Integrating Unmanned aerial vehicle Laser Scanning (ULS) and Terrestrial Laser Scanning (TLS) data in complex forest environments remains a significant challenge. Despite the availability of numerous registration algorithms, robust comparative studies are limited by the lack of reliable multi-platform benchmark datasets. In this study, we introduce the first multiplatform benchmark dataset for ULS-TLS point cloud registration in forests, encompassing 17 plots from seven diverse regions with about 1.56 billion points. The dataset is categorized into three difficulty levels based on overlap ratio and rigid overlap.We evaluated the performance of five registration algorithms against this benchmark. Chen2022 achieved the highest accuracy with a 100% success rate across all difficulty levels. While Wu2024 demonstrated robust performance in lower difficulties but faced challenges in more complex scenarios. We also found that terrain variations and rigid overlap significantly impacted registration accuracy, particularly for algorithms reliant on individual tree positions such as Hyypp¨a2021 and Feng2024. These findings underscore the need for improved data collection strategy, ground filtering techniques, and feature matching algorithms to enhance performance in challenging environments. We present the first openly accessible multi-platform benchmark dataset for forested regions and anticipate that future research will expand this work to additional areas. The dataset can be downloaded from: DatasetDownloadLink.

Accurate acquisition of tree species and forest age information is crucial for forest ecosystem protection and sustainable development. Although such information can be obtained through remote sensing technology, accurate extraction of tree species and forest age still faces significant challenges due to the remote sensing data characteristics and the complex and variable mountainous natural geographical environment. This article uses the Landsat-NDVI long-term series data from 1991 to 2021 to address this problem. A growth characteristic change model is established for the main dominant tree species in Shangri-La city, including Pinus yunnanensis, Pinus densata Mast, Picea & Abies, and Quercus acutissima, throughout the growth cycle in vertical zones at different altitudes. Besides, the study compared the normalized difference vegetation index (NDVI) time-series data of the area to be identified with the growth characteristic model to match the appropriate growth range automatically, and their similarity is determined to complete tree species classification. The forest age is then obtained based on the optimal matching point of the NDVI time-series data in the growth model. This method can simultaneously obtain tree species and forest age information and acquire forest age when the image duration exceeds the remote sensing image record. Ultimately, the overall accuracy of tree species classification reached 83.42%, and the forest age fitting results also showed a high degree of correlation, with a coefficient of determination ($R^{2}$) of 0.92. The root mean square error (RMSE) of forest age in Shangri-La, dominantly covered by mature forests/overmature forests, has dropped to 10.59. This study significantly improves remote sensing technology’s accuracy and application efficiency in multiparameter inversion of forest resources while providing a new technical means for forest resource management and ecological protection.

The settlement and tilt of a communication base station’s cabinet can harm electronic components, affecting stable operation. A 3D model measures cabinet deformation using LiDAR data, facilitating timely maintenance. While limited studies address cabinet segmentation, existing methods, relying on line or plane detection, face challenges with interference from objects sharing features with the cabinet. This paper proposes a cabinet point cloud segmentation method that combines cluster analysis and plane Point Cloud Coverage (PCC). The method preprocesses the point cloud, analyzes the differences in normal vector characteristics in different points neighborhoods, segments the point cloud based on the given normal vector value, performs Euclidean clustering, and extracts the planar point cloud using the RANSAC algorithm to judge if it meets the characteristics of the cabinet. The proposed method is validated with nine datasets, demonstrating a 98.23% average F1-score. Results confirm the algorithm’s accuracy in extracting cabinet point cloud, showcasing its versatility compared to common algorithms.

