Changes In Vegetation Phenology Research Articles

Linking Vegetation Phenology to Net Ecosystem Productivity: Climate Change Impacts in the Northern Hemisphere Using Satellite Data

With global climate change, linking vegetation phenology with net ecosystem productivity (NEP) is crucial for assessing vegetation carbon storage capacity and predicting terrestrial ecosystem changes. However, there have been few studies investigating the relationship between vegetation phenology and NEP in the middle and high latitudes of the Northern Hemisphere. This study comprehensively analyzed vegetation phenological changes and their climate drivers using satellite data. It also investigated the spatial distribution and climate drivers of NEP and further analyzed the sensitivity of NEP to vegetation phenology. The results indicated that the average land surface phenology (LSP) was dominated by a monotonic trend in the study area. LSP derived from different satellite products and retrieval methods exhibited relatively consistent responses to climate. The average SOS and POS for different retrieval methods showed a higher negative correlation with nighttime temperatures compared to daytime temperatures. The average EOS exhibited a higher negative correlation with daytime temperatures than a positive correlation. The correlations between VPD and the average SOS, POS, and EOS showed that the proportion of negative correlations was higher than that of positive correlations. The average annual NEP ranged from 0 to 1000 gC·m−2. The cumulative trends of NEP were mainly monotonically increasing, accounting for 61.04%, followed by monotonically decreasing trends, which accounted for 17.95%. In high-latitude regions, the proportion of positive correlation between VPD and NEP was predominant, while the proportion of negative correlation was predominant in middle-latitude regions. The positive and negative correlations between soil moisture and NEP (48.08% vs. 51.92%) were basically consistent in the study area. The correlation between SOS and POS with NEP was predominantly negative. The correlation between EOS and NEP was overall characterized by a greater proportion of negative correlations than positive correlations. The correlation between LOS and NEP exhibited a positive relationship in most areas. The sensitivity of NEP to vegetation phenological parameters (SOS, POS, and EOS) was negative, while the sensitivity of NEP to LOS was positive (0.75 gC·m−2/d for EVI vs. 0.63 gC·m−2/d for LAI vs. 0.30 gC·m−2/d for SIF). This study provides new insights and a theoretical basis for exploring the relationship between vegetation phenology and NEP under global climate change.

Reversal of the Spatiotemporal Patterns at the End of the Growing Season of Typical Steppe Vegetation in a Semi-Arid Region by Increased Precipitation

Vegetation phenology serves as a sensitive indicator of climate change. However, the mechanism of the hydrothermal role in vegetation phenology changes is still controversial. Utilizing the data on the Fraction of Absorbed Photosynthetically Active Radiation (FPAR) from MODIS and meteorological data, the study employed the dynamic threshold method to derive the end of the growing season (EOS). The research delved into the spatiotemporal patterns of the EOS for typical steppe vegetation in the semi-arid region of Inner Mongolia spanning the period from 2003 to 2022. Furthermore, the investigation scrutinized the response of EOS to temperature and precipitation dynamics. The results showed that (1) the dynamic threshold method exhibited robust performance in the EOS of typical steppe vegetation, with an optimal threshold of 45% and a Root Mean Square Error (RMSE) of 5.5 days (r = 0.81); (2) the spatiotemporal patterns of the EOS of typical steppe vegetation in the semi-arid region experienced a noteworthy reversal from 2003 to 2022; (3) the lag effects of precipitation and temperature on the EOS were found, and the lag time scales were mainly 1 month and 2 months. The increase in precipitation in August was the key reason for the reversal of the EOS, and satisfying the precipitation was a prerequisite for the temperature to delay the EOS. The study emphasizes the important role of water availability in regulating the response of the EOS to hydrothermal factors and highlights the utility and reliability of FPAR in monitoring the EOS of typical steppe vegetation.

