Accurately Crown asymmetry is a common morphological response to heterogeneous environmental pressures, and this asymmetry directly influences the physiological processes of tree growth. Advances in UAV-Lidar technology have facilitated the acquisition of high-precision crown data, providing new pathways to characterize crown structural variations and investigate their impacts on tree growth. In this study, we propose a novel crown asymmetry index (Ihctsa), based on UAV-LiDAR data and time-series tool, to characterize crown asymmetry. Using a Cunninghamia lanceolata plantation in southeastern China as a case study, we analyzed the size-dependent effects of crown asymmetry on tree growth. The results revealed a significant reversal in the effect of crown asymmetry as tree size increased. Specifically, crown asymmetry exhibited a significant negative effect on the growth of small DBH trees, whereas it showed a significant positive effect on the growth of large DBH trees. Compared to the traditional Mean Circularity Index (IMC), the Ihctsa index captures the structural characteristics of crown variations and can determine the DBH threshold value (≈ 19.77 cm) for growth response at which transition from negative to positive. Furthermore, the LiDAR-based crown overlap index (CI2) demonstrated superior methodological suitability for separating thinning effects compared to the classic Hegyi index (CI1). This study demonstrates the feasibility and effectiveness of utilizing LiDAR for spatial competition analysis, providing a new methodological reference for forest ecology research.

The transition from monoculture plantations to mixed-species stands is a key strategy in China for improving productivity and climatic resilience. However, the long-term productivity outcomes of different transformation measures, particularly under future climate change, remain insufficiently quantified. Here, we developed a hybrid modeling framework combining the process-based 3-PGmix model with machine learning to simulate and efficiently extrapolate spatiotemporal dynamics of biomass and net primary productivity (NPP) over 40 years following mix-species transformation. In this framework, machine learning serves as a computational surrogate to emulate 3-PGmix outputs, enabling rapid scenario exploration while retaining the structural assumptions of the underlying process model. Results indicate that the effects of species mixing on stand productivity are stage-dependent, with the strongest productivity gains occurring in mixed stands containing approximately 70% coniferous and 30% broadleaved species. In the study area, converting pure Cunninghamia lanceolata plantations to mixed stands with Schima superba at this proportion is projected to increase total productivity by 3336.7 t year-1 by 2060 (equivalent to 1.2 t ha-1 year-1). Across both SSP245 and SSP585 scenarios, mixed stands consistently exhibited lower relative biomass decline than pure C. lanceolata stands-particularly during mid-rotation stages-indicating greater stability under projected climate trajectories. Sensitivity analysis shows that productivity losses are primarily driven by temperatures exceeding optimal growth thresholds. Maximum temperature effects outweighed the compensatory influences of precipitation and minimum temperatures. For computational efficiency, Random Forest (RF) was selected as a surrogate emulator of 3-PGmix simulations. It outperformed artificial neural networks (ANN) and multiple linear regression (MLR) in reproducing simulated NPP patterns, demonstrating high approximation accuracy and numerical stability within the modeled parameter space. This hybrid framework therefore functions as a computationally efficient extension of 3-PGmix for largescale scenario analysis. It provides an interpretable platform for evaluating plantation transformation strategies under projected climate change in subtropical China.

The RSEI index has been used widely in ecological environment quality assessment， but its application in high vegetation coverage areas in southern China remains relatively limited. To monitor and evaluate the ecological environment quality in the region more effectively， indices of humidity （WET）， heat （LST）， and dryness （NDBSI） factors and the comprehensive vegetation index mNDVI were introduced to construct the modified remote sensing ecological index （MRSEI） by principal component analysis. Then， the GEE cloud platform and ArcGIS 10.8 platform were used to analyze the spatial and temporal distribution and driving mechanisms of ecological quality in Jiangle County from 2000 to 2020. The results were as follows： ① Compared with RSEI， the average correlation between MRSEI and the principal components was higher. In the three experimental areas， its contrast was increased by 10.538， 2.923， and 8.558， and its entropy was increased by 0.024， 0.046， and 0.025， respectively. ② The overall change of MRSEI in Jiangle County was an increase， with an average annual increment of 0.010. Areas with good ecological quality accounted for the largest proportion of the county， ranging from 33.61% to 38.28%， while the proportion of county areas classified as poor or poorer was about 10%. The proportion of areas defined as stable in ecological change ranged from 42.23% to 59.05%， although this fluctuated significantly from year to year. Moreover， the ecological environment was more prone to deterioration in the southwest and northern regions， and urban construction expanded outward from the central areas. Through the 20 years， the land area with improved ecological quality reached 424.34 km2， indicating a significant improvement in Jiangle County's ecological environment. ③ Land use type and slope were the primary factors influencing spatial variations in ecological environment quality， with annual average precipitation also playing a significant role. The interactions among driving factors led to some degree of improvement， with the interaction between land use type and annual average precipitation having the strongest influence on MRSEI spatial differentiation， contributing 36.1% to the variation. This study provides a scientific basis for ecological environment monitoring and sustainable development in Jiangle County.

