The leaf area index (LAI) influences surface energy distribution and carbon cycling, making it a crucial structural parameter of vegetation. Terrestrial laser scanning (TLS) point cloud data (PCD) have become an important data source for estimating LAI and vertically stratified LAI. Commonly used methods for estimating LAI from PCD are either voxel-based methods or image-based methods that rely on two-dimensional (2D) projections of PCD. However, the accuracy of these methods is critically influenced by leaf clumping in 3D space and the leaf inclination angle distribution (LIAD)-factors that remain challenging to quantify in practice. In this study, a new model (FDTLS) is developed by integrating fractal theory with analytical geometry. The model employs the canopy fractal dimension (FD) to estimate LAI and plant area index (PAI), including their vertical profiles, overcoming the limitations of traditional methods. This model effectively considers leaf clumping in 3D space and is insensitive to the LIAD. Multistation stitched PCDs from 25 simulated heterogeneous plots, including mixed forests, structurally stratified forests, open-canopy forests, and dense broadleaf forests, as well as two field plots (one coniferous and one broadleaf), were employed to validate the model. The reference LAI for the field plots was obtained using an allometric approach and a litter collection method. Notably, the FDTLS method exhibited remarkable stability across varying LIAD types, maintaining a relative PAI variation of just 6.12%, whereas all comparison methods exceeded 90%. In contrast to the other methods (RRMSE > 18.24% for simulated plots; RRMSE > 22.8% for field plots), the FDTLS method achieved substantially improved accuracy, with RRMSE of 15.42% for simulated plots and 7.4% for field plots. Furthermore, FDTLS accurately estimated stratified PAI for simulated plots, yielding a mean absolute deviation of 0.35. Additional sensitivity analyses revealed that the method remained robust to various practical factors, including point cloud completeness, scanning resolution, registration error, and leaf radius measurement error. These attributes position the FDTLS method as a robust tool for advancing LAI measurements, with particular utility in satellite product validation and related applications. Supporting resources are available at: https://github.com/CloudyCUG/FDTLS_Tool.

The Qinghai-Tibet Plateau spans a vast area with highly complex natural conditions, making ecosystem classification and the delineation of field boundaries challenging. Ongoing climate warming has intensified glacial melting and thawing of permafrost, further complicating surface hydrological processes. The frequent alternations of redox conditions driven by soil water saturation make it difficult to distinguish the boundaries between wetlands and grasslands. Consequently, traditional wetland definitions and classification frameworks are inadequate for depicting the spatiotemporal patterns of alpine wetlands on the plateau, constraining a deeper understanding of the ecosystems. In this study, a novel multilevel framework for wetland classification is proposed. By integrating long-time-series Landsat 5/7/8 images, multisource environmental data, and field investigations, a 30-m annual dataset of alpine wetlands distribution on the plateau for 2000–2020 was constructed on the basis of a cloud computing platform to analyze spatiotemporal patterns and their driving factors. The results indicate that the overall classification accuracy is 0.92, which is highly consistent with that of high-resolution images. Over the past two decades, the total area of alpine wetlands on the plateau ranged from approximately 9.0×104 to 1.2×105 km2, accounting for approximately 4