High-quality point cloud annotation is labor-intensive and time-consuming, but it serves as a critical factor driving the success of LiDAR point cloud semantic segmentation. Leveraging low-quality labels in LiDAR point cloud processing is overlooked, despite the fact that noisy annotation has low labeling costs and abundant cross-modal resources (e.g., labels from images). To this end, we thoroughly investigate the performance of airborne LiDAR point cloud semantic segmentation models using noisy labels for the first time and find that it is closely related to object categories and learning stages. Then we propose a new semantic segmentation framework for LiDAR point cloud noisy learning called adaptive dynamic noise label correction (ADNLC), which consists of weak category priority, dynamic monitoring (DM), and historical choice (HC). With these methods, we can adaptively correct the noise labels of different categories according to their specific learning situations. Finally, we provide a comprehensive process for noise simulation, accuracy evaluation, and comparisons in airborne LiDAR point cloud learning from noisy labels. We conduct experiments on the ISPRS 3-D Labeling Vaihingen and Large-scale ALS data for Semantic Labeling in Dense Urban Areas (LASDU) datasets, and the results show that our ADNLC outperforms baseline methods by 30% and 16%, respectively, verifying the superiority of ADNLC and demonstrating the potential of noise labels in LiDAR data processing.

As the most complex community structure in terrestrial ecosystems, tropical rainforests still employ traditional manual surveys to obtain spatial information on trees, which are time-consuming and laborious with significant errors. Light detection and ranging (LiDAR) can provide high-quality 3-D point cloud data, but using it to extract structural information of trees and shrubs in complex forests remains a technical challenge for point cloud mapping. Therefore, this article proposes a solution for realizing the accurate separation of LiDAR point cloud data for trees and shrubs within complex forests such as tropical rainforests. The method first preprocesses the data to obtain understory point cloud data. The local curvature (L-curvature) features are then utilized to perform preliminary separation. Then, the growth segmentation is performed based on the normal vector and the curvature magnitude to obtain the clustered objects. Accordingly, trees and shrubs are identified according to the features of the segmented clustered objects. Finally, eight tropical rainforest plots were selected to evaluate and analyze the performance of the method. The research results indicate that the accuracy of tree extraction in tropical rainforests using this method can reach over 91%. The accuracy and efficiency of this method for tree-shrub separation are superior to other methods. This study provides essential support for investigating forest understory vegetation characteristics and the spatial growth distribution of tropical rainforest trees and shrubs. It lays a foundation for the subsequent application and promotion of terrestrial LiDAR when investigating vegetation information in complex forest scenarios.

Unmanned aerial vehicle laser scanning (ULS) and terrestrial laser scanning (TLS) registration is an essential method for acquiring comprehensive forest structural information and conducting forest resource inventories. Due to the sparsity of the understory point cloud in ULS, existing methods for forest area point cloud registration have limitations in processing. To address this issue, this study proposes an efficient and robust coarse registration algorithm for forest area TLS-ULS point clouds. First, the tree top points are obtained based on the neighborhood maximum and HeightAnd angle threshold constraint methods, which are used as keypoints to construct an irregular triangular mesh. Line segment feature descriptors are then constructed for each mesh edge to establish matching relationships for registration transformation. The experimental results obtained using multiple airborne and terrestrial point cloud datasets from different regions demonstrate that the proposed algorithm does not rely on tree trunk attributes and has no strict density requirements for airborne point clouds. High registration accuracy is achieved for eight test plots in two study areas, with translation and rotation errors of 0.28° and 0.12 m, respectively, and an average pointwise error of 0.14 m. This indicates that the proposed algorithm has high registration accuracy and strong robustness, making it suitable for TLS-ULS registration in forest scenes.

