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Abstract Aims Robot-assisted total hip arthroplasty (rTHA) boasts superior accuracy in implant placement, but there is a lack of effective assessment in perioperative management in the context of enhanced recovery after surgery (ERAS). This study aimed to compare the effectiveness and safety of rTHA versus conventional total hip arthroplasty (cTHA) in ERAS-managed patients. Methods In this prospective trial, a total of 60 eligible patients aged between 18 and 80 years were randomly divided into two groups to undergo either rTHA or cTHA. The primary outcomes included blood loss parameters. Secondary outcomes were the duration of the operation, surgical time, WOMAC pain score, WOMAC stiffness score, WOMAC physical function score, Harris score, and postoperative complications. Results The study cohort analyzed 59 eligible participants, 30 of whom underwent rTHA and 29 of whom underwent cTHA. Analysis could not be conducted for one patient due to severe anemia. Notably, the cTHA group had a significantly shorter surgical time than the rTHA group (69.49 ± 18.97 vs. 104.20 ± 19.63 min, P

Abstract Benefiting from the advantageous features of structural diversity and resource renewability, organic electroactive compounds are considered as attractive cathode materials for aqueous Zn‐ion batteries (ZIBs). In this review, we discuss the recent developments of organic electrode materials for aqueous ZIBs. Although the proton (H+) storage chemistry in aqueous Zn‐organic batteries has triggered an overwhelming literature surge in recent years, this topic remains controversial. Therefore, our review focuses on this significant issue and summarizes the reported electrochemical mechanisms, including pure Zn2+ intercalation, pure H+ storage, and H+/Zn2+ co‐storage. Moreover, the impact of H+ storage on the electrochemical performance of aqueous ZIBs is discussed systematically. Given the significance of H+ storage, we also highlight the relevant characterization methods employed. Finally, perspectives and directions on further understanding the charge storage mechanisms of organic materials are outlined. We hope that this review will stimulate more attention on the H+ storage chemistry of organic electrode materials to advance our understanding and further its application.

To improve the simulation accuracy of vegetation canopy reflectance in optical bands, the Radiosity Applicable to Porous IndiviDual objects (RAPID) model has been upgraded to better deal with branches in the latest RAPID4. Previous versions of RAPID (RAPID1 and RAPID3) neglected branches in porous objects in optical bands, while RAPID2 emphasized them in microwave bands. This inconsistency needed to be addressed to establish a unified radiosity-based simulation framework. By incorporating branches in RAPID4, we have improved several aspects of the model, including the random dynamic projection process, the equivalent reflectance or transmittance, the single scattering estimation, the multiple scattering solution, and the bidirectional reflectance factor (BRF) calculation. Three-dimensional trees from the fifth RAdiation transfer Model Intercomparison (RAMI-V) have been used to test the contribution of branches on BRF. Comparisons with a ray-tracing-based LESS model (the LargE-Scale remote sensing data and image Simulation framework) on RAMI-V scenes show a general agreement on BRF (R2 ≥ 0.96 and root mean square error ranging from 0.014 to 0.054). The major biases occur in a realistic scene (i.e., HET51_WWO_TLS) created from terrestrial laser scanning data. Sensitivity analysis has been conducted to show the branch contribution on BRF in optical domain. Without considering dense branches, the BRF error can exceed 0.1.

