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The fine modeling of the lunar gravity field is a fundamental task for establishing lunar altitude references, conducting research on lunar interior structure, and other scientific endeavors. The lunar gravity field model constructed solely based on spacecraft orbit data has limited spatial resolution, making it challenging to meet the demands of fine gravity field research. The acquisition of high-resolution terrain models presents new opportunities for the study of precise lunar gravity fields. This paper reviews the current status and advancements in fine lunar gravity field modeling, including research efforts and evaluation methods for global lunar gravity field models, principles and technologies for fine gravity field modeling, as well as related research outcomes. On this basis, this paper systematically summarizes the key theoretical issues and technical challenges in the theory of lunar gravity field modeling based on high-resolution terrain models. These include data updating, error correction in high-frequency gravity field modeling, calculation efficiency, and the series divergence problem in spherical harmonic analysis. In addition, future research directions are discussed. This work will provide references for gravity field research on the Moon and other terrestrial planets.

With the rapid development of LiDAR and artificial intelligence technologies, 3D point cloud semantic segmentation has become a highlight research topic. This technology is able to significantly enhance the capabilities of building information modeling, navigation and environmental perception. However, current deep learning-based methods primarily rely on voxelization or multi-layer convolution for feature extraction. These methods often face challenges in effectively differentiating between homogeneous objects or structurally adherent targets in complex real-world scenes. To this end, we propose a Graph Transformer point cloud semantic segmentation network (ASGFormer) tailored for structurally adherent objects. Firstly, ASGFormer combines Graph and Transformer to promote global correlation understanding in the graph. Secondly, spatial index and position embedding are constructed based on distance relationships and feature differences. Through a learnable mechanism, the structural weights between points are dynamically adjusted, achieving adaptive spatial structure within the graph. Finally, dummy nodes are introduced to facilitate global information storage and transmission between layers, effectively addressing the issue of information loss at the terminal nodes of the graph. Comprehensive experiments are conducted on the various real-world 3D point cloud datasets, analyzing the effectiveness of proposed ASGFormer through qualitative and quantitative evaluations. ASGFormer outperforms existing approaches with of 91.3% for OA, 78.0% for mAcc, and 72.3% for mIoU on S3DIS dataset. Moreover, ASGFormer achieves 72.8%, 45.5%, 81.6%, 70.1% mIoU on ScanNet, City-Facade, Toronto 3D and Semantic KITTI dataset, respectively. Notably, the proposed method demonstrates effective differentiation of homogeneous structurally adherent objects, further contributing to the intelligent perception and modeling of complex scenes.

Abstract Activation of the Hippo pathway by angiomotins to limit colorectal cancer progression is prevalent, whereas the regulation of angiomotins remains elusive. In this study, we uncover the involvement of an upregulated E3 ubiquitin ligase called RNF166, which destabilizes angiomotins, activates YAP, and is associated with a poor prognosis in colorectal cancer patients. Mechanistically, RNF166 specifically recognizes PARsylated angiomotin, a modification mediated by tankyrase at specific amino acid residues (D506, E513, E516, and E528). The tankyrase inhibitor XAV939, effectively prevents RNF166-dependent destabilization of angiomotins and subsequent activation of YAP. Additionally, YAP-5SA, a constitutively active form of YAP, rescues colorectal cancer progression following knockdown of RNF166. Importantly, the C-terminus of RNF66, particularly the Di19-ZF domain, is the crucial region responsible for recognizing ADP-ribosylated angiomotins. Together, this work not only sheds light on the regulation of the Hippo pathway in colorectal cancer but also uncovers a novel poly(ADP-ribose)-binding domain, which may serve as a potential therapeutic target for intervention.

Semantic segmentation of large-scale point clouds is crucial for advancing smart city infrastructure and supporting autonomous driving technology. However, existing semantic segmentation techniques designed for indoor environments often struggle to adapt to vast outdoor scenes. Moreover, networks for large-scale scenes face challenges such as limited receptive fields and computational complexity, hindering their ability to accurately perceive small target features. To address these challenges, we propose PVCFormer, a novel cross-attention architecture that leverages both point and voxel representations. By feeding concurrently sampled data at varying voxel resolutions into the network, PVCFormer enhances the segmentation of small-scale features while expanding the receptive field. Additionally, the cross-transformer block facilitates better fusion of point and voxel features, and the introduction of CosFormer improves the computational efficiency of the network.Simultaneously, we introduce SYSU9, a new dataset labeled with 9 categories covering an area of over 7 square kilometers, to serve as a benchmark for evaluating point cloud semantic segmentation algorithms. We proposed two model versions, PVCFormer-CA and PVCFormer-SA. PVCFormer-CA achieves an overall accuracy of 92.4 % on SensatUrban, 94.6 % on DALES, and 91.1 % on SYSU9. For semantic segmentation, PVCFormer-CA achieves 61.5 % mIoU on SensatUrban, 73.6 % mIoU on DALES, and 62.4 % mIoU on SYSU9.Our experiments demonstrated promising results in large-scale outdoor point cloud semantic segmentation and introduce novel methodologies leveraging attention mechanisms for handling large-scale point clouds.

