Phenology Dynamics Research Articles

Large herbivores link plant phenology and abundance in Arctic tundra

Abstract Plant phenological dynamics have been well studied in relation to abiotic conditions and climate change, but comparatively poorly studied in relation to herbivory. In contrast, plant abundance dynamics have been well studied in relation to abiotic conditions and herbivory, but poorly studied in relation to phenology. Consequently, the contribution of herbivory to plant phenological dynamics and therefrom to plant abundance dynamics remains obscure. We conducted a 9-year herbivore exclusion experiment to investigate whether herbivory might link plant phenological and abundance dynamics in arctic tundra. From 2009 to 2017, we monitored annual green-up timing and abundance of nine plant taxa, including deciduous shrubs, forbs, and graminoids, on plots that were either grazed or experimentally exclosed from herbivory by caribou (Rangifer tarandus) and muskoxen (Ovibos moschatus). In 62% of cases, green-up occurred earlier under herbivory, and in 75% of cases abundance was greater under herbivory, compared to green-up and abundance under herbivore exclusion. Moreover, taxa that responded to herbivory with earlier green-up also had comparatively greater abundance later in the growing season. Conversely, taxa that responded to herbivory with delayed green-up exhibited comparatively lower abundance later in the growing season. Hence, well-documented influences of large herbivores on plant abundance and community composition in arctic tundra may relate, at least to some extent, to influences of herbivory on plant phenology. We recommend that ongoing and future assessments of the contribution of herbivores to plant abundance and community responses to climate change, especially in the Arctic, should also consider impacts of herbivores on plant phenology.

Citizen science engagement reveals patterns of long-term persistence of an at risk butterfly in three metropolitan centers

Insect declines have been documented for about twenty years worldwide, and the more recent Bee Colony Collapse Disorder has brought public attention to the issue. With this in mind, the persistence and thriving of an iconic, once-extirpated butterfly, the Atala (Eumaeus atala), in three major metropolitan centers located in southeastern Florida is remarkable and deserves close examination as to how it happened. The Atala butterfly is a charismatic species mostly residing in an urban dominated landscape, and has been a prime target of citizen science engagement since the second re-discovery of the species in 1979 in coastal Miami. Three citizen science actions were instrumental in this recovery: reintroductions, year-long population monitoring afterwards, and participation in annual butterfly counts. In this research, we analyzed three different sets of monitoring data gathered by citizen volunteers to help us determine factors that may influence the Atala butterfly’s persistence. We found that the most labor- and time-demanding citizen science data allowed us to define fine scale population phenological dynamics enabling us to quantify the anecdotal biannual “crash and eruption” cycles, while the median level long-term engagement data revealed a coarse, multiple year crash and eruption population cycle. Our analyses illustrated that the contribution of on-going data collection gained through committed citizen science actions play a critical part in the species conservation strategy, allowing us to apprehend new integral knowledge of the butterfly’s biology, as well as factors influencing its population persistence and distribution.

A leaf age-dependent light use efficiency model for remote sensing the gross primary productivity seasonality over pantropical evergreen broadleaved forests.

Tropical and subtropical evergreen broadleaved forests (TEFs) contribute more than one-third of terrestrial gross primary productivity (GPP). However, the continental-scale leaf phenology-photosynthesis nexus over TEFs is still poorly understood to date. This knowledge gap hinders most light use efficiency (LUE) models from accurately simulating the GPP seasonality in TEFs. Leaf age is the crucial plant trait to link the dynamics of leaf phenology with GPP seasonality. Thus, here we incorporated the seasonal leaf area index of different leaf age cohorts into a widely used LUE model (i.e., EC-LUE) and proposed a novel leaf age-dependent LUE model (denoted as LA-LUE model). At the site level, the LA-LUE model (average R2 = .59, average root-mean-square error [RMSE] = 1.23 gC m-2 day-1) performs better than the EC-LUE model in simulating the GPP seasonality across the nine TEFs sites (average R2 = .18; average RMSE = 1.87 gC m-2 day-1). At the continental scale, the monthly GPP estimates from the LA-LUE model are consistent with FLUXCOM GPP data (R2 = .80; average RMSE = 1.74 gC m-2 day-1), and satellite-based GPP data retrieved from the global Orbiting Carbon Observatory-2 (OCO-2) based solar-induced chlorophyll fluorescence (SIF) product (GOSIF) (R2 = .64; average RMSE = 1.90 gC m-2 day-1) and the reconstructed TROPOspheric Monitoring Instrument SIF dataset using machine learning algorithms (RTSIF) (R2 = .78; average RMSE = 1.88 gC m-2 day-1). Typically, the estimated monthly GPP not only successfully represents the unimodal GPP seasonality near the Tropics of Cancer and Capricorn, but also captures well the bimodal GPP seasonality near the Equator. Overall, this study for the first time integrates the leaf age information into the satellite-based LUE model and provides a feasible implementation for mapping the continental-scale GPP seasonality over the entire TEFs.

