficiency estimation in other similar desert areas.

Leaf area index (LAI) is a key ecological indicator for describing the structure of canopies and for modelling energy exchange between atmosphere and biosphere. While LAI of the forest overstory can be accurately assessed over large spatial scales via remote sensing, LAI of the forest understory (LAIu) is still largely ignored in ecological studies and ecosystem modelling due to the fact that it is often too complex to be destructively sampled or approximated by other site parameters. Additionally, so far only few attempts have been made to retrieve understory LAI via remote sensing, because dense canopies with high LAI are often hindering retrieval algorithms to produce meaningful estimates for understory LAI. Consequently, the forest understory still constitutes a poorly investigated research realm impeding ecological studies to properly account for its contribution to the energy absorption capacity of forest stands. This study aims to compare three conceptually different indirect retrieval methodologies for LAIu over a diverse panel of forest understory types distributed across Europe. For this we carried out near-to-surface measurements of understory reflectance spectra as well as digital surface photography over the extended network of Integrated Carbon Observation System (ICOS) forest ecosystem sites. LAIu was assessed by exploiting the empirical relationship between vegetation cover and light absorption (Beer-Lambert- Bouguer law) as well as by utilizing proposed relationships with two prominent vegetation indices: normalized difference vegetation index (NDVI) and simple ratio (SR). Retrievals from the three methods were significantly correlated with each other (r = 0.63–0.99, RMSE = 0.53–0.72), but exhibited also significant bias depending on the LAI scale. The NDVI based retrieval approach most likely overestimates LAI at productive sites when LAIu > 2, while the simple ratio algorithm overestimates LAIu at sites with sparse understory vegetation and presence of litter or bare soil. The purely empirical method based on the Beer-Lambert law of light absorption seems to offer a good compromise, since it provides reasonable LAIu values at both low and higher LAI ranges. Surprisingly, LAIu variation among sites seems to be largely decoupled from differences in climate and light permeability of the overstory, but significantly increased with vegetation diversity (expressed as species richness) and hence proposes new applications of LAIu in ecological modelling.

Phenological mismatches between migratory birds and food availability driven by global warming have been found to influence the fitness and population dynamics of several herbivorous goose species in Arctic breeding sites. However, the effect of phenological mismatches at wintering sites have been inadequately studied. We used long-term data to explore the consequences of water regime changes on phenological mismatch (i.e., days between arrival of geese and timing of the optimal Normalized Difference Vegetation Index [NDVIopt]) and detect the relative role of the mismatch as a driver of annual variations in abundance and distribution of the Lesser White-fronted Goose Anser erythropus (a vulnerable species on the IUCN Red List) in East Dongting Lake, which is the one of this species’ largest wintering sites worldwide. The NDVI of the Carex meadow (i.e., the dominant vegetation in East Dongting Lake wetland and also the major food resource for the geese) was used as the phenology metric. Results showed that the water recession pattern greatly influenced food conditions when geese arrived at the site. Early water recession led to significantly higher NDVI, whereas late water recession led to significantly lower NDVI than did the optimal water recession. However, the suitable habitat area was significantly larger under the optimal water recession pattern. Both early and late water level recessions caused greater mismatches and resulted in lower geese abundance and a more concentrated distribution. NDVI was positively correlated with aboveground biomass and negatively correlated with N% of Carex spp. High biomass and low N% related to high NDVI caused by early water recession may indicate unfavorable food conditions at the time that geese arrive and reduced sustainable food availability in mid-winter, while a low NDVI and limited habitat area caused by late water recession may indicate an insufficient food supply. In conclusion, non-optimal water recession led to a greater phenological mismatch and resulted in limited habitat quality (e.g., lower leaf N%) under early water recession but limited habitat quantity (e.g., smaller habitat area and lower aboveground biomass) under late water recession, and ultimately threatened the Lesser White-fronted Goose. Our results contributed to explaining the decline of the Lesser White-fronted Goose population in this wintering site.

The upstream of Minjiang River is an important ecological barrier and one of the main water sources of the Chengdu Plain and upstream Yangtze River. However the ecological balance is vulnerable in this area. Terrain undulation has a certain impact on vegetation coverage in mountain areas. However, it is not clear how the influence of topography on vegetation coverage changes with pixel scale. This study proposed a framework by combining Discrete Wavelet Transform (DWT) and Geographically Weighted Regression (GWR) to detect the relationships between topography and vegetation in a typical fragile ecological area. The satellite-derived Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM) were combined to examine the terrain undulation impacts on vegetation coverage at different scales in a small watershed of the Upper Minjiang River. The study results showed that approximate components of NDVI had consistently strong spatial autocorrelation at multi-scale. Elevation and south-facing index of aspect were positively correlated with NDVI, whereas slope was negatively correlated with NDVI. The order of correlation between terrain attributes and NDVI was elevation > aspect > slope. The correlation of NDVI with elevation and slope increased as the decomposition scale increased, whereas the correlation between NDVI and south-facing index of aspect did not show obvious change rule with increasing scale. The spatial heterogeneity of impact degree of elevation decreased as the scale parameter increased, but slope and aspect did not show obviously smoothing effect as the scale parameter increased. The findings will help identifying the favorable topographic position for vegetation growth and its controlling mechanisms and provide a new insight for ecological restoration practices.

The use of time series of vegetation indices obtained from satellites has become a highly relevant source of data in studies of land degradation assessment and monitoring. However, information about future vegetation dynamics, which is key for early warnings oriented at land management decisions, is still lacking. Trend-cycle was recently proposed as an indicator that represents a smoothed version of a seasonally adjusted data series, which provides information on long-term movements (trend) while including changes in direction underlying the series (cycles). The aim was to estimate the direction and speed of change of the Normalized Difference Vegetation Index (NDVI) trend-cycles as a complementary information of the rhythm of change between cyclic phases of vegetation productivity. In particular, we estimate the first and second derivative of the end-point of the trend-cycle function, as a measure of the direction the function is going and the speed of change, respectively. The potential advantage of our proposal is the fast processing for large areas and its sensitivity to capturing shifts in temporal dynamics for short time series data. This information can be used as a proxy to build scenarios of the future behaviour of vegetation dynamics, which is a relevant issue to move forward in the development of early warning tools for adaptive land management.

Understanding the response of vegetation spring phenology to climatic factors in arid regions is crucial for projecting the land-climate interactions of arid ecosystems under climate change. The Xinjiang region, an important part of northern China with complex climatic features, was selected as a case study. We investigated the impact of temperature, precipitation, and solar radiation on the start of the vegetation growing season (SOS) using a long-term series of the normalized difference vegetation index, climate data from 1982 to 2014, and the partial least squares regression (PLS) method. The primary findings were following: (1) the regional scaling of the mean SOS ranged from 95 to 113 day of year (DOY) and showed distinct spatial heterogeneity for different vegetation types. The relative percentage of the area that exhibited advanced SOS (65.80%) was larger than that of the area that exhibited delayed SOS (34.20%), with a regional advancing tendency of 0.19 days per year during the study period. Different vegetation types showed an advancing SOS, which shifted to delaying after 2005. (2) Temperature significantly affected the SOS for various vegetation types. Generally, the warming spring temperatures led to advanced SOS, whereas reduced warming in spring after 2005 reversed the SOS trends. The higher solar radiation occurring from late winter to spring also contributed to the advance in the SOS. Conversely, the increasing precipitation during this period caused a delay in the SOS. These results can provide a useful reference for improving the vegetation phenology models of arid regions.