Water shortages have become a major constraint on China's industrial development. Iron and steel industrial parks have a huge demand for water resources and complex production technologies. Therefore, it is very important to study the distribution, transfer and loss of water resources in industrial parks in order to improve the ability to refine water resources management. The purpose of this study was to reveal the water flow in industrial parks by using the principle of water balance and to provide a method for quantification and characteristic recognition of water resources in industrial production processes. In this research, an iron and steel industrial park in North China was chosen as the case study. In order to calculate the water balance of the whole steel production processes, the industrial park was divided into four levels and 110 water units according to the pipe network system and production processes. Based on the results of multi-level and multi-node water balance, this paper analyzed the water intake structure and water consumption structure of industrial parks, and provided the methods to optimize the allocation of water resources and reduce the consumption of fresh water in industrial production processes. The results of the study showed that the energy department accounted for 60.8% of the total water withdrawal of the industrial park. There were 6,249 m3/day of fresh water in the industrial park, which could be replaced by reclaimed water from urban sewage. Evaporation and pipe network leakage were the main water consumption factors in the steel park, which contributed 91.3% of the water consumption. Under the guidance of the research results, the evaporation water consumption of the industrial park was reduced by 8,412 m3/day, and pipe leakage was reduced by 600 m3/day. This article demonstrates the application of the water balance principle in complex water use systems, which is helpful for water resources management based on water use processes. HIGHLIGHTS A water balance network model based on the industrial production process is proposed.; The model quantifies the transfer and loss of water resources during steel production, making the previously neglected water visible.; The water intake structure evaluates the amount of fresh water that can be replaced by unconventional water resources in the industrial park and establishes the flow relationship between urban sewage and industrial water.; Water consumption structure found the main water consumption factors, location and quantity of the industrial park. The problem of unclear characteristics of industrial water consumption has been solved.;

Accurate and rapid estimation of canopy cover (CC) is crucial for many ecological and environmental models and for forest management. Unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR) systems represent a promising tool for CC estimation due to their high mobility, low cost, and high point density. However, the CC values from UAV-LiDAR point clouds may be underestimated due to the presence of large quantities of within-crown gaps. To alleviate the negative effects of within-crown gaps, we proposed a pit-free CHM-based method for estimating CC, in which a cloth simulation method was used to fill the within-crown gaps. To evaluate the effect of CC values and within-crown gap proportions on the proposed method, the performance of the proposed method was tested on 18 samples with different CC values (40−70%) and 6 samples with different within-crown gap proportions (10−60%). The results showed that the CC accuracy of the proposed method was higher than that of the method without filling within-crown gaps (R2 = 0.99 vs 0.98; RMSE = 1.49% vs 2.2%). The proposed method was insensitive to within-crown gap proportions, although the CC accuracy decreased slightly with the increase in within-crown gap proportions.

This study evaluated the potential effects of mannan oligosaccharide (MOS) on the health status of common carp under ammonia stress. The experimental fish were equally divided into four groups. The control group was fed with a basal diet. The ammonia stress group (Am group) was fed with a basal diet and set in the culture environment with 0.15 mg/L NH3. The MOS group was fed 0.2% MOS with a basal diet. The treatment group (MOS/Am group) was fed 0.2% MOS feed under ammonia exposure (NH3, 0.15 mg/L). These groups were fed for 30 d. The results showed that under ammonia stress, the growth performance decreased significantly, and the activities of non-specific immune factors, intestinal digestive enzymes, and antioxidant enzymes decreased significantly. Dietary supplementation of MOS, the growth performance, the activities of non-specific immune factors, intestinal digestive enzymes, and antioxidant enzymes increased. Histopathological studies showed that adding MOS reduced liver, gill, and intestine tissue damage under ammonia exposure. Our study suggests that adding MOS to the diet can improve growth performance, immunity, antioxidant capacity, and intestinal health of the common carp. MOS can effectively alleviate the oxidative damage and inflammatory response caused by ammonia poisoning to common carp.

Rice–fish coculture (RF) is a small ecosystem in which microorganisms are widely distributed in the fish, water environment, soil, and plants. In order to study the positive effects of microorganisms on common carp and rice in the RF ecosystem, a total of 18 strains with growth-promoting ability were screened from common carp (Cyprinus carpio) gut contents, among which three strains had the ability to produce both DDP-IV inhibitors and IAA. The strain with the strongest combined ability, FYN-22, was identified physiologically, biochemically, and by 16S rRNA, and it was initially identified as Bacillus licheniformis. As the number of metabolites secreted by the strain under natural conditions is not sufficient for production, the FYN-22 fermentation medium formulation was optimized by means of one-factor-at-a-time (OFAT) experiments and response surface methodology (RSM). The results showed that, under the conditions of a soluble starch concentration of 10.961 g/l, yeast concentration of 2.366 g/l, NH4Cl concentration of 1.881 g/l, and FeCl3 concentration of 0.850 g/l, the actual measured number of FYN-22 spores in the fermentation broth was 1.913 × 109 CFU/ml, which was 2.575-fold improvement over the pre-optimization value. The optimized fermentation solution was used for the immersion operation of rice seeds, and, after 14 days of incubation in hydroponic boxes, the FYN-22 strain was found to have a highly significant enhancement of 48.31% (p

