Green-up Date Research Articles

Quantifying City- and Street-Scale Urban Tree Phenology from Landsat-8, Sentinel-2, and PlanetScope Images: A Case Study in Downtown Beijing

Understanding the phenology of urban trees can help mitigate the heat island effect by strategically planting and managing trees to provide shade, reduce energy consumption, and improve urban microclimates. In this study, we carried out the first evaluation of high spatial resolution satellite images from Landsat-8, Sentinel-2, and PlanetScope images to quantify urban street tree phenology in downtown Beijing. The major research goals are to evaluate the consistency in pixel-level spring–summer growth period phenology and to investigate the capacity of high-resolution satellite observations to distinguish phenological transition dates of urban street trees. At the city scale, Landsat-8, Sentinel-2, and PlanetScope show similar temporal NDVI trends in general. The pixel-level analysis reveals that green-up date consistency is higher in areas with medium (NDVI > 0.5) to high (NDVI > 0.7) vegetation cover when the impacts of urban surfaces on vegetation reflectance are excluded. Similarly, maturity date consistency significantly increases in densely vegetated pixels with NDVI greater than 0.7. At the street scale, this study emphasizes the efficacy of NDVI time series derived from PlanetScope in quantifying the phenology of common street tree genera, including Poplars (Populus), Ginkgos (Ginkgo), Chinese Scholars (Styphnolobium), and Willows (Salix), in downtown Beijing to improve urban vegetation planning. Based on PlanetScope observations, we found that the four street tree genera have unique phenological patterns. Interestingly, we found that the trees along many major streets, where Chinese Scholars are the major tree genus, have later green-up dates than other areas in downtown Beijing. In conclusion, the three satellite observation datasets prove to be effective in monitoring street tree phenology during the spring–summer growth period in Beijing. PlanetScope is effective in monitoring tree phenology at the street scale; however, Landsat-8 may be affected by the mixture of land covers due to its relatively coarse spatial resolution.

Growing‐season climate as an explanation of spatial variations in temperature sensitivity of green‐up on Tibetan Plateau

AbstractTemperature sensitivity (TS) of the green‐up date (GUD) of plants is crucial for the prediction of grassland phenology that is important for animal husbandry and pasture management. Spatial variations in the TS are known to reflect interannual temperature variability and/or accumulated precipitation preceding the GUD (pre‐GUD). However, whether spatial TS variations are related to the interaction between pre‐GUD temperature variability and precipitation, which is a potential indicator of frost risk, remains unclear. Furthermore, because the interaction between interannual temperature variability and accumulated precipitation following the GUD (post‐GUD) can exert selection pressure on the plant life cycle, it may also be involved in shaping the spatial TS pattern. Using long‐term ground observations of GUD on the Tibetan Plateau, we show that TS is more negative (greater GUD advance per unit temperature increase) in areas with more pre‐GUD precipitation and low pre‐GUD interannual temperature variability, but less negative in areas with more pre‐GUD precipitation and high pre‐GUD interannual temperature variability. This result is likely because more pre‐GUD precipitation facilitates sprout and leaf development under stable temperature conditions, whereas it increases frost risk when the temperature variability is high. In contrast, TS magnitude decreases with increases in post‐GUD precipitation in areas where post‐GUD interannual temperature variability is low, but increases with post‐GUD precipitation in areas where post‐GUD interannual temperature variability is high. We speculate that because hydrothermal demands for leaf growth from the onset of green‐up to maturity are more easily fulfilled when interannual temperature variability is lower and precipitation is higher, green‐up need not be sensitive to pre‐GUD temperature. In contrast, high post‐GUD precipitation likely aggravates low‐temperature constraints on leaf growth when temperature variability is high, resulting in greater TS to maximize growing season length. These results suggest that spatial TS variations on the Tibetan Plateau are likely associated with adaptations of leaf‐out phenology to background pre‐GUD climatic conditions together with selection pressure from post‐GUD conditions.