Multibeam echo sounding sonar, as an active acoustic detection technology, is the mainstream equipment for current seabed topography surveying. The accuracy of the seabed topographic model is significantly influenced by anomalies in survey. Addressing the limitations in stability and comprehensiveness in anomaly processing of existing detection algorithms, this article proposes a novel anomaly detection algorithm based on prior constraints. This algorithm integrates the distribution characteristics of various types of anomalies and classifies them meticulously. A robust estimation of the detection model is achieved through geostatistical methods to enhance the accuracy of seabed topography representation. Experimental results demonstrate that our method excels in error control. Tests on one simulated dataset and two field-collected datasets showed type I errors between 1.6% and 3%, type II errors between 5.3% and 17%, and total error rates between 1.9% and 3.1%. Compared to existing classic algorithms, our method not only preserves the terrain features but also achieves comprehensive detection of different types of anomalies.

Abstract Background To explore the diagnostic value of Caprini risk assessment model (2005) combined with D-dimer for deep vein thrombosis, and to exclude patients with low incidence of thrombosis who might not need anticoagulation after surgery. Methods A total of 171 colorectal cancer patients who underwent surgery from January 2022 to August 2022 were enrolled in this study. Caprini risk assessment model was used to evaluate patients the day before surgery, and full-length venous ultrasonography of lower extremity was used to assess whether patients had thrombosis one day before surgery and the sixth day after surgery. The value of D-dimer was measured by enzyme-linked immunosorbent assays on the first day after surgery, and clinical data of patients were collected during hospitalization. Results A total of 171 patients were divided into IPC Group and IPC + LMWH Group according to whether low molecular weight heparin (LMWH) were used to prevent thrombus after surgery. Eventually, 17.6% (15/85) patients in IPC Group and 7% (6/86) patients in IPC + LMWH Group developed DVT. Through separate analysis of IPC Group, it is found that Caprini score and D-dimer were independent risk factors for DVT (Caprini OR 3.39 [95% CI 1.38–8.32]; P = 0.008, D-Dimer OR 6.142 [95% CI 1.209–31.187]; P = 0.029). The area under ROC curve of Caprini risk assessment model is 0.792 (95% CI 0.69–0.945, P 10 and D-dimer > 0.835 μg/mL, the incidence of DVT is 38.7% (12/31) and LMWH should be considered. Conclusions The Caprini risk assessment model and D-dimer can provide more accurate risk stratification for patients after laparoscopic radical resection of colorectal cancer.

Accurate extraction of individual trees has theoretical and practical significance for improving forest management and productivity levels. Although airborne laser scanning (ALS) technology is frequently utilized for large-scale forest mapping and the detection of individual trees, the challenges of detecting individual trees in multi-layered and deciduous forests remain for most canopy-based methods. This study aimed to develop an efficient individual tree detection method capable of delineating trees with various heights, especially for sub-canopy trees. Inspired by the “from bottom to top” growth characteristics of tree branches, we propose an oriented search and clustering method, which clusters tree points upwards to the local tops, making it more adaptable to the roughly conical growth characteristics of a forest’s tree canopy. On the NEWFOR dataset, our approach demonstrated comparable overall performance to that of state-of-the-art methods. In the non-dominant layers of multi-layered forests, our method achieved RMSmatch values of 30% in the 2–5 m range, 31% in the 5–10 m range, and 55% in the 10–15 m range, demonstrating the best extraction performance. A practical case study was conducted on selected plots of ALS point clouds acquired from the Bavarian Forest National Park (BFNP), with 1704 trees identified. This work provides a more reliable and efficient method for the future forest point cloud segmentation of individual trees; the method better meets the needs of forest inventory and resource management and lays a foundation for good forest management.