In the present study, the effect of supplementation with dandelion extract (DE) in common carp feed. Seven levels of DE supplementation to the basal diet were evaluated, i.e., 0 %, 0.5 %, 1 %, 2 %, 4 %, 7 %, and 10 % (50 % polysaccharide content), for a feeding period of 56 days. The addition of DE to the diet increased weight gain rate, specific growth rate,and protein efficiency ratio of common carp in DE2, DE3, DE4, DE5 and DE6 group. In contrast, it significantly reduced feed conversion ratio. Interestingly, DE supplementation did not have a significant effect on survival rate, condition factor, viscerosomatic index, and hepatosomatic index (P > 0.05). Moreover, DE supplementation improved the content of growth hormone (GH) and insulin-like growth factor (IGF-I) hormones in common carp serum, which was correlated with upregulation of genes coding for these two hormones in hepatopancreas (P

Soil quality (SQ) is critical to sustainable agricultural development. It is sensitive to the crop yield, especially in thin-layer black-soil regions, which have experienced severe degradation in recent years. However, how to evaluate the SQ and its influence on land productivity is not clear in regions with thin black-soil coverage. Therefore, an integrated soil quality index (SQI) was constructed using diverse datasets along a 30 km transect in a typical thin-layer black-soil region of China. The results showed that obvious soil degradation was observed in this area. Black-soil thickness (BST), soil organic matter (SOM), and the total nitrogen (TN) content were the most strongly correlated with corn yield among the 13 investigated indexes, with Pearson coefficients of 0.65, 0.39 and 0.34, respectively. The minimum-dataset-based SQI using six soil properties within 0–30 cm was the optimal solution for SQ evaluation in the study area. The good performance of the established SQI using the optimal method was supported by its strong correlation with the corn yield, with a Pearson coefficient and linear R2 of 0.75 and 0.56, respectively. The BST identified by differences in colour across the soil profile provided powerful information for the SQI, the value of which would be underestimated by 8% if this index were ignored. The linear R2 between the SQI and corn yield decreased from 0.56 to 0.49 when the BST index was removed. This study showed the significance of improving the SQ in thin-layer black-soil regions. The core of soil management is to prevent the losses of surface black soil and improve the SOM content in this region. These findings can help farmers and decision makers adopt proper measures to improve SQ and thereby crop yield.

Understanding post-fire forest recovery is critical to the study of forest carbon dynamics. Many previous studies have used multispectral imagery to estimate post-fire recovery, yet post-fire forest structural development has rarely been evaluated in the Great Xing’an Mountain. In this study, we extracted the historical fire events from 1987 to 2019 based on a classification of Landsat imagery and assessed post-fire forest structure for these burned patches using Global Ecosystem Dynamics Investigation (GEDI)-derived metrics from 2019 to 2021. Two drivers were assessed for the influence on post-fire structure recovery, these being pre-fire canopy cover (i.e., dense forest and open forest) and burn severity levels (i.e., low, moderate, and high). We used these burnt patches to establish a 25-year chronosequence of forest structural succession by a space-for-time substitution method. Our result showed that the structural indices suggested delayed recovery following the fire, indicating a successional process from the decomposition of residual structures to the regeneration of new tree species in the post-fire forest. Across the past 25-years, the dense forest tends toward greater recovery than open forest, and the recovery rate was faster for low severity, followed by moderate severity and high severity. Specifically, in the recovery trajectory, the recovery indices were 21.7% and 17.4% for dense forest and open forest, and were 27.1%, 25.8%, and 25.4% for low, moderate, and high burn severity, respectively. Additionally, a different response to the fire was found in the canopy structure and height structure since total canopy cover (TCC) and plant area index (PAI) recovered faster than relative height (i.e., RH75 and RH95). Our results provide valuable information on forest structural restoration status, that can be used to support the formulation of post-fire forest management strategies in Great Xing’an Mountain.