Environmental economic profiles of expressway construction via life cycle assessment

The construction process of expressway is characterized by high energy and materials consumption, which poses overwhelming potential threat to the physical environment. In order to coordinate contradiction between expressway construction and concomitant environment issues, this study quantitatively assessed environmental impact of different sub-projects and presented an environmental price model to characterize the overall environmental impact of construction materials. The results showed that the bridge, culvert, and tunnel sub-projects produced the most significant environmental impact. Compared with onsite construction and transportation, material production contributed the largest proportion (approximately 90%) of the life cycle environmental impact. Most of the environmental burdens were only associated with a few types of materials (e.g., steel, iron, and cement). Steel, in particular, contributed to 29.73%–80.47% in the four categories of environmental impact (including global warming potential (GWP), fine particulate matter production (FPMF), terrestrial acidification (TA), and ozone formation health (OF-H) in four sub-projects (including bridge and culvert, tunnel, safety measures and embedded pipelines (SMEP), and green and environmental protection facilities (GEPF)).Notably, certain materials contributed significantly to only specific environmental impact categories of environmental impact. Cement, wood, and explosives contributed significantly to GWP, land use (LU), and substance of ozone depletion (SOD)contributing 51.55%, 70.87%, and 54.95%, respectively. Environmental impact ratio can indicate the matching degree between the environmental impact of a material and its production process. For some materials with high environmental impact ratio, such as iron, brick and cement, a focus on green update and iteration of production technologies of such construction materials are required. This study identifies the environmentally sensitive links of different expressway construction sub-projects and provides a decision-making basis for the selection of construction materials and upgradation and iteration of production technologies from the perspective of environmental price, thus providing scientific guidance to green expressway construction.

A soil-air temperature model to determine the start of season phenology of deciduous forests

Forest ecosystems play a major role in sequestering atmospheric carbon dioxide, which can help offset the detrimental effects of anthropogenic carbon emissions. However, climate change has and will continue to affect the phenology of forest ecosystems’ carbon uptake, changing both the “carbon uptake transition date” - when forests shift from being a net carbon source to sink - and the “green-up date” reflecting the onset of bud burst. Previous studies have shown that a forest's carbon uptake transition date correlates to the date when soil temperature warms enough to surpass mean annual air temperature (soil-air temperature model). However, we still don't know if this simple relationship holds across different sites or over longer time periods. In this study, we explore the relationship between climate and both types of phenological transition dates using over 200 site years of data between 1997 and 2022. Using flux tower data from 18 sites across North America and Europe, we derive three potential carbon uptake transition dates corresponding to the dates when 10%, 25%, and 50% of seasonal net ecosystem exchange (NEE) amplitude is reached. Using PhenoCam data, we then derive three potential green-up dates corresponding to when 10%, 25%, and 50% of total seasonal green chromatic coordinate (GCC) is reached (the greenness model). We evaluate our model estimates using concordance coefficients, a metric of agreement between two measures, to determine which process, carbon uptake or budburst, is best predicted by the soil-air temperature model and to what extent. We find that variation in phenological relationships can be attributed to different regional and bioclimatic groups, highlighting potential biome-specific strengths and limitations of the soil-air temperature model. This model offers a simple approach to better understand phenological transitions and identify potential and limitations for a simple universal SOS prediction approach in deciduous forests.

Magnitude and direction of green-up date in response to drought depend on background climate over Mongolian grassland

