Advancements in Monitoring Tree Phenology Under Global Change: A Comprehensive Review

Abstract

This comprehensive review explores recent advancements in monitoring tree phenology in the context of global change. As climate change continues to alter ecosystems worldwide, understanding tree phenology has become increasingly crucial for predicting ecological responses and informing conservation strategies. This review examines traditional ground-based observation methods, highlights their strengths and limitations, and discusses the integration of modern technologies such as remote sensing, digital cameras, and sensor networks. Special attention is given to the role of citizen science initiatives in expanding phenological data collection. This review also addresses the challenges posed by global change in tree phenology monitoring, including shifting phenological patterns and data integration complexities. Furthermore, it explores the applications of phenological data in climate change research, ecosystem management, and biodiversity conservation. The paper concludes by identifying future directions and emerging technologies that promise to revolutionize tree phenology monitoring, emphasizing the need for interdisciplinary collaboration and standardized methodologies to enhance our understanding of tree phenology in a rapidly changing world.

1. Introduction

Global change encompasses a range of environmental transformations driven by human activities, including climate change [1], land-use change [2], and alterations in biogeochemical cycles [3]. Climate change, characterized by rising temperatures [4], shifting precipitation patterns [5], and increased frequency of extreme weather events [6], has profound effects on ecosystems. Land-use changes, such as urbanization and deforestation, further exacerbate these impacts by fragmenting habitats and altering local microclimates [7,8]. Additionally, disturbances in biogeochemical cycles, such as nitrogen deposition, can influence nutrient availability and ecosystem productivity [9,10].

Tree phenology, the study of recurring biological events in trees, plays a critical role in ecosystem functioning [11,12]. Phenological events, such as bud burst, leaf expansion, flowering, and leaf senescence, are highly sensitive to environmental changes [13,14]. These events regulate key ecological processes, including photosynthesis, carbon sequestration, and species interactions [15]. For example, the timing of flowering can affect pollinator availability [16], whereas leaf senescence influences nutrient cycling and soil organic matter decomposition [17]. Investigating tree phenology is crucial for understanding tree fitness, competition for resources, and the impacts of climate change on ecosystems [12,18]. Our analysis revealed that over 80% of tree phenology studies originate from just nine countries (Figure 1). The United States and China are the most prominent, accounting for 24.51% and 15.84% of the studies, respectively. Other significant contributors include Brazil, Canada, France, Spain, Japan, and the United Kingdom, whose respective shares range from 4.22% to 8.06%. This distribution underscores a notable research imbalance, with many regions remaining underrepresented. Factors such as limited research capacity and funding constraints may contribute to this disparity. Future research efforts should prioritize the exploration of underrepresented areas to achieve a more comprehensive understanding of global tree phenology.

Under global change, the importance of tree phenology research is further highlighted. Climate change has led to shifts in phenological events, with many temperate tree species experiencing earlier spring activities and delayed autumn events [13]. However, these trends are modified by concurrent increases in drought frequency and intensity under climate change scenarios. Reduced water availability and prolonged drought conditions can paradoxically accelerate senescence in some species and ecosystems [19]. These complex phenological responses may trigger cascading effects across ecosystem structures and functions. For example, earlier leaf-out can increase the growing season length and potentially increase carbon uptake [20], but it may also expose trees to a higher risk of late spring frost damage [21]. Moreover, altered phenology can disrupt the synchronization between trees and associated species, such as pollinators and herbivores, leading to changes in species interactions [20]. In addition, tree phenology research helps us understand the feedback mechanisms between vegetation and climate [22,23]. For example, increased vegetation productivity due to earlier spring phenology can lead to biophysical impacts such as changes in surface albedo and evapotranspiration, which in turn can influence local and regional climates [24,25]. This highlights the need for comprehensive studies that integrate phenology with other ecological and climatic processes to better predict and manage the impacts of global change on forest ecosystems.

However, traditional tree phenology monitoring methods face several challenges. First, monitoring tree-crown scale leaf phenology via remote sensing is difficult, and the number of leaf phenological events characterized by remotely sensed phenological metrics is limited [26]. Second, different phenological metric extraction methods for characterizing the same field phenological event may yield varying accuracies [27]. Third, our understanding of root phenology remains limited because of the inherent difficulties in monitoring root growth [12]. Last, compared with the onset of the growing season, much less is known about its end, despite its critical importance for understanding processes such as carbon uptake and the nutrient cycle [28,29,30].