Accuracy evaluation is an essential step in validating image segmentation results. Existing metrics, such as overall accuracy and F1-score, mainly concern the range and consistency of the semantic labels of image segments, which may not be sufficiently sensitive to detect missing or segmentation errors in small patches. To address this issue, this study proposes the investigator accuracy (IA) metric, which focuses on the location accuracy of single patches by evaluating the capture accuracy of their near-center subregions and category weight to determine the image segmentation quality. Before evaluating the IA metric, we optimize the grayscale dilation algorithm to separate each identified patch from the image without converting the data format and then distinguish each patch as embedded or nonembedded. Next, we use an iterative grayscale erosion approach to assess the distance-to-center weight, which is a crucial parameter for evaluating the IA of each pixel in a single patch. In addition, we apply a parallel analysis scheme to improve the efficiency of the IA evaluation. The results indicate that the capture accuracy of near-center subregions and the category weight of a single patch affect its IA. Unlike commonly used metrics, the IA is independent of the area ratio and the number of patches belonging to multiple landcover types. The output of the intermediate analysis steps can be used to produce thematic maps showing the distribution density of target patches.

Abstract Light detection and ranging (LiDAR) data can provide 3D structural information of objects and are ideal for extracting individual tree parameters, and individual tree segmentation (ITS) is a vital step for this purpose. Various ITS methods have been emerging from airborne LiDAR scanning (ALS) or unmanned aerial vehicle LiDAR scanning (ULS) data. Here, we propose a new individual tree segmentation method, which couples the classical and efficient watershed algorithm (WS) and the newly developed connection center evolution (CCE) clustering algorithm in pattern recognition. The CCE is first used in ITS and comprehensively optimized by considering tree structure and point cloud characteristics. Firstly, the amount of data is greatly reduced by mean shift voxelization. Then, the optimal clustering scale is automatically determined by the shapes in the projection of three different directions. We select five forest plots in Saihanba, China and 14 public plots in Alpine region, Europe with ULS or ALS point cloud densities from 11 to 3295 pts/m2. Eleven ITS methods were used for comparison. The accuracy of tree top detection and tree height extraction is estimated by five and two metrics, respectively. The results show that the matching rate (Rmatch) of tree tops is up to 0.92, the coefficient of determination (R2) of tree height estimation is up to .94, and the minimum root mean square error (RMSE) is 0.6 m. Our method outperforms the other methods especially in the broadleaf forests plot on slopes, where the five evaluation metrics for tree top detection outperformed the other algorithms by at least 11% on average. Our ITS method is both robust and efficient and has the potential to be used especially in coniferous forests to extract the structural parameters of individual trees for forest management, carbon stock estimation, and habitat mapping.

As a critical prerequisite for semantic facade reconstruction, accurately separating wall and protrusion points from facade point clouds is required. The performance of traditional separation methods is severely limited by facade conditions, including wall shapes (e.g., nonplanar walls), wall compositions (e.g., walls composed of multiple noncoplanar point clusters), and protrusion structures (e.g., protrusions without regularity, repetitive, or self-symmetric features). This study proposes a more widely applicable wall and protrusion separation method. The major principle underlying the proposed method is to transform the wall and protrusion separation problem as a ground filtering problem and to separate walls and protrusions using ground filtering methods, since the 2 problems can be solved using the same prior knowledge, that is, protrusions (nonground objects) protrude from walls (ground). After transformation problem, cloth simulation filter was used as an example to separate walls and protrusions in 8 facade point clouds with various characteristics. The proposed method was robust to the facade conditions, with a mean intersection over union of 90.7%, and had substantially higher accuracy compared with the traditional separation methods, including region growing-, random sample consensus-, multipass random sample consensus-based, and hybrid methods, with mean intersection over union values of 69.53%, 49.52%, 63.93%, and 47.07%, respectively. Besides, the proposed method was general, since existing ground filtering methods (including the maximum slope, progressive morphology, and progressive triangular irregular network densification filters) can also perform well.

A synchronous case of small bowel adenocarcinoma(SAB) is reported, accompanied with gastrointestinal stromal tumor(GIST),and gangliocytomain in an elderly woman with neurofibromatosis type 1 (NF-1). A 67-year-old female was hospitalized with the chief complaint of abdominal pain, the computed tomography scan indicated a large bowel mass. Multiple tumors were found in the small intestine, through which two larger tumors (7 cm and 1.5 cm) were resected. A novel germline NF1 mutation and a PMS2 mutation were identified after genetic testing, followed by the exploration of possible relationship between them in promoting tumorigenesis. Our results suggest multiple gastrointestinal tumors emerging in NF1 patients, and genetic testing can better guide postoperative treatment in a more efficient way.