Understanding how biotic and environmental drivers jointly shape forest carbon dynamics over time is essential for climate-adapted management of subtropical forests. We investigated the long-term interactions between biotic factors, environmental factors, and forest carbon dynamics in the subtropical forests of Jiangxi Province, China, over the period 1989–2019. The High Accuracy Surface Modelling (HASM) multi-source data fusion method integrates ground observation points with area-wide data from remote sensing and existing datasets to simulate the spatial distribution of forest carbon density across the entire study area. In Zixi, forest carbon density increased most rapidly between 1989 and 2009, after which the rate slowed as forest stands matured. Structural Equation Modelling (SEM) disentangled direct and indirect effects of drivers, and identified species richness and community-weighted functional traits as key positive drivers of aboveground carbon density. The influence of environmental factors reversed over the study period. Under ongoing global warming, the combined effects of altitude, temperature, and precipitation shifted from suppressing to reinforcing carbon accumulation in later years, increasingly operating through pathways mediated by functional traits. These findings enhance our understanding of carbon dynamics in subtropical forests and underline the importance of preserving species richness, especially in subtropical mountain forest. This study provides valuable insights for adaptive forest management and climate change mitigation strategies, aiming to improve ecosystem resilience and sustain carbon sequestration efforts in the face of ongoing global warming.

Tree species mixing is a well-known forest management strategy, often playing a beneficial role in forest productivity, stability and ecosystem biodiversity. However, whether species mixing mitigates the impacts of climate anomalies such as drought on tree growth remains controversial. This study compared the growth, climate sensitivity and drought response between pure and mixed forests with different mixing proportions of Chinese fir in Southeastern China using the normalized difference vegetation index (NDVI) and tree-ring width (TRW) respectively. NDVI time series were extracted from 57 monoculture and 50 mixture plots using the Landsat remote sensing satellite spanning 1986–2022, and tree rings were collected from 324 trees in pure forests and 182 trees in mixed forests. Then, the effects of tree-level, neighborhood, and climate drivers were investigated using the interpretable XGBoost model. No evidence showed that Chinese fir benefited from the admixture with Masson pine. Species mixing had neutral or negative effects on the growth of Chinese fir and its drought response, regardless of whether NDVI or TRW was used. NDVI was more sensitive to atmospheric moisture compared to TRW, but differences between pure and mixed forests were more pronounced in TRW. Tree growth and its response to drought in mixed forests were influenced by tree characteristics > neighborhood traits > climate, and their effects varied with the mixing proportions of species. Vapor pressure deficit (VPD) had a greater impact on tree growth than soil moisture. In mixed forests, the negative impact of VPD on tree growth was the smallest in the mixing proportion of 5:5, which also experienced the lowest growth reduction caused by droughts. Our findings suggested that species mixtures did not always facilitate all mixed species, but rather even reduced the growth and resilience to multi-year droughts of a particular species. However, appropriate mixing proportions of species could minimize the downside effects while maintaining the ecological advantages of species mixing.