Artificial intelligence(AI)is increasingly transforming ecological research by enabling large-scale environmental observation,complex data analysis,and automated pattern recognition.Although numerous studies have demonstrated the technical advantages of AI in vegetation mapping,biodiversity monitoring,and ecological modeling,considerably less attention has been paid to how AI reshapes the organization and generation of scientific knowledge.Existing discussions primarily focus on algorithmic performance or ecological applications,while the underlying knowledge production process remains insufficiently understood.Using the development of the 1:500000 Vegetation Formation Map of Grasslands on the Qinghai-Tibetan Plateau as a representative case,this study investigates how ecological theory,remote sensing observations,and AI are integrated throughout the research process.Rather than evaluating AI as merely a computational tool,this paper aims to explain its role in interdisciplinary ecological research and to clarify how human researchers and intelligent systems jointly contribute to scientific knowledge generation. This study adopts a qualitative case study approach combining technical process analysis with science and technology studies.The complete workflow of vegetation mapping was reconstructed,including field investigation,vegetation classification,remote sensing data acquisition,deep learning-based classification,vegetation map production,ecological interpretation,and scientific validation.The analysis focuses on how ecological knowledge is embedded into AI-assisted mapping through vegetation classification systems,sample labeling,variable selection,and model construction.Theoretical interpretation draws upon interdisciplinary research theory,Mode 2 knowledge organization,and the Fourth Paradigm of data-intensive scientific discovery.Rather than treating these theories as alternative explanations,they are employed to analyze different dimensions of the research process,namely interdisciplinary capability integration,changes in research organization,data-driven scientific discovery,and the emergence of Human-AI collaborative knowledge production. The case demonstrates that the production of the Qinghai-Tibetan Plateau vegetation formation map represents more than a technological achievement in ecological mapping.First,the mapping task exceeded the capability of any individual discipline because it simultaneously required ecological classification knowledge,regional-scale remote sensing observations,and AI-based high-dimensional data analysis.These heterogeneous capabilities were integrated into a unified research framework to accomplish regional vegetation mapping.Second,the study reveals a distinctive mechanism of in-model knowledge integration.Ecological knowledge was not only involved in defining research questions and interpreting final results,but was continuously embedded into the AI workflow through vegetation classification systems,sample labeling,feature selection,and model constraints.Consequently,ecological expertise became part of the computational learning process rather than remaining external to it.This finding provides a concrete explanation of how interdisciplinary knowledge integration can occur within AI-supported scientific research.Third,AI functioned neither as a simple analytical tool nor as an autonomous scientific agent.Instead,it served as a knowledge intermediary connecting ecological theory with remotely sensed observations.By learning the correspondence between field survey data and multi-source environmental variables,the AI model extended localized ecological knowledge to regional spatial scales,enabling the identification of vegetation formations across the entire Qinghai-Tibetan Plateau.However,AI-generated spatial patterns did not automatically become scientific conclusions.They required subsequent ecological interpretation,comparison with historical vegetation maps,long-term observations,warming experiments,and ecological process studies before meaningful scientific explanations could be established.Finally,the case exhibits a dual-loop pathway of scientific discovery.Unlike the conventional hypothesis-driven paradigm,in which theories generate hypotheses subsequently verified by observations,important scientific findings in this study first emerged from AI-assisted analysis of massive ecological datasets.Newly identified vegetation changes subsequently stimulated ecological explanation and causal investigation.Data-driven pattern discovery generated new scientific questions,whereas theory-driven interpretation established ecological mechanisms,together forming a dual-loop pathway of scientific discovery. This study argues that AI is reshaping ecological research not by replacing scientists but by reorganizing the relationships among theory,observations,and computation.The Qinghai-Tibetan Plateau vegetation mapping project illustrates a new mode of Human-AI collaborative knowledge production characterized by interdisciplinary capability integration,in-model knowledge integration,AI functioning as a knowledge intermediary,and a dual-loop pathway linking data-driven discovery with theory-driven explanation.At the same time,this collaborative mode depends upon several essential conditions,including mature theoretical frameworks,high-quality observational datasets,and continuous human interpretation.Its significance therefore lies not in demonstrating that AI can replace scientific reasoning,but in revealing how intelligent systems can become integral participants in scientific knowledge generation while maintaining the indispensable role of human expertise.These findings contribute to a deeper understanding of methodological transformation in ecological research and provide a conceptual framework for analyzing AI-enabled scientific knowledge production in other data-intensive disciplines.

Terrestrial ecosystems integrity (TEI), which reflects the capacity of ecosystems to maintain structural and functional stability under external disturbances, is a critical concept for guiding ecosystem conservation, restoration, and service valuation. However, a unified evaluation framework for terrestrial ecosystems integrity remains lacking. Existing studies on ecosystem integrity within the Yellow River Basin have predominantly focused on specific regions, with limited basin-wide assessments and insufficient integration of ecosystem structure, function, and processes. To bridge these gaps, this study developed an a comprehensive TEI evaluation system and evaluated the spatiotemporal patterns of TEI for the Yellow River Basin from 2000 to 2020, utilizing remote sensing imagery and statistical yearbooks as primary data sources. The index system incorporates seven key indicators including Splitting Index, Landscape Shape Index, GDP Per Capita and four others, utilizing remote sensing imagery and statistical yearbooks as primary data sources. Results revealed significant spatial heterogeneity in TEI, with a “high in the south, low in the north” pattern. Between 2000 and 2020, the overall TEI of the basin exhibited a polarization trend, with the most severe decline observed in the Inner Mongolia Autonomous Region. This study provides a novel framework for assessing TEI and provides a scientific foundation for informed ecosystem management strategies within the region in the Yellow River Basin.