Increasing drought is one major consequence of ongoing global climate change and is expected to cause significant changes in vegetation phenology, especially for naturally vulnerable ecosystems such as grassland. However, the linkage between the response characteristic of green-up date (GUD) to drought and background climate remains largely unknown. Here, we focused on how the GUD of Mongolian grassland responds to extreme drought events (EDE). We first extracted the GUD from the MODIS Enhanced Vegetation Index data during 2001–2020 and identified the preseason EDE using the standardized precipitation evapotranspiration index data. Subsequently, we quantified the response of GUD to preseason EDE (DGUD) in each pixel as the difference in GUD between drought and normal years. The effect of 12 factors on DGUD was analyzed using the random forest algorithm. The results showed that the GUD under EDE may delay or advance by > 20 days compared to normal years. For the regions with mean annual temperature > −2 °C, the GUD was delayed under EDE due to the dominant role of water restriction on GUD, while the GUD was advanced under EDE in colder areas due to the warmer temperature during drought. However, the magnitude of delay in GUD under drought was greater in regions with less precipitation and more severe droughts. Our results could help to develop appropriate management strategies to mitigate the impacts of drought on grasslands.

The Sensitivity of Green-Up Dates to Different Temperature Parameters in the Mongolian Plateau Grasslands

The rise in global average surface temperature has promoted the advancement of spring vegetation phenology. However, the response of spring vegetation phenology to different temperature parameters varies. The Mongolian Plateau, one of the largest grasslands in the world, has green-up dates (GUDs) with unclear sensitivity to different temperature parameters. To address this issue, we investigated the responses of GUDs to different temperature parameters in the Mongolian Plateau grasslands. The results show that GUDs responded significantly differently to changes in near-surface temperature (TMP), near-surface temperature maximum (TMX), near-surface temperature minimum (TMN), and diurnal temperature range (DTR). GUDs advanced as TMP, TMX, and TMN increased, with TMN having a more significant effect, whereas increases in DTR inhibited the advancement of GUDs. GUDs were more sensitive to TMX and TMN than to TMP. The sensitivity of GUDs to DTR showed an increasing trend from 1982 to 2015 and showed this parameter’s great importance to GUDs. Our results also show that the spatial and temporal distributions of temperature sensitivity are only related to temperature conditions in climatic zones instead of whether they are arid.

Varying effects of tree cover on relationships between satellite-observed vegetation greenup date and spring temperature across Eurasian boreal forests

Earth observation satellites have facilitated the quantification of how vegetation phenology responds to climate warming on large scales. However, satellite image pixels may contain a mixture of multiple vegetation types or species with diverse phenological responses to climate variability. It is unclear how these mixed pixels affect the statistical relationships between satellite-derived vegetation phenology and climate factors. Here, we aim to investigate the impacts of percent tree cover (PTC), a measure of mixed pixel, on the statistical relationships between satellite-derived vegetation greenup date (GUD) and spring air temperature across Eurasian boreal forests at a 0.05° spatial resolution. We estimated GUD using Moderate Resolution Imaging Spectroradiometer (MODIS) time series data. The responses of GUD to interannual variation in spring temperature (April to May) during 2001–2020 were characterized by correlation coefficient (RTAM) and sensitivity (STAM). We then evaluated the local impacts of PTC on spatial variations in RTAM and STAM using partial correlation analysis through spatial moving windows. Our results indicate that, for most areas, forests with higher PTC were associated with stronger RTAM and STAM. Moreover, PTC had stronger local impacts on RTAM and STAM than mean annual temperature and temperature seasonality for 37.3% and 27.4% of the moving windows, respectively. These impacts were spatially varying and different among forest types. Specifically, deciduous broadleaf forests and deciduous needleleaf forests tend to have a higher proportion of these impacts compared to other forest types. Our findings demonstrate the nonnegligible effects of PTC on the statistical responses of GUD to temperature variability at coarse spatial resolution (0.05°) across Eurasian boreal forests.