Urban land surface temperature (LST) plays a crucial role in observing and comprehending energy exchange within urban environments. Urban geometric structure and material composition are critical parameters to characterize urban thermal radiation accurately. In this article, an urban thermal radiation analytical model based on the sky view factor (UTRAM-SVF) was developed by considering the multiple scattering within the urban canopy and the radiation composition of urban components. The cross-comparison of the proposed method was conducted in four ways: the discrete anisotropic radiation transfer (DART) model, urban effective emissivity model based on SVF (UEM-SVF), Landsat 8 Thermal Infrared Sensor (TIRS) data, and Airborne Hyperspectral Scanner (AHS) TIR data. Compared with DART, the results revealed that the root-mean-square error (RMSE) of radiance by the UTRAM-SVF model is 0.04 W/(m$^{2}\cdot $sr$\cdot \mu $m). Furthermore, two field applications were conducted using Landsat 8 TIRS data and AHS TIR data. The differences of at-sensor radiance from UTRAM-SVF model and Landsat 8 TIRS data vary from 0.05 to 0.3 W/(m$^{2}\cdot $sr$\cdot \mu $m), and the RMSE is 0.17 W/(m$^{2}\cdot $sr$\cdot \mu $m). The results of AHS TIR images on the UTRAM-SVF model present that the average biases of at-sensor radiance are 0.07 and 0.19 W/(m$^{2}\cdot $sr$\cdot \mu $m) for two TIR channels. The cross-comparison results show that the proposed method outperformed the UEM-SVF model in evaluating radiance over the complex and heterogenous urban areas (SVF <0.4). The UTRAM-SVF model can be used to monitor urban thermal radiation based on high-resolution TIR data and can further be helpful to retrieve high-resolution urban LST.

Leaf area index (LAI) is a significant parameter controlling a lot of physical and biological processes related to vegetation on the Earth's surface. Previously, an LAI estimation model of ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2)/Atlas (Advanced Topographic Laser Altimeter System) has been established and the accuracy of ICESat-2 LAI has been evaluated using optical images. However, this model hasn't been tested with airborne data. To demonstrate the effectiveness of ICESat-2 LAI, this study applied the model to airborne LiDAR data in the Saihanba National Nature Reserve in the same season. Results showed that the coefficient of determination $(R^{2})$ of ICESat-2 LAI was 0.63 and the root mean square error (RMSE) is $1.03(n=26,\ p < 0.001)$ , ICESat-2 has the inversion capability of LAI comparable to airborne lidar. These findings may help in promoting the LAI estimation model and broadening the application fields of the photon-counting LiDAR data.

Photon-counting Light Detection and Ranging (LiDAR) can penetrate a certain depth of water and provide reliable date support for water depth information extraction. Taking the islands and reefs in the South China Sea as an example, this paper uses the only in-orbit spaceborne photon-counting LiDAR - Ice, Cloud and land Elevation Satellite-2/ Advanced Topographic Laser Altimeter System (ICESat-2/ATLAS) to carry out research on depth extraction and accuracy evaluation in shallow water areas of islands and reefs. Aiming at difference in the density distribution of water surface and bottom photons, this study respectively utilizes the interval estimate and the modified Ordering Points to Identify the Clustering Structure (OPTICS) to filter out noise photons, and the modified OPTICS is changed twice by filter parameters. Then, bathymetric extraction is realized by refraction correction and tide correction. Finally, the manual labeling photons and airborne bathymetric LiDAR data of South China Sea is used to evaluate the denoising and bathymetric accuracy.

Land cover classification is important for effectively protecting and developing land resources. This study investigates the joint use of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) data and Landsat 8 Operational Land Imager (OLI) data in land cover classification with random forest (RF) in Yunnan province, China, to explore the application potential of photon counting Lidar data in land cover classification. The contributions of this paper are: (1) The joint use of ICESat-2 and Landsat 8 image datasets can provide better land cover classification accuracy, achieving 10% and 3% accuracy gains for five types(forest/low-vegetation/water/construction land/barren) and four types (vegetation/water/construction-land/barren)of land cover, respectively. (2) The proposed feature selection improves the overall accuracy by 1.5% and 1% for five and four land cover types, respectively. (3) The accuracy of the land cover classification reached 82% and 98% for five and four types of land cover. (4) The terrain factors, the number of canopy photons, and solar conditions significantly impact land cover classification for a complex terrain area.