The field of tree phenology monitoring has undergone significant transformations with the advent of new technologies [31,32]. Traditional ground-based methods have been complemented by remote sensing, digital cameras, and sensor networks, enabling researchers to collect data at unprecedented spatial and temporal scales [33,34]. This technological evolution has facilitated a shift from descriptive studies to predictive modeling, enhancing our ability to forecast phenological responses to global change. International initiatives, such as the Global Earth Observation System of Systems (GEOSS), have further supported these advancements by promoting data sharing and standardization [35].

2. Traditional Ground-Based Observation Methods and Their Evolution

2.1. Strengths and Limitations of Classic Methods

Traditional ground-based observation methods have served as the cornerstone of tree phenology monitoring for decades [36,37]. These methods rely on direct visual observations of individual trees or stands, with researchers meticulously recording the timing of key phenological events, such as budburst, leaf expansion, flowering, and leaf senescence [38,39,40]. To ensure consistency and comparability across studies, standardized protocols such as the BBCH scale (Biologische Bundesanstalt, Bundessortenamt, and CHemical industry scale) have been widely adopted [41,42].

Ground-based methods offer several advantages. They provide high-resolution, species-specific data, which are invaluable for understanding local phenological patterns and validating remote sensing observations [43]. Additionally, these methods allow researchers to capture subtle phenological changes that may not be detectable through other techniques [44]. However, ground-based approaches are not without limitations. They are inherently labor intensive and time-consuming [45], which restricts their spatial coverage and temporal frequency. Furthermore, observer bias and variability in the interpretation of phenological stages can introduce inconsistencies in the data [46]. These limitations highlight the need for complementary methods, such as remote sensing and sensor networks, to overcome the constraints of traditional ground-based observations and achieve broader-scale phenological monitoring.

2.2. Technological Innovations and Upgrades

Recent technological advancements have revolutionized tree phenology monitoring by enhancing traditional ground-based methods through the integration of automated sensor systems, collaborative observation networks, and cutting-edge remote sensing technologies [17,47]. These innovations have significantly improved the spatial and temporal resolution of phenological data, enabling researchers to address the limitations of traditional approaches and gain deeper insights into the impacts of global change on forest ecosystems.

2.2.1. Remote Sensing Improvements

The Sentinel-2 and Landsat series of high-resolution satellites have significantly enhanced the spatial and temporal resolution of phenological monitoring [48,49]. These satellites provide frequent, detailed data that improve the accuracy of tree-crown-scale leaf phenology monitoring [50]. Additionally, unmanned aerial vehicles (UAVs) equipped with multispectral sensors offer unprecedented spatial resolution, capturing fine-scale phenological changes at the tree-crown level [51]. These advancements overcome the limitations of traditional remote sensing, such as coarse spatial resolution and infrequent data acquisition [34]. However, when dealing with UAVs, it is crucial to address the issues of revisit frequency and coverage capacity. Acquiring large surfaces with high revisit frequencies remains a challenge because of the limited flight time and range of UAVs. Moreover, there are limitations in using UAVs for phenology monitoring, including regulatory restrictions, data processing complexity, and the need for skilled operators. In combination with these technologies, automated sensor systems—including sap flow monitors and microcore samplers—provide continuous, high-resolution data on tree physiological processes [52]. For example, sap flow sensors elucidate the relationship between phenological events and water use dynamics [53], whereas microcore sampling tracks cambial activity and growth patterns [54]. Together, these tools enable a more comprehensive understanding of phenological dynamics across scales (Figure 2).

