Abstract Accurate estimation of individual tree above‐ground biomass (AGB) and its component‐wise allocation is crucial for advancing ecological research and forest management. However, current biomass estimation methods, such as destructive sampling and allometric equation–based approaches, face limitations in both operational efficiency and cost‐effectiveness, and only destructive sampling can provide component‐wise biomass measurements, which is impractical for large‐scale studies or repeated measurements. In this study, we present a terrestrial laser scanning (TLS)‐based workflow integrating wood–leaf separation, voxel‐based foliage estimation and detailed 3D reconstruction of tree architecture to achieve accurate estimation of individual tree AGB and its component‐wise allocation. A total of 68 trees were scanned to obtain high‐resolution TLS data and subsequently destructively harvested to acquire field reference measurements for validation. The results demonstrate that the workflow achieved high accuracy in predicting AGB at the individual tree level (coefficient of determination/R2 = 0.88, root mean squared error/RMSE = 16.83 kg, mean absolute error/MAE = 12.18 kg), significantly outperforming estimates derived from locally calibrated allometric equations (R2 = 0.61, RMSE = 29.86 kg, MAE = 24.52 kg). Furthermore, this study provides evidence of the strong capability of TLS in estimating branch‐level biomass, with high accuracy achieved across branch orders (R2 ranging from 0.66 to 0.91, RMSE from 3.55 to 380 g and MAE from 2.97 to 290 g). By providing precise, non‐destructive estimates of biomass distribution across branches and leaves, this workflow demonstrates strong potential for improving the accuracy of tree biomass quantification, supporting investigations of resource allocation strategies, and enhancing forest carbon monitoring.

Excessive tree mortality is a global concern and remains poorly understood as it is a complex phenomenon. We lack global and temporally continuous coverage on tree mortality data. Ground-based observations on tree mortality, e.g., derived from national inventories, are very sparse, and may not be standardized or spatially explicit. Earth observation data, combined with supervised machine learning, offer a promising approach to map overstory tree mortality in a consistent manner over space and time. However, global-scale machine learning requires broad training data covering a wide range of environmental settings and forest types. Low altitude observation platforms (e.g., drones or airplanes) provide a cost-effective source of training data by capturing high-resolution orthophotos of overstory tree mortality events at centimeter-scale resolution. Here, we introduce deadtrees.earth, an open-access platform hosting more than two thousand centimeter-resolution orthophotos, covering more than 1,000,000 ha, of which more than 58,000 ha are manually annotated with live/dead tree classifications. This community-sourced and rigorously curated dataset can serve as a comprehensive reference dataset to uncover tree mortality patterns from local to global scales using space-based Earth observation data and machine learning models. This will provide the basis to attribute tree mortality patterns to environmental changes or project tree mortality dynamics to the future. The open nature of deadtrees.earth, together with its curation of high-quality, spatially representative, and ecologically diverse data will continuously increase our capacity to uncover and understand tree mortality dynamics.

Foliar nitrogen (N) uptake varied widely across 13 tree species and leaf ages in natural tropical and subtropical forests. Species with higher N demand and denser stomata had higher foliar N uptake, especially in mature leaves. N uptake in old leaves was more influenced by PAR and soil conditions.

Accurate quantification of forest structural parameters, such as tree height (H), crown vertical projection area (CPA) and crown volume (CV), is essential for precise estimation of forest carbon sequestration, monitoring succession dynamics, and improving carbon cycle models. In natural forests characterized by high species diversity and complex stand structures, the capability of terrestrial laser scanning (TLS) and unmanned aerial vehicle laser scanning (UAV-LS) to measure forest structural parameters across different tree heights for coniferous and broadleaved species, remains unevaluated under the influence of canopy shading effects. This study investigated deciduous broadleaf -Korean pine forests by integrating TLS and UAV-LS point clouds using geographic coordinates and combining inventory data to identify tree species from individual tree point clouds. The fused point cloud of forest structural parameters served as a baseline dataset to evaluate TLS and UAV-LS accuracy during the period of no leaf cover. The results showed a strong correlation between TLS and UAV-LS with the fused point cloud (R2 = 0.96–0.99) TLS and UAV-LS had greater accuracy in measuring H, CPA and CV for coniferous trees than for broadleaf trees, with smaller D-rRMSE differences for conifers (0.7

