Mixed forests are increasingly recognized for their superior ecological functioning and productivity compared to pure stands, primarily due to enhanced resource utilization and more complex canopy structures. However, not all species combinations enhance productivity. Pinus massoniana, widely used in subtropical reforestation, often forms structurally simple canopies in pure stands, limiting productivity. Our study utilized multi-source LiDAR—Unmanned Aerial Vehicle and Handheld Mobile Laser Scanning with a LiDAR-based Solar Radiation Attenuation Model to investigate how canopy structure and solar radiation affected forest productivity in subtropical pure forests (Ligustrum lucidum, LLP; Pinus massoniana, PMP; Liquidambar formosana, LFP) and mixed forests (P. massoniana-L. formosana, PMLF; P. massoniana-L. lucidum, PMLL). The results showed that both PMLF and PMLL exhibited overyielding relative to their corresponding pure stands, attributed to multi-layered canopy structures and enhanced light interception. Crucially, PMLL demonstrated effective vertical niche differentiation and mutual overyielding among species, indicating an advantageous combination. In contrast, PMLF benefited L. formosana but suppressed P. massoniana, resulting in less pronounced gains. Increased canopy structural complexity significantly promotes light interception, which directly drives productivity. These findings highlight the importance of species selection and canopy configuration in optimizing stand productivity and guiding sustainable forest management.

Carbon neutrality has emerged as a critical issue both in China and globally, playing a significant role in mitigating climate change, protecting the environment, and enhancing energy security. Understanding the regional distribution of carbon sequestration potential is essential for informing land use and industrial planning strategies. Existing research primarily focuses on the carbon sequestration capacity of forests, while there is an inadequate assessment of the overall regional carbon sequestration potential within the context of land use. To address these research gaps, a Regional Carbon Sequestration Potential Index (RCSPI) was developed to quantitatively evaluate carbon sequestration potential across different regions. This index incorporates multi-source remote sensing variables and environmental factors. This study utilized free and open-source remote sensing data products, including above-ground biomass (AGB), gross primary productivity (GPP), fractional vegetation cover (FVC), and land use classification, to calculate the RCSPI and generate a Pixel Carbon Sequestration Potential Index (PCSPI) map. This approach provides a more comprehensive evaluation of carbon sequestration potential from the pixel scale to regional scale. The performance of the RCSPI was tested using data from 103 county-level regions in Hubei Province. The results showed that the RCSPI showed good ability to facilitate the analysis of distinct carbon sequestration potential patterns, which accruing to the heterogeneity of resource conditions across different regions. Furthermore, we identified three distinct patterns of carbon sequestration potential. Each pattern corresponds to the three indicators of GPP, AGB, and potential carbon sequestration space (F), each exhibiting unique characteristics. Utilizing these patterns, managers can make informed adjustments to regional land management and forestry development policies, which is crucial for achieving carbon neutrality.

Pinus armandii Franch., a key native conifer in China, faces severe Dendroctonus armandi infestation, threatening forest ecosystems. Reducing infestation probability and enhancing resistance are essential for transforming Pinus armandii forests. This study investigates the correlation between stand structure, topographic factors, and the probability of Pinus armandii infestation by Dendroctonus armandi. Based on these correlations, it selects suitable mixed-species combinations of native tree species with low infestation probabilities that are adapted to regional characteristics. A random survey was conducted in 58 plots (6,021 trees) in Shennongjia. Logistic regression and analysis of variance (ANOVA) revealed: (1) Infestation rate increased with elevation, peaking at 84.38% above 2,000 m; between 1,500-2,100 m, probability rose 4.3% per 100 m elevation gain; (2) Steeper slopes (> 25°) reduced infestation (46.03%), with risk decreasing 1.9% per 1° slope increase (0°-40°); (3) Larger DBH (> 30 cm) trees had higher infestation (82.93%), increasing 4.5% per 1 cm DBH; (4) Higher species mingling (four neighboring non-Pinus trees) lowered infestation to 63.39%, reducing risk by 54.3% per mingling unit; (5) Healthy Pinus armandii were frequently neighbored by Litsea pungens, Carpinus cordata, Phellodendron chinense, and Betula platyphylla. Prioritizing slopes > 25° and elevations < 2,000 m for afforestation, mixed with Litsea pungens, Carpinus cordata, or Betula platyphylla, can mitigate infestation. These findings provide actionable strategies to enhance Pinus armandii forest resilience against Dendroctonus armandi threats.

