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.

The spatio-temporal distribution of biocrusts can be used to monitor regional water resources in desert ecosystems. However, a lack of biocrust products from remotely sensed images with fine spatial resolution (FSR) limits scientific research in this area. To address this issue, we establish an estimation model for biocrusts (EMBC) in three steps for FSR images and map the large-scale fractional biocrust coverage (FBC) in deserts using Sentinel-2 images with a spatial resolution of 10 m. Firstly, we develop a fraction biocrust cover index (FBCI) based on radiative transfer theory. Next, a multi-angle calculation equation involvingFBCIis established and the parameters for a pixel dichotomy model are solved by an inverse method using a linear kernel-driven model. Finally, this pixel dichotomy model withFBCIis used to calculateFBC. We validate the model using field measurements and compare the validation results with those estimated by a random forest model and a backpropagation neural network model. This comparison demonstrates that the value ofFBCestimated by EMBC is highly consistent with field measurements (root mean square error (RMSE) = 0.0774, systematic deviation = -4.05%). Furthermore, the values of FBC estimated with EMBC and the two other models show a high level of consistency in terms of spatial distribution (RMSEFBCin a desert and is an important technique for monitoring drought in an arid environment.

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.

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.

Terrestrial laser scanning (TLS) is widely used in forest inventory surveys. Diameter at breast height (DBH) is one of the most important parameters in the forest inventory survey. There are many methods to estimate DBH. In this study, cylinder fitting algorithm, circle fitting algorithm and Hough transform algorithm are used to estimate DBH of two larches of different ages to find a better DBH extraction algorithm. Compared with the circle fitting algorithm and Hough transform algorithm, the cylinder fitting algorithm achieves the highest accuracy. In addition, it is worth noting that different structure of the trees may affect the accuracy of these methods greatly.

Terrestrial laser scanning (TLS) can acquire high-precision point cloud data within a short time span and has received a lot of attention in the research on forest resource inventory. At present, most of the methods for estimating tree parameters are based on the point cloud of the single tree. This study obtains forest parameters based on the plot-level TLS data. Larch trees in the plot are detected and tree height, diameter at breast height (DBH) and crown projection area of single larch are estimated. The results show that the trees detection accuracy of each plot is relatively high in this study, DBH and tree height can be estimated with relatively higher accuracies with R2values of 0.949 and 0.77, respectively, and root mean squared error (RMSE) value of 2.98 cm and 1.5897 m, respectively. Our results also show that the estimation accuracies of the forest parameters based on the plot-level are similar to that based on the single-level. The proposed method in this study performs much better than the conventional single-level methods in workload and automation.

Filling depressions is a commonly used preprocessing step for the automatic extraction of drainage networks from raster digital elevation models (DEMs). The Priority-Flood algorithm is the fastest depression-filling algorithm for floating-point DEMs. Most of the variants of the Priority-Flood algorithm processes disjoint depressions using one single priority queue, without taking advantage of the fact that the disconnected depressions can be filled independently and that the running times can be reduced accordingly. This study proposes a new algorithm to process sub-watersheds independently for the generic floating-point DEMs. The proposed algorithm draws largely on the Priority-Flood algorithm, identifies and processes each sub-watersheds independently, which provides more insight into the Priority-Flood algorithm. Its efficiency in processing small DEM datasets can be used to process each small tiles in tile-based parallel filling of depressions.

Terrestrial laser scanning (TLS) can acquire high-precision point cloud data within a short time span and it has received a lot of attention in the study of forest structures. In this study the height and diameter at breast height (DBH) of larches are estimated based on TLS data and then the larch allometric model is used to estimate the above-ground biomass (AGB) of single tree in plots located at Saihanba National Forest Park, Heibei Province, China. The results show that DBH and tree height can be estimated with relatively higher accuracies with R2values of 0.95 and 0.80, respectively, and root mean squared error (RMSE) value of 2.91 cm and 1.54 m, respectively. In addition, the AGB is estimated based on the retrieved DBH and tree height. The results show that tree parameters and AGB can be estimated from TLS data.