Vegetation Phenology Changes and Recovery after an Extreme Rainfall Event: A Case Study in Henan Province, China

Extreme rainfall can severely affect all vegetation types, significantly impacting crop yield and quality. This study aimed to assess the response and recovery of vegetation phenology to an extreme rainfall event (with total weekly rainfall exceeding 500 mm in several cities) in Henan Province, China, in 2021. The analysis utilized multi-sourced data, including remote sensing reflectance, meteorological, and crop yield data. First, the Normalized Difference Vegetation Index (NDVI) time series was calculated from reflectance data on the Google Earth Engine (GEE) platform. Next, the ‘phenofit’ R language package was used to extract the phenology parameters—the start of the growing season (SOS) and the end of the growing season (EOS). Finally, the Statistical Package for the Social Sciences (SPSS, v.26.0.0.0) software was used for Duncan’s analysis, and Matrix Laboratory (MATLAB, v.R2022b) software was used to analyze the effects of rainfall on land surface phenology (LSP) and crop yield. The results showed the following. (1) The extreme rainfall event’s impact on phenology manifested directly as a delay in EOS in the year of the event. In 2021, the EOS of the second growing season was delayed by 4.97 days for cropland, 15.54 days for forest, 13.06 days for grassland, and 12.49 days for shrubland. (2) Resistance was weak in 2021, but recovery reached in most areas by 2022 and slowed in 2023. (3) In each year, SOS was predominantly negatively correlated with total rainfall in July (64% of cropland area in the first growing season, 53% of grassland area, and 71% of shrubland area). In contrast, the EOS was predominantly positively correlated with rainfall (51% and 54% area of cropland in the first and second growing season, respectively, and 76% of shrubland area); however, crop yields were mainly negatively correlated with rainfall (71% for corn, 60% for beans) and decreased during the year of the event, with negative correlation coefficients between rainfall and yield (−0.02 for corn, −0.25 for beans). This work highlights the sensitivity of crops to extreme rainfall and underscores the need for further research on their long-term recovery.

Earlier peak photosynthesis timing potentially escalates global wildfires.

More intense fire weather due to climate change is implicated as a key driver of recent extreme wildfire events. As fuel stock, the role of vegetation and its phenology changes in wildfire dynamics, however is not fully appreciated. Using long-term satellite-based burned areas and photosynthesis observations, we reveal that an earlier peak photosynthesis timing (PPT) potentially acts to escalate subsequent wildfires, with an increase in the global average burned fraction of 0.021% (∼2.20 Mha) for every additional day of PPT advancement. Satellite observations and Earth System modeling consistently show that this fire escalation is likely due to intensified drought conditions and increased fuel availability associated with the climate feedback arising from earlier PPT. Current fire-enabled dynamic global vegetation models can reproduce the observed negative correlation between PPT and burned area but underestimate the strength of the relationship notably. Given the continued PPT advancement owing to climate change, the bioclimatic effects of vegetation phenology change suggest a potentially pervasive upward pressure on future wildfires.

Spatial and Temporal Variations of Vegetation Phenology and Its Response to Land Surface Temperature in the Yangtze River Delta Urban Agglomeration

In the Yangtze River Delta urban agglomeration, which is the region with the highest urbanization intensity in China, the development of cities leads to changes in land surface temperature (LST), while vegetation phenology varies with LST. To investigate the spatial and temporal changes in vegetation phenology and its response to LST in the study area, this study reconstructed the time series of the enhanced vegetation index (EVI) based on the MODIS EVI product and extracted the vegetation phenology indicators in the study area from 2002 to 2020, including the start of the growing season (SOS), the end of the growing season (EOS), and the growing season length (GSL), and analyzed the temporal–spatial patterns of vegetation phenology and LST in the study area, as well as the correlation between them. The results show that (1) SOS was advanced, EOS was postponed, and GSL was extended in the study area from 2002 to 2020, and there were obvious differences in the vegetation phenology indicators under different land covers and cities; (2) LST was higher in the southeast than in the northwest of the study area from 2002 to 2020, with an increasing trend; and (3) there are differences in the response of vegetation phenology to LST across land covers and cities, and SOS responds differently to LST at different times of the year. EOS shows a significant postponement trend with the annual mean LST increase. Overall, we found differences in vegetation phenology and its response to LST under different land covers and cities, which is important for scholars to understand the response of vegetation phenology to urbanization.