Registration of unmanned aerial vehicle laser scanning (ULS) and ground light detection and ranging (LiDAR) point clouds in forests is critical to create a detailed representation of a forest structure and an accurate retrieval of forest parameters. However, tree occlusion poses challenges for those registration methods used artificial markers, and some automated registration methods have low time-efficiency due to the process of object (e.g., tree, crown) segmentation. In this study, we propose an automated and time-efficient method to register ULS and ground LiDAR (including terrestrial and backpack laser scanning) forest point clouds. Registration involves coarse alignment and fine registration, where the coarse alignment is divided into vertical and horizontal alignment. The vertical alignment is implemented by rotating grounds to the horizontal plane, and the horizontal alignment is achieved by canopy image matching. During image matching, vegetation points are projected onto the horizontal plane to obtain two binary images, and then, canopy shape feature, which is described by a two-point congruent set and canopy overlap, is used to match the binary images. Finally, we implement coarse alignment of ULS and ground LiDAR datasets by combining the results of ground alignment and image matching and finish fine registration in six plantation forest plots with sizes of 0.03 ha to 0.25 ha. Experimental results show that the ULS and ground LiDAR data in different plots are registered, of which the coarse alignment errors are less than 0.20 m in the horizontal direction, the final registration accuracy is less than 0.15 m, and the average runtime is less than 1 s. Our study demonstrates the effectiveness of the proposed strategy and has able to perform accurate and quick registration of ULS and ground LiDAR data from plantation forests with different attributes.

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.

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.

Tree growth is an important indicator of forest health and can reflect changes in forest structure. Traditional tree growth estimates use easy-to-measure parameters, including tree height, diameter at breast height, and crown diameter, obtained via forest in situ measurements, which are labor intensive and time consuming. Some new technologies measure the diameter of trees at different positions to monitor the growth trend of trees, but it is difficult to take into account the growth changes at different tree levels. The combination of terrestrial laser scanning and quantitative structure modeling can accurately estimate tree structural parameters nondestructively and has the potential to estimate tree growth from different tree levels. In this context, this paper estimates tree growth from stem-, crown-, and branch-level attributes observed by terrestrial laser scanning. Specifically, tree height, diameter at breast height, stem volume, crown diameter, crown volume, and first-order branch volume were used to estimate the growth of 55-year-old larch trees in Saihanba of China, at the stem, crown, and branch levels. The experimental results showed that tree growth is mainly reflected in the growth of the crown, i.e., the growth of branches. Compared to one-dimensional parameter growth (tree height, diameter at breast height, or crown diameter), three-dimensional parameter growth (crown, stem, and first-order branch volumes) was more obvious, in which the absolute growth of the first-order branch volume is close to the stem volume. Thus, it is necessary to estimate tree growth at different levels for accurate forest inventory.

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

Abstract Background The stem curve of standing trees is an essential parameter for accurate estimation of stem volume. This study aims to directly quantify the occlusions within the single-scan terrestrial laser scanning (TLS) data, evaluate its correlation with the accuracy of the retrieved stem curves, and subsequently, to assess the capacity of single-scan TLS to estimate stem curves. Methods We proposed an index, occlusion rate, to quantify the occlusion level in TLS data. We then analyzed three influencing factors for the occlusion rate: the percentage of basal area near the scanning center, the scanning distance and the source of occlusions. Finally, we evaluated the effects of occlusions on stem curve estimates from single-scan TLS data. Results The results showed that the correlations between the occlusion rate and the stem curve estimation accuracies were strong (r = 0.60–0.83), so was the correlations between the occlusion rate and its influencing factors (r = 0.84–0.99). It also showed that the occlusions from tree stems were the main factor of the low detection rate of stems, while the non-stem components mainly influenced the completeness of the retrieved stem curves. Conclusions Our study demonstrates that the occlusions significantly affect the accuracy of stem curve retrieval from the single-scan TLS data in a typical-size (32 m × 32 m) forest plot. However, the single-scan mode has the capacity to accurately estimate the stem curve in a small forest plot (