Leaf Area Index (LAI) is a critical biophysical parameter for characterizing vegetation canopy structure and function. However, fine-scale LAI estimation remains challenging due to limitations in spatial resolution and structural detail in traditional remote sensing data and the insufficiency of single-index models like the LiDAR Penetration Index (LPI) in capturing canopy complexity. This study proposes a multi-scale LAI estimation approach integrating high-density UAV-based LiDAR data with LPI and point cloud texture features. A total of 40 field-sampled plots were used to develop and validate the model. LPI was computed at three spatial scales (5 m, 10 m, and 15 m) and corrected using a scale-specific adjustment coefficient (μ). Texture features including roughness and curvature were extracted and combined with LPI in a multiple linear regression model. Results showed that μ = 15 provided the optimal LPI correction, with the 10 m scale yielding the best model performance (R2 = 0.40, RMSE = 0.35). Incorporating texture features moderately improved estimation accuracy (R2 = 0.49, RMSE = 0.32). The findings confirm that integrating structural metrics enhances LAI prediction and that spatial scale selection is crucial, with 10 m identified as optimal for this study area. This method offers a practical and scalable solution for improving LAI retrieval using UAV-based LiDAR in heterogeneous forest environments.

HMLS (Handheld Mobile Laser Scanning) and UAV (Unmanned Aerial Vehicle) LiDAR are increasingly utilized in forest inventory due to their efficiency and portability. However, challenges such as occlusions, low vertical overlap, and varying point cloud density complicate the fusion of these two datasets. In this study, we propose a novel two-stage method to match HMLS and UAV LiDAR data with different point density at complicate forest with dense canopy cover. The first stage optimizes voxel size selection for varying cloud densities and performs feature extraction. The second stage addresses gross error elimination through the truncated least squares method and performs feature matching using K-D Tree nearest neighbor indexing in combination with Singular Value Decomposition (SVD). The method was tested in 27 forest plots with varying vertical overlaps and stand conditions across Hubei Province, China and compared with four registration methods: Coarse-to-Global Adjustment Strategy (CGAS), Optimized Coarse-to-Fine Algorithms (OCFA), Generalized-ICP (GICP), and Bidirectional-Pearson Improved Method (BPIM). Results show that the proposed approach significantly improves registration accuracy, with error reductions of up to 0.096 m, 0.284 m, and 0.425 m under lower (0.37-0.56), moderate (0.58-0.73), and higher (0.77-0.95) canopy cover, respectively. Stand conditions and tree species influence registration accuracy. The results demonstrate higher accuracy in plots with lower canopy cover, steeper slopes, and fewer shrubs. Coniferous forests, with straighter trunks and fewer branches, provide more distinct feature points, leading to better accuracy than broadleaf forests. Additionally, UAV and HMLS matching accuracy is influenced by flight altitude, with higher altitudes increasing registration errors due to the decreased point density of UAV LiDAR.

Assessing forest loss from snow and ice storms is vital for disaster evaluation and sustainable management. Traditional optical remote sensing methods, which focus on horizontal canopy changes, struggle to capture vertical stand alterations caused by snow and ice storms. This study introduces the LiDAR Forest Structure Change Index (LFSCI), a novel index that employs bitemporal unmanned aerial vehicle (UAV) LiDAR point data to comprehensively evaluate changes in the vertical distribution of forest stands. Following ice storms in Shizishan, Wuhan, China in early 2024, research was conducted to compare the performance of LFSCI with traditional metrics, such as canopy cover (CC), Leaf Area Index (LAI), and tree height (TH), across two spatial scales (grid and individual tree). LFSCI was evaluated at nine point densities (5-177 pt/m2). Through validation with field-measured stand volume changes from 43 plots, LFSCI showed superior correlation (R2 = 0.64 for grids, 0.59 for trees) in comparison to CC (R2 = 0.52), LAI (R2 = 0.38), and TH (R2 = 0.16). Higher point densities enhanced accuracy, with 50 pt/m2 recommended for effective snow and ice storm impact detection. Pure broad-leaved forests were more susceptible to loss in comparison to mixed conifer-broadleaf forests, mixed broadleaf forests, and needle forests. Additionally, stands characterized by greater tree heights, steeper slopes, and shaded conditions were more vulnerable to damage than those in other environments.