Machine Learning Vegetation Filtering of Coastal Cliff and Bluff Point Clouds

Coastal cliffs erode in response to short- and long-term environmental changes, but predicting these changes continues to be a challenge. In addition to a chronic lack of data on the cliff face, vegetation presence and growth can bias our erosion measurements and limit our ability to detect geomorphic erosion by obscuring the cliff face. This paper builds on past research segmenting vegetation in three-band red, green, blue (RGB) imagery and presents two approaches to segmenting and filtering vegetation from the bare cliff face in dense point clouds constructed from RGB images and structure-from-motion (SfM) software. Vegetation indices were computed from previously published research and their utility in segmenting vegetation from bare cliff face was compared against machine learning (ML) models for point cloud segmentation. Results demonstrate that, while existing vegetation indices and ML models are both capable of segmenting vegetation and bare cliff face sediments, ML models can be more efficient and robust across different growing seasons. ML model accuracy quickly reached an asymptote with only two layers and RGB images only (i.e., no vegetation indices), suggesting that these more parsimonious models may be more robust to a range of environmental conditions than existing vegetation indices which vary substantially from one growing season to another with changes in vegetation phenology.

Refined Analysis of Vegetation Phenology Changes and Driving Forces in High Latitude Altitude Regions of the Northern Hemisphere: Insights from High Temporal Resolution MODIS Products

The vegetation patterns in high-latitude and high-altitude regions (HLAR) of the Northern Hemisphere are undergoing significant changes due to the combined effects of global warming and human activities, leading to increased uncertainties in vegetation phenological assessment. However, previous studies on vegetation phenological changes often relied on long-term time series of remote sensing products for evaluation and lacked comprehensive analysis of driving factors. In this study, we utilized high temporal resolution seamless MODIS products (MODIS-NDVISDC and MODIS-EVI2SDC) to assess the vegetation phenological changes in High-Latitude-Altitude Regions (HLAR) of the Northern Hemisphere. We quantified the differences in vegetation phenology among different land-use types and determined the main driving factors behind vegetation phenological changes. The results showed that the length of the growing season (LOS) derived from MODIS-NDVISDC was 8.9 days longer than that derived from MODIS-EVI2SDC, with an earlier start of the growing season (SOS) by 1.5 days and a later end of the growing season (EOS) by 7.4 days. Among different vegetation types, deciduous needleleaf forests exhibited the fastest LOS extension (p < 0.01), while croplands showed the fastest LOS reduction (p < 0.05). Regarding land-use transitions, the conversion of built-up land to forest and grassland had the longest LOS. In expanding agricultural areas, the LOS of land converted from built-up land to cropland was significantly higher than that of other land conversions. We analyzed human activities and found that as the human footprint gradient increased, the LOS showed a decreasing trend. Among the climate-related factors, the dominant response of phenology to temperature was the strongest in the vegetation greening period. During the vegetation browning period, the temperature control was weakened, and the control of radiation and precipitation was enhanced, accounting for 20–30% of the area, respectively. Finally, we supplement and prove that the highest contributions to vegetation greening in the Northern Hemisphere occurred during the SOS period (May–June) and the EOS period (October). Our study provides a theoretical basis for vegetation phenological assessment under global change. It also offers new insights for land resource management and planning in high-latitude and high-altitude regions.

Earlier spring greening in Northern Hemisphere terrestrial biomes enhanced net ecosystem productivity in summer

The northern terrestrial biomes are being remarkably altered by climate change. Higher springtime temperature induces the earlier greening of vegetation, which may further influence ecosystem functions during the subsequent season. However, the response of summer net ecosystem productivity to spring vegetation greenness and phenology changes has not yet been quantified. To understand the impact of such phenological changes on terrestrial carbon sink of the following season, here we integrate remotely-sensed vegetation data and model simulations of carbon flux with an explainable machine learning approach. We find that the lagged effects of widespread earlier spring greening are increasing the summer ecosystem carbon sink across the northern vegetated areas (30° to 90°N) from 1982 to 2015. In particular, response disparities exist in non-agricultural biomes, and the vegetation with moderate tree coverage is more sensitive to earlier spring greening. Furthermore, modest tree restoration can strengthen the beneficial effects of earlier spring greening. This study improves our understanding of interseasonal vegetation-climate-carbon coupling that drives the key ecological feedback within climate change projections.