Spaceborne light detection and ranging (LiDAR) provides a promising method for large-scale characterizing leaf area index (LAI). However, the quality of point cloud data from spaceborne LiDAR, especially Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), is susceptible to atmosphere and background noise, introducing considerable uncertainty in LAI retrieval. Thus, efficiently screening out the high-quality point cloud is a significant guarantee for high-quality LAI retrieval. In this study, we proposed a quality control (QC) method that employed the number of 10-m windows without ground points in the ICESat-2 100-m segment as the QC flag. This method divided segments into 11 QC flags from 0 to 10 and was applied to LAI retrieval across Chinese forests from 2019 to 2020. The field measurements at locations identical to ICESat-2 ground tracks were used to validate the ICESat-2 LAI at different QC flags. The results showed that the proposed method effectively improved point cloud quality recognition and LAI accuracy, with ICESat-2 LAI (QC <3) reducing root mean square error (RMSE) by 26.36% compared with all ICESat-2 LAIs. It also showed good agreement with Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Land Surface Satellite (GLASS) LAI and mitigated saturation issues in passive optical imagery. The ICESat-2 LAI with QC <3 performed better in deciduous broadleaved, evergreen needle-leaved, deciduous needle-leaved, and mixed forests (MFs), but not in evergreen broadleaved forests (EBFs). ICESat-2 LAI was particularly adept at capturing high-LAI values, which had the highest proportion of LAI values over 6.0 compared with MODIS and GLASS LAI. The proposed method has the potential for large-scale and high-quality LAI retrieval using ICESat-2 data on a global scale.

Spaceborne light detection and ranging (LiDAR) waveform sensors require accurate signal simulations to facilitate prelaunch calibration, postlaunch validation, and the development of land surface data products. However, accurately simulating spaceborne LiDAR waveforms over heterogeneous forests remains challenging because data-driven methods do not account for complicated pulse transport within heterogeneous canopies, whereas analytical radiative transfer models overly rely on assumptions about canopy structure and distribution. Thus, a comprehensive simulation method is needed to account for both the complexity of pulse transport within canopies and the structural heterogeneity of forests. In this study, we propose a framework for spaceborne LiDAR waveform simulation by integrating a new radiative transfer model - the canopy voxel radiative transfer (CVRT) model - with reconstructed three-dimensional (3D) voxel forest scenes from small-footprint airborne LiDAR (ALS) point clouds. The CVRT model describes the radiative transfer process within canopy voxels and uses fractional crown cover to account for within-voxel heterogeneity, minimizing the need for assumptions about canopy shape and distribution and significantly reducing the number of input parameters. All the parameters for scene construction and model inputs can be obtained from the ALS point clouds. The performance of the proposed framework was assessed by comparing the results to the simulated LiDAR waveforms from DART, Global Ecosystem Dynamics Investigation (GEDI) data over heterogeneous forest stands, and Land, Vegetation, and Ice Sensor (LVIS) data from the National Ecological Observatory Network (NEON) site. The results suggest that compared with existing models, the new framework with the CVRT model achieved improved agreement with both simulated and measured data, with an average R2 improvement of approximately 2% to 5% and an average RMSE reduction of approximately 0.5% to 3%. The proposed framework was also highly adaptive and robust to variations in model configurations, input data quality, and environmental attributes. In summary, this work extends current research on accurate and robust large-footprint LiDAR waveform simulations over heterogeneous forest canopies and could help refine product development for emerging spaceborne LiDAR missions.