Legacy effects following drought are widely detected across worldwide forests, significantly affecting the growth recovery and susceptibility of trees after droughts. Thinning is a common forest management practice used to alter tree growth and climate-growth relationships. Although the effects of thinning on tree response during drought have been investigated, how thinning modulates post-drought legacy effects remains largely unknown. In this study, based on tree-ring data of 140 trees, we examined the effects of thinning on post-drought legacy effects using the quantile mixed effect model for Chinese fir (Cunninghamia lanceolata) in Southeastern China. The tree-ring data were stratified sampling from a thinning experiment applied 10 years ago in 8-year-old plantations, and included four thinning intensities (20 %, 25 %, 33 %, and 50 % reduction of tree number) and an unthinned control treatment. Drought legacy effects of tree growth positively depended on tree social status with the magnitudes and variations larger in higher status classes. Dominant large trees without thinning management had the greatest drought legacy effects. Although the effects of thinning varied slightly at different quantiles, they all indicated that thinning mitigated the growth legacies after drought and the reduction effect was more pronounced with increasing thinning intensity. Thinning could also reduce post-drought climate sensitivities, but only after moderate thinning (20 % and 25 % thinning intensity). Heavier thinning (33 % and 50 % thinning intensity) instead enhanced tree growth responses to climate changes following drought. Thinning intensity needs to be carefully considered to really reap the post-drought benefits of forest thinning management. Our findings suggested that mild thinning offered an alleviation of climate dependency following drought in addition to reducing drought legacy effects on growth, benefiting tree recovery from drought. The results of this study are useful to inform management adaptive strategies for drought-vulnerable plantations under increasingly frequent droughts.

The tree canopy represents a fundamental element of tree-related information. However, achieving precise canopy information from remote sensing images remains a significant challenge due to varying canopy sizes, mutual overlap, and diverse woodland environments. This study aims to leverage high -resolution Chinese fir images captured by an unmanned aerial vehicle (UAV) from a state forest farm in Jiangle County, Fujian Province, China. The images are integrated with the Mask R-CNN model to autonomously extract attributes at both individual and stand levels, facilitating precise forest mapping at the level of individual trees. The fusion entails a corresponding band saturation-weighted approach between RGB images and thermally-enhanced Canopy Height Model (CHM) images. The fusion threshold is set equal to the weight assigned to the CHM, and the weight of the RGB is computed as 1 minus the fusion threshold, ranging from 0 to 1 in intervals of 0.1. The dataset is then trained using three instance segmentation models: feature extraction networks based on ResNet50, ResNet101, and ResNeXt101, respectively. An instance merging approach based on the canopy crossoccupancy ratio is introduced, to enhance the accuracy of individual tree and stand-level attributes extraction, as well as forest mapping for individual tree canopies by using extensive stand images. This study focuses on evaluating the performance of instance segmentation models using two critical metrics: Bounding Box Average Precision (Box-AP) and Segmentation Average Precision (Segm-AP). The results highlighted that the Mask R-CNN model integrated with ResNeXt101, coupled with sample fusion using a threshold of 0.1, demonstrated exceptional performance. The accuracy of segmentation is acceptable, with Box-AP at 51.697 % and Segm-AP at 54.946 %. The extractions of individual level attributes, crown area, north - south crown width, and east - west crown width yielded R 2 of 0.933, 0.871, and 0.877, respectively. As for stand-level attributes, canopy density, and individual population extraction resulted in R 2 values of 0.901 and 0.912, respectively. Relative to the original images (with a fusion threshold of 0), segmentation accuracy was improved for all combinations, with the optimal configuration showing a 0.019 increase in R 2 for canopy density and a 0.014 increase in R 2 for individual population extraction. Furthermore, Box-AP and Segm-AP exhibit enhancements of 10.795 % and 10.746 %, respectively. The instances-merging method enhances the extraction accuracy of individual population by 5%. This study underscores the precision and efficacy of the Mask R-CNN instance-based segmentation model and fusion strategy, providing a robust support for the integration of deep learning in canopy extraction research. Its implications are of great significance for large-scale forestry surveys and the advancement of precision forestry, highlighting the substantial potential.

Crown development is closely related to the biomass and growth rate of the tree and its width (CW) is an important covariable in growth and yield models and in forest management. To date, various CW models have been proposed. However, limited studies have explicitly focused on additive and inherent correlation of crown components and total CW as well as the influence of competition on crown radius from the corresponding direction. In this study, two model systems were used, i.e., aggregation method system (AMS) and disaggregation method system (DMS), to develop crown width additive model systems. For calculating spatially explicit competition index (CI), four neighbor tree selection methods were evaluated. CI was decomposed into four cardinal directions and added into the model systems. Results show that the power model form was more proper for our data to fit CW growth. For each crown radius and total CW, height to the diameter at breast height (HDR) and basal area of trees larger than the subject tree (BAL) significantly contributed to the increase of prediction accuracy. The 3-m fixed radius was optimal among the four neighborhoods selection ways. After adding decomposed competition Hegyi index into model systems AMS and DMS, the prediction accuracy improved. Of the model systems evaluated, AMS based on decomposed CI provided the best performance as well as the inherent correlation and additivity properties. Our study highlighted the importance of decomposed CI in tree CW modelling for additive model systems. This study focused on methodology and could be applied to other species or stands.