Characterizing Vegetation Phenology Shifts on the Loess Plateau over Past Two Decades

Phenology is a critical mirror reflecting vegetation growth and has a major impact on terrestrial ecosystems. The Loess Plateau (LP) is a paramount ecological zone in China that has experienced considerable vegetation changes. However, understanding the dynamics of vegetation phenology is limited by ambiguous vegetation interpretation and anthropogenic-induced forces. This study combined the multi-climatic and anthropogenic datasets to characterize the interactions between phenology shifts and environmental variables. The principal findings were as follows: (1) Phenological shifts exhibit spatial heterogeneity and an interannually increasing trend in greenness (R2 > 0.6, p < 0.05). Notably, SOS (the start of the growing season) advances while EOS (the end of the growing season) delays in both the southeastern and northwestern regions. (2) SOS and EOS, primarily in the range of 100–150 and 285–320 days, respectively. Phenological changes vary depending on vegetation types. The forest has an early SOS, within 80–112 days, and a delayed EOS, within 288–320 days. The SOS of shrub is mainly within 80–144 days. (3) EOS shows a strong response to the preseason of each climate variable. Precipitation (R = 0.76), soil moisture (R = −0.64), and temperature (R = 0.89) are the governing determinants in shaping vegetation phenology. In addition, agriculture and urbanization play a significant role in shaping the spatial variations of SOS. These findings provide a basis for a systematic understanding of the processes that affect vegetation growth, which is crucial for maintaining the health and sustainability of arid and semiarid ecosystems.

Characterisation of Two Vineyards in Mexico Based on Sentinel-2 and Meteorological Data

In Mexico, viticulture represents the second source of employment in the agricultural area after the fruit and vegetable sector. In developed countries, remote sensing is widely used for vineyard monitoring; however, this tool is barely used in the developing countries of Iberoamerica. In this research, our overall objective is to characterise two vineyards in the state of Queretaro (Mexico) using Sentinel-2 and meteorological data, specifically spectral and thermal indices. Results show that spectral indices obtained from Sentinel-2 bands have adequately characterised the phenological dynamics of the different varieties of the vineyards. The Modified Soil-Adjusted Vegetation Index (MSAVI) was adequately used to discriminate between the first stages of vineyards, while the Normalized Difference Vegetation Index (NDVI) was useful for monitoring vineyards during the rest stages of vineyards. Thermal indices have shown that the best grape varieties are those that can adapt to both cooler and warmer temperatures, have a reasonable ripening period, and can produce wines with balanced acidity and flavours. In conclusion, the combination of meteorological (including thermal indices) and remote sensing data (NDVI and MSAVI) provide information for choosing a suitable grape variety for this region.

Macrophenological dynamics from citizen science plant occurrence data

Abstract Phenological shifts across plant species is a powerful indicator to quantify the effects of climate change. Today, mobile applications with automated species identification open new possibilities for phenological monitoring across space and time. Here, we introduce an innovative spatio‐temporal machine learning methodology that harnesses such crowd‐sourced data to quantify phenological dynamics across taxa, space and time. Our algorithm links individual phenological responses across thousands of species and geographical locations, using a similarity measure. The analysis draws on nearly ten million plant observations collected through the AI‐based plant identification app Flora Incognita in Germany from 2018 to 2021. Our method quantifies changes in synchronisation across the annual cycle. During the growing season, synchronised behaviour can be encoded by a few characteristic macrophenological patterns. Nonlinear spatio‐temporal changes of these patterns can be efficiently quantified using a data compressibility measure. Outside the growing season, the phenological synchronisation diminishes introducing noise into the patterns. Despite biases and uncertainties associated with crowd‐sourced data, for example due to human data collection behaviour, our study demonstrates the feasibility of deriving meaningful indicators for monitoring plant macrophenology from individual plant observations. As crowd‐sourced databases continue to expand, our approach holds promise to study climate‐induced phenological shifts and feedback loops.