Understanding evapotranspiration (ET) dynamics under community composition transitions in grasslands is crucial for interpreting alpine ecosystem responses to climate change. We investigated variations in ET and its components during the growing season across five alpine grassland transition types in the Source Region of the Yellow River (SRYR) from 1986 to 2018, integrating climatic, vegetation, and soil factors. Under warming and wetting conditions, ET increased significantly by 1.17 mm yr−1, accounting for 79.39% of annual precipitation, while soil moisture declined slightly. A pronounced temperature–precipitation decoupling emerged between alpine meadow-origin (AM-origin) and alpine steppe-origin (AS-origin) transitions, indicating differential hydrological responses driven by community composition. Vegetation growth increased across all transitions, yet its regulation of ET components varied by transition type. Transpiration dominated ET increases, contributing over 80% in AM-origin and 100% in AS-origin transitions. Soil evaporation exhibited contrasting trends: decreasing in AS-origin transitions due to enhanced soil insulation from vegetation growth, but increasing in AM-origin transitions, thereby reducing soil moisture. Interannual ET growth rates and seasonal fluctuations were greater in AM-origin than in AS-origin transitions. A critical turning point in ET trends, caused by changes in precipitation, revealed the divergent hydrological trajectories among the transitions. In AM-origin transitions, temperature primarily drove ET increases, causing soil drying (strongest in AM to TS), whereas in AS-origin transitions, precipitation dominated, resulting in soil wetting (more pronounced in AS to AM). These findings demonstrate that the directionality of compositional transitions governs hydrological responses more strongly than absolute vegetation states.

There are numerous ecological challenges that are of considerable concern caused by urbanization and natural calamities. The spatial pattern of the landscape is of great significance in maintaining species diversity and regulating material cycles and individual ecophysiological processes as a medium for ecosystem service functions. Resolving the conflicting relationships between people and land necessitates the precise identification of essential ecological sources. Therefore, there is an urgent need to investigate the relationship between ecological sources and ecosystem service values (ESVs) and to optimize the matter and energy.connectivity network. The study optimizes ecological network simulation using a penetration threshold, population density inverse distance formula, and geodetector weight analysis, constructs ecological spatial networks and evaluates their structure using the minimum cumulative resistance (MCR) model and complex network analysis, and discusses the relationship between network topological characteristics (NTCs) and the spatial pattern of the various ESVs. The results show that the major sources and corridors are primarily along the banks of the Wei River and the Fen River. The clustering coefficient has a significant negative correlation with both water supply and water regulation service values. It is found that the sources with higher ecological service values related to water resources have better network connectivity. This result plays a major reference role for the identification of several crucial ecological components, such as sources, nodes and stepping stones, in the middle Yellow River basin (MYRB). Ultimately, we propose strengthening the ecological protection of the eastern part of the Qinling Mountains and increasing the ecological communication capacity of the forestland on both sides of the river valley to create an optimized ecological pattern.

Aboveground biomass (AGB) of shrubs and low-statured trees constitutes a substantial portion of the total carbon pool in temperate forest ecosystems, contributing much to local biodiversity, altering tree-regeneration growth rates, and determining above- and belowground food webs. Accurate quantification of AGB at the shrub layer is crucial for ecological modeling and still remains a challenge. Several methods for estimating understory biomass, including inventory and remote sensing-based methods, need to be evaluated against measured datasets. In this study, we acquired 158 individual terrestrial laser scans (TLS) across 45 sites in the Yanshan Mountains and generated metrics including leaf area and stem volume from TLS data using voxel- and non-voxel-based approaches in both leaf-on and leaf-off scenarios. Allometric equations were applied using field-measured parameters as an inventory approach. The results indicated that allometric equations using crown area and height yielded results with higher accuracy than other inventory approach parameters (R2 and RMSE ranging from 0.47 to 0.91 and 12.38 to 38.11 g, respectively). The voxel-based approach using TLS data provided results with R2 and RMSE ranging from 0.86 to 0.96 and 6.43 to 21.03 g. Additionally, the non-voxel-based approach provided similar or slightly better results compared to the voxel-based approach (R2 and RMSE ranging from 0.93 to 0.96 and 4.23 to 11.27 g, respectively) while avoiding the complexity of selecting the optimal voxel size that arises during voxelization.

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Leaf area (LA) parameters are crucial in ecosystem studies. As ecophysiological models advance toward finer detail, accurately estimating LA at various scales becomes essential, particularly for diverse units like urban individual trees. Several algorithms based on terrestrial laser scanning (TLS) data have been developed to obtain the LA of individual trees. However, their use at the stand level needs further research. In this study, the comparative shortest-path algorithm (CSP) is introduced for the automatic individual tree segmentation, thereby facilitating the application of the path length distribution method (PATH) for LA estimation at the stand level. Using high-density TLS data, we presented a bottom-up estimation of stand LA index (LAI) from 50 individual tree measurements and validated the results at different scales. At the tree scale, the LA derived from TLS and the allometric model were highly correlated, with an $R$ -value of 0.83. At the stand scale, the proposed method provides consistent results with the allometric and TRAC instrument measurements, performing better than vertical upward photography. Generally, 23 shared stations under the forest are enough to accurately obtain the LA of 50 trees and the LAI in an urban forest stand. Sensitivity analysis shows that the method is not sensitive to TLS scan resolution and parameters used in tree crown envelope reconstruction. The proposed bottom-up approach provides a new way of estimating the LAI at stand level using TLS and has the advantage of providing multilevel LA information and avoiding the scale effect.