The Impact of Urbanization on Spatial–Temporal Variation in Vegetation Phenology: A Case Study of the Yangtze River Delta, China

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

Effects of land surface temperatures on vegetation phenology along urban–rural local climate zone gradients

ContextUrbanization and local urban climate have multiple impacts on vegetation phenology in urban and suburban areas. Understanding these effects and their interactions with the surface urban heating effect remains limited.ObjectiveWe employed a time series of Earth observation data to analyze land surface phenology (LSP) dynamics and related environmental drivers in the highly urbanized Pearl River Delta (PRD) region.MethodsFirst, local climate zone (LCZ) maps were generated from Earth observation datasets of 2000 and 2019. Second, LSP (i.e., start, end, and length of season) were extracted from vegetation indices for 2000–2019. Thirdly, land surface temperature (LST) was used as an explanatory variable based on the LCZ of cities. Finally, interannual trends of LSP and their association with LST were analyzed, depending on the distance gradient of vegetation to compact high-rise buildings.ResultsUrban surface characteristics showed that LSP in regions dominated by compact and high-rise urban areas presented significant spatiotemporal variation at the start and end of season than those dominated by open, mid-rise, and low-rise areas. The impacts of spring and autumn LST in the daytime on LSP were slightly more substantial than those in the nighttime. The association of decreasing spring LST in the daytime with a delayed start of season is especially pronounced in urban domains.ConclusionsThe results indicate that vegetated areas adjacent to urban domains presented greater spatiotemporal dynamics than suburban and rural regions. Our study emphasizes the dependence of spatiotemporal changes in vegetation phenology on the effects of urban surface warming.

Combined Effects of Meteorological Factors, Terrain, and Greenhouse Gases on Vegetation Phenology in Arid Areas of Central Asia from 1982 to 2021

Spatiotemporal variations in Central Asian vegetation phenology provide insights into arid ecosystem behavior and its response to environmental cues. Nevertheless, comprehensive research on the integrated impact of meteorological factors (temperature, precipitation, soil moisture, saturation vapor pressure deficit), topography (slope, aspect, elevation), and greenhouse gases (carbon dioxide, methane, nitrous oxide) on the phenology of Central Asian vegetation remains insufficient. Utilizing methods such as partial correlation and structural equation modeling, this study delves into the direct and indirect influences of climate, topography, and greenhouse gases on the phenology of vegetation. The results reveal that the start of the season decreased by 0.239 days annually, the length of the season increased by 0.044 days annually, and the end of the season decreased by 0.125 days annually from 1982 to 2021 in the arid regions of Central Asia. Compared with topography and greenhouse gases, meteorological factors are the dominant environmental factors affecting interannual phenological changes. Temperature and vapor pressure deficits (VPD) have become the principal meteorological elements influencing interannual dynamic changes in vegetation phenology. Elevation and slope primarily regulate phenological variation by influencing the VPD and soil moisture, whereas aspect mainly affects the spatiotemporal patterns of vegetation phenology by influencing precipitation and temperature. The findings of this study contribute to a deeper understanding of how various environmental factors collectively influence the phenology of vegetation, thereby fostering a more profound exploration of the intricate response relationships of terrestrial ecosystems to environmental changes.

Contrasting responses to microhabitat and temperature determine breeding habitat differentiation between two Viola‐feeding butterflies

Abstract Since widespread monitoring began in 1976 in the UK, habitat‐specialist butterfly populations have declined dramatically. The main driver is habitat degradation, caused primarily by land‐use change, perhaps interacting with changes in vegetation phenology. Here, we focus on two declining species: Boloria selene (Dennis & Schiffermüller) and Boloria euphrosyne L., Lepidoptera: Nymphalidae. We hypothesise that these species differ in their preferred breeding habitat, and this is driven by differences in their temperature preferences, mediated by vegetation cover. We use mark‐release‐recapture techniques and oviposition observations to characterise and compare adult distribution, habitat use and oviposition site preferences of the two species. Egg‐laying females of both species are shown to occur in areas with relatively high abundance of the larval food plants, Viola spp. (violets), principally V. riviniana, and they oviposit where Viola spp. abundance is locally high. However, in contrast to B. selene, ovipositing B. euphrosyne tends to occur in areas with relatively short and sparse cover of vegetation. B. euphrosyne oviposit in sites with a relatively high plant surface temperature irrespective of ambient temperatures, in contrast with B. selene in which the temperature of oviposition sites increases as ambient temperature increases. These differential temperature strategies likely underlie differences in breeding habitat preference. Microclimatic cooling caused by increased vegetation growth in spring may be one reason B. euphrosyne is declining in the UK, while both B. euphrosyne and B. selene may be affected by declining Viola spp. availability. Our data provide further evidence that drivers of butterfly declines can be multi‐factorial, and paradoxically, that thermophilic species do not necessarily benefit from climate warming if responses of other species result in cooling of their habitats.