With the increasing sizes of digital elevation models (DEMs), there is a growing need to design parallel schemes for existing sequential algorithms that identify and fill depressions in raster DEMs. The Priority-Flood algorithm is the fastest sequential algorithm in the literature for depression identification and filling of raster DEMs, but it has had no parallel implementation since it was proposed approximately a decade ago. A parallel Priority-Flood algorithm based on the fastest sequential variant is proposed in this study. The algorithm partitions a DEM into stripes, processes each stripe using the sequential variant in many rounds, and progressively identifies more slope cells that are misidentified as depression cells in previous rounds. Both Open Multi-Processing (OpenMP)- and Message Passing Interface (MPI)-based implementations are presented. The speed-up ratios of the OpenMP-based implementation over the sequential algorithm are greater than four for all tested DEMs with eight computing threads. The mean speed-up ratio of our MPI-based implementation is greater than eight over TauDEM, which is a widely used MPI-based library for hydrologic information extraction. The speed-up ratios of our MPI-based implementation generally become larger with more computing nodes. This study shows that the Priority-Flood algorithm can be implemented in parallel, which makes it an ideal algorithm for depression identification and filling on both single computers and computer clusters.

Various methods are available for the planar segmentation of point clouds from terrestrial laser scanning. In this letter, a new method is proposed to extract planar features from the range image of a point cloud scanned from one standpoint. In this method, a plane is parameterized by its normal vector and the distance from the origin. The algebraic derivation of the parameters is presented in this letter. The parameters are calculated based on the gradient value of a pixel in the range image. The multiple-band synthetic image of planar parameters is segmented using the Iso cluster unsupervised classification method. Experimental plane segmentation results using range images of two point clouds are illustrated. In comparison with existing methods, the proposed method gives an exact estimation of the planar parameters and can handle planes of any orientation.

Under the indoor building environment, when the fires and other accidents occur, how to effectively organize the masses evacuation and fire rescue, is closely related to the safety of people's lives and property and has become a critical problem of public concern. This paper presents an improved ant colony algorithm (ACO) to solve the problem of how to optimize the evacuation route and rescue route when an accident occurs. According to the key factors affecting people emergency evacuation, such as indoor building environment, fire and its combustion products, problem of path's optimal selection, etc., we propose an emergency evacuation model, based on the model it can give an optimal evacuation route for the mass and an optimal rescue route for the firefighters. We also analyzes the search results, it shows that the search results is robust and reasonable.

In China, drought is one of the major environmental disasters, which bring great harm to the people. The middle reach of Yangtze River is the most important base to produce grains in China. Influenced by the summer monsoon, the drought occurs frequently. In our paper, the NDVI and LST from MODIS data were utilized to calculate the TVDI (Temperature Vegetation Dryness Index), which were used to monitor the drought of the study area. Meteorological drought indices were calculated from 10-day precipitation, temperature and evaporation data of 94 meteorological stations, including precipitation standardized variables, dryness and relative moisture index were used to analyze the degree of drought and the area of drought. The results showed that TVDI is significantly related to soil moisture.

Multiple scans are generally required to fully reconstruct three-dimensional models of botanical trees. An algorithm for the automatic registration of tree point clouds from terrestrial laser scanning is proposed in this letter. The method extracts skeletons from the point clouds and conducts skeleton-based registration automatically. It uses a closed-form solution for the fine registration of the skeletons. An ICP procedure is applied to the roughly aligned tree point clouds to refine registration results of the skeleton-based method. Two example trees are registered using the proposed algorithm. The algorithm does not require a perfect skeleton to be extracted. No manual coarse registration is needed. The algorithm runs slower than our previously proposed method but with better registration accuracy. The algorithm contributes to the automatic marker-free tree point cloud registration and improves the registration accuracy of existing methods.

Multiple scans are generally required to fully reconstruct 3-D models of botanical trees. An algorithm for the automatic registration of tree point clouds scanned from terrestrial laser scanners is proposed in this letter. The method extracts skeletons from the point cloud and conducts coarse registration automatically. It defines a distance measure between two skeleton segments and a mapping cost function between two skeletons. The coarse registration is refined using the Gauss-Newton method. Three example trees, including a Populus euphratica tree scanned in the lower reaches of the Heihe River basin, are registered using the proposed algorithm. The algorithm does not require a perfect skeleton to be extracted. No manual coarse registration is needed. The algorithm contributes to the automatic marker-free tree point cloud registration and improves field scanning efficiency by making the placement of markers unnecessary.