Due to the difference of factors, such as lighting conditions, shooting environments, and time, there is compound brightness difference between adjacent images, which includes local brightness difference and radiometric difference. This paper proposed a method to eliminate the compound brightness difference of adjacent images after mosaicking, named local to global radiometric balance. It includes the brightness compensation model and brightness approach model. Firstly, the weighted average value of each row and column of image are calculated to express the brightness change; secondly, according to weighted average value, the brightness compensation model is built; thirdly, combined with the blocking method, the brightness compensation model is applied to image. Based on the value after above process, the brightness approach model is established to make the gray value of adjacent images approach to the mosaic line. In the paper, the standard deviation, MSE (mean square error) and mean value are used as the measure indices of the effect of brightness balance. The three groups of experimental results show that compared with the brightness stretch method, the histogram equalization method and the radiometric balance method, the local to global radiometric balance method not only realizes compound brightness balance, but also has better visual effects than others.

3D building models are important for many applications related to human activities in urban environments. However, due to the high complexity of the building structures, it is still difficult to automatically reconstruct building models with accurate geometric description and semantic information. To simplify this problem, this article proposes a novel approach to automatically decompose the compound buildings with symmetric roofs into semantic primitives by exploiting local symmetry contained in the building structure. In this approach, the proposed decomposition allows the overlapping of neighbor primitives and each decomposed primitive can be represented as a parametric form, which simplify the complexity of the building reconstruction and facilitate the integration of LiDAR data and aerial imagery into a parameters optimization process. The proposed method starts by extracting isolated building regions from the LiDAR point clouds. Next, point clouds belonging to each compound building are segmented into planar patches to construct an attributed graph, and then the local symmetries contained in the attributed graph are exploited to automatically decompose the compound buildings into different semantic primitives. In the final step, 2D image features are extracted depending on the initial 3D primitives generated from LiDAR data, and then the compound building is reconstructed using constraints from LiDAR data and aerial imagery by a nonlinear least squares optimization. The proposed method is applied to two datasets with different point densities to show that the complexity of building reconstruction can be reduced considerably by decomposing the compound buildings into semantic primitives. The experimental results also demonstrate that the traditional model driven methods can be further extended to the automated reconstruction of compound buildings by using the proposed semantic decomposition method.

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.

In this paper, a primitive-based 3D building roof modeling method, by integrating LiDAR data and aerial imagery, is proposed. The novelty of the proposed modeling method is to represent building roofs by geometric primitives and to construct a cost function by using constraints from both LiDAR data and aerial imagery simultaneously, so that the accuracy potential of the different sensors can be tightly integrated for the building model generation by an integrated primitive’s parameter optimization procedure. To verify the proposed modeling method, both simulated data and real data with simple buildings provided by ISPRS (International Society for Photogrammetry and Remote Sensing), were used in this study. The experimental results were evaluated by the ISPRS, which demonstrate the proposed modeling method can integrate LiDAR data and aerial imagery to generate 3D building models with high accuracy in both the horizontal and vertical directions. The experimental results also show that by adding a component, such as a dormer, to the primitive, a variant of the simple primitive is constructed, and the proposed method can generate a building model with some details.

Separating point clouds into ground and non-ground points is a preliminary and essential step in various applications of airborne light detection and ranging (LiDAR) data, and many filtering algorithms have been proposed to automatically filter ground points. Among them, the progressive triangulated irregular network (TIN) densification filtering (PTDF) algorithm is widely employed due to its robustness and effectiveness. However, the performance of this algorithm usually depends on the detailed initial terrain and the cautious tuning of parameters to cope with various terrains. Consequently, many approaches have been proposed to provide as much detailed initial terrain as possible. However, most of them require many user-defined parameters. Moreover, these parameters are difficult to determine for users. Recently, the cloth simulation filtering (CSF) algorithm has gradually drawn attention because its parameters are few and easy-to-set. CSF can obtain a fine initial terrain, which simultaneously provides a good foundation for parameter threshold estimation of progressive TIN densification (PTD). However, it easily causes misclassification when further refining the initial terrain. To achieve the complementary advantages of CSF and PTDF, a novel filtering algorithm that combines cloth simulation (CS) and PTD is proposed in this study. In the proposed algorithm, a high-quality initial provisional digital terrain model (DTM) is obtained by CS, and the parameter thresholds of PTD are estimated from the initial provisional DTM based on statistical analysis theory. Finally, PTD with adaptive parameter thresholds is used to refine the initial provisional DTM. These contributions of the implementation details achieve accuracy enhancement and resilience to parameter tuning. The experimental results indicate that the proposed algorithm improves performance over their direct predecessors. Furthermore, compared with the publicized improved PTDF algorithms, our algorithm is not only superior in accuracy but also practicality. The fact that the proposed algorithm is of high accuracy and easy-to-use is desirable for users.