Although good performance has been recently achieved in point cloud semantic segmentation based on deep learning, it has not been promoted due to differences in actual scenes and the time-consuming production of labeled data sets. Unsupervised domain adaptation (UDA) aims to solve the problem of how to adapt the classifier from one scene (source domain) to another unlabeled scene (target domain), which can reduce the performance drop caused by the domain shift. Since spatial information is important for light detection and ranging (LiDAR) point and the causes of domain gap in image and point cloud tasks are different, projecting point cloud into image for processing is not suitable and how to apply the image-oriented domain adaptation (DA) methods to point cloud is not trivial. In this letter, we propose a 3D point-based UDA method for point cloud semantic segmentation. This model introduces point- and set-level domain adaptive modules to achieve feature alignment between the domains. We evaluate the proposed method with two experiments, including cross terrain adaptation and airborne to mobile adaptation. Compared with the results without using DA, the mean intersection over union (mIoU) increased by 10.45% and 24.69%, respectively, indicating the effectiveness of our method.

As an essential parameter in forestry, crown base height (CBH) faces many tasks. The methods are still developing for estimating it. Unmanned aerial vehicles (UAVs) light detection and ranging (LiDAR) supplies new, massive, and high-density data for estimating CBH. Many methods had been generated to compute CBH indirectly using regression-based ways or directly using geometric/statistical LiDAR-based ways. However, there were few methods to deal with the problem of understory, trunk, and noise points caused by high-density UAV data. A robust method was first proposed in this study to directly estimate CBH from LiDAR data, which contained two significant skills: 1) understory vegetation removal for each tree using a polynomial curve and 2) computing CBH by kernel densification of the elevation frequency histogram of LiDAR data. It could tolerate the understory and trunk points better through kernel convolution. The method proposed in this study and a previous simple model were applied in a crabapple plot in the Huailai Remote Sensing Comprehensive Experimental Station, Hebei, China, and verified by field-measured data. It was inspiring that our method is slightly better, and the mean CBH of LiDAR-derived trees was only 1.60 cm higher than that of field-measured trees. The mean absolute error (MAE) of CBH was 4.91 cm,${R}^{2}$was 0.73, the root-mean-squared error (RMSE) was 8.29 cm, and the bias was 2.68% for these trees. Generally, this method showed strong usability for high-density UAV LiDAR data and high precision for measuring CBH of low trees.

As many LiDAR point cloud processing steps, such as reconstruction, are often time- and memory-consuming, dividing LiDAR point clouds into subregions is common and necessary during preprocessing. However, the existing data dividing methods rely on tedious manual work or regular grids and result in oversegmentation around cutting lines. In this letter, we propose a new gap-based data dividing method for various LiDAR point clouds that can minimize the intersections between cutting lines and objects. The basic idea is to find a set of optimal paths that consist of gaps between objects as potential cutting lines. The experiments and comparisons in three data sets demonstrate that the proposed method is much better than the baseline method in terms visual inspection and cutting line quality.

Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) shows great potential for forest height retrieval. However, there are abundant noise photons in the ICESat-2 data, which make the accurate extraction of global forest heights challenging. In this letter, a novel algorithm based on the clustering method of ordering points to identify the clustering structure (OPTICS) was proposed to remove noise photons. First, we modified the circular shape of the search area in the OPTICS algorithm to an elliptical shape. Second, a distance ordering of all photons was generated using the modified OPTICS algorithm. Finally, signal photons were effectively detected using distance thresholds set by the Otsu method. To evaluate the algorithm performance, both the simulated and real ICESat-2 data were applied to our proposed algorithm. In addition, we compared our algorithm with another noise removal algorithm based on the modified density-based spatial clustering of applications with noise (DBSCAN). The results show that our algorithm works well in distinguishing the signal and noise photons as indicated by high$F$values. Compared with the modified DBSCAN, our algorithm performs better in filtering out noise photons regardless of the simulated or real ICESat-2 data sets. In addition, the results also indicate that our algorithm is robust because it is insensitive to the clustering parameters. Overall, the new proposed algorithm is effective for removing noise photons in the ICESat-2 data.

