Point cloud registration is a prominent topic in computer vision research, with applications including target identification, 3D reconstruction, SLAM, and others. The capture of key points and matching point pairs has evolved into a critical technology for point cloud registration. In this paper, we propose an approach that extracts key points using the Intrinsic Shape Signature (ISS) algorithm. Additionally, we combine the Fast Point Feature Histograms (FPFH) descriptor with curvature to obtain matching point pairs, thereby establishing a solid foundation for point cloud registration. The experiment shows that, compared to the 3D-SIFT technique, the key points derived by the ISS algorithm have a more uniform distribution. Moreover, our proposed combination of the FPFH descriptor with curvature extracts matching point pairs more effectively than using the FPFH descriptor alone.

Fine mapping of retrogressive thaw slumps (RTSs) holds paramount significance in the study of permafrost degradation and carbon exchange. We propose a lightweight and enhanced semantic segmentation network (LessNet) for automatically mapping the RTSs from Sentinel-2 images. LessNet is constructed on the encoder-decoder framework with innovative incorporation of attention mechanism and dual-level semantic features fusion. The lightweight architecture of LessNet eliminates the need for pre-training, and the network hyperparameters are automatically updated based on the training dataset, which allows for fast convergence of supervised learning. Experiments conducted in the Beiluhe region of the Tibetan Plateau highlight the robustness and competitive performance of the model.

Canopy gaps affect the spatial distribution of radiation in the canopy. The estimation of gap fraction (GF) is important for the study of leaf area index (LAI). Terrestrial laser scanning (TLS) has been widely used for retrieving canopy structure parameters through massive high-resolution spatial samples in the form of 3-D point cloud data sets. Monte Carlo simulations can be used to obtain approximate solutions to quantitative problems through a large number of random sampling while avoiding complicated mathematical calculations. Monte Carlo simulations are suitable for analyzing TLS data and could help overcome resolution reductions inherent to current point cloud processing approaches. However, few studies have applied the Monte Carlo method to capture the canopy structure features implicit in high-resolution point clouds. This letter proposes a method for estimating the GF based on Monte Carlo simulations. A large number of randomly simulated laser beams were emitted to identify the canopy and gaps according to the discrimination distance, and the results were compared with the GFs derived from digital hemispherical photography (DHP). In general, the proposed TLS method estimated smaller GFs than the DHP method since DHP was vulnerable to exposure conditions and complex canopy structure. The GF estimated by the two methods is consistent ($R^{2} =0.7522$), indicating the effectiveness of the Monte Carlo method.

The flow direction calculation over flat surfaces in a digital elevation model (DEM) is vital for hydrological analysis. The Garbrecht and Martz (G&M) algorithm is a widely used algorithm to obtain correct hydrological flow patterns over flat surfaces. In this study, we propose an efficient variant over the fastest variant of the G&M algorithm. The proposed variant introduces three improvements and significantly reduces redundant computation. For all tested DEMs, the running time of our proposed variant is 26% to 49% shorter than that of the fastest variant. The proposed variant can be used to obtain correct hydrological flow patterns over flat surfaces with less time.

As one of the most important components of plants, the reconstruction of high-precision leaf models is a critical step for building tree models. According to the morphological structure of leaves, this paper proposes a leaf reconstruction method based on the laser point cloud data. The moving least squares method is used to fit the leaf surface to extract the complex profile information of the leaf, and the delaunay triangulation algorithm is used to reconstruct the three-dimensional model of the fitted leaf point cloud data. The results show that the proposed method can not only realize the three-dimensional reconstruction of the leaf, but also reflect the morphological characteristics of real leaf.

Forest resources are of great significance in regulating climate, maintaining biodiversity, and providing ecological products. Accurate identification of tree species is the basis for research and utilization of forest resources. This study combined the characteristics of multi-source data, based on the AISA EAGLE II hyperspectral images and airborne LiDAR point clouds which were obtained in August, 2016. Point cloud characteristics, spectral and texture characteristics were extracted from both datasets. Then SVM was used to classify the main tree species of Genhe experimental area. The results showed that tree species classification accuracy can be improved by using airborne LiDAR and hyperspectral image features.