Assessment of Phenological Dynamics of Different Vegetation Types and Their Environmental Drivers with Near-Surface Remote Sensing: A Case Study on the Loess Plateau of China.

Plant phenology is an important indicator of the impact of climate change on ecosystems. We have continuously monitored vegetation phenology using near-surface remote sensing, i.e., the PhenoCam in a gully region of the Loess Plateau of China from March 2020 to November 2022. In each image, three regions of interest (ROIs) were selected to represent different types of vegetation (scrub, arbor, and grassland), and five vegetation indexes were calculated within each ROI. The results showed that the green chromatic coordinate (GCC), excess green index (ExG), and vegetation contrast index (VCI) all well-captured seasonal changes in vegetation greenness. The PhenoCam captured seasonal trajectories of different vegetation that reflect differences in vegetation growth. Such differences may be influenced by external abiotic environmental factors. We analyzed the nonlinear response of the GCC series to environmental variables with the generalized additive model (GAM). Our results suggested that soil temperature was an important driver affecting plant phenology in the Loess gully region, especially the scrub showed a significant nonlinear response to soil temperature change. Since in situ phenology monitoring experiments of the small-scale on the Loess Plateau are still relatively rare, our work provides a reference for further understanding of vegetation phenological variations and ecosystem functions on the Loess Plateau.

Acoustic phenology of tropical resident birds differs between native forest species and parkland colonizer species

Most birds are characterized by a seasonal phenology closely adapted to local climatic conditions, even in tropical habitats where climatic seasonality is slight. In order to better understand the phenologies of resident tropical birds, and how phenology may differ among species at the same site, we used ~70 000 hours of audio recordings collected continuously for two years at four recording stations in Singapore and nine custom‐made machine learning classifiers to determine the vocal phenology of a panel of nine resident bird species. We detected distinct seasonality in vocal activity in some species but not others. Native forest species sang seasonally. In contrast, species which have had breeding populations in Singapore only for the last few decades exhibited seemingly aseasonal or unpredictable song activity throughout the year. Urbanization and habitat modification over the last 100 years have altered the composition of species in Singapore, which appears to have influenced phenological dynamics in the avian community. It is unclear what is driving the differences in phenology between these two groups of species, but it may be due to either differences in seasonal availability of preferred foods, or because newly established populations may require decades to adjust to local environmental conditions. Our results highlight the ways that anthropogenic habitat modification may disrupt phenological cycles in tropical regions in addition to altering the species community.

Significant phenological response of forest tree species to climate change in the Western Carpathians

Abstract This study aims to analyse the phenological dynamics of tree species in response to changes in climatic conditions over the last two climate-normal periods (CLINO 1961−1990 and 1991−2020). We focused on the main climax tree species (Quercus species, Fagus sylvatica L., Abies alba Mill., Picea abies [L.] Karst., and Pinus mugo Turra) dominating eight altitudinal forest vegetation zones (FVZ) and alluvial forests of Western Carpathians and adjacent lowlands in Central Europe. The phenological phases analysed in this study were first leaf (BBCH11) and general leaf colouring (BBCH94) for deciduous species, and the onset of new shoots (BBCH10) for evergreen conifers. The results of partial correlations confirmed that temperature is the main driving factor explaining the shifted onset of phenological phases for the species considered. Furthermore, deciduous species growing in the lower (and warmer) FVZ showed a certain level of drought sensitivity related to the earlier BBCH94. The identified trends in phenology were species-specific and differed between the individual FVZs. The most pronounced shifts toward the earlier BBCH10 and BBCH11 were found in the upper FVZ of the vertical distribution range of species. The presented results will support our understanding of the mechanisms underlying environmental control of tree phenology. This is crucial for predicting how the growing season of trees will be constrained by climate change-induced conditions in individual FVZ.