LiDAR footprint, defined as the illumination area of LiDAR sensor on the ground, is the fundamental unit that the sensor collects information from. The design of footprint size crucially influences the acquired LiDAR signals. For large-footprint full-waveform LiDAR, a well-designed footprint size is indispensable to acquire accurate and complete vertical profiles of scene targets. The methods that design the footprint size are increasingly needed to satisfy various application requirements. In this study, an analytical method to designing the footprint size is proposed for forest and topography applications. It is established based on a mixture Gaussian model and the designed footprint size ensures the signals of vegetation and ground can be completely extracted. Experiment results with our method show that the footprint size is preferably in the range of 10.6–25.0 m for forest application, while it is less than 32.3 m for topography application. The intersection of the two sets satisfies both applications. Furthermore, a series of sensibility studies were performed to analyze the influence of multiple key parameters to the optimal footprint size, including the scene characteristics, instrumental configurations, and application requirements. This study provides a theoretical basis for the design of future large-footprint full-waveform laser altimeters.

This study aims to understand the effect of surface topography on vegetation high-order backscatterings of large-footprint full-waveform LiDAR. Most previous studies in exploring LiDAR scattering mechanisms were carried out over relatively flat areas. To explore the canopy scattering mechanism on slope terrain, this study implements the Discrete Anisotropic Radiative Transfer (DART) model to simulate the LiDAR single- and multiple-scattering waveforms in mountainous forest scene. Results show that 1) terrain slope changes the LiDAR scattering components at different time delays by changing spatial distribution of scene elements; 2) the ratio of multiple scattering to total intensity changes little with the terrain slope (less than 2%); 3) multiple scattering contributes most when the ground vertical extent is close to the canopy vertical extent. These findings may help in better understanding the canopy scattering processes on the slope terrain.

The Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission provides effective data for measuring global ground elevation and forest height. Unlike the ICESat, ICESat-2 emits three pairs of beams. Each pair includes a strong beam and a weak beam with an energy ratio of 4:1. To evaluate the performance of ICESat-2's strong and weak beams in estimating ground elevation and forest height, the ICESat-2 data in four different scenarios were analyzed; 1) ICESat-2's strong beams in the daytime, 2) ICESat-2's weak beams in the daytime, 3) ICESat-2's strong beams in the nighttime, and 4) ICESat-2's weak beams in the nighttime. The results indicate that the weak beams perform as well as strong beams in estimating ground elevations due to high coefficient of determination (R2) values and low root-mean-squared error (RMSE) values regardless of the daytime and nighttime data. While for forest height estimation, ICESat-2's weak beams perform worse than strong beams and the daytime data perform worse than the nighttime data. These results suggest that all ICESat-2 data are suitable for ground elevation extraction, while the ICESat-2's weak beams in the daytime are not suited to estimate forest height.

Ground filtering is an essential process for further classification of other features from a point cloud. This letter aims to develop an efficient method for the automatic recognition of a ground surface in urban environments from a point cloud acquired by mobile laser scanning (MLS). The MLS point cloud has large amounts of data. To decrease the calculation effort, the point clouds are first segmented into regular voxels using an octree structure. Then, voxel cloud connectivity segmentation (VCCS) is applied to generate supervoxels, which alleviate the issue of boundary cross between voxels and ground truth. Finally, several geometric features on the voxel scale are selected for a support vector machine (SVM) to label segments as a ground surface and nonground objects. For an objective comparison with other methods, the accuracy of the proposed method is evaluated by public MLS data sets. Experimental results show that all ground type objects can be well recognized with least susceptibility to scenic change.

Multispectral LiDAR systems have been proved to have potential for retrieving forest structural and physiological parameters from waveforms of various wavelengths. However, multiple scattering occurring in complex forest canopy directly affects the waveform shape. This study combined a leaf optical model, a forest structure model, and a LiDAR process model to simulate multispectral LiDAR waveforms in single and multiple scattering cases and then assessed the effect of multiple scattering on forest physiological indicators. Results showed that 1) multiple scattering increases the waveform intensity, especially at near-infrared wavelength; 2) multiple scattering greatly affects the retrieval of physiological parameters of the understory; 3) when leaf chlorophyll content is low, forest physiological indicator was relative-seriously overestimated due to the multiple scattering effect. These findings may shed some light on our understanding of multiple scattering mechanism and accurately retrieving forest physiological parameters from multi-spectral LiDAR waveforms.