Individual trees are fundamental to urban ecosystems as they play an important role in energy transfer, pollutant removal, and habitat formation. Leaf area (LA) is an important factor to quantify the effect of individual trees on urban ecosystems. Terrestrial laser scanners (TLSs) are widely recognized as the most accurate devices for tree structural measurements. However, they face challenges in estimating LA from LA index (LAI) for individual trees primarily due to arbitrary and confusing horizontal projection areas. Occlusion and clumping effects further hinder the objective and accurate LA measurements of individual trees. Therefore, we developed the slant leaf area index-based method (SLAIM) to estimate the LA of individual trees from single-scan TLS data by introducing the concept of slant leaf area index (SLAI). SLAI quantifies the amount of leaves along the view direction, and it can be retrieved at given view zeniths using gap probability. Subsequently, LA can be accumulated by SLAI across the whole crown. Tests with simulated and field-measured TLS point clouds demonstrate SLAIM's accuracy, with the relative errors (REs) in LA below 10% in most cases. Stratified LA validation reveals an $R<^>{2}$ exceeding 0.77 across all realistic crowns, along with a root-mean-square error (RMSE) under 2 m2. SLAIM's advantages include compatibility with single-scan point clouds, effective correction of clumping effects, and consideration of variations in leaf projection coefficients at different zeniths. SLAIM proves more efficient and practical for actual LA measurements, showcasing its potential for advanced urban ecosystem research.

Structural information of grassland changes on the Tibetan Plateau is essential for understanding alterations in critical ecosystem functioning and their underlying drivers that may reflect environmental changes. However, such information at the regional scale is still lacking due to methodological limitations. Beyond remote sensing indicators only recognizing vegetation productivity, we utilized multivariate data fusion and deep learning to characterize formation-based plant community structure in alpine grasslands at the regional scale of the Tibetan Plateau for the first time and compared it with the earlier version of Vegetation Map of China for historical changes. Over the past 40 years, we revealed that (1) the proportion of alpine meadows in alpine grasslands increased from 50% to 69%, well-reflecting the warming and wetting trend; (2) dominances of Kobresia pygmaea and Stipa purpurea formations in alpine meadows and steppes were strengthened to 76% and 92%, respectively; (3) the climate factor mainly drove the distribution of Stipa purpurea formation, but not the recent distribution of Kobresia pygmaea formation that was likely shaped by human activities. Therefore, the underlying mechanisms of grassland changes over the past 40 years were considered to be formation dependent. Overall, the first exploration for structural information of plant community changes in this study not only provides a new perspective to understand drivers of grassland changes and their spatial heterogeneity at the regional scale of the Tibetan Plateau, but also innovates large-scale vegetation study paradigm.

Predicting future lake levels under climate change is critical for advancing our understanding of hydrological processes in a changing environment. However, continuous and long-term prediction of lake levels is challenging due to discrepancies in multi-source data and the lack of integration of hydrological models and climate scenarios. Physical and statistical models have been used for lake levels prediction, however, physical models are difficult to calibrate and statistical models often fail to account for the effects of climate changes. In this study, a lake level prediction model was proposed by assimilating the hydrophysical model based on water balance and the ICESat-2 observations using Variational Bayesian Monte Carlo. The model can predict monthly lake levels combining CMIP6-SWAT climate-driven projections and ICESat-2 observations. Short-term validation over 24 months showed the R2 was 0.91 and the RMSE was 5 cm between the proposed model and the ICESat-2 observation. The accuracy is superior to both the hydrophysical model based on water balance and the Prophet time series model. Long-term validation from 1978 to 2021 showed the proposed model has the potential to enhance prediction accuracy within 2 to 3 years compared with the hydrophysical model based on water balance and it is suitable for predicting long-term lake level trends. Notably, it successfully predicted the significant turning point around 2005 where lake levels shift from decline to increase based on past data. The water levels of Lake Qinghai were predicted to rise at a rate of 3.7 cm per year by 2050 under the SSP2-4.5 scenario. The proposed assimilation model combines the strengths of hydrological modeling based on water balance (incorporating the effects of climate change) and the latest ICESat-2 lake level observations (incorporating the effects of recent historical lake levels), improving the accuracy of short-term lake levels and long-term lake level trends prediction. Moreover, as the model is based on satellite remote sensing observations, it has the potential to be applied to any lake globally.