Recent Cereal Phenological Variations under Mediterranean Conditions

This study analyzes the temporal patterns of rainfed cereal phenology extracted from the GIMMS NDVI3g dataset in the main cereal-growing regions under a Mediterranean climate in Spain, Portugal, France and Italy during the period 1982–2022. The series before and after the beginning of the 21st century were analyzed separately. Phenological parameters were extracted using the modified dynamic threshold method, and their trends were analyzed. Correlation analyses were performed to study the relationships among these parameters and to analyze the influence of hydroclimatic variables on the start (SOS) and end (EOS) of the growing season. Results showed a temporal reversal in phenological trends between both study periods, coinciding with the global warming hiatus. In the first period (1982–2002), SOS and EOS advanced (−7.5 and −3.1 days, respectively), and the length of growing season (LOS) increased. However, during the second stage (2003–2022), SOS and EOS were delayed (7.5 and 1.7 days, respectively), and LOS decreased. Similar dynamics were observed for the influence of the hydroclimatic variables on SOS and EOS, stronger in the first period and weaker in the second. This study provides valuable information on the phenological dynamics of rainfed cereals that may be useful for their management and planning in climate change scenarios.

Spawning fish maintains trophic synchrony across time and space beyond thermal drivers.

Increasing ocean temperature will speed up physiological rates of ectotherms. In fish, this is suggested to cause earlier spawning due to faster oocyte growth rates. Over time, this could cause spawning time to become decoupled from the timing of offspring food resources, a phenomenon referred to as trophic asynchrony. We used biological data, including body length, age, and gonad developmental stages collected from >125,000 individual Northeast Arctic cod (Gadus morhua) sampled between 59 and 73° N in 1980-2019. Combined with experimental data on oocyte growth rates, our analyses show that cod spawned progressively earlier by about a week per decade, partly due to ocean warming. It also appears that spawning time varied by more than 40 days, depending on year and spawning location. The significant plasticity in spawning time seems to be fine-tuned to the local phytoplankton spring bloom phenology. This ability to partly overcome thermal drivers and thus modulate spawning time could allow individuals to maximize fitness by closely tracking local environmental conditions important for offspring survival. Our finding highlights a new dimension for trophic match-mismatch and should be an important consideration in models used to predict phenology dynamics in a warmer climate.

Land use change and coastal water darkening drive synchronous dynamics in phytoplankton and fish phenology on centennial timescales

AbstractAt high latitudes, the suitable window for timing reproductive events is particularly narrow, promoting tight synchrony between trophic levels. Climate change may disrupt this synchrony due to diverging responses to temperature between, for example, the early life stages of higher trophic levels and their food resources. Evidence for this is equivocal, and the role of compensatory mechanisms is poorly understood. Here, we show how a combination of ocean warming and coastal water darkening drive long‐term changes in phytoplankton spring bloom timing in Lofoten Norway, and how spawning time of Northeast Arctic cod responds in synchrony. Spring bloom timing was derived from hydrographical observations dating back to 1936, while cod spawning time was estimated from weekly fisheries catch and roe landing data since 1877. Our results suggest that land use change and freshwater run‐off causing coastal water darkening has gradually delayed the spring bloom up to the late 1980s after which ocean warming has caused it to advance. The cod appear to track phytoplankton dynamics by timing gonadal development and spawning to maximize overlap between offspring hatch date and predicted resource availability. This finding emphasises the importance of land–ocean coupling for coastal ecosystem functioning, and the potential for fish to adapt through phenotypic plasticity.

Impact of Preseason Climate Factors on Vegetation Photosynthetic Phenology in Mid-High Latitudes of the Northern Hemisphere.