Tree stem detection is a key step toward retrieving detailed stem attributes from terrestrial laser scanning (TLS) data. Various point-based methods have been proposed for the stem point extraction at both individual tree and plot levels. The main limitation of the point-based methods is their high computing demand when dealing with plot-level TLS data. Although segment-based methods can reduce the computational burden and uncertainties of point cloud classification, its application is largely limited to urban scenes due to the complexity of the algorithm, as well as the conditions of natural forests. Here we propose a novel and simple segment-based method for efficient stem detection at the plot level, which is based on the curvature feature of the points and connected component segmentation. We tested our method using a public TLS dataset with six forest plots that were collected for the international TLS benchmarking project in Evo, Finland. Results showed that the mean accuracies of the stem point extraction were comparable to the state-of-art methods (>95%). The accuracies of the stem mappings were also comparable to the methods tested in the international TLS benchmarking project. Additionally, our method was applicable to a wide range of stem forms. In short, the proposed method is accurate and simple; it is a sensible solution for the stem detection of standing trees using TLS data.

Sensitive, selective and fast detection of chemical warfare agents is necessary for anti-terrorism purposes. In our search for functional materials sensitive to dimethyl methylphosphonate (DMMP), a simulant of sarin and other toxic organophosphorus compounds, we found that zinc oxide (ZnO) modification potentially enhances the absorption of DMMP on a manganese dioxide (MnO2) surface. The adsorption behavior of DMMP was evaluated through the detection of tiny organophosphonate compounds with quartz crystal microbalance (QCM) sensors coated with ZnO-modified MnO2 nanofibers and pure MnO2 nanofibers. Experimental results indicated that the QCM sensor coated with ZnO-modified nanostructured MnO2 film exhibited much higher sensitivity and better selectivity in comparison with the one coated with pure MnO2 nanofiber film. Therefore, the DMMP sensor developed with this composite nanostructured material should possess excellent selectivity and reasonable sensitivity towards the tiny gaseous DMMP species.

Surface shortwave (SW) irradiation is the primary driving force of energy exchange in the atmosphere and land interface. The global climate is profoundly influenced by irradiation changes due to the special climatic condition in Antarctica. Remote-sensing retrieval can offer only the instantaneous values in an area, whilst daily cycle and average values are necessary for further studies and applications, including climate change, ecology, and land surface process. When considering the large values of and small diurnal changes of solar zenith angle and cloud coverage, we develop two methods for the temporal extension of remotely sensed downward SW irradiance over Antarctica. The first one is an improved sinusoidal method, and the second one is an interpolation method based on cloud fraction change. The instantaneous irradiance data and cloud products are used in both methods to extend the diurnal cycle, and obtain the daily average value. Data from South Pole and Georg von Neumayer stations are used to validate the estimated value. The coefficient of determination (R2) between the estimated daily averages and the measured values based on the first method is 0.93, and the root mean square error (RMSE) is 32.21 W/m2 (8.52%). As for the traditional sinusoidal method, the R2 and RMSE are 0.68 and 70.32 W/m2 (18.59%), respectively The R2 and RMSE of the second method are 0.96 and 25.27 W/m2 (6.98%), respectively. These values are better than those of the traditional linear interpolation (0.79 and 57.40 W/m2 (15.87%)).

Separating point clouds into ground and non-ground measurements is an essential step to generate digital terrain models (DTMs) from airborne LiDAR (light detection and ranging) data. However, most filtering algorithms need to carefully set up a number of complicated parameters to achieve high accuracy. In this paper, we present a new filtering method which only needs a few easy-to-set integer and Boolean parameters. Within the proposed approach, a LiDAR point cloud is inverted, and then a rigid cloth is used to cover the inverted surface. By analyzing the interactions between the cloth nodes and the corresponding LiDAR points, the locations of the cloth nodes can be determined to generate an approximation of the ground surface. Finally, the ground points can be extracted from the LiDAR point cloud by comparing the original LiDAR points and the generated surface. Benchmark datasets provided by ISPRS (International Society for Photogrammetry and Remote Sensing) working Group III/3 are used to validate the proposed filtering method, and the experimental results yield an average total error of 4.58%, which is comparable with most of the state-of-the-art filtering algorithms. The proposed easy-to-use filtering method may help the users without much experience to use LiDAR data and related technology in their own applications more easily.