2.2.2. Phenological Metric Extraction

Phenological metrics can be estimated via various methods, such as processing techniques for digital images of vegetation and automated procedures for satellite Earth observation time series [55,56]. The integration of machine learning and deep learning technologies has revolutionized the extraction of phenological metrics from remote sensing data [57,58]. These automated methods process large datasets with high accuracy and efficiency, enabling the identification of phenological events at large scales. Furthermore, multiplatform software, such as the “Pheno-Synthesis Software Suite” (PS3 version 2021) [59], integrates ground-based observations, near-surface imagery, satellite data, and climate datasets. This holistic approach addresses the challenges of inconsistent phenological metric extraction and provides a comprehensive understanding of phenological dynamics. Collaborative observation networks, such as the USA National Phenology Network and the European Phenology Network [60,61], further enhance these efforts by establishing standardized observation guidelines and facilitating data sharing across sites. These networks have expanded the spatial and temporal coverage of phenological data, enabling researchers to detect trends and patterns at regional and global scales.

2.2.3. Root Phenology Monitoring

Advances in root monitoring technologies, such as minirhizotrons and root sensors, have opened new avenues for studying below-ground phenology [62,63,64]. These tools enable nondestructive, continuous observations of root growth and development, offering valuable insights into root phenological processes [65]. By combining field data with modeling and simulation techniques, researchers can better predict root dynamics and their responses to environmental changes. Automated sensor systems, such as sap flow monitoring, further complement these efforts by providing continuous data on above-ground physiological processes, creating a more integrated understanding of whole-tree phenology.

2.2.4. Monitoring of the End of the Growing Season

The use of multitemporal satellite data and advanced analytical techniques has improved monitoring at the end of the growing season [34,66]. By analyzing temporal changes in vegetation indices, researchers can more accurately determine the timing of leaf senescence and other late-season phenological events [27,67]. Global and regional initiatives, such as the Global Phenological Monitoring (GPM) Programme [68] and the American PhenoCam network [69], promote standardized data collection and sharing, enhancing our understanding of both the onset and the end of the growing season. Collaborative observation networks play a critical role in these efforts by providing large-scale, standardized phenological data that support the validation and refinement of remote sensing-based models [17,70].

Collectively, these technological innovations have addressed many of the challenges associated with traditional phenology monitoring methods. By providing high-resolution, continuous, and standardized data, these advancements have significantly enhanced our ability to monitor and understand tree phenology. This progress is critical for predicting the impacts of global change on forest ecosystems and informing sustainable management practices.

3. Challenges Posed by Global Change in Tree Phenology Monitoring

3.1. Interference from Nonclimatic Factors

Global change introduces a range of nonclimatic factors that can complicate tree phenology monitoring [71,72]. For example, light pollution from artificial lighting in urban areas can alter the photoperiod and disrupt the phenological cycles of light-sensitive tree species [73]. Compared with their rural counterparts, streetlights can delay leaf senescence in urban trees, extending their growing season by 15–20 days [74,75]. Additionally, changes in atmospheric composition, such as elevated levels of CO₂ and ozone, can influence tree phenology through complex physiological mechanisms [76]. CO₂ fertilization may increase growth and prolong the growing season [77], whereas ozone exposure can cause oxidative stress and accelerate leaf senescence [78].

3.2. Data Science Challenges

The integration of diverse phenological datasets is challenging due to their heterogeneity [79]. Combining ground observations, remote sensing, and sensor data requires advanced methods [43,80]. Bayesian hierarchical models help merge these multisource datasets effectively [81]. Data assimilation techniques, such as the Ensemble Kalman Filter (EnKF), further improve model accuracy [82]. Uncertainty quantification is also crucial for reliable analysis. Monte Carlo simulations assess variability in phenological predictions [83]. Sensitivity analyses identify key factors influencing uncertainty [84]. Together, these approaches enhance the robustness of phenological models. Clear communication of uncertainty supports better decision-making in ecological and climate studies.

4. Applications of Tree Phenology Data

4.1. Ecosystem Service Assessment

Phenological data play a crucial role in assessing ecosystem services and informing management decisions by enabling the development of predictive models and early warning systems [85,86]. Phenological events, such as the timing of leaf emergence and senescence, directly influence ecosystem productivity [87], and models that couple phenology with net primary productivity (NPP) can improve predictions of climate change impacts on carbon sequestration and forest growth [88]. Additionally, phenological data support disaster risk reduction by enhancing early warning systems—for example, forecasting flowering periods helps mitigate frost damage to trees [89]—while monitoring leaf area dynamics aids in wildfire risk assessments by detecting shifts in vegetation dryness [90]. Further integration of phenological data with remote sensing and machine learning can refine these applications, providing near-real-time insights for adaptive ecosystem management [91].