Cropland expansion delays vegetation spring phenology according to satellite and in-situ observations

The global climate change-induced advanced spring green-up dates (GUDs) of vegetation in the past decades have been well documented. However, anthropogenic effects on GUDs are still unclear, and the impacts of widespread cropland expansion on GUDs are rarely quantified. Here, we examine the differences in GUDs between agroecosystems and natural ecosystems using both satellite and in-situ observations across China’s ecosystems with a latitude ≥ 30°N and explore the potential effects of cropland expansion on vegetation spring phenology. We find that agroecosystems had significantly later GUDs than adjacent natural ecosystems according to satellite observations, with a delay of 22.26 ± 16.32 (Mean ± 1STD) days and 18.11 ± 11.75 days compared with woodland and grassland ecosystems, respectively. The later GUDs for agroecosystems is more distinct in the Northeast China Plain and Xinjiang Oasis region (>30 days), where land reclamation was remarkable. We also find a distinct delayed GUD trend (> 5 day/yr, p < 0.001) during 2001–2014 in a cropland expansion region in Xinjiang Province, which is much higher than the usual advancement of GUDs due to warming (usually ≤ 1 day/yr). In-situ and satellite observations of delayed GUDs agree better in agroecosystems than in forest and grassland ecosystems. More attention is needed to better understand the important role of cropland expansion in driving inter-annual change of vegetation spring phenology in addition to climate change, and integration of realistic crop green-up timing into climate models is also needed for accurate climate simulations.

Plant Phenology Simulation and Trigger Threshold Based on Total Climatic Production Factors—A Case Study of Stipa krylovii Phenology

In response to the new concept of the impact of total climate production factors on plant phenology, this study verifies the feasibility of simulating plant phenology and triggering thresholds based on total climatic production factors by using the phenological and meteorological observation data of S. krylovii plants from 1985 to 2018 at the Xilinhot National Climate Observatory of China Meteorological Administration. The results indicate that the total climate production factors have a significant impact on plant phenological changes and can be effectively utilized for simulating phenology and determining triggering thresholds. The mutation of cumulative climate production potential based on total climate production factors can effectively indicate the green-up date and the wilting date of S. krylovii plants, and their triggering thresholds depend on the parameters of climate resource changes and the characteristics of plant biology, which are (0.085, −5.363) and (0.086, −27.620), respectively. The cumulative climate production potential based on total climate production factors can effectively indicate the heading date of S. krylovii plants, and its triggering thresholds also depend on the parameters of climate resource changes and the characteristics of plant biology, which is (394.632, −38,026.268). Furthermore, the results support the viewpoint that abrupt changes to the climate determine the beginning and ending of plant growth, while the accumulative climate resources determine the other phenological dates. This study provides new ideas for the study of plant phenology.

Decreasing elevational gradient in peak photosynthesis timing on the Tibetan Plateau

The Tibetan Plateau has experienced elevation-dependent climate warming, which has profoundly influenced the seasonal dynamics of plant growth. However, how peak photosynthesis timing (DOYPmax), a key component of plant growth seasonality, responds to climate change on the Tibetan Plateau remains unclear. Here, we investigated the change in the elevational gradient in DOYPmax on the Tibetan Plateau during 2001–2018 and its drivers. We mapped the primary climatic constraint on plant photosynthesis and found that it was temperature at 3000–4000 m a.s.l. in the central and eastern parts but soil water at 4000–5000 m a.s.l. in the southwestern part. DOYPmax increased by 1.0 ± 0.1 days/100 m with increasing elevation from 3000 to 5000 m a.s.l., but the elevational gradient in DOYPmax decreased by -0.8 ± 0.2 days/100 m/decade during 2001–2018 due to both direct and indirect effects of climate change. Specifically, at 3000–4000 m a.s.l., DOYPmax was delayed because of the direct effect of the increase in soil water. At 4000–5000 m a.s.l., DOYPmax advanced due to the warming-induced earlier green-up date in the central plateau and the drought-induced later spring phenology in the southwestern plateau. Such a change in plant growth seasonality should alter seasonal carbon cycle of alpine ecosystems, summer diets of the migrant herbivores and alpine plant–animal interactions along elevation gradients, but further efforts are needed to clarify how herbivores that migrate long distances, e.g., Tibetan antelope, adapt to changes in the elevational gradient in peak plant growth timing.