基于建立的小兴安岭南麓红松树轮宽度标准年表,分析红松径向生长与该地区温度和降水间的关系以及1982年升温突变对此相关性的影响.结果表明:6月平均温度与树轮宽度年表在变暖前后始终呈极显著负相关,是该地区红松径向生长的主要限制因子.基于此构建的区域1843-1982年6月平均温度重建方程稳定可靠.重建温度序列的偏暖时期和偏冷时期分别持续7年和29年,偏暖时段为1915-1921年,偏冷时段为1880-1891年和1932-1948年.小波分析结果显示6月平均温度存在2-7a周期变化.空间相关分析结果表明重建温度序列能很好的代表小兴安岭南麓及附近区域的温度变化.本研究拓展了研究区现有的气候数据,可为掌握小兴安岭气候变化规律和科学预测未来气候提供数据支撑.

Digital aerial photograph (DAP) data is processed based on Structure from Motion (SfM) algorithm and regional net adjustment method to generate digital surface discrete point clouds similar to Light Detection and Ranging (LiDAR) and digital orthophoto mosaic (DOM) similar to optical remote sensing image. In this study, we obtained high-resolution images of mature forests of Chinese fir by unmanned aerial vehicle (UAV) flying through cross-route flight, and then reconstructed the three-dimensional point clouds in the UAV aerial area by SfM technique. The point cloud segmentation (PCS) algorithm was used for the individual tree segmentation, and the F-score of the three sample plots were 0.91, 0.94, and 0.94, respectively. Individual tree biomass modeling was conducted using 155 mature Chinese fir forests which were correctly segmented. The relative root mean squared error (rRMSE) values of random forest (RF), bagged tree (BT) and support vector regression (SVR) were 34.48%, 35.74% and 40.93%, respectively. Our study demonstrated that DAP point clouds had great potential to extract forest vertical parameters and could be applied successfully in individual tree segmentation and individual tree biomass modeling.

Crown, with many dimensions greatly influences the stem taper of a tree. However, few taper models have accounted for its impact on diameter estimation. In order to investigate and quantify the effects of various crown factors on stem taper and develop new taper models incorporating crown information for Cunninghamia lanceolata in Southeast China, a sample data of 1100 taper measurements from 108 trees and two different modeling methods were utilized. A set of traditional non-linear regression (NLR) models with linear and non-linear functions composed of crown factors introduced respectively, were developed for stem diameter prediction, as well as artificial neural network (ANN) models based on different input variables. ANN technology was applied to variable screening prior to developing models, and the evaluation statistics and graphics were used to assess the models. The results showed that crown length (CL) and height to live crown base (HCB) had larger influence on the model accuracy than other crown variables, demonstrating that variables screening based on ANN is feasible and efficient when numerous potential variables are available. The accuracy of taper model was improved when incorporating different crown variables or their combinations in terms of higher R2 and lower RMSE, however, the degree of improvement varied, depending on the variables added and the modelling approach. The inclusion of CR and HCB presented the highest improvement, whether using ANN or NLR modelling methods. The ANN model decreased 13.96 % in RMSE and 17.00 % in MAE. Similarly, the NLR model reduced 2.1 % in RMSE and 1.78 % in MAE. This study indicated that the refined models with crown variables included were more in line with the biological logic of nature and the accuracy has been improved, although the improvement of nonlinear regression models was not as significant as expected. In addition, it also suggested that in forest resource inventory, ANN was a recommended technique for variable screening and an alternative method for model development.