Although the Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimetry System (ICESat/GLAS) has been used for urban monitoring, however, previous studies focused on extracting the maximum building height within the footprint. In fact, the full-waveform recording of GLAS data makes it possible to extract multiple building heights. However, the uncertainty of the spatial distribution and reflectance creates considerable challenges for the fine inversion of multiple building heights within the footprint. In this letter, we proposed an inversion method of multiple building heights using GLAS data assisted by QuickBird imagery. First, the GLAS waveform and the auxiliary optical imagery were processed to extract some spectral, horizontal, and vertical information as the prior knowledge of the inversion model. Then, the multiple building heights were inversed from the optimal simulated waveform based on the 3-D geometric optical and radiative transfer (GORT) model. The building heights measured by airborne LiDAR were used to validate the inversed building heights. The results demonstrated that the proposed inversion method achieved the building height estimation accurately and precisely ($R^{2} = 0.971$, rRMSE = 13.2%, and$n = 430$). This letter may shed some light on extracting multiple-level heights within the footprint using satellite LiDAR full-waveform data.

Due to errors in sensors and positioning, there exist mismatches between different phases of mobile laser scanning point clouds, which impede the application of point cloud, such as changing detection and deformation monitoring. To rectify such mismatches, we designed a 3-D deep feature construction method for point cloud registration. The proposed method combines two 3-D convolutional neural networks into a uniform deep learning model to extract 3-D deep features. First, the corresponding points and noncorresponding points are set to train the deep learning model to minimize the distance between corresponding points’ features and maximize the distance between features of noncorresponding points. Second, in the test phase, the 3-D deep feature for each keypoint was extracted by the trained deep learning model. This could be used to determine the corresponding points by the$k$-dimensional tree and random sample consensus (RANSAC) algorithm. Finally, a transformation matrix was calculated based on the corresponding points and was then applied to point cloud registration. The experimental results illustrated that the proposed method of using 3-D deep features is more efficient at a corresponding point search than representatives of three existing methods. It also improved registration accuracy.

Building detection is valuable for 3-D building reconstruction and urban management. In this letter, a vegetation mask-based connected filter (VMCF) algorithm is proposed to discriminate building regions from light detection and ranging (LiDAR) data using the following steps. First, digital surface model (DSM) data are obtained by the interpolation of a LiDAR point cloud, and a top-hat transform is introduced to remove outliers. Second, a vegetation mask is derived by using the entropy and normalized difference vegetation index extracted from the DSM and aerial imagery, respectively. Third, a stack of nested binary images is generated by slicing the DSM data into different levels, and in each level, the connected components are acquired by using vegetation-mask-based connected analysis. Finally, a tree structure is constructed using a max-tree algorithm, and then building regions are derived by analyzing the area difference of the corresponding nodes of the tree in adjacent levels. The proposed VMCF algorithm is validated using three test areas provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation. The experimental results show that building regions in the LiDAR data can be effectively detected by the proposed method, and the detection rates of three test areas are 89%, 91.8%, and 90.9%, respectively.

Canopy height models (CHMs) were utilized to detect treetops and estimate individual-tree parameters. The treetop detection based on CHMs was affected by surface topography and crown characteristics. However, their effects have not been well studied. Therefore, this paper aimed at assessing the impacts of aforementioned factors to facilitate treetop identification from LiDAR-derived CHMs. To fulfill this objective, we first extended and improved the previous models for cases with various terrains. Then, a new theoretical model was developed to quantify treetop displacements for ellipsoidal tree crowns. Finally, we further analyzed the treetop displacements due to terrain slope, crown radius, crown shape, and offset distance to the slope surface. Our analysis indicates that the vertical displacement increases exponentially with terrain slope; thus, the effect of terrain slope must be considered over extremely steep areas; larger errors are observed for trees with a large crown radius; the treetop displacements are highly correlated with crown shape, the effect of topographic normalization can be neglected for conical crowns with a large crown angle, and the elliptical crown shape can reduce the treetop detection errors; and treetop displacements increases with offset distance to the slope surface inCase 2, while opposite results are observed inCase 5. In addition, the results also demonstrate that the effect of slope-distorted CHMs may be quite different for different types of tree crowns and terrains. Overall, this paper makes a significant contribution to the development of theoretical models for quantifying treetop displacements. Furthermore, our findings provide a theoretical basis and guidance for better identifying treetops from LiDAR-derived CHMs.