Context The traditional drying method, sun drying, for Chrysanthemum morifolium Ramat. cv. Hang-ju (Compositae) (HJ) is widely replaced by sulphur fumigation (SF), which has an unknown effect on its efficacy. Objective To investigate protective effects of nonfumigated HJ (NHJ) and sulphur-fumigated HJ (SHJ) water extracts against oxidative stress and lipid peroxidation. Materials and methods Sprague-Dawley rats were administered high-fat diet to induce hyperlipidaemia and randomly divided into eight groups (n = 6): control, fenofibrate, NHJ and SHJ extracts (1, 2 or 4 g crude drugs/kg/d; intragastric administration for 8 weeks). Serum total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), superoxide dismutase (SOD) and malondialdehyde (MDA) levels were detected. Human umbilical vein endothelial cells (HUVECs) were treated with NHJ and SHJ extracts (50, 100 or 200 μg/mL) for 24 h, followed by oxidized low-density lipoprotein (ox-LDL, 20 μg/mL) for 2 h in vitro. Cellular reactive oxygen species (ROS), SOD and MDA levels and apoptosis were evaluated. Results NHJ was more effective than SHJ in decreasing serum TG, TC, LDL-C, LDL/HDL and MDA while increasing serum HDL-C and SOD levels at high doses. SHJ (IC50=19.9 mg/mL) suppressed HUVEC growth stronger than NHJ (IC50=186.7 mg/mL). At 200 μg/mL, NHJ was more effective than SHJ in downregulating ROS and MDA levels, reducing HUVECs apoptosis rate and elevating SOD activity in ox-LDL-treated HUVECs. Conclusions SF causes oxidative damage and attenuates antioxidative activity in ox-LDL-treated HUVECs, which promotes lipid peroxidation. SF is not recommended for processing HJ.

Forest canopy cover (FCC) plays an important role in many ecological, hydrological and forestry applications. For large-scale applications, FCC is usually estimated from remotely sensed data by inverting radiative transfer models (RTMs) or using data-driven regressions. In this article, we proposed a hybrid model, which combines a 3-D RTM and transfer learning-based convolutional neural network (T-CNN), to estimate FCC from very high-resolution satellite images (e.g., Chinese GaoFen-2, 1 m resolution with 4 bands). Unlike common hybrid models that are purely trained with simulation data, T-CNN combines simulation data-based pre-training and actual data-based transfer learning, which is a widely used technique in artificial intelligence for fine-tuning models. The performance of T-CNN was compared with a random forest (RF) model and two general CNN models, including CNN trained with actual dataset only (data-CNN) and CNN trained with RTM simulation data only (RTM-CNN). Results on the independent validation dataset (not used in training stage) showed that T-CNN had higher accuracy (RMSE = 0.121, R2 = 0.83), compared with RF (RMSE = 0.26, R2 = 0.61), Data-CNN (RMSE = 0.142, R2 = 0.81), and RTM-CNN (RMSE = 0.144, R2 = 0.73), which indicates that T-CNN has a strong transferability. Tests on different training sizes showed that T-CNN (0.084 < RMSE < 0.108) provided constantly better performances than RF (0.116 < RMSE < 0.122) and data-CNN (0.103 < RMSE < 0.128), which demonstrates the potential of T-CNN as an alternative to RTM-based inversion and data-driven regressions to estimate FCC, especially when training data is imbalanced and inadequate.