Slower changes in vegetation phenology than precipitation seasonality in the dry tropics.

The dry tropics occupy ~40% of the tropical land surface and play a dominant role in the trend and interannual variability of the global carbon cycle. Previous studies have reported considerable changes in the dry tropical precipitation seasonality due to climate change, however, the accompanied changes in the length of the vegetation growing season (LGS)-the key period of carbon sequestration-have not been examined. Here, we used long-term satellite observations along with in-situ flux measurements to investigate phenological changes in the dry tropics over the past 40 years. We found that only ~18% of the dry tropics show a significant (p ≤ .1) increasing trend in LGS, while ~13% show a significant decreasing trend. The direction of the LGS change depended not only on the direction of precipitation seasonality change but also on the vegetation water use strategy (i.e. isohydricity) as an adaptation to the long-term average precipitation seasonality (i.e. whether the most of LGS is in the wet season or dry season). Meanwhile, we found that the rate of LGS change was on average ~23% slower than that of precipitation seasonality, caused by a buffering effect from soil moisture. This study uncovers potential mechanisms driving phenological changes in the dry tropics, offering guidance for regional vegetation and carbon cycle studies.

Temporal and Spatial Changes of Vegetation Phenology and Its Correlation With Net Primary Productivity in Northeast China From 2001 to 2022

Temporal and Spatial Changes of Vegetation Phenology and Its Correlation With Net Primary Productivity in Northeast China From 2001 to 2022

Land Surface Temperature Sensitivity to Changes in Vegetation Phenology Over Northern Deciduous Forests

AbstractChanges in vegetation phenology are important contributors to seasonal climate change over the northern land surface. However, the biogeophysical effects of phenological shifts on land surface temperature (LST) remain uncertain. Here, we demonstrated the sensitivity of LST to advanced start of growing season (SOS) and delayed end of growing season (EOS) during corresponding green‐up and senescence periods over northern deciduous forests. Moreover, the mechanism responsible for LST sensitivity was assessed by quantifying the contributions of biogeophysical properties to LST sensitivity by applying a two resistance mechanism method to an experimental set using Community Land Model version 5. LST sensitivities to advanced SOS and delayed EOS were −2.1 × 10−2 K day−1 and −2.8 × 10−2 K day−1, respectively, indicating cooling effects by phenological shifts. The different responses of the aerodynamic resistance to SOS and EOS could explain the higher LST sensitivity to EOS than that to SOS. The decrease in the aerodynamic resistance in response to delayed EOS was three times larger than that to advanced SOS, thereby inducing a larger surface cooling effect during senescence period through enhanced turbulent heat transfer. Thus, the reduced aerodynamic resistance could explain the cooling effect of the phenological shift, which is an important consideration for projecting seasonal climate change.

Responses of Vegetation Phenology to Urbanization in Plateau Mountains in Yunnan, China