The current individual tree competition indexes cannot provide information on the competitive pressure of trees in different directions. To quantitatively describe the magnitude of competitive pressure on trees in different directions, this study proposes a method to calculate the magnitude of competitive pressure in the east, west, south, and north directions based on the Hegyi competition index. The Hegyi competition index was assumed as a vector and it is decomposed in two vertical directions by attributing a weight in each direction. Under the condition that the Hegyi index is constant, the Hegyi index is allocated to the four directions. Correlation analysis between the Hegyi competition index components in the four directions of trees and the crown radius of the corresponding azimuth was carried out. The crown radius prediction model containing the Hegyi competition index component was established by using Generalized additive models (GAMs). The results show that the Hegyi competition index component was significantly negatively correlated with the crown radius in the corresponding direction, and the correlation coefficient with the crown radius in the corresponding direction was the largest compared with other directions. The crown radius in the four directions decreased with the increase of the Hegyi competition index component in the corresponding direction, indicating that the competition pressure on the crown in a specific direction inhibited the growth and extension of the crown in this direction. After adding the Hegyi competition index component, the fitting effect of crown radius models in different directions is greatly improved. In general, the Hegyi competition index component quantitatively expresses the competitive pressure of trees in different directions. The proposed method has particular significance for the study of tree crown morphology and provides a new idea for studying the competitive growth relationship of plants.

Climate change caused by industrial carbon emissions and land use/land cover changes is a widely concerning issue around the world and is closely related to the global carbon cycle [...]

Crown width is one of the most important crown dimensions that influence tree growth and survival. Accurate crown width prediction is vital for forest management. However, measuring crown width is time-consuming and labor-intensive, making it necessary to construct a convenient and accurate crown width prediction model. Nowadays, machine learning technologies have already been increasingly used to accurately predict tree growth, but there is still a lack of systematic and comprehensive comparison. This paper provided a comparative analysis of various machine learning methods (i.e., the Linear Regression, the Least Absolute Shrinkage and Selection Operator, the k-NearestNeighbors, the Random Forest, the Gradient Boosting Decision Tree, the Support Vector Regression, the Voting Regressor and the Multi-Layer Perceptron), simple crown width-diameter of breast height model, generalized crown width-diameter of breast height model and nonlinear mixed-effect crown width model for estimating the crown width of Larix olgensis in terms of the coefficient of determination, root mean squared error, mean absolute error, and mean absolute percentage error using hold-out validation and 10-fold cross-validation. The study showed that machine learning algorithms performed better than common nonlinear regression and nonlinear mixed-effect models. Specifically, the voting regressor and random forest algorithm yielded the highest prediction quality of crown width among the different models. In addition, from a practical point of view, the advantage of machine learning is that its implementation does not require crown width measurements. On the contrary, the calibration of the mixed-effect model requires prior information, which limits its use.

Parameter sensitivity analysis can determine the influence of the input parameters on the model output. Identification and calibration of critical parameters are the crucial points of the process model optimization. Based on the Extended Fourier Amplitude Sensitivity Test (EFAST) and the Morris method, this paper analyzes and compares the parameter sensitivity of the annual mean net primary productivity (NPP) of Larix olgensis Henry forests in Jilin Province simulated by the Lund–Potsdam–Jena dynamic global vegetation model (LPJ model) in 2009–2014 and 2000–2019, and deeply examines the sensitivity and influence of the two methods to each parameter and their respective influence on the model’s output. Moreover, it optimizes some selected parameters and re-simulates the NPP of Larix olgensis forests in Jilin Province from 2010 to 2019. The conclusions are the following: (1) For the LPJ model, the sensitive and non-influential parameters could be identified, which could guide the optimization order of the model and was valuable for model area applications. (2) The results of the two methods were similar but not identical. The sensitivity parameters were significantly correlated (p < 0.05); parameter krp was the most sensitive parameter, followed by parameters αm, αa and gm. These sensitive parameters were mainly found in the photosynthesis, water balance, and allometric growth modules. (3) The EFAST method had a higher precision than the Morris method, which could calculate quantitatively the contribution rate of each parameter to the variances of the model results; however, the Morris method involved fewer model running times and higher efficiency. (4) The mean relative error (MRE) and mean absolute error (MAE) of the simulated value of LPJ model after parameter optimization decreases. The optimized annual mean value of NPP from 2010 to 2019 was 580 g C m−2 a−1, with a mean annual growth rate of 2.13%, exhibiting a fluctuating growth trend. The MAE of the simulated value of LPJ model after parameter optimization decreases.