The underappreciated importance of solar radiation in constraining spring phenology of temperate ecosystems in the Northern and Eastern United States

Spring phenology of temperate ecosystems is highly sensitive to climate change, generating various impacts on many important terrestrial surface biophysical processes. Although various prognostic models relying on environmental variables of temperature and photoperiod have been developed for spring phenology, comprehensive ecosystem-scale evaluations over large landscapes and long-time periods remain lacking. Further, environmental variables other than temperature and photoperiod might also importantly constrain spring phenology modelling but remain under-investigation. To address these issues, we leveraged around 20-years datasets of environmental variables (from Daymet and GLDAS products) and the spring phenology metric (i.e., the greenup date) respectively derived from MODIS and PhenoCams across 108 sites in the Northern and Eastern United States. We firstly cross-compared MODIS-derived greenup date with official PhenoCams product with high accuracy (R2 = 0.70). Then, we evaluated the three prognostic models (i.e., Growing Degree Date (GDD), Sequential (SEQ) and optimality-based (OPT)) with MODIS-derived spring phenology, assessed the model residuals and their associations with soil moisture, rainfall, and solar radiation, and revised the two photoperiod-relevant models (SEQ, OPT) by replacing the daylength variable with solar radiation, which was found to contribute the most to model residuals. We found that 1) all models demonstrated good capability in characterizing spring phenology, with OPT performing the best (RMSE = 8.04 ± 5.05 days), followed by SEQ (RMSE = 10.57 ± 7.77 days) and GDD (RMSE = 10.84 ± 8.42 days), 2) all models displayed high model residuals showing tight correlation with solar radiation (r = 0.45–0.75), and 3) the revised models that included solar radiation significantly performed better with an RMSE reduction by 22.08%. Such results are likely because solar radiation better constrains early growing season plant photosynthesis than photoperiod, supporting the hypothesis of spring phenology as an adaptive strategy to maximize photosynthetic carbon gain (approximated by solar radiation) while minimizing frost damage risk (captured by temperature). Collectively, our study reveals the underappreciated importance of solar radiation in constraining spring phenology of temperate ecosystems, and suggests ways to improve spring phenology modelling and other phenology-related ecological processes.

Climate-driven land surface phenology advance is overestimated due to ignoring land cover changes

Global warming has led to earlier spring green-up dates (GUDs) in recent decades with significant consequences for global carbon and hydrologic cycles. In addition to changes in climate, land cover change (LCC), including interchanges between vegetation and non-vegetation, and among plants with different functional traits, may also affect GUD. Here, we analyzed how satellite-derived GUD from 1992 to 2020 was impacted by changes in temperature, precipitation, standardized precipitation evapotranspiration index (SPEI), solar radiation, and LCC for the Northern Hemisphere (>30° N). While the climate variables had larger impact overall, variability in GUD was controlled by LCC for 6% of the Northern Hemisphere, with systematically earlier or later changes among transitions between different land cover types. These changes were found mainly along the southeastern coast of the United States, in Central-north Europe, and across northeastern China. We further showed that climate change attribution of earlier GUD during 1992–2020 was overestimated by three days when the impact of LCC was ignored. Our results deepen the understanding of how LCC impacts GUD variability and enables scientists to more accurately evaluate the impact of climate change on land surface phenology.

Climate lags and genetics determine phenology in quaking aspen (Populus tremuloides).

Spatiotemporal patterns of phenology may be affected by mosaics of environmental and genetic variation. Environmental drivers may have temporally lagged impacts, but patterns and mechanisms remain poorly known. We combine multiple genomic, remotely sensed, and physically modeled datasets to determine the spatiotemporal patterns and drivers of canopy phenology in quaking aspen, a widespread clonal dioecious tree species with diploid and triploid cytotypes. We show that over 391 km2 of southwestern Colorado: greenup date, greendown date, and growing season length vary by weeks and differ across sexes, cytotypes, and genotypes; phenology has high phenotypic plasticity and heritabilities of 31-61% (interquartile range); and snowmelt date, soil moisture, and air temperature predict phenology, at temporal lags of up to 3 yr. Our study shows that lagged environmental effects are needed to explain phenological variation and that the effect of cytotype on phenology is obscured by its correlation with topography. Phenological patterns are consistent with responses to multiyear accumulation of carbon deficit or hydraulic damage.