Abstract. Long-term, high-resolution canopy cover data are essential for understanding grassland ecosystem dynamics and informing sustainable management. However, existing products are largely limited to coarse spatial resolutions, constraining their utility for high-precision, large-scale analyses. In this study, we collected over 16,000 drone image tiles (30 m × 30 m) from 2,144 sites across China and developed a machine learning model to produce a spatially seamless, 30 m annual dataset of national grassland canopy cover from 1990 to 2023 by integrating drone and Landsat-series imagery. The model achieves high predictive accuracy (R2 = 0.73, RMSE = 18.4 %) and robust temporal transferability (R2 = 0.68, RMSE = 20.6 %). Comparisons with existing large-scale products demonstrated significantly improved accuracy and reduced residual artifacts, underscoring the robustness of our approach across diverse grassland types and time periods. Spatiotemporal analysis indicated a multi-decadal mean canopy cover of 43.80 ± 18.69 % across China’s grasslands. Over the 34-year period, 41.76 % of grasslands exhibited significant increases, 57.16 % showed nonsignificant change, and 1.08 % experienced significant declines. Climatic factors—including drought, precipitation, and temperature—emerged as the dominant drivers of canopy cover dynamics at the national scale, although their effects exhibited pronounced spatial heterogeneity. In contrast, anthropogenic pressures played a secondary role overall but could override climatic influences at local scales. Collectively, these findings, together with the long-term, high-resolution canopy cover dataset developed in this study, provide an essential basis for advancing the understanding of grassland ecosystem dynamics and for supporting evidence-based conservation and sustainable management strategies, particularly under intensifying climate change and increasing frequency of extreme events. The national grassland canopy cover dataset generated in this study is archived on Zenodo and can be freely downloaded from https://doi.org/10.5281/zenodo.20301123 (Jiang et al., 2026).

Ecosystem carbon use efficiency (CUE) is a key indicator of an ecosystem's capacity to function as a carbon sink. While previous studies have predominantly focused on how climate and resource availability affect CUE through physiological processes during the growing season, the role of canopy structure in regulating carbon and energy exchange, especially its interactions with winter climate processes and nitrogen use efficiency (NUE) in shaping ecosystem CUE in semi-arid grasslands, remains insufficiently understood. Here, we conducted a 5-year snow manipulation experiment in a temperate grassland to investigate the effects of deepened snow on ecosystem CUE. We measured ecosystem carbon fluxes, soil nitrogen concentration, species biomass, plants' nitrogen concentration, canopy height and cover and species composition. We found that deepened snow increased soil nitrogen availability, while the concurrent rise in soil moisture facilitated nutrient acquisition and utilization. Together, these changes supported greater biomass accumulation per unit of nitrogen uptake, thereby enhancing NUE. In addition, deepened snow favoured the dominance of C3 grasses, which generally exhibit higher NUE and greater height than C3 forbs, providing a second pathway that further elevated community-level NUE. The enhanced NUE, through both physiological efficiency and compositional shifts, promoted biomass production and facilitated the development of larger canopy volumes. Larger canopy volumes under deepened snow increased gross primary production through improved light interception, while the associated increase in autotrophic maintenance respiration was moderated by higher NUE. Besides, denser canopies reduced understorey temperatures throughout the day, particularly at night, thereby suppressing heterotrophic respiration. Ultimately, deepened snow increased ecosystem CUE by enhancing carbon uptake while limiting respiratory carbon losses. Synthesis. These findings demonstrated the crucial role of biophysical processes associated with canopy structure and NUE in regulating ecosystem CUE, which has been largely overlooked in previous studies. We also highlight the importance of winter processes in shaping carbon sequestration dynamics and their potential to modulate future grassland responses to climate change.