During the period preceding the vegetation growing season (GS), temperature emerges as the pivotal factor determining phenology in northern terrestrial ecosystems. Despite extensive research on the impact of daily mean temperature (Tmean) during the preseason period, the influence of diurnal temperature range (DTR) on vegetation photosynthetic phenology (i.e., the impact of the plant photosynthetic cycle on seasonal time scale) has largely been neglected. Using a long-term vegetation photosynthetic phenology dataset and historical climate data, we examine vegetation photosynthetic phenology dynamics and responses to climate change across the mid-high latitudes of the Northern Hemisphere from 2001 to 2020. Our data reveal an advancing trend in the start of the GS (SOS) by -0.15 days per year (days yr-1), affecting 72.1% of the studied area. This is particularly pronounced in western Canada, Alaska, eastern Asia, and latitudes north of 60°N. Conversely, the end of the GS (EOS) displays a delaying trend of 0.17 days yr-1, impacting 62.4% of the studied area, especially northern North America and northern Eurasia. The collective influence of an earlier SOS and a delayed EOS has resulted in the notably prolonged length of the GS (LOS) by 0.32 days yr-1 in the last two decades, affecting 70.9% of the studied area, with Eurasia and western North America being particularly noteworthy. Partial correlation coefficients of the SOS with preseason Tmean, DTR, and accumulated precipitation exhibited negative values in 98.4%, 93.0%, and 39.2% of the study area, respectively. However, there were distinct regional variations in the influence of climate factors on the EOS. The partial correlation coefficients of the EOS with preseason Tmean, DTR, and precipitation were positive in 58.6%, 50.1%, and 36.3% of the region, respectively. Our findings unveil the intricate mechanisms influencing vegetation photosynthetic phenology, holding crucial significance in understanding the dynamics of carbon sequestration within terrestrial ecosystems amidst climate change.

Synergistic Impacts of Built-Up Characteristics and Background Climate on Urban Vegetation Phenology: Evidence from Beijing, China

Vegetation is an important strategy for mitigating heat island effects, owed to its shading and evaporative cooling functions. However, urbanization has significantly affected regional vegetation phenology and can potentially weaken the cooling potential of vegetation. Previous studies have mainly focused on national and regional vegetation phenology, but local-scale vegetation phenology and dynamic variations in built-up areas remain unclear. Therefore, this study characterized the vegetation phenology in the densely built-up area of Beijing, China over the period of 2000–2020 based on high-resolution NDVI data using Savitzky–Golay filtering and explored its spatiotemporal characteristics and drivers. The results indicate that the vegetation phenology exhibits significant spatial heterogeneity and clustering characteristics. Compared with vegetation in peripheral blocks, vegetation in central urban blocks generally has an earlier start in the growing season (SOS), later end in the growing season (EOS), and a longer growing season length (GSL). However, the overall distribution of these parameters has experienced a process of decentralization along with urbanization. In terms of drivers, vegetation phenology indicators are mainly influenced by background climate. Specifically, SOS and GSL are mainly affected by temperature (TEP), whereas EOS is mainly influenced by annual precipitation (PRE). Additionally, local environmental factors, particularly the percentage of water body (WAP), also have an impact. Notably, the local environment and background climate have a synergistic effect on vegetation phenology, which is greater than their individual effects. Overall, this study extends the current knowledge on the response of vegetation phenology to urbanization by investigating long-term vegetation phenology dynamics in dense urban areas and provides new insights into the complex interactions between vegetation phenology and built environments.

Variation of the Start Date of the Vegetation Growing Season (SOS) and Its Climatic Drivers in the Tibetan Plateau.

Climate change inevitably affects vegetation growth in the Tibetan Plateau (TP). Understanding the dynamics of vegetation phenology and the responses of vegetation phenology to climate change are crucial for evaluating the impacts of climate change on terrestrial ecosystems. Despite many relevant studies conducted in the past, there still remain research gaps concerning the dominant factors that induce changes in the start date of the vegetation growing season (SOS). In this study, the spatial and temporal variations of the SOS were investigated by using a long-term series of the Normalized Difference Vegetation Index (NDVI) spanning from 2001 to 2020, and the response of the SOS to climate change and the predominant climatic factors (air temperature, LST or precipitation) affecting the SOS were explored. The main findings were as follows: the annual mean SOS concentrated on 100 DOY-170 DOY (day of a year), with a delay from east to west. Although the SOS across the entire region exhibited an advancing trend at a rate of 0.261 days/year, there were notable differences in the advancement trends of SOS among different vegetation types. In contrast to the current advancing SOS, the trend of future SOS changes shows a delayed trend. For the impacts of climate change on the SOS, winter Tmax (maximum temperature) played the dominant role in the temporal shifting of spring phenology across the TP, and its effect on SOS was negative, meaning that an increase in winter Tmax led to an earlier SOS. Considering the different conditions required for the growth of various types of vegetation, the leading factor was different for the four vegetation types. This study contributes to the understanding of the mechanism of SOS variation in the TP.