Increased precipitation leads to earlier green-up and later senescence in Tibetan alpine grassland regardless of warming

It is debatable whether warming or increased precipitation primarily drives the changes of spring and autumn phenology in alpine grasslands at high elevations like the Tibetan Plateau. We aim to test the hypothesis that increased precipitation and soil moisture rather than warming significantly advance spring green-up dates (GUD) of dominant species in a semiarid alpine grassland, while both increases of temperature and precipitation delay their autumn senescence dates (SD). We conducted a 2-year manipulative experiment with infrared warming (ambient, +2 °C) and precipitation increase for each of rainfall events (ambient, +15 %, +30 %) during the growing season in a Tibetan alpine grassland. GUD and SD of three dominant species and the relevant soil temperature (ST) and moisture (SM) were observed. Rainy season onset as well as Pre-GUD or Pre-SD (30 days before GUD or SD) mean air-temperature (T-30d) and precipitation (P-30d) and relevant soil temperature (ST-30d) and moisture (SM-30d) were calculated for each experimental treatment. GUD dates of the three dominant species were advanced by increased precipitation rather than by warming, which showed a robust positive correlation with rainy season onset. SD dates were independently delayed by both increases of temperature and precipitation. There was no interactive effect of warming and increased precipitation on GUD and SD across species and years. In general, GUD had a significant negative correlation with Pre-GUD P-30d (SM-30d) but not with Pre-GUD T-30d (ST-30d), while SD showed a significant positive correlation with Pre-SD T-30d and P-30d or Pre-SD ST-30d and SM-30d. Our data support the hypothesis, indicating that spring and autumn phenology of monsoon-adapted alpine vegetation are more sensitive to precipitation change than to warming. The prolonged growing season length under increased temperature and precipitation is more depended on the delay of autumn senescence than the advance of spring green-up.

Sensitivity of forest phenology in China varies with proximity to forest edges

BackgroundShifts in forest phenological events serve as strong indicators of climate change. However, the sensitivity of phenology events to climate change in relation to forest origins has received limited attention. Moreover, it is unknown whether forest phenology changes with the proximity to forest edge. MethodsThis study examined the green-up dates, dormancy dates, time-integrated NDVI (LiNDVI, a measure of vegetation productivity in growing season), and their sensitivities to climatic factors along the gradients of distance (i.e. proximity) to forest edge (0–2 ​km) in China's natural forests (NF) and planted forests (PF). For the analysis, field-surveyed data were integrated with Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI from 2000 to 2022. ResultsOur results reveal that PF had earlier green-up dates, later dormancy dates, and higher LiNDVI than NF. However, green-up sensitivities to temperature were higher at the edges of NF, whereas no such pattern was observed in PF. Conversely, the sensitivity of dormancy dates remains relatively stable from the inner to the edge of both NF and PF, except for a quadratic change in dormancy date sensitivity to precipitation found in NF. Additionally, we found that the green-up sensitivity to temperature increased with decreasing proximity to edge in NF evergreen forests, while it showed the opposite trend in PF evergreen forests. Furthermore, we observed that the precipitation impact on green-up dates shifts from postponing to advancing from the inner to the edge of NF, whereas precipitation dominantly postpones PF's green-up dates regardless of the proximity to edge. The LiNDVI exhibits higher sensitivity to precipitation at the edge areas, a phenomenon observed in NF but not in PF. ConclusionsThese results suggest that the responses of forests to climate change vary with the distance to the edge. With increasing edge forests, which results from fragmentation caused by global changes, we anticipate that desynchronized phenological events along the distance to the edge could alter biogeochemical cycles and reshape ecosystem services such as energy flows, pollination duration, and the tourism industry. Therefore, we advocate for further investigations of edge effects to improve ecosystem modelling, enhance forest stability, and promote sustainable tourism.