The demand for leaf area index (LAI) retrieval from spaceborne full-waveform LiDAR increases due to its direct sampling of the three-dimensional forest structure at a near-global scale. However, the nonrandomness (i.e., clumping effect) of canopy composition limits the reliability of LAI derived from two common methods. They either assume a homogeneous scene in the footprint or just correct for the large gaps-induced between-crown clumping. The clumping in the crown is still an unaddressed issue. We proposed a method to compensate occlusion (i.e., lower canopy layers are occluded by the upper canopy in the process of LiDAR measurement), through which the vertical canopy profile can be resolved from the waveform. Further, we developed a method of deriving relative path length distribution that can reflect the heterogeneity of the canopy from the occlusion-corrected waveform. In addition to correcting the between-crown clumping, we corrected the within-crown clumping further using the derived relative path length distribution, based on path length distribution (PATH) theory. We used simulated waveform data with known LAI and GLAS data with corresponding field-measured LAI to test the performance of our and the other two common LAI retrieval methods. Results show that the errors of our approach are the lowest (with an error generally below 10% and the maximum error below 20%, compared with up to 69% and 47% for the other two methods), and it is relatively stable in various scenes. This study demonstrated the potential of improving LAI retrieval through full utilization of full-waveform data.

Accurate wall-to-wall estimation of forest crown cover is critical for a wide range of ecological studies. Notwithstanding the increasing use of UAVs in forest canopy mapping, the ultrahigh-resolution UAV imagery requires an appropriate procedure to separate the contribution of understorey from overstorey vegetation, which is complicated by the spectral similarity between the two forest components and the illumination environment. In this study, we investigated the integration of deep learning and the combined data of imagery and photogrammetric point clouds for boreal forest canopy mapping. The procedure enables the automatic creation of training sets of tree crown (overstorey) and background (understorey) data via the combination of UAV images and their associated photogrammetric point clouds and expands the applicability of deep learning models with self-supervision.Based on the UAV images with different overlap levels of 12 conifer forest plots that are categorized into “I”, “II” and “III” complexity levels according to illumination environment, we compared the self-supervised deep learning-predicted canopy maps from original images with manual delineation data and found an average intersection of union (IoU) larger than 0.9 for “complexity I” and “complexity II” plots and larger than 0.75 for “complexity III” plots. The proposed method was then compared with three classical image segmentation methods (i.e., maximum likelihood, Kmeans, and Otsu) in the plot-level crown cover estimation, showing outperformance in overstorey canopy extraction against other methods. The proposed method was also validated against wall-to-wall and pointwise crown cover estimates using UAV LiDAR and in situ digital cover photography (DCP) benchmarking methods. The results showed that the model-predicted crown cover was in line with the UAV LiDAR method (RMSE of 0.06) and deviate from the DCP method (RMSE of 0.18). We subsequently compared the new method and the commonly used UAV structure-from-motion (SfM) method at varying forward and lateral overlaps over all plots and a rugged terrain region, yielding results showing that the method-predicted crown cover was relatively insensitive to varying overlap (largest bias of less than 0.15), whereas the UAV SfM-estimated crown cover was seriously affected by overlap and decreased with decreasing overlap. In addition, canopy mapping over rugged terrain verified the merits of the new method, with no need for a detailed digital terrain model (DTM). The new method is recommended to be used in various image overlaps, illuminations, and terrains due to its robustness and high accuracy. This study offers opportunities to promote forest ecological applications (e.g., leaf area index estimation) and sustainable management (e.g., deforestation).