The boundary feature is of great significance in describing the object shape and constructing 3D model of object. Accurate extraction of boundary features is important to the visualization of the object. This paper presented an accurate extraction algorithm of the indoor boundary features based on point cloud data. The amount of the data was downsampled by the voxelgrid filter. The boundary features of the indoor were extracted by using the angle criterion based on the normal vector and the statistical filtering algorithm. The results show that the boundary features of the indoor can be accurately extracted by the proposed method.

Rice is one of the most important crops in the world. Rice usually grows in tropical and subtropical areas where are usually rainy and cloudy. Under this weather condition, optical remote sensing has a great limitation, but the synthetic aperture radar (SAR) runs well. This paper utilized C band multi-temporal Radarsat-2 data, which is acquired in 2016, to extract quad-polarized backscattering coefficients, Cloude-Pottier and Freeman-Durden decomposition parameters for establishing classification features. Random forest (RF) algorithm was employed as the classifier. The Overall Accuracy (OA) is 82.9% when only the data on May 15thwas used. After adding the data on July 26th, the OA increased to 88.8%. Then, the variable importance was estimated by using out-of-bag (OOB) data. The results indicate that using RF would effectively identify the ground objects in the rice planting area.

The tree skeleton is one of the important parameters for building 3D model of the tree. The accurate extraction of tree skeleton is of great significance for tree visualization. This paper proposed an effective method for extracting tree skeleton of individual trees. The complexity of canopy structure was reduced by slicing. The leaf and wood components were separated by combining classification and segmentation methods. L1-median algorithm was used to extract the tree skeleton points accurately. The results show that the method can accurately extract the tree skeleton from terrestrial LiDAR data. The extracted skeleton had a good conformance with the point cloud of the tree in morphology.

Fraction of absorbed photosynthetically active radiation (FPAR) refers to the ratio of photosynthetically active radiation (PAR) absorbed by the green part of the vegetation canopy to the total PAR. Accurate and quantitative monitoring of rice FPAR is of great significance for cultivation management and yield estimation. High spatial and temporal resolution optical images, and all-time all-weather radar images are widely used in crop monitoring. In this study, a rice FPAR synergistic inversion model was established using a combination of optical and radar remote sensing dataset. The results suggest that the estimated FPAR is more approximate to the measured values, compared with the commonly used MODIS FPAR product. The FPAR spatiotemporal distribution maps are generated based on the synergistic inversion model.

The wood structure is an important parameter that represents the geometrical and topological characteristics of trees. Accurate extraction of the wood component of a tree is of great importance in visualizing trees. Light detection and ranging (LiDAR) has been applied to obtain the 3-D structural properties of vegetation. However, it is difficult to separate the wood and leaf components from point clouds data in situations where wood and leaves are mixed and overlapping. This letter proposes an effective method for extracting wood point cloud of individual trees based on different geometric features of leaves and wood by combining classification and segmentation methods. Magnolia grandiflora and Cinnamomum camphor trees were scanned using a high-resolution terrestrial LiDAR. The complexity of the structure of the canopy was reduced using a slicing method. The wood and/or leaf components were classified using the K-means and random sampling consistency (RANSAC) algorithm. The cylindrical segmentation method based on the RANSAC algorithm was used for the precise extraction of the wood component in wood and leaf mixed point clouds. The trunk under the canopy was extracted completely. The average recall and precision in extracting canopy wood point clouds of the Magnolia grandiflora and Cinnamomum camphor achieved 94.60%, 92.02% and 93.62%, 91.46%, respectively. The results indicate that the proposed method has the potential for accurately extracting the wood point cloud from terrestrial LiDAR data.