Satellite observed delaying effects of increased winds on spring green-up dates

Wind speeds have substantially changed over the Northern Hemisphere during the past a few decades, and such changes have been shown to have important implications for the growth, chemical composition, structure, and morphology of terrestrial ecosystems. However, the effects of wind change on the spring leaf green-up date (GUD) remain largely unknown. Using both 89 sites of flux measurements (972 site-year in total) and satellite observations over 1982–2015, here we show that higher winds in the spring preseason contributed to the delayed GUD for 29.8% of northern ecosystems, and various factors controlled the strength of the delaying effects. We further show that the earlier GUD with climate change was offset roughly by 24.4 ± 3.7% due to the increase of wind. Higher winds potentially caused delayed GUD by changes in soil conditions and differences between daytime and night-time temperatures. A higher precipitation could likely compensate the drying effects of winds on GUD. Among different vegetation types, soil water and water use efficiency were the most important regulators of the sensitivity of GUD to wind for all plant functional types, while evapotranspiration regulated grasslands and shrubs. Our results identified the new role of wind change on the spring phenology in respective of satellite observations, and future ecosystem models might consider these effects for a better representation of the responses of vegetation phenology to climate change.

Revegetation affects the response of land surface phenology to climate in Loess Plateau, China

Land surface phenology (LSP), defined as the plant's growth rhythm retrieved from satellite sensing products, is proven to shift with climate change and affect the carbon cycles of terrestrial ecosystems. Global afforested area is largely increasing and consequently affecting local and global climate. However, how and to what extent revegetation affects LSP remains relatively unexplored. Here we investigated the difference in four LSPs (i.e., greenup, maturity, senescence, and dormancy) and the response of LSP to climate between restored and native vegetation on Loess Plateau, China, where a remarkable process of vegetation restoration happened during 1982–2015. Most study regions showed a longer growing season (LOS) over time, specifically, with a slight delay in greenup but a relatively large delay in senescence. We found that air temperature was the dominant factor affecting greenup and maturity, while precipitation mostly controlled the senescence and dormancy in the study area. Under similar climate conditions, the LSP of restored vegetation (i.e., restored forest and grassland) showed a significant difference (p < 0.05) from native ones during 1999–2015. Compared to the native forest, restored forest from cropland and grassland showed a delayed greenup date by 0.3 and 3.6 days (p < 0.05) and an advanced dormancy date of 6.6 and 9.0 days (p < 0.05), respectively. Furthermore, the restored vegetation became less sensitive to air temperature than native vegetation, while the restored forest was more sensitive to precipitation, and its growth was affected by the water limitation to a larger extent in the study area. Our study highlights the necessity of considering land use management and its effect on the LSP change to better understand the effect of afforestation on global climate and carbon cycles.

Towards an Automated Approach for Monitoring Tree Phenology Using Vehicle Dashcams in Urban Environments.

Trees in urban environments hold significant value in providing ecosystem services, which will become increasingly important as urban populations grow. Tree phenology is highly sensitive to climatic variation, and resultant phenological shifts have significant impact on ecosystem function. Data on urban tree phenology is important to collect. Typical remote methods to monitor tree phenological transitions, such as satellite remote sensing and fixed digital camera networks, are limited by financial costs and coarse resolutions, both spatially and temporally and thus there exists a data gap in urban settings. Here, we report on a pilot study to evaluate the potential to estimate phenological metrics from imagery acquired with a conventional dashcam fitted to a car. Dashcam images were acquired daily in spring 2020, March to May, for a 2000 m stretch of road in Melksham, UK. This pilot study indicates that time series imagery of urban trees, from which meaningful phenological data can be extracted, is obtainable from a car-mounted dashcam. The method based on the YOLOv3 deep learning algorithm demonstrated suitability for automating stages of processing towards deriving a greenness metric from which the date of tree green-up was calculated. These dates of green-up are similar to those obtained by visual analyses, with a maximum of a 4-day difference; and differences in green-up between trees (species-dependent) were evident. Further work is required to fully automate such an approach for other remote sensing capture methods, and to scale-up through authoritative and citizen science agencies.