The fraction of photosynthetically active radiation (FPAR) is a key parameter in controlling mass and energy exchanges between vegetation and atmosphere. LiDAR data-derived canopy vertical structural information and hyperspectral image-derived vegetation spectral information can be considered as complementary for vegetation FPAR estimation. To the best of our knowledge, few studies have estimated vegetation FPAR by both LiDAR and hyperspectral data based on physical models. This letter aims to explore the ability of combining airborne LiDAR and hyperspectral data to retrieve maize FPAR based on the energy budget balance principle. First, canopy gap probability and openness were estimated from airborne LiDAR data. Next, canopy reflectance and soil background reflectance were retrieved from hyperspectral image. Then, we estimated maize FPAR based on the energy budget balance principle. Finally, model validity was assessed byin situdata and results showed the physical FPAR estimation model estimated maize FPAR accurately. These results indicated that the physical method proposed in this letter was efficient and reliable to estimate maize FPAR, and FPAR retrieval can benefit from the complementary nature of LiDAR-captured canopy structural information and hyperspectral-detected vegetation spectral characteristics.

The accurate estimation of terrain slope is very important for accurately monitoring the elevation and mass changes of glacier using laser altimeter. In this paper, a novel physical model was proposed for accurately estimating withinfootprint terrain slope. The new proposed model was built based on overlapping footprints of the geoscience laser altimeter system (GLAS) data, namely, using altitude angle, footprint size, shape, orientation, terrain aspect, and ground extent. Ground extent estimation models were established on the basis of linear regression analyses between: 1) GLAS-derived waveform extent and airborne topographic mapper (ATM)-derived ground extent and 2) GLAS-derived waveform width and ATM-derived ground extent, respectively. In addition, the terrain slopes estimated from the overlapping footprints were validated by ATM data and compared with the slopes calculated from surface elevations, i.e., from ASTER global digital elevation model (DEM) (GDEM) and GLAS elevation. Results showed that the accuracy of waveform width-predicted ground extents (R2= 0.868, RMSE = 0.686 m, n = 20, and p-value2= 0.776, RMSE = 0.824 m, n = 20, and p-value <; 0.0001), which indicated that waveform width is more suitable for estimating ground extent. Slopes estimated from the new proposed model have a strong consistency with those calculated from ATM data (Corrcoef = 0.786, bias = 0.654°, SD = 1.368°, and RMSE = 1.452°). Additionally, results also indicated that the new proposed model performs much better than the methods based on ASTER GDEM and the GLAS surface elevation in estimating within-footprint terrain slope due to higher correlation, lower bias, standard deviation, and RMSE.

The accurate estimation of within-footprint slope is very important for measuring earth’s surface characteristics using satellite light detection and ranging (LiDAR) data. Several models have previously been proposed for slope estimation; however, these models have limitations in either accuracy or applicability. Therefore, the main purpose of this paper is to explore the influence of various factors (e.g., ground vertical extent, footprint size, footprint shape, and footprint orientation) on slope estimation to better estimate the within-footprint slope using large-footprint waveform LiDAR data. The results indicated that the absolute slope error due to the coupling effect of ground vertical extent and footprint size increased with an increase in the ratio of ground vertical extent and footprint size, while the relative slope error had an opposite trend. The slope error caused by footprint shape was relatively low when the footprint eccentricity was small. However, the slope error due to footprint shape grew rapidly when the footprint eccentricity became larger; thus, it is essential to fully take into account the influence of footprint shape on within-footprint slope estimation. In addition, the results suggest that the slope error changed regularly based on the intersection angle between footprint orientation and terrain aspect. This paper also provided guidance for the determination of an easy and practical model for within-footprint slope estimation. The determination of best model is dependent on the value of intersection angle. Once the intersection value is given, the best model can easily be determined. Using the best model, the within-footprint terrain slope can be estimated with high accuracy.