Climate dictates wildfire activity around the world. But East and Southeast Asia are an apparent exception as fire-activity variation there is unrelated to climatic variables. In subtropical China, fire activity decreased by 80% between 2003 and 2020 amid increased fire risks globally. Here, we assessed the fire regime, vegetation structure, fuel flammability and their interactions across subtropical Hubei, China. We show that tree basal area (TBA) and fuel flammability explained 60% of fire-frequency variance. Fire frequency and fuel flammability, in turn, explained 90% of TBA variance. These results reveal a novel system of scrubland–forest stabilized by vegetation–fire feedbacks. Frequent fires promote the persistence of derelict scrubland through positive vegetation–fire feedbacks; in forest, vegetation–fire feedbacks are negative and suppress fire. Thus, we attribute the decrease in wildfire activity to reforestation programs that concurrently increase forest coverage and foster negative vegetation–fire feedbacks that suppress wildfire.

Understanding canopy nitrogen (N) and phosphorus (P) differences is crucial for optimizing plant nutrient distribution and management. This study evaluated leaf N and P content in citrus trees across three cultivation modes: traditional mode (TM), wide-row and narrow-plant mode (WRNPM), and fenced mode (FM). We used hyperspectral data for non-destructive quantification and compared 1080 leaf samples from upper, middle, and lower canopy layers. Four models—Random Forest (RF), Backpropagation Neural Network (BPNN), Partial Least Squares (PLS), and Support Vector Machine (SVM)—were employed for leaf N and P estimation. Results showed that the TM had significantly lower N content compared to the WRNPM and FM, while the WRNPM exhibited higher P content. The canopy layer had minimal impact on N and P in the FM, and leaves in the upper layer had higher nutrient content in the WRNPM and TM. RF provided the best estimation accuracy, with R2 values of 0.66 for N and 0.72 for P. The cultivation mode and canopy layer significantly influenced the estimation accuracy, with the TM yielding the highest R2, followed by the WRNPM and FM obtaining the lowest accuracy. The labor-saving cultivation mode had different nutrient utilization efficiency compared to the TM. The cultivation mode and canopy layer should be considered when hyperspectral data were used for estimating the leaf N and P content. The study can offer new insights for precise fertilization strategies in fruit trees.

Effectively evaluating and estimating the photosynthetic capacities of different poplar genotypes is essential for selecting and breeding poplars with high productivity. This study measured leaf hyperspectral reflectance, net photosynthetic rate (Pn), transpiration rate (Tr), intercellular CO2 concentration (Ci), and stomatal conductance (Gs) across the upper-, middle- and lower-layer leaves of six poplar genotypes. Photosynthetic capacities and spectral differences were assessed among these genotypes. By analyzing the correlation of photosynthetic parameters and spectral characteristics, the photosynthetic parameters were also estimated from hyperspectral parameters using BP neural networks. Significant differences were observed in the photosynthetic parameters among six poplar genotypes. Populus tremula x P. alba exhibited the highest photosynthetic rate, while Populus hopeiensis showed the lowest. Leaves in the middle layer demonstrated greater photosynthetic capacities than those in the other layers. Leaf reflectance among the six poplar genotypes differed significantly in the ranges of 400-760 nm, 800-1,300 nm, 1,500-1,800 nm, and 1,900-2,000 nm. Values for MTCI, WI, REP, PRI, and first-order derivative at 891 nm also showed significant differences. Hyperspectral parameters, including first-order derivative spectra (FDS), raw spectral reflectance, and photosynthetic parameters, showed strong correlations in the red light (670 nm), near-infrared (760-940 nm), and short-wave infrared (1,800-2,500 nm). Four photosynthetic parameters including P n , T r , C i , and Gs were estimated using BP neural network models and R2 were 0.56, 0.44, 0.35, and 0.35, respectively. The present results indicate that hyperspectral reflectance can effectively distinguish between different poplar genotypes and estimate photosynthetic parameters, highlighting its great potential for studying plant phenomics.

The canopy radiative transfer model enables quantitative inversion of vegetation biophysical parameters by rapidly simulating spectral information. However, structural input parameters, such as the leaf inclination angle distribution (LAD), are often set to empirical values in classic models, leading to simulation errors. Laser point cloud technology has developed rapidly in recent years, and the 3D structure of vegetation can be detailed using laser sensors and processing software, providing the necessary conditions for accurate solutions of radiative transfer models of the canopy. However, the mechanisms for improving existing models of canopy radiative transfer by introducing laser point clouds have not yet been theoretically analyzed in detail. In this study, a precise solution was proposed by incorporating the accurate LAD obtained from laser point clouds into the intermediate function of the traditional Scattering by Arbitrarily Inclined Leaves (SAIL) model, which is a widely used multiangle radiative transfer model. The calculation of the leaf inclination angle and the derivation of the improved model were detailed in this paper. Experiments from different observation angles and bands compared the similarity between the actual measured spectra and the simulated spectra for both the traditional SAIL model and the proposed model with accurate structural parameters. The overall similarity of the proposed model with respect to the conventional SAIL model was improved, indicating that introducing laser point clouds improved the simulation accuracy. The proposed model achieved more significant improvements in the blue 450 nm and red 670 nm bands. Moreover, the similarity improvements differed between different angles. Finally, we analyzed the sensitivity of the structural parameters of the SAIL model and the proposed model using local and global sensitivity analysis methods.