The process of urbanization alters the distribution of land use and gives rise to certain climatic modifications that have a direct influence on vegetation phenology. Mountainous areas have a fragile biological environment, and vegetation phenology is relatively sensitive to urbanization. However, there is a paucity of research on the impact of urbanization in mountainous areas on vegetation phenology. The Urban Agglomeration of Central Yunnan (UACY) is located on the Yunnan–Guizhou Plateau in China. This study explored the vegetation phenological changes in different terrains from 2001 to 2020 in the UACY based on remote sensing data. Using the dynamic gradient method, we examined the response of vegetation phenology to urbanization from three aspects: urban–rural gradient, urbanization intensity (UI), and population density. The results showed that landform has a significant impact on SOS (start of growing season), with a topographic relief difference of 200 m acting as the dividing line. The findings derived from the dynamic gradient analysis indicate that UI has the most significant effect. SOS advances by 5.77 days (R2 = 0.96), EOS (end of growing season) advances by 2.30 days (R2 = 0.83), and LOS (length of growing season) lengthens by 2.59 days (R2 = 0.87) for every 10% increase in UI. This study has the potential to serve as a valuable resource for future urban planning and administration in the UACY. Additionally, it could provide decision-making support for the development of mountainous urban agglomerations in ecological environments.

Effects of Ecological Restoration and Climate Change on Herbaceous and Arboreal Phenology.

With global climate change, changes in vegetation phenology have become increasingly evident. Horqin Sandy Land is located near the eastern part of the West Liaohe River. It is the largest sandy land in China and its ecological environment is fragile. Investigating the changes in vegetation phenology in these sandy areas and determining the relationship between vegetation phenology and meteorological factors are of great importance for predicting the impacts of future climate change and understanding the response mechanisms of ecosystems. In this study, we used the time series of the Normalized Difference Vegetation Index (NDVI) from 2000 to 2021 and extracted the vegetation phenology in the Horqin Sandy Land using high-order curve fitting methods, including the start date of the growing season (SOS), the end date of the growing season (EOS), and the length of the growing season (LOS). We analyzed their temporal variation and used partial correlation analysis to determine their relationship with meteorological factors (temperature and precipitation). In addition, we compared the phenology and microclimate of forest and grassland within the study area. In the Horqin Sandy Land, the vegetation SOS was concentrated between the 115th and 150th day, the EOS was concentrated between the 260th and 305th day, and the LOS ranged from 125 to 190 days. Over the past 22 years, the SOS, EOS, and LOS of vegetation in the Horqin Sandy Land showed trends of delay, shift, and extension, with rates of change of 0.82 d/10a, 5.82 d/10a, and 5.00 d/10a, respectively. The start date of the growing season in the Horqin Sandy Land was mainly influenced by precipitation in April of the current year, while the end date was mainly influenced by precipitation in August of the current year. Overall, the SOS in the forested areas of the Horqin Sandy Land was slightly later than in the grasslands, but the EOS in the forested areas was significantly later than in the grasslands, resulting in a longer LOS in the forests. In addition, annual precipitation and the rate of precipitation increase were higher in the forested areas than in the grasslands, but soil temperature was higher in the grasslands than in the forests. Vegetation phenology in the Horqin Sandy Land has undergone significant changes, mainly manifested in the delayed end date of the growing season, the extended length of the growing season, and the differences between forest and grassland. This indicates that climate change has indeed affected phenological changes and provides a theoretical basis for subsequent ecological restoration and desertification prevention efforts in the region.

Urban-rural gradient in vegetation phenology changes of over 1500 cities across China jointly regulated by urbanization and climate change

A comprehensive investigation on the long-term changes of urban vegetation phenology (△UVP) is critical for a better understanding of changes in urban ecosystems. However, the urban–rural gradient of the △UVP across cities with diverse climate zones and city sizes, as well as the associated drivers, remains largely unclear. Here, using multi-source remote sensing data from 2001 to 2019, we investigated the urban–rural gradient (including six urban–rural subzones, i.e., the old urban core, new urban core, new urban area, suburban area, exurban area, and rural area) of the △UVP across 1500 + cities in China and analyzed the impacts of urbanization level and climate change on the △UVP. First, over the past two decades, the △UVP during spring (i.e., the changes of the start of growing season, termed △SOS) is generally negative (advanced SOS; 73 % ± 4 %) and that during autumn (i.e., changes of the end of growing season, termed △EOS) is mainly positive (delayed EOS; 63 % ± 3 %) in all urban–rural subzones. The spatial distribution of the △UVP based on enhanced vegetation index (EVI) data is relatively consistent with that based on solar-induced chlorophyll fluorescence (SIF) data. Along the urban–rural gradient, both the SOS advance and EOS delay exhibit an increasing first, decreasing later, and finally steady curve, with the maximum occurring in the new urban area at − 12.4 ± 18.5 days (for △SOS) and 3.8 ± 9.9 days (for △EOS). Second, the SOS changes from an advance to a delay from the middle temperate to southern subtropical zones, while the EOS changes from a delay to an advance. The magnitudes of both △SOS and △EOS show a general trend of increasing first and then decreasing with decreasing latitude. Third, the SOS advance and EOS delay generally decrease with city size for the old urban core, increase initially and decrease later with city size for the three suburban subzones, while they show little deviations with city size for the new urban core and new urban area. Finally, in the three urban subzones, the mean △UVP across all the cities is contributed more by climate change (mean: △SOS: 73 %; △EOS: 78 %) than by urbanization level (mean: △SOS: 27 %; △EOS: 22 %). Our findings can improve the understanding of urban vegetation changes in the context of rapid urbanization and climate change.