The Qinghai–Tibet Plateau is rich in water resources with numerous lakes, rivers, and glaciers, and, as a source of many rivers in Central Asia, it is known as the Asian Water Tower. Under global climate change, it is critical to understand the current influencing factors on surface water area in this region. Although there are numerous studies on surface water mapping, they are still limited by temporal/spatial resolution and record length. Moreover, the complicated topographic condition makes it challenging to map the surface water accurately. Here, we proposed an automatic two-step annual surface water classification framework using long time-series Landsat images and topographic information based on the Google Earth Engine (GEE) platform. The results showed that the producer accuracy (PA) and user accuracy (UA) of the surface water map in the Qinghai–Tibet Plateau in 2020 were 99% and 90%, respectively, and the Kappa coefficient reached 0.87. Our dataset showed high consistency with high-resolution images, indicating that the proposed large-scale water mapping method has great application potential. Furthermore, a new annual surface water area dataset on the Qinghai–Tibet Plateau from 2000 to 2020 was generated, and its relationship with climate, vegetation, permafrost, and glacier factors was explored. We found that the mean surface water area was about 59 481 km2, and there was a significant increasing trend (=322 km2/year, $p < 0.01$ ) during 2000–2020 in the plateau. Greening, warming, and wetting climate conditions contributed to the increase of surface water area. Active layer thickness and permafrost types may be the most related to the decrease of surface water area. This study provides important information for ecological assessment and protection of the plateau and promotes the implementation of sustainable development goals related to surface water resources.

Vegetation cover fraction (fCover) and related quantities are basic yet critical vegetation structure variables in various disciplines and applications. Ground- and aerial-based proximal and remote sensing techniques have been widely adapted across multiple spatial extents. However, the definitions of fCover-related nomenclatures have not yet been fully standardized, leading to confusing terms and making comparing historic measures difficult. With the issues potentially arising from an increasing diversity of fCover and related quantities estimation methods and corresponding uncertainties, there is also a growing need to spread knowledge on the current advances, challenges, and perspectives, especially in the context of no such existing review for groundand aerial- based estimation. This paper provides the current knowledge mainly concerning passive image-based methods and active light detection and ranging (LiDAR) -based methods. We first harmonized the definitions of fCover and its related quantities (e.g., effective canopy cover, crown cover, stratified vegetation cover, and canopy fraction). Secondly, the typical applications of fCover and related quantities over a range of scales, fields, and ecosystems were summarized. Thirdly yet importantly, we offered a comprehensive review of traditional non-imaging methods, image-based methods (e.g., segmentation, unmixing, and spectral retrieval), point cloudbased methods (e.g., rasterization), and LiDAR return-based methods (e.g., return number index and return intensity retrieval) across different platforms (i.e., ground, unmanned aerial vehicle (UAV) and airplane). Our investigation of fCover and related quantities estimation touches upon various vegetation ecosystems, including agriculture cropland, grassland, wetland, and forest. Finally, the current challenges and future directions were discussed, such as image signal processing under complex heterogeneous surfaces and stratified cover and nonphotosynthesis cover retrieval. We, therefore, expect that this review may offer an insight into fCover and related quantities estimation and serve as a reference for remote sensing scientists, agronomists, silviculturists, and ecologists.

