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Large-scale and precise measurement of mangrove canopy height is crucial for understanding and evaluating wetland ecosystems' condition, health, and productivity. This study generates a global mangrove canopy height map with a 30 m resolution by integrating Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) photon-counting light detection and ranging (LiDAR) data with multi-source imagery. Initially, high-quality mangrove canopy height samples were extracted using meticulous processing and filtering of ICESat-2 data. Subsequently, mangrove canopy height models were established using the random forest (RF) algorithm, incorporating ICESat-2 canopy height samples, Sentinel-2 data, TanDEM-X DEM data and WorldClim data. Furthermore, a global 30 m mangrove canopy height map was generated utilizing the Google Earth Engine platform. Finally, the global map's accuracy was evaluated by comparing it with reference canopy heights derived from both space-borne and airborne LiDAR data. Results indicate that the global 30 m resolution mangrove height map was found to be consistent with canopy heights obtained from space-borne (r = 0.88, Bisa = -0.07 m, RMSE = 3.66 m, RMSE% = 29.86 %) and airborne LiDAR (r = 0.52, Bisa = -1.08 m, RMSE = 3.39 m, RMSE% = 39.05 %). Additionally, our findings reveal that mangroves worldwide exhibit an average height of 12.65 m, with the tallest mangrove reaching a height of 44.94 m. These results demonstrate the feasibility and effectiveness of using ICESat-2 data integrated with multi-source imagery to generate a global mangrove canopy height map. This dataset offers reliable information that can significantly support government and organizational efforts to protect and conserve mangrove ecosystems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Similarity measurement is one of the key tasks in spatial data analysis. It has a great impact on applications i.e., position prediction, mining and analysis of social behavior pattern. Existing methods mainly focus on the exact matching of polylines which result in the trajectories. However, for the applications like travel/drive behavior analysis, even for objects passing by the same route the trajectories are not the same due to the accuracy of positioning and the fact that objects may move on different lanes of the road. Further, in most cases of spatial data mining, locations and sometimes sequences of locations on trajectories are most important, while how objects move from location to location (the exact geometries of trajectories) is of less interest. For the abovementioned situations, the existing approaches cannot work anymore. In this paper, we propose a grid aware approach to convert trajectories into sequences of codes, so that shape details of trajectories are neglected while emphasizing locations where trajectories pass through. Experiments with Shanghai Float Car Data (FCD) show that the proposed method can calculate trajectories with high similarity if these pass through the same locations. In addition, the proposed methods are very efficient since the data volume is considerably reduced when trajectories are converted into grid-codes., Competing Interests: All authors declare that they have no conflicts of interest.

This paper presents a theoretical method for separating bending and torsion of shape sensing sensor to improve sensing accuracy during its deformation. We design a kind of shape sensing sensor by encapsulating three fibers on the surface of a flexible rod and forming a triangular FBG sensors array. According to the configuration of FBG sensors array, we derive the relationship between bending curvature and bending strain, and set up a function about the packaging angle of