Winter Wheat Phenology Variation and Its Response to Climate Change in Shandong Province, China

Crop phenology is considered to be an important indicator reflecting the biophysical and physiological processes of crops facing climate change. Therefore, quantifying crop phenology change and its relationship with climate variables is of great significance for developing agricultural management and adaptation strategies to cope with global warming. Based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) product, winter wheat green-up date, heading date, jointing date, and maturity date were first retrieved by Savitzky–Golay (S-G) filtering and threshold methods and then the variation of winter wheat phenology and its correlation with mean (Tmean), minimum (Tmin), and maximum (Tmax) temperature and precipitation (Pre) during 2003–2019 were comprehensively analyzed in Shandong Province, China. Results showed that green-up date, jointing date, heading date, and maturity date generally ranged from 50–70 DOY, 75–95 DOY, 100–120 DOY, and 130–150 DOY. Winter wheat phenology presented a spatial pattern of the South earlier than the North and the inland earlier than the coastal regions. For every 1° increase in latitude/longitude, green-up date, jointing date, heading date, and maturity date were respectively delayed by 3.93 days/0.43 days, 2.31 days/1.19 days, 2.80 days/1.14 days, and 2.12 days/1.09 days. Green-up date and jointing date were both advanced in the West and delayed in the Eastern coastal areas and the South, and heading date and maturity date respectively showed a widespread advance and a delayed tendency in Shandong Province, however, the trend of winter wheat phenological changes was generally insignificant. In addition, green-up date, jointing date, and heading date generally presented a significant negative correlation with mean/minimum temperature, while maturity date was positively associated with the current month maximum temperature, notably in the West of Shandong Province. Regarding precipitation, a generally insignificant relationship with winter wheat phenology was detected. Results in this study are anticipated to provide insight into the impact of climate change on winter wheat phenology and to supply reference for the agricultural production and field management of winter wheat in Shandong Province, China.

Identification of the Spring Green-Up Date Derived from Satellite-Based Vegetation Index over a Heterogeneous Ecoregion

Multiple methods have been developed to identify the transition threshold from the reconstructed satellite-derived normalized difference vegetation indices (NDVI) time series and to determine the inflection point corresponding to a certain phenology phase (e.g., the spring green-up date (GUD)). We address an issue that large uncertainties might occur in the inflection point identification of spring GUD using the traditional satellite-based methods since different vegetation types exhibit asynchronous phenological phases over a heterogeneous ecoregion. We tentatively developed a Maximum-derivative-based (MDB) method and provided inter-comparisons with two traditional methods to detect the turning points by the reconstructed time-series data of NDVI for identifying the GUD against long-term observations from the sites covered by a mixture of deciduous forest and herbages in the Pan European Phenology network. Results showed that higher annual mean temperature would advance the spring GUD, but the sensitive magnitudes differed depending on the vegetation type. Therefore, the asynchronization of phenological phases among different vegetation types would be more pronounced in the context of global warming. We found that the MDB method outperforms two other traditional methods (the 0.5-threshold-based method and the maximum-ratio-based method) in predicting the GUD of the subsequent-green-up vegetation type when compared with ground observation, especially at sites with observed GUD of herbages earlier than deciduous forest, while the Maximum-ratio-based method showed better performance for identifying GUDs of the foremost-green-up vegetation type. Although the new method improved in our study is not universally applicable on a global scale, our results, however, highlight the limitation of current inflection point identify algorithms in predicting the GUD derived from satellite-based vegetation indices datasets in an ecoregion with heterogeneous vegetation types and asynchronous phenological phases, which makes it helpful for us to better predict plant phenology on an ecoregion-scale under future ongoing climate warming.