Building digitalization is an important component of digital city development. Terrestrial laser scanning (TLS) technology provides a high-precision and high-density data source to guarantee building digitalization and reconstruction. However, when these large scattered points cloud are used to build a 3D digital model, the points of building boundaries extraction is an essential issue. In this paper, the edge detection method for 2D images is extended to the 3D points cloud to achieve rapid, complete and accurate boundaries points for building. Firstly, based on the coplanar conditions, the points cloud data of a building are divided into different patches and then converted into 2D images according to the depth dimension of each patch. Secondly, the points of each plane are then extracted by an improved Laplace image edge detection method. Finally, the boundary points extracted by the method proposed are validated using tested data. It shows that the proposed method is effective and robust: the extraction accuracy for the building boundary points is up to 85%, notably for cases with more complicate features.

This letter is aimed to compare the performance of canopy height models (CHMs) derived from airborne laser scanning (ALS) data and unmanned aerial vehicle (UAV) stereo images in the extraction of individual tree height and crown size. Treetops were identified using the local maximum algorithm from the Gaussian filtered CHMs. A parabola fitting was used to determine the crown size. Factors affecting the delineation results, such as point cloud density and the spatial distribution and growing status of trees, were analyzed. The results showed that the UAV stereo images, together with the ALS-derived digital elevation model (DEM), can achieve better performance than ALS data alone based on our data set. The match ratio between delineated and field-measured trees varied significantly, with the highest ratio of 66.94% obtained by UAV in the young aspen forest and the lowest ratio of 33.76% obtained by ALS in the old forest. Aside from the influence of point density, this letter also shed light on the important role that the spatial distribution and growing status of trees play in the delineation of individual trees. To conclude, integrating UAV stereo images with the ALS-derived DEM is effective in delineating individual tree attributes in small-scale windbreaks, which provides some suggestions for the future management of agriculture land.

Digitization and modeling of city buildings is always one of the most important issues in Virtual 3D City. Traditional methods are usually expensive and time-consuming. Nowadays reconstruction methods based on LiDAR (Light Detection And Range) data have been widely used [1] due to its powerful capacity of data acquisition. Airborne Laser Scanning (ALS) data have got some promising results [2]. However the obvious disadvantage is that it can hardly get the profile information except for the roof of building, and the result is that people can not get complete building model. On the contrary, Terrestrial Laser Scanning (TLS) could get points cloud of buildings with high density and accuracy by multi-stations scanning for realistic 3D model reconstruction. The information includes not only building roofs, but also building profiles. In this paper, the authors use TLS data, and explore an automatic model reconstruction approach to establish digital building model. The registered cloud points from multiple scanning stations are taken as input parameters. Profiles information of building is extracted from raw points cloud by a segmentation algorithm. And a regular polyhedron model of building is finally presented. The experiment results show that the automatic reconstruction procedure and algorithms are easy to implement and can get reasonable building model after facade boundary regularization.

A waveform decomposition method, i.e., multiscale wavelet analysis, is proposed in this letter for light detection and ranging waveform characterization and average tree height estimation. First, the waveform decomposition was applied to ICESat/Geoscience Laser Altimeter System (GLAS) data to extract the waveform characteristics (waveform peaks) through performing Gaussian wavelet functions at five increasing scales. Then, the waveform length and average tree height were derived from the waveform decomposition information. This method was applied to the GLAS waveform data in Yunnan province, China, for average tree height estimation. Finally, the results were validated by field measurements and compared with the relevant parameters in the GLA14 product provided by NASA. This study indicates that, for simple-peak waveforms, the waveform decomposition was consistent with that of the GLA14 product, while for bimodal or multipeak waveforms, the result was more accurate and reasonable than that of the GLA14 product.