Leaf Area Index(LAI)is a key parameter that characterizes the structure of vegetation canopies,and the indirect measurement of LAI has always been an important research topic in vegeta-tion remote sensing.Terrestrial Laser Scanning(TLS),with its efficient and precise three-dimensional observation capability,has been widely used for LAI estimation.At the same time,TLS provides possi-bilities for the inversion of more refined leaf area parameters,such as the three-dimensional distribution of Foliage Area Volume Density(FAVD).This paper reviews the main methods for retrieving leaf area parameters based on TLS from a methodological perspective,discussing their advantages,limitations,and influencing factors.The indirect measurement of leaf area parameters based on TLS can be classi-fied into four categories:the gap fraction-based method,the contact frequency-based method,the com-puter graphics theory-based method,and the ecophysiological model-based method.These methods dif-fer in theoretical foundations,data organization,etc.,and are suitable for the retrieval of various leaf area parameters at different scales.Among them,the mainstream approach currently relies on the mea-surement and analysis of gap fraction,and it has evolved into three categories based on different forms of point cloud organization:2D images,pulses,and voxels.Methods based on contact frequency,computer graphics theory,and ecophysiological models are also emerging,providing new avenues for leaf area pa-rameter inversion and can be further optimized to improve the accuracy and efficiency of LAI estima-tion.Existing methods have basically covered the inversion of various leaf area parameters at different scales.From stand-scale LAI to more refined individual-tree-scale Vertical Foliage Profile(VFP)and voxel-scale FAVD,leaf area parameter inversion based on TLS continues to advance in a refined and three-dimensional direction.In this process,clumping effects,non-uniform path lengths,and occlusion ef-fects are important factors that affect measurement accuracy and require further research and calibra-tion.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) employs a unique multibeam photon counting approach to acquire a near-continuously sampled profile and provides more precise technology for mapping the leaf area index (LAI) at the global scale. The inversion accuracy of LAI is affected by the clumping effect, which has been an open question for spaceborne laser scanning (SLS). Here, we present a segmented method based on the path length distribution model to calculate the clumping-corrected LAI independently using ICESat-2 data. The results showed that the LAI derived by the proposed method with a 200 m segment was consistent with the airborne laser scanning (ALS)-derived LAI, with a root mean squared error (RMSE) of 0.37. A satisfactory agreement (RMSE $=1.03$ ) was also shown between moderate resolution imaging spectroradiometer (MODIS) LAI and ICESat-2 LAI. Moreover, the LAI derived by the proposed method was on average 31.72% higher than the LAIe derived by Beer’s law, which indicated that the proposed method achieved the purpose of correcting the clumping effect. The gap probability was calculated by the 200 m moving window and the path length distribution was obtained by the 1 m moving window as the model input had the highest accuracy. In addition, the limitation of the point cloud data and the time lag of ICESat-2 acquisitions and ALS observations may affect the inversion accuracy of LAI. This study proposed a feasible way to correct the clumping effect and invert LAI independently using ICESat-2 data, which has the potential to characterize vegetation structure precisely at regional and global scales.

Grassland degradation threatens ecosystem function and livestock production, partly induced by soil nutrient deficiency due to the lack of nutrient return to soils, which is largely ascribed to the intense grazing activities. Therefore, nitrogen (N) fertilization has been widely adopted to restore degraded Qinghai-Tibetan Plateau (QTP) grasslands. Despite numerous field manipulation studies investigating its effects on alpine grasslands, the patterns and thresholds of plant response to N fertilization remain unclear, thus hindering the prediction of its influences on the regional scale. Here, we established a random forest model to predict N fertilization effects on plant productivity based on a meta-analysis synthesizing 88 publications in QTP grasslands. Our results showed that N fertilization increased the aboveground biomass (AGB) by 46.51 %, varying wildly among plant functional groups. The positive fertilization effects intensified when the N fertilization rate increased to 272 kg ha-1 yr-1, and decreased after three years of continuous fertilization. These effects were more substantial when applying ammonium nitrate compared to urea. Further, a machine learning model was used to predict plant productivity response to N fertilization. The total explained variance and mean squared residuals ranged from 49.41 to 75.13 % and 0.011-0.058, respectively, both being the highest for grasses. The crucial predictors were identified as climatic and geographic factors, background AGB without N fertilization, and fertilization methods (i.e., rate, form, and duration). These predictors with easy access contributed 62.47 % of the prediction power of grasses' response, thus enhancing the generalizability and replicability of our model. Notably, if 30 % of yak dung is returned to soils on the QTP, the grassland productivity and plant carbon pool are predicted to increase by 5.90-6.51 % and 9.35-10.31 g C m-2 yr -1, respectively. Overall, the predictions of this study based on literature synthesis enhance our understanding of plant responses to N fertilization in QTP grasslands, thereby providing helpful information for grassland management policies. Conflict of interest: The authors declare no conflict of interest.