Due to the important role of forests in carbon neutrality, it is a big task in accurately calculating and predicting forest carbon storage and carbon sink capacity in recent years. However, considering the factors on the capacity of forest carbon sequestration, ecologists and foresters consider different models to evaluate the forest carbon sink ability at different scales, with foresters focusing more on forest growth models, while ecologists adding more climate and environmental factors, which may result in inconsistent results in carbon storage. Therefore, constructing an integrated model by combining the forestry and ecology models is essential for accurately quantifying and characterizing the forest carbon sink potential at the regional scale. Here, we proposed a new forest carbon sink potential index (FCSPI), which is defined as fractional deficiency of the current forest carbon to its maximum level, based on forest permanent plots, climate, and edaphic data to evaluate the forest sink potential ability from stand level to zonal scale across the northern subtropical zone in Hubei province, China, which coupled the stand growth model and climate-productivity model. The results at stand level showed that the R2 and RMSE of FCSPI were 0.78 and 0.072 respectively, which indicated that the FCSPI is an intuitive, highly practical, straightforward, easy, and rapid to implement methodology for forest carbon sequestration assessment. Moreover, FCSPI can conveniently extended from stand scale to zonal scale based on the site quality index for carbon sink (SIC) and stand age variables, which were derived from the opened climate, edaphic, and topographic data. The results of wall-to-wall FCSPI across the subtropical forest in Hubei province reveal the current and future carbon potential sequestration, which can help managers to focus on forest management for climate-smart actions and planning in forest ecosystem services framework.

Urban heat islands are representative problems in urban environments. The impact of spectral indexes on land-surface temperature (LST) under different urban forms, climates, and functions is not fully understood. Local climate zones (LCZs) are used to characterize heterogeneous cities. In this study, we quantified the contribution of three cities to high-temperature zones and surface urban heat island intensity (SUHII) across LCZs and seasons, used Welch and Games–Howell tests to analyze the difference in LST, then described the spatial pattern characteristics of LST, and used a geographically weighted regression model to analyze the relationship between spectral indexes and LST. The results showed that compact midrise, compact low-rise (LCZ 3), large low-rise (LCZ 8), heavy industry (LCZ 10), and bare rock or paved (LCZ E) contributed greatly to high-temperature zones and had strong SUHII. There were 92–98% significant differences between different LCZs. The spatial aggregation of LST gradually weakened with a decrease in temperature. The modified normalized difference water index (MNDWI) in most LCZs of all seasons for Wuhan could reduce LST well, while MNDWI only had cooling effects in winter for Nanjing and Shanghai. Normalized difference vegetation index (NDVI) in most LCZs performed a cooling role during summer and transition seasons (spring and autumn), while it showed a warming effect in winter. The cooling effect of NDVI in open building types was stronger than that of compact building types, while the cooling effect of MNDWI was better in compact building types than in open building types. With the increase of normalized difference built-up index (NDBI), all LCZs showed warming effects, and the magnitude of LST increase varied in different cities and seasons. These results contribute further insight into thermal environment in heterogeneous urban areas.

Analyzing and comparing the effects of labor-saving cultivation modes on photosynthesis, as well as studying their vertical canopy architecture, can improve the tree structure of high-quality and high-yield citrus and selection of labor-saving cultivation modes. The photosynthesis of 1080 leaves of two labor-saving cultivation modes (wide-row and narrow-plant mode and fenced mode) comparing with the traditional mode were measured, and nitrogen content of all leaves and photosynthetic nitrogen use efficiency (PNUE) were determined. Unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) data were used to assess the vertical architecture of three citrus cultivation modes. Results showed that for the wide-row and narrow-plant and traditional modes leaf photosynthetic CO2 assimilation rate, stomatal conductance, and transpiration rate of the upper layer were significantly higher than those of the middle layer, and values of the middle layer were markedly higher than those of the lower layer. In the fenced mode, a significant difference in photosynthetic factors between the upper and middle layers was not observed. A vertical canopy distribution had a more significant effect on PNUE in the traditional mode. Leaves in the fenced mode had distinct photosynthetic advantages and higher PNUE. UAV-based LiDAR data effectively revealed the differences in the vertical canopy architecture of citrus trees by enabling calculating the density and height percentile of the LiDAR point cloud. The point cloud densities of three cultivation modes were significantly different for all LiDAR density slices, especially at higher canopy heights. The labor-saving modes, particularly the fenced mode, had significantly higher height percentile data.