Differential effects of climatic and non-climatic factors on the distribution of vegetation phenology trends on the Tibetan plateau

The study of vegetation phenology changes is important because it is a sensitive indicator of climate change, affecting the exchange of carbon, energy and water fluxes between the land and the atmosphere. Previous studies have focused on the effects of climatic factors among environmental factors on vegetation phenology, thus the effects of non-climatic factors among environmental factors have not been well quantified. This study endeavors to scrutinize the spatiotemporal inconsistency in the start-of-season (SOS) and the end-of-season (EOS) on the Tibetan Plateau (TP) and to quantify the effects of environmental factors on phenology. To this end, the Moderate-resolution Imaging Spectroradiomater (MODIS) Normalized Difference Vegetation Index (NDVI) data from 2001 to 2018 and four common used methods were employed to extract SOS and EOS, and the site data was used to select the most appropriate phenology results. The Geodetector model was used to assess and measure the explanatory power of different environmental factors. The research results indicate that temperature exerts a more substantial impact on phenology than precipitation on TP. non-climatic factors such as longitude, latitude, and elevation are more influential in determining the distribution of phenological trends than climatic factors. Among these non-climatic factors, latitude has the most prominent effect on the trends of SOS. Furthermore, non-climatic factors exhibit a stronger effect on SOS, whereas EOS is more susceptible to climatic factors and less influenced by non-climatic factors. These discoveries bear great significance in comprehending the intricate outcomes of regional changes on vegetation phenology and enhancing phenology models.

Effects of Climate Change and Urbanization on Vegetation Phenology in the Bucharest Metropolitan Area

Being an essential issue in global warming, the response of urban vegetation to climate change and urbanization has become an increasing concern at both the local and global levels. This study aims to investigate the effect of the urban environment on vegetation phenology for the Bucharest metropolitan area in Romania and to identify the potential climate drivers that influence key phenology in the urban environment. In this study, we comprehensively analyzed the response of urban vegetation phenology shifts due to climate variability and urbanization in the Bucharest metropolitan area from a spatiotemporal perspective during the 2002- 2022 period. Through synergy use of time series of the main climate variables, Air temperature -AT, land surface temperature (LST), and biophysical variables derived from MODIS Terra/Aqua satellite and in-situ data, this study developed a complex statistical and spatial regression analysis. Green space was measured with satellite-derived vegetation indicators Normalized Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI), Net Primary Production (NPP) data, which captures the combined availability of urban parks, street trees, forest, and periurban agricultural areas. Leaf Area Index (LAI) and Photosynthetically active radiation (FPAR) indicators have been used to characterize the effects of meteorological parameters and urbanization impacts on vegetation phenology and their changes. The results show that the response of vegetation phenology to urbanization level and climate parameters variability has a distinct spatiotemporal difference across the urban/periurban gradient. The findings of this study show that the land surface temperature anomalies associated with urbanization-induced climate warming, especially during strong summer heat waves and under urban heat islands alter urban vegetation biophysical properties, directly impacting its phenology shifts. At the metropolitan scale, the urban thermal environment directly impacts vegetation phenology patterns. The quantitative findings of this study are of great importance for understanding the complex impacts of urbanization and climate changes on vegetation phenology and for developing models to predict vegetation phenological changes under future urbanization.