A model for predicting the phenology of Philaenus spumarius.

The design and implementation of Philaenus spumarius control strategies can take advantage of properly calibrated models describing and predicting the phenology of vector populations in agroecosystems. We developed a temperature-driven physiological-based model based on the system of Kolmogorov partial differential equations to predict the phenological dynamics of P. spumarius. The model considers the initial physiological age distribution of eggs, the diapause termination process, and the development rate functions of post-diapausing eggs and nymphal stages, estimated from data collected in laboratory experiments and field surveys in Italy. The temperature threshold and cumulative degree days for egg diapause termination were estimated as 6.5°C and 120 DD, respectively. Preimaginal development rate functions exhibited lower thresholds ranging between 2.1 and 5.0°C, optimal temperatures between 26.6 and 28.3°C, and upper threshold between 33.0 and 35°C. The model correctly simulates the emergence of the 3rd, 4th, and 5th nymphal instars, key stages to target monitoring actions and control measures against P. spumarius. Precision in simulating the phenology of the 1st and 2nd nymphal stages was less satisfactory. The model is a useful rational decision tool to support scheduling monitoring and control actions against the late and most important nymphal stages of P. spumarius.

Evaluation of PlanetScope-detected plant-specific phenology using infrared-enabled PhenoCam observations in semi-arid ecosystems

Phenology detection from remotely sensed data remains challenging in semi-arid ecosystems due to the unique spatial heterogeneity and irregular temporal growth in plants. PlanetScope imagery, with fine spatial and temporal resolutions, is revolutionizing the earth observation sector. It has demonstrated its effectiveness in monitoring phenology dynamics across various terrestrial ecosystems. However, the quality and accuracy of PlanetScope data for depicting plant growth development and detecting phenological metrics (phenometrics) in semi-arid environments have not been systematically examined. In this study, we evaluated the capability of PlanetScope for monitoring plant-specific phenology across the semi-arid western United States, by comparing phenometrics (onsets of greenup, maturity, senescence, and dormancy) retrieved from time series of two PlanetScope vegetation indices (VI), which are EVI2 (two-band Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index), with a set of PhenoCam observations at 15 sites. To conduct a comprehensive comparison, PhenoCam time series and phenometrics were extracted from infrared-enabled PhenoCam EVI2 and NDVI, as well as commonly used PhenoCam GCC (Green Chromatic Coordinate). Our results show that (1) time series of PlanetScope VI were consistent with PhenoCam GCC and VI time series during greenup phase but moderately comparable during senescence phase, with an average R2 of 0.67 and 0.57 for greenup and senescence phases, respectively; (2) phenometrics derived from PlanetScope VI exhibited better agreement with those from PhenoCam GCC and VI in greenup phase (greenup and maturity onsets) than in senescence phase (senescence and dormancy onsets), with an average R2 of 0.81, 0.84, 0.72, and 0.53 for greenup, maturity, senescence, and dormancy onsets, respectively; (3) PlanetScope-detected senescence onset and dormancy onset were systematically later than PhenoCam-based retrievals with a mean systematic bias of 12.6 days and 18.2 days, respectively; (4) phenometrics derived from PlanetScope VI were more comparable with PhenoCam VI retrievals than with PhenoCam GCC retrievals, which are reflected in better correlations and smaller bias between phenometrics, especially during senescence phase; and (5) PlanetScope and PhenoCam EVI2 time series produced the most comparable phenometrics, suggesting that EVI2 is an optimal index for vegetation phenology detections from different sensors. In summary, this study suggests PlanetScope has the ability to detect plant-specific phenology and to improve our understanding of phenology dynamics in heterogeneous semi-arid ecosystems at fine scales.

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.