4.2. Policy and Management Interfaces

Phenological data are increasingly being integrated into policy and management frameworks, providing science-based solutions for climate action and urban resilience [92]. Under the Paris Agreement, phenological indicators serve as critical tools for monitoring national commitments, where phenology-based corrections significantly increase the accuracy of forest carbon stock assessments [93]. For example, shifts in leaf longevity and growing season length directly affect carbon sequestration rates, enabling more precise reporting of greenhouse gas emissions [94]. In urban forestry planning, phenological data guide evidence-based species selection by identifying trees whose growth cycles align with local climate patterns [95]. This approach not only improves canopy cooling during heatwaves but also reduces vulnerability to late-spring frosts—a key consideration as climate variability increases [96,97]. Emerging technologies, such as near-real-time phenological monitoring via satellite imagery and IoT sensors [98], are further transforming these applications, allowing adaptive management at unprecedented spatial and temporal resolutions.

5. Future Directions

5.1. Emerging Frontiers in Technology Integration

The future of tree phenology monitoring lies in the integration of advanced technologies and interdisciplinary approaches, enabling unprecedented insights into forest ecosystems under global change [31]. Emerging multiomics approaches—combining phenological observations with genomic, transcriptomic, and metabolomic data—are revealing the molecular drivers of phenological events [99,100]. For example, gene expression networks linked to budburst timing [101] and drought-induced metabolite shifts [102] now provide mechanistic explanations for the observed phenological responses to climate variability. Concurrently, digital twin forests are revolutionizing monitoring paradigms by creating dynamic, data-rich virtual replicas of real-world forests [103]. These twins integrate near-real-time satellite imagery, IoT sensor networks (e.g., sap flow, microclimate), and individual tree 3D modeling (LiDAR-derived architectures) to simulate forest responses at hyperresolution (<1 m²). Pilot systems, such as Finland’s “Forest Digital Twin” [104], already demonstrate predictive skill in projecting pest outbreaks and carbon sequestration shifts on the basis of phenological anomalies. However, challenges persist in scaling omics-to-ecosystem linkages and reducing computational costs for global twin deployments—areas where machine learning and edge computing show high potential [105].

5.2. Global Collaboration Mechanisms

Addressing the challenges of global change requires coordinated international efforts through innovative frameworks and open science principles [106]. Spearheaded by organizations such as the Food and Agriculture Organization (FAO), the proposed GPM Programme seeks to establish standardized protocols for worldwide phenological monitoring, integrating ground observations, satellite data (e.g., PhenoCam networks), and citizen science platforms (e.g., iNaturalist) to ensure data harmonization across borders. Concurrently, the open science movement is revolutionizing data practices [107]: machine-readable formats such as PhenoML enable seamless metadata sharing, whereas blockchain-based systems cryptographically secure data provenance significantly reduces interoperability barriers [108]. International collaborations are demonstrating measurable impacts through standardized phenological monitoring. The International Phenological Network (IPN) and the World Meteorological Organization’s Global Phenological Monitoring (WMO-GPM) framework have harmonized data from multiple countries, improving vegetation-climate model accuracy in midlatitude regions [68]. However, persistent tropical coverage gaps (with a relatively low proportion of WMO-GPM stations in rainforests) underscore the urgency of initiatives such as the Group on Earth Observations Biodiversity Observation Network (GEO BON), which prioritizes sensor deployment in biodiversity hotspots. Emerging technologies in these regions—such as PhenoCam automated image analysis and low-cost IoT sensors—are reducing observation costs while maintaining high data accuracy.

6. Conclusions

The field of tree phenology monitoring has made significant strides in recent years, driven by technological advancements and the urgent need to understand ecosystem responses to global change. The integration of traditional methods with modern technologies has expanded our capacity to monitor phenological events across diverse spatial and temporal scales. However, the challenges posed by global change continue to test the limits of current monitoring approaches. Future research must focus on developing innovative solutions, fostering interdisciplinary collaboration, and promoting global data sharing to enhance our understanding of tree phenology in a rapidly changing world. By doing so, we can better predict and mitigate the impacts of global change on forest ecosystems, ensuring their resilience and sustainability for future generations.