To address the demands of precision agriculture and the measurement of plant photosynthetic response and nitrogen status, it is necessary to employ advanced methods for estimating chlorophyll content quickly and non-destructively at a large scale. Therefore, we explored the utilization of both linear regression and machine learning methodology to improve the prediction of leaf chlorophyll content (LCC) in citrus trees through the analysis of hyperspectral reflectance data in a field experiment. And the relationship between phenology and LCC estimation was also tested in this study. The LCC of citrus tree leaves at five growth seasons (May, June, August, October, and December) were measured alongside measurements of leaf hyperspectral reflectance. The measured LCC data and spectral parameters were used for evaluating LCC using univariate linear regression (ULR), multivariate linear regression (MLR), random forest regression (RFR), K-nearest neighbor regression (KNNR), and support vector regression (SVR). The results revealed the following: the MLR and machine learning models (RFR, KNNR, SVR), in both October and December, performed well in LCC estimation with a coefficient of determination (R2) greater than 0.70. In August, the ULR model performed the best, achieving an R2 of 0.69 and root mean square error (RMSE) of 8.92. However, the RFR model demonstrated the highest predictive power for estimating LCC in May, June, October, and December. Furthermore, the prediction accuracy was the best with the RFR model with parameters VOG2 and Carte4 in October, achieving an R2 of 0.83 and RMSE of 6.67. Our findings revealed that using just a few spectral parameters can efficiently estimate LCC in citrus trees, showing substantial promise for implementation in large-scale orchards.

Under the background of beautiful countryside construction and digital village strategy, the digital integration, management, expression and application of rural landscape resources are the basic work to improve digital management of rural ecology and human settlements in China.A big data application platform for rural landscape resources was constructed to solve the multi-source heterogeneity and difficulty in integrating rural landscape resource data, applying 3S and multi-source data fusion technology.The overall functional structure was described around the construction process of the core modules of the three platforms including the integration and storage of multi-scale rural landscape resource data, the function of classification and evaluation of rural landscape resources, and the visualization of rural landscape resource data.The integration and display of multi-scale and multi-source rural landscape resource data in the national territory-region-local-village domain was realized by building a distributed “space-attribute integration” rural landscape resource big data application platform.On one hand, the platform offers data channels and digital platform support for rural planning, construction, and management; on the other hand, it will provide methods and ideas for constructing rural big data platforms in other fields to help the development of digital villages.

随着"互联网+"新经济模式的发展,数字化、网络化、智慧化成为各个行业新的发展契机.随着"智慧林业"的提出,智慧林业人才培养被纳入林学专业人才培养计划.本文在调研就业市场对智慧林业人才的需求、学生对信息技术的了解和掌握程度的基础上,分析了 18所林业院校中林学专业关于信息技术类相关课程的设置和实践情况,并以华中农业大学林学专业的智慧林业人才培养模式为例,基于智慧林业人才核心技能需求,提出了多学科交叉融合的智慧林业人才培养思路和实现路径.

The accurate identification of forest tree species is important for forest resource management and investigation. Using single remote sensing data for tree species identification cannot quantify both vertical and horizontal structural characteristics of tree species, so the classification accuracy is limited. Therefore, this study explores the application value of combining airborne high-resolution multispectral imagery and LiDAR data to classify tree species in study areas of different altitudes. Three study areas with different altitudes in Muyu Town, Shennongjia Forest Area were selected. Based on the object-oriented method for image segmentation, multi-source remote sensing feature extraction was performed. The recursive feature elimination algorithm was used to filter out the feature variables that were optimal for classifying tree species in each altitude study area. Four machine learning algorithms, SVM, KNN, RF, and XGBoost, were combined to classify tree species at each altitude and evaluate the accuracy. The results show that the diversity of tree layers decreased with the altitude in the different study areas. The texture features and height features extracted from LiDAR data responded better to the forest community structure in the different study areas. Coniferous species showed better classification than broad-leaved species within the same study areas. The XGBoost classification algorithm showed the highest accuracy of 87.63% (kappa coefficient of 0.85), 88.24% (kappa coefficient of 0.86), and 84.03% (kappa coefficient of 0.81) for the three altitude study areas, respectively. The combination of multi-source remote sensing numbers with the feature filtering algorithm and the XGBoost algorithm enabled accurate forest tree species classification.