Pine wilt disease (PWD) is one of the most destructive forest diseases that has led to rapid wilting and mortality in susceptible host pine trees. Spatially explicit detection of pine wood nematode (PWN)-induced infestation is important for forest management, policy making, and practices. Previous studies have mapped forest disturbances in response to various forest diseases and/or insects over large areas using remote-sensing techniques, but these efforts were often constrained by the limited availability of ground truth information needed for the calibration and validation of moderate-resolution satellite algorithms in the process of linking plot-scale measurements to satellite data. In this study, we proposed a two-level up-sampling strategy by integrating unmanned aerial vehicle (UAV) surveys and high-resolution Radarsat-2 satellite imagery for expanding the number of training samples at the 30-m resampled Sentinel-1 resolution. Random forest algorithms were separately used in the prediction of the Radarsat-2 and Sentinel-1 infestation map induced by PWN. After data acquisition in Muping District during August and September 2021, we first verified the ability of a deep-learning-based object detection algorithm (i.e., YOLOv5 model) in the detection of infested trees from coregistered UAV-based RGB images (Average Precision (AP) of larger than 70% and R2 of 0.94). A random forest algorithm trained using the up-sampling UAV infestation map reference and corresponding Radarsat-2 pixel values was then used to produce the Radarsat-2 infestation map, resulting in an overall accuracy of 72.57%. Another random forest algorithm trained using the Radarsat-2 infestation pixels with moderate and high severity (i.e., an infestation severity of larger than 0.25, where the value was empirically set based on a trade-off between classification accuracy and infection detectability) and corresponding Sentinel-1 pixel values was subsequently used to predict the Sentinel-1 infestation map, resulting in an overall accuracy of 87.63%, where the validation data are Radarsat-2 references rather than UAV references. The Sentinel-1 map was also validated by independent UAV surveys, with an overall accuracy of 76.30% and a Kappa coefficient of 0.45. We found that the expanded training samples by the integration of UAV and Radarsat-2 strengthened the medium-resolution Sentinel-1-based prediction model of PWD. This study demonstrates that the proposed method enables effective PWN infestation mapping over multiple scales.

Digital aerial photogrammetry (DAP) has emerged as an alternative to airborne laser scanning (ALS) for forest inventory applications, as it offers a low-cost and flexible three-dimensional (3D) point cloud. Unlike the forest inventory attributes (e.g., tree height and diameter at breast height), the relative ability of DAP and ALS in predicting canopy structural variables (i.e., canopy cover and leaf area index (LAI)) has not been sufficiently investigated by previous studies. In this study, we comprehensively compared the canopy cover and LAI estimates using DAP- and ALS-based methods over 166 selected tropical forest sample plots with seven different tree species and forest types. We also explored the relationship between field-measured aboveground biomass (AGB) and the LAI estimates. The airborne LAI estimates were subsequently compared with the Sentinel-2-based LAI values that were retrieved using a one-dimensional radiative transfer model. The results demonstrated that the DAP-based method generally overestimated the two canopy variables compared to ALS-based methods but with relatively high correlations regardless of forest type and species (R2 of 0.80 for canopy cover and R2 of 0.76 for LAI). Under different forest types and species, the R2 of canopy cover and LAI range from 0.64 to 0.89 and from 0.54 to 0.87, respectively. Apparently, different correlations between AGB and LAI were found for different forest types and species where the mixed coniferous and broad-leaved forest shows the best correlation with R2 larger than 0.70 for both methods. The comparison with satellite retrievals verified that the ALS-based estimates are more consistent with Sentinel-2-based estimates than DAP-based estimates. We concluded that DAP data failed to provide analogous results to ALS data for canopy variable estimation in tropical forests.

Accurate estimates of canopy cover (CC) are central for a wide range of forestry studies. As direct measurements are impractical, indirect optical methods have often been used to estimate CC from the complement of gap fraction measurements obtained with restricted-view sensors. In this short note we evaluated the influence of the image pixel resolution (ground sampling distance; GSD) on CC estimation in poplar plantations obtained from field (cover photography; GSD < 1 cm), unmanned aerial (UAV; GSD