A study of annual tree-wise LiDAR intensity patterns of boreal species observed using a hyper-temporal laser scanning time series

This study introduces the annual tree-wise intensity patterns of three boreal tree species, silver birch (Betula pendula Roth.), Scots pine (Pinus sylvestris L.), and Norway spruce (P,icea abies H. Karst.), observed from a long-term hyper-temporal point cloud dataset collected with a permanent laser scanning (LiDAR) station. An annual LiDAR intensity pattern refers to the trend of variations of tree-wise calibrated LiDAR intensity values over the course of a year, primarily linked to species-specific phenological characteristics. Such pattern was discovered from hyper-temporal (76 observations between April 2020 and April 2021) high resolution (0.01 m 3D point spacing at 100 m distance) point cloud observations acquired using a single wavelength (1550 nm) static LiDAR system installed on a climate monitoring tower at the Hyytiälä forest research station in central Finland. A set of experiments was designed to explore the interactions among tree-wise calibrated LiDAR intensity, species, data quality, and observation dates. Thus, to provide practical suggestions for expectable accuracies of tree species classification using diverse types of LiDAR systems and data acquisition strategies. It was revealed that by combining high spatial resolution (up to 100,000 points/m2) with bi-weekly observations (two scans per week except winter period), the average tree species classification accuracy reaches 96.8% for the three boreal tree species in the study area, where the few misclassified cases are attributed to inaccurate individual tree segmentation. This suggests that the annual tree-wise LiDAR intensity patterns, which reflects the tree-wise phenological dynamics, are robust and species-specific. When the spatial resolution was reduced to 5 points/ m2, the average accuracy of species classification among the three studied species reached 85% when using bi-temporal data from spring and autumn. Our analysis further indicates that arranging data acquisition with a two-week variability in late autumn and late spring could yield higher accuracy in species classification. Furthermore, our study revealed the importance of subcanopy information for species classification, specifically in distinguishing different coniferous species. When limited to upper canopy data (as in simulated airborne LiDAR scenarios), achieving a tree species classification accuracy of over 80% required either high point density (e.g. 8000 points/m2) combined with bi-temporal collection in a year, or low point density (5 points/ m2) with a dense time series (e.g., 76 time points within a year), which showcased the possibility to compensate low spatial resolution with high temporal resolution, and vice versa, in capturing species-specific phenological characteristics of trees.

Spatial-temporal heterogeneity of spring phenology in boreal forests as estimated by satellite solar-induced chlorophyll fluorescence and vegetation index

Changes in spring phenology in boreal forests around the subarctic region have a profound impact on the global carbon and water cycles. To date, photosynthesis and greenness dynamics in evergreen coniferous forests have been found to be decoupled on a large scale, but whether the spatial heterogeneity of seasonal dynamics in boreal forests is coupled in various datasets remains unclear. Therefore, based on the solar-induced chlorophyll fluorescence (SIF) retrieved by satellite and the modelled SIF (GOME-2 SIF and GOSIF) during 2007–2018, we calculated the start of the growing season (SOS) of boreal forests (45–70°N) and compared the results with 2-band Enhanced Vegetation Index (EVI2) phenological products (MCD12Q2 C6) and flux data (Gross primary productivity, GPP) phenology. The spatial-temporal variations in the SOS were analyzed by linear regression and piecewise linear regression. The results exhibited that GOME-2 SIF showed an SOS of 11 days earlier than GOSIF, 8 days earlier than EVI2 in North America, and 5 days earlier than GOSIF, 3 days earlier than EVI2 in Eurasia. However, all relationships between SOSs of the three remote sensing datasets were significant in North America and Eurasia, and the significance was higher in Eurasia. With increasing latitude and longitude, almost all SOSs estimated from the three datasets significantly increased. Also, both in North America and Eurasia, the SOSs exhibited a trend of being advanced to being delayed from the western to the eastern parts of the continents over 2007–2018. This indicated the spatial consistency of photosynthesis and greenness dynamics in the spring phenology of boreal forests, especially in Eurasia. In this study, the validity of the spatial-temporal variation in spring phenology of boreal forests based on GOME-2 SIF retrieved from satellites and MCD12Q2 C6 was confirmed, and further exploration is needed to determine their potential advantages.