The accurate determination of the Diameter at Breast Height (DBH) of Moso bamboo is crucial for estimating biomass and carbon storage in Moso bamboo forests. In this research, we utilized handheld LiDAR point cloud data to extract the DBH of Moso bamboo and enhanced the accuracy of diameter fitting by optimizing denoising parameters. Specifically, we fine-tuned two denoising parameters, neighborhood point number and standard deviation multiplier, across five gradient levels for denoising. Subsequently, DBH fitting was conducted on data processed with varying denoising parameters, followed by a precision evaluation to investigate the key factors influencing the accuracy of Moso bamboo DBH fitting. The research results indicate that a handheld laser was used to scan six plots, from which 132 single Moso bamboo trees were selected. Out of these, 122 single trees were successfully segmented and identified, achieving an accuracy rate of 92.4% in identifying single Moso bamboo trees, with an average accuracy of 95.64% in extracting DBH for individual plants; the mean error was ±1.8 cm. Notably, setting the minimum neighborhood point to 10 resulted in the highest fitting accuracy for DBH. Moreover, the optimal standard deviation multiplier threshold was found to be 1 in high-density forest plots and 2 in low-density forest plots. Forest condition and slope were identified as the primary factors impacting the accuracy of Moso bamboo DBH fitting.

This paper takes the Chaohu Lake Basin as the study area, constructs a remote sensing ecological index (RSEI) based on Landsat TM/OLI series remote sensing data from 1999 to 2020 through the Google Earth Engine cloud computing platform, and uses spatial autocorrelation and geodetectors and other methods to conduct large-scale, long time-series dynamic monitoring and analysis of the ecological environmental quality of the Chaohu Lake Basin. The results show the following: (1) the mean value of the RSEI in the study area increased from 0.68 in 1999 to 0.72 in 2020, showing an overall improvement trend, and the ecological environment of the study area was mainly excellent and good. (2) The global Moran’s I index in the study area was greater than 0. The ecological environmental quality of the Chaohu Lake Basin showed a clustering trend in terms of global autocorrelation with significant positive spatial correlation, and the “high-high” clustering and “low-low” clustering characteristics were most obvious from 1999 to 2020. (3) The ecological environment in the study area has been influenced by multiple factors, among which human factors had a greater impact on the ecological environment of Chaohu Lake Basin in 2008, which led to a decline in ecological environment quality.

Moso bamboo, widely distributed in subtropical regions of China, plays an important role in forest management and carbon cycle research. However, accurate estimation of tree density and aboveground biomass (AGB) remains challenging due to the unique characteristics of Moso bamboo forests in their growth and stand structure. This research aims to develop a new procedure for bamboo tree density and AGB estimation based on UAV-LiDAR and sample plots from multiple sites through comparative analysis of the incorporation of two groups of variables—regular point cloud metrics (e.g., height, point density) and layered texture metrics—and three modeling methods—multiple linear regression (MLR), mixed-effects modeling (MEM), and hierarchical Bayesian modeling (HBM). The results showed that incorporating layered texture metrics with regular variables substantially improved the estimation accuracy of both tree density and AGB. Among these models, HBM achieved the highest predictive performance, yielding coefficient of determination (R²) values of 0.54 for tree density and 0.59 for AGB, with corresponding relative root mean square errors (rRMSE) of 21.46% and 17.97%. This study presents a novel and effective method for estimating Moso bamboo tree density and AGB using multi-site UAV-LiDAR and sample plots, offering a scientific basis for precise management and carbon stock assessment.