Nutrient resorption from leaves and translocation to twigs and other woody tissues during leaf senescence is a crucial strategy for plant nutrient conservation. The nutrients retained in twigs provide essential resources for new growth, especially when root nutrient acquisition is restricted by low soil temperatures during early spring. However, the interconnections between leaf nutrient resorption and twig nutrient accumulation during autumn, and their influence on spring phenology, remain poorly understood. We selected 20 woody species with a wide range of leaf traits in a common garden and investigated the relationships among leaf resorption efficiency, twig accumulation efficiency of nitrogen (N) and phosphorus (P), and autumn-spring phenology. We found that leaf N resorption (54.27%) was significantly higher than leaf P resorption (42.42%). Additionally, twig N accumulation efficiency (40.00%) was significantly higher than twig P accumulation (18.37%), with both positively correlated with leaf nutrient resorption efficiency. Species with acquisitive traits exhibited higher N and P resorption efficiency, along with higher P accumulation efficiency in twigs. Soil fertility had a relatively minor influence on both leaf nutrient resorption and twig nutrient accumulation. In addition, autumn and spring phenological events were linked to plant internal nutrient dynamics. Species with later leaf shedding and shorter leaf fall duration in autumn tended to exhibit greater leaf P resorption efficiency. Furthermore, species with higher twig nutrient accumulation efficiency showed earlier bud break and a longer period of leaf-out in the subsequent spring. Modular network analysis and structural equation model further indicated that leaf nutrient resorption was strongly related to leaf economic traits, while leaf nutrient resorption was indirectly linked to bud-break timing through twig nutrient accumulation. Synthesis. Our findings suggest that plant internal nutrient dynamics are not only consequences of phenology but may also influence phenological timing. These results highlight the importance of nutrient resorption and storage strategies in regulating seasonal growth patterns and indicate that internal nutrient cycling may affect plant performance and ecosystem functioning under varying environmental conditions.

Forest above-ground biomass (AGB) is a crucial measure of forest carbon storage and plays a critical role in global carbon monitoring. While terrestrial laser scanning (TLS) serves as a powerful tool for AGB estimation, existing methods face key limitations. These include reliance on computationally intensive individual-tree segmentation, which struggles in dense forests with overlapping canopies, or on oversimplified structural attributes, such as diameter at breast height (DBH) and tree height, or being site-specific. Here, we present a cross-site scalable and computationally efficient modeling workflow for community-level AGB estimation using multiplatform TLS-derived metrics, without requiring tree-level point cloud segmentation. Besides community-level DBH extracted via a point-cloud-segmentation-free workflow, we selected community-level canopy structure metrics, such as leaf area densities, canopy entropy, etc. to train an AGB estimation model via Random Forest. We used data from five ecologically diverse sites across East Asia, spanning a 20° latitudinal gradient, to train and cross-validate our model. The model achieved high predictive accuracy () and showed consistent performance across all sites. Compared to site-specific models, the cross-site model performed as accurate, with canopy structural features accounting for over 40% of the explained variance and improving performance by 13.89% relative to the site-specific DBH-and-height-only model. Furthermore, our evidence suggested that such improvement was indeed driven by the inclusion of canopy structural information. However, leave-one-site-out validation showed limited transferability to environmentally distinct sites, highlighting the need for broader training datasets. Overall, our workflow provides an efficient alternative for community-level AGB estimation and underscore the importance of multidimensional canopy attributes for improving forest carbon assessment across heterogeneous ecosystems.

Abstract Canopy structural diversity varies systematically with climate and species diversity across boreal, temperate, and tropical biomes. Yet, how this latitudinal variation affects forest productivity—particularly the role of structural diversity in mediating effects of climate on productivity across biomes—remains unresolved. By synthesizing airborne laser scanning data and ground-based forest inventories spanning boreal to tropical forests, we show that beyond its direct effects on forest productivity, climate influences productivity indirectly by regulating canopy structural diversity—a significant, yet previously underappreciated mechanism whose precise magnitude is challenging to isolate. Notably, we observe a pronounced latitudinal congruence between hotspots of structural diversity and productivity. This positions structural diversity not only as a complementary indicator of productivity but also as a critical mediator of the influence of climate and species diversity, essential for improved forecasting.

Tropical forests are increasingly affected by drought, yet the factors that control post-drought ecosystem resilience—the capacity to withstand disturbances—are not fully understood. Here we use temporal autocorrelation of satellite-derived vegetation greenness to quantify ecosystem resilience following 142,444 severe drought events across tropical forests from 2003 to 2022. We show that resilience declined in 68.8% of areas after droughts, particularly in dry environments, whereas 20.3% of areas with increased resilience were located in moist tropical forests. More intense and prolonged droughts led to a pronounced decline in resilience. Mean annual precipitation was identified as the most important regulator influencing resilience changes after drought, while soil phosphorus was the most consistent regulator across forest biomes, exhibiting widespread mitigating effects on resilience loss. Along decreasing precipitation gradients, the mitigating effect of soil phosphorus on post-drought resilience loss intensified. These findings provide insights into how tropical forests respond to drought and offer practical guidance for region-specific, adaptive forest management under a changing climate. Drought disturbances are reducing the recovery capacity of tropical forests, especially in drier conditions, but soil phosphorus can mitigate this impact, according to a satellite-based analysis of ecosystem resilience.