Pine wilt disease (PWD) is a global destructive threat to forests which has been widely spread and has caused severe tree mortality all over the world. It is important to establish an effective method for forest managers to detect the infected area in a large region. Remote sensing is a feasible tool to detect PWD, but the traditional empirical methods lack the ability to explain the signals and can hardly be extended to large scales. The studies using physically-based models either ignore the within-canopy heterogeneity or rely too much on prior knowledge. In this study, we propose an approach to retrieve PWD infected areas from medium-resolution satellite images of two phases based on the simulations of an extended stochastic radiative transfer model for forests infected by pests (SRTP). A small amount of prior knowledge was used, and a change of background soil was considered in this approach. The performance was evaluated in different study sites. The inversion method performs best in the three-dimensional model LESS simulation sample plots (R2 = 0.88, RMSE = 0.059), and the inversion accuracy decreases in the real forest sample plots. For Jiangxi masson pine stand with large coverage and serious damage, R2 = 0.57, RMSE = 0.074; and for Shandong black pine stand with sparse and a small number of single plant damage, R2 = 0.48, RMSE = 0.063. This study indicates that the SRTP model is more feasible for pest damage inversion over different regions compared with empirical methods. The stochastic radiative transfer theory provides a potential approach for future monitoring of terrestrial vegetation parameters.

Leaf angle distribution (LAD) is an important attribute of forest canopy architecture and affects the solar radiation regime within the canopy. Terrestrial laser scanning (TLS) has been increasingly used in LAD estimation. The point clouds data suffer from the occlusion effect, which leads to incomplete scanning and depends on measurement strategies such as the number of scans and scanner location. Evaluating these factors is important to understand how to improve LAD, which is still lacking. Here, we introduce an easy way of estimating the LAD using open source software. Importantly, the influence of the occlusion effect on the LAD was evaluated by combining the proposed complete point clouds (CPCs) with the simulated data of 3D tree models of Aspen, Pin Oak and White Oak. We analyzed the effects of the point density, the number of scans and the scanner height on the LAD and G-function. Results show that: (1) the CPC can be used to evaluate the TLS-based normal vector reconstruction accuracy without an occlusion effect; (2) the accuracy is slightly affected by the normal vector reconstruction method and is greatly affected by the point density and the occlusion effect. The higher the point density (with a number of points per unit leaf area of 0.2 cm−2 to 27 cm−2 tested), the better the result is; (3) the performance is more sensitive to the scanner location than the number of scans. Increasing the scanner height improves LAD estimation, which has not been seriously considered in previous studies. It is worth noting that relatively tall trees suffer from a more severe occlusion effect, which deserves further attention in further study.

Vegetation cover estimation for overstory and understory layers provides valuable information for modeling forest carbon and water cycles and refining forest ecosystem function assessment. Although previous studies demonstrated the capability of light detection and ranging (LiDAR) in the three-dimensional (3D) characterization of forest overstory and understory communities, the high cost inhibits its application in frequent and successive survey tasks. Low-cost commercial red−green−blue (RGB) cameras mounted on unmanned aerial vehicles (UAVs), as LiDAR alternatives, provide operational systems for simultaneously quantifying overstory crown cover (OCC) and understory vegetation cover (UVC). We developed an effective method named back-projection of 3D point cloud onto superpixel-segmented image (BAPS) to extract overstory and forest floor pixels using 3D structure-from-motion (SfM) point clouds and two-dimensional (2D) superpixel segmentation. The OCC was estimated from the extracted overstory crown pixels. A reported method, called half-Gaussian fitting (HAGFVC), was used to segement green vegetation and non-vegetation pixels from the extracted forest floor pixels and derive UVC. The UAV-based RGB imagery and field validation data were collected from eight forest plots in Saihanba National Forest Park (SNFP) plantation in northern China. The consistency of the OCC estimates between BAPS and canopy height model (CHM)-based methods (coefficient of determination: 0.7171) demonstrated the capability of the BAPS method in the estimation of OCC. The segmentation of understory vegetation was verified by the supervised classification (SC) method. The validation results showed that the OCC and UVC estimates were in good agreement with reference values, where the root-mean-square error (RMSE) of OCC (unitless) and UVC (unitless) reached 0.0704 and 0.1144, respectively. The low-cost UAV-based observation system and the newly developed method are expected to improve the understanding of ecosystem functioning and facilitate ecological process modeling.