Forest resource surveys are of vital importance for grasping the current status of forest resources, formulating management strategies, and evaluating ecosystem functions. Traditional manual measurement methods have numerous limitations in complex forest environments. The emergence of LiDAR technology has provided a new approach. Backpack LiDAR has been increasingly applied due to its portability and flexibility. However, there is a lack of comprehensive research on the influence of different scanning routes on data quality and analysis results. In this study, forest plots of four tree species, namely Carya cathayensis, Cinnamomum camphora, Koelreuteria bipinnata, and Quercus acutissima in Chuzhou City, Anhui Province, were selected as the research objects. Six scanning routes were designed to collect point cloud data using backpack LiDAR. After preprocessing, including denoising and ground point classification, diameter at breast height (DBH) fitting and accuracy evaluation were carried out. The results indicated that the individual tree recognition rates of C. cathayensis, C. camphora, and K. bipinnata reached 100%, while that of Q. acutissima was between 64.71% and 78.07% and was significantly affected by the scanning route. The DBH fitting accuracy of each tree species varied among different routes. For example, C. cathayensis had high accuracy in routes 1 and 6, and C. camphora had high accuracy in routes 1 and 3. Tree species characteristics, scanning routes, and data processing methods jointly affected the DBH fitting accuracy. This study provides a basis for the application of backpack LiDAR in forest resource surveys. Although backpack LiDAR has advantages, it is still necessary to optimize data acquisition schemes targeting tree species characteristics and improve point cloud data processing algorithms to promote its in-depth application in the forestry field.

Accurate quantification of campus vegetation carbon stocks is essential for advancing carbon neutrality goals and refining urban carbon management strategies. This study pioneers the integration of drone and backpack LiDAR data to overcome limitations in conventional carbon estimation approaches. The Comparative Shortest-Path (CSP) algorithm was originally developed to segment tree crowns from point cloud data, with its design informed by metabolic ecology theory—specifically, that vascular plants tend to minimize the transport distance to their roots. In this study, we deployed the Comparative Shortest-Path (CSP) algorithm for individual tree recognition across 897 campus trees, achieving 88.52% recall, 72.45% precision, and 79.68% F-score—with 100% accuracy for eight dominant species. Diameter at breast height (DBH) was extracted via least-squares circle fitting, attaining >95% accuracy for key species such as Magnolia grandiflora and Triadica sebifera. Carbon storage was calculated through species-specific allometric models integrated with field inventory data, revealing a total stock of 163,601 kg (mean 182.4 kg/tree). Four dominant species—Cinnamomum camphora, Liriodendron chinense, Salix babylonica, and Metasequoia glyptostroboides—collectively contributed 84.3% of total storage. As the first integrated application of multi-platform LiDAR for campus-scale carbon mapping, this work establishes a replicable framework for precision urban carbon sink assessment, supporting data-driven campus greening strategies and climate action planning.

Few-shot remote sensing scene classification aims to accurately recognize unseen scene categories using only a scarce number of labeled samples, which has emerged as a research hotspot in the field of remote sensing image interpretation. However, remote sensing images intrinsically suffer from large intra-class variations, high inter-class similarities, and complex background interferences. Traditional few-shot learning methods typically perform feature metric learning in Euclidean space, making it difficult to capture the non-Euclidean geometric distribution characteristics of remote sensing features, and they often neglect the spatial structural information embedded in feature maps. To address these issues, this paper proposes a novel few-shot remote sensing scene classification method, termed ZSFS-RGBN, which integrates a Zigzag Scanning Feature Sequence with a Riemannian Geometric Barycenter Network. Specifically, ResNet12 is first employed as the backbone to extract deep convolutional feature maps from both the support and query sets. Second, a Zigzag scanning strategy is introduced to reorganize the two-dimensional feature maps into one-dimensional feature sequences, thereby effectively preserving the spatial locality and structural continuity of the features. Third, an autoregressive moving average (ARMA) model is constructed to characterize the spatial dependencies of the feature sequences, and its state parameters are mapped onto a symmetric positive definite (SPD) matrix manifold, enabling the deep semantic representations of remote sensing scenes in a non-Euclidean geometric space. Finally, a Riemannian geometric barycenter network is designed to learn the Riemannian barycenter of each category on the SPD manifold, where a joint loss function is introduced to simultaneously optimize intra-class compactness and inter-class separability. Comprehensive experiments are conducted on three public remote sensing scene datasets: NWPU-RESISC45, UC Merced Land-Use, and WHU-RS19. Experimental results demonstrate that the proposed method consistently outperforms several representative state-of-the-art approaches under both 5-way 1-shot and 5-way 5-shot settings. Furthermore, ablation studies verify the effectiveness of each component within the proposed framework.