Accurate assessment of leaf functional traits is crucial for a diverse range of applications from crop phenotyping to parameterizing global climate models. Leaf reflectance spectroscopy offers a promising avenue to advance ecological and agricultural research by complementing traditional, time-consuming gas exchange measurements. However, the development of robust hyperspectral models for predicting leaf photosynthetic capacity and associated traits from reflectance data has been hindered by limited data availability across species and environments. Here we introduce the Global Spectra-Trait Initiative (GSTI), a collaborative repository of paired leaf hyperspectral and gas exchange measurements from diverse ecosystems. The GSTI repository currently encompasses over 7500 observations from 397 species and 41 sites gathered from 36 published and unpublished studies, thereby offering a key resource for developing and validating hyperspectral models of leaf photosynthetic capacity. The GSTI database is developed on GitHub (https://github.com/plantphys/gsti, last access: 4 January 2026) and published to ESS-DIVE 10.15485/2530733, Lamour et al., 2025). It includes gas exchange data, derived photosynthetic parameters, and key leaf traits often associated with traditional gas exchange measurements such as leaf mass per area and leaf elemental composition. By providing a standardized repository for data sharing and analysis, we present a critical step towards creating hyperspectral models for predicting photosynthetic traits and associated leaf traits for terrestrial plants.

Shrub fractional abundance (SFA), the proportion of shrub cover per unit area, serves as a critical indicator of environmental aridity and ecosystem health in arid and semi-arid regions, particularly across the Mongolian steppe. However, large-scale SFA mapping in Mongolian steppe ecosystems remains challenging due to the small crown size of shrubs, their sparse distribution, and spectral overlap with coexisting low vegetation (e.g., grasses and herbs), which hinders accurate detection using coarser-resolution satellite data or traditional field surveys. To address these challenges, we developed a two-step approach that integrates very-high-resolution (VHR) imagery, time-series Sentinel-2 data, and deep learning techniques. First, we generated high-accuracy benchmark maps of individual shrub crowns from 0.5 m VHR imagery by combining manual segmentation with a hybrid deep learning framework (Dino V2 and convolutional neural networks). Second, we used these shrub crown maps as training data to build an XGBoost model for predicting SFA from 20 m Sentinel-2 time-series data, leveraging phenological information to improve estimation. We validated our approach across 70 sites (1km2 each) in the Inner Mongolia Autonomous Region, which is representative of Mongolian steppe ecosystems. From VHR imagery, we mapped 1.31 million shrub crowns with an accuracy of R2 = 0.92. Scaling up with Sentinel-2 data yielded regional SFA maps with an R2 = 0.60. Further SHAP (SHapley Additive exPlanations) analysis on the developed XGBoost model revealed that phenological metrics (particularly observations in early-May, mid-July, and late-September), which distinguish shrub phenology from that of other land cover types (e.g., grasses and bare soil), were the most influential predictors of SFA. Finally, our regional SFA maps uncovered unimodal relationships between shrub distribution and climate variables, peaking at mean annual minimum temperatures near 0 degrees C and annual precipitation around 200 mm. Collectively, these findings demonstrate how the integration of multi-source remote sensing and machine learning can overcome historical limitations in SFA mapping, enabling accurate, spatially continuous assessments across vast Inner-Mongolian steppe ecosystems. Our framework has the potential to be applied to other steppe ecosystems and dryland ecosystems across the Mongolian steppe and beyond, offering a foundation for improved monitoring and ecological impact assessments in the face of global climate changes.

Recreating the physical structure of the world in three dimensions offers insights into the fine-grained changes of ecosystems and the principles of ecosystem organization. However, suitable data to synchronously measure how animals interact with their environment in three dimensions are scarce. This dimensional discrepancy between three-dimensional animal information and environmental structural data can have major implications for ecological studies. Here, we introduce a LiDAR-based trapping sampling method centered on the concept of three-dimensional animal remote sensing, aiming to enhance animal ecological research by synchronously collecting three-dimensional animal information and habitat structural data. Such efforts are valuable because they establish an effective bridge between animals and their three-dimensional habitat, facilitating a deeper understanding of their interactions and ecosystem dynamics.