为评估吉林省落叶松林的生产力现状并为我国森林生态系统生产力和植被监测研究提供基础数据,以吉林省落叶松林为研究对象,基于吉林省及其周边100 km范围内41个气象站点资料,采用LPJ-DGVM模型模拟了 2000-2019年吉林省落叶松林近20年的净初级生产力,并采用线性回归趋势分析、变异系数、Hurst指数和相关性分析法对其时空变化、稳定性及其与气候因子的相关关系进行了分析.结果表明:(1)2000-2019年吉林省落叶松林年均净初级生产力(NPP)为592 g C m-2 a-1,年均增长率为2.81％,随时间推移呈现波动增长的趋势(β=14.55,R2=0.784,P＜0.01).(2)NPP变异系数为0.07-2.33,均值为0.48,除幼龄林外,整体波动较小.Hurst指数介于0.441-0.849之间,均值为0.612,未来吉林省落叶松林NPP呈增加趋势.(3)吉林省落叶松林NPP存在明显的空间异质性,北部和南部区域NPP较高,是近20年NPP增长较快的区域.(4)2000-2019年吉林省落叶松林年均NPP与年总降水、生长季降水量之间均不显著(P＞0.05),与年均温呈显著正相关(P＜0.05),与生长季均温为极显著正相关(P＜0.01),该阶段内温度比降水更能对吉林省落叶松林NPP的年际变化产生影响.LPJ模型模拟吉林省落叶松林2000-2019年NPP与样地实测值极显著相关(P＜0.01),可以用于模拟吉林省落叶松林的NPP.

Increasing numbers of explanatory variables tend to result in information redundancy and “dimensional disaster” in the quantitative remote sensing of forest aboveground biomass (AGB). Feature selection of model factors is an effective method for improving the accuracy of AGB estimates. Machine learning algorithms are also widely used in AGB estimation, although little research has addressed the use of the categorical boosting algorithm (CatBoost) for AGB estimation. Both feature selection and regression for AGB estimation models are typically performed with the same machine learning algorithm, but there is no evidence to suggest that this is the best method. Therefore, the present study focuses on evaluating the performance of the CatBoost algorithm for AGB estimation and comparing the performance of different combinations of feature selection methods and machine learning algorithms. AGB estimation models of four forest types were developed based on Landsat OLI data using three feature selection methods (recursive feature elimination (RFE), variable selection using random forests (VSURF), and least absolute shrinkage and selection operator (LASSO)) and three machine learning algorithms (random forest regression (RFR), extreme gradient boosting (XGBoost), and categorical boosting (CatBoost)). Feature selection had a significant influence on AGB estimation. RFE preserved the most informative features for AGB estimation and was superior to VSURF and LASSO. In addition, CatBoost improved the accuracy of the AGB estimation models compared with RFR and XGBoost. AGB estimation models using RFE for feature selection and CatBoost as the regression algorithm achieved the highest accuracy, with root mean square errors (RMSEs) of 26.54 Mg/ha for coniferous forest, 24.67 Mg/ha for broad-leaved forest, 22.62 Mg/ha for mixed forests, and 25.77 Mg/ha for all forests. The combination of RFE and CatBoost had better performance than the VSURF–RFR combination in which random forests were used for both feature selection and regression, indicating that feature selection and regression performed by a single machine learning algorithm may not always ensure optimal AGB estimation. It is promising to extending the application of new machine learning algorithms and feature selection methods to improve the accuracy of AGB estimates.

We propose a fundamental theorem for eco-environmental surface modelling (FTEEM) in order to apply it into the fields of ecology and environmental science more easily after the fundamental theorem for Earth’s surface system modeling (FTESM). The Beijing-Tianjin-Hebei (BTH) region is taken as a case area to conduct empirical studies of algorithms for spatial upscaling, spatial downscaling, spatial interpolation, data fusion and model-data assimilation, which are based on high accuracy surface modelling (HASM), corresponding with corollaries of FTEEM. The case studies demonstrate how eco-environmental surface modelling is substantially improved when both extrinsic and intrinsic information are used along with an appropriate method of HASM. Compared with classic algorithms, the HASM-based algorithm for spatial upscaling reduced the root-mean-square error of the BTH elevation surface by 9 m. The HASM-based algorithm for spatial downscaling reduced the relative error of future scenarios of annual mean temperature by 16%. The HASM-based algorithm for spatial interpolation reduced the relative error of change trend of annual mean precipitation by 0.2%. The HASM-based algorithm for data fusion reduced the relative error of change trend of annual mean temperature by 70%. The HASM-based algorithm for model-data assimilation reduced the relative error of carbon stocks by 40%. We propose five theoretical challenges and three application problems of HASM that need to be addressed to improve FTEEM.