Plant phenology, the study of recurring plant life history events’ timing, is a key indicator of global environmental change and significantly impacts ecosystem functions and services. Land surface phenology (LSP) characterizes plant phenology by monitoring seasonal plant canopy structure dynamics via satellites. Numerous studies have demonstrated that ecosystem-scale LSP variability is mainly driven by climate and environmental conditions across different ecosystems. However, significant spatial and temporal phenological variations are still observed within local landscapes where environmental conditions are relatively similar. This suggests that biotic factors may be important to regulating LSP variability, but their role in determining phenological variability has been underexplored. To address this knowledge gap, we selected four temperate forest sites with minor topographic relief to ensure the homogeneity of environmental conditions and examined how functional traits regulate intra-site spatial and temporal LSP variability. We combined plant functional traits derived from remote sensing data with multi-year Harmonized LandSat-Sentinel-2 (HLS) data to investigate the effects of functional traits on phenological variability. For spatial LSP variability, we assessed the extent to which functional traits could explain the variation in the start of season (SOS) and end of season (EOS). We found that functional traits showed a substantial explanatory power for spatial phenological variability across all the study sites, with cross-validation correlations (cv) ranging from 0.50 to 0.85. For temporal LSP variability, we used multi-year series of the two band Enhanced Vegetation Index (EVI2) to calculate the cumulative deviation of EVI2 values from their long-term means, which served as an indicator of temporal phenological variability. Functional traits also significantly contributed to the temporal variability across all sites, with cv ranging from 0.46 to 0.71. Furthermore, our results show that plant traits related to vegetation competitive ability and productivity (e.g., canopy height, plant area index, and leaf mass per area), are crucial to explaining intra-site phenological variability, but their relative contributions vary among different sites. Collectively, these results demonstrate that functional traits play a critical role in regulating intra-site spatial and temporal LSP variability, and plants employ diverse strategies to cope with the environment, which ultimately impacts various ecological processes.

Increasing combined heat and drought extremes due to climate change heighten the risk of crop failure, underscoring the need for improved stress diagnosis for effective management strategies. However, current plant physiology indicators struggle to differentiate crop stresses in hot-dry environments. This study proposes using specific leaf metabolites, detectable by leaf reflectance spectra, for more precise identification of heat and drought stress compared to traditional methods. We conducted two rounds of one-week drought treatments under heat stress on soybean seedlings. Throughout the experiment, we monitored stomatal conductance, reflectance spectra, and metabolites, including Abscisic Acid (ABA), Jasmonic Acid (JA), Salicylic Acid (SA), and proline (Pro), on a daily basis. Our findings revealed that ABA and JA exhibited differential sensitivities to drought and heat stress, respectively. In contrast, stomatal conductance was unable to differentiate between the two stressors. Using partial least-squares regression (PLSR), we determined that both ABA and JA could be detected via leaf spectroscopy with moderate predictive performance (R2 = 0.53, relative RMSE =14.28 %; R2 = 0.53, relative RMSE = 14.96 %) and exhibited distinct sensitive spectral signatures. The metabolite-derived, stress-specific spectral models enable more precise and earlier diagnosis and differentiation of stress in a hotdry environment than traditional physiological indicators (e.g., relying on stomatal conductance). This study provides an example of using metabolites as novel stress indicators, which could contribute to precision agriculture, offering the potential for accurate, stress-specific, and pre-physiological detection of crop health.

Accurate quantification of tree populations within regions is critical for evaluating forest ecosystem conditions and developing effective forest management strategies[1].High-quality tree cen-sus data,collected through field surveys and remote sensing tech-nologies,is fundamental to China's sustainable development and environmental conservation initiatives.

Two fundamental aspects that characterize the diversity of natural forests are species richness and structural diversity. Our understanding of the fine-grained patterns and drivers of tree species richness and structural diversity in many regions has been limited by lack of spatially representative vegetation-plot data. Here we use data on 314,613 trees from 3,396 plots to elucidate spatial patterns, determinants and future potential of tree species richness and structural diversity in natural forests across China. We find that the patterns and their dominant drivers differed between tree species richness and structural diversity. Precipitation seasonality is the foremost predictor of species richness, whereas forest age is the leading predictor of structural diversity. Projections based on future climate scenarios SSP126 and SSP245 highlight the potential for substantial increases in fine-grained species richness (~36%) and structural diversity (~27%) by 2100. While this increase in diversity could enhance carbon sequestration, it may also pose threats to endangered species due to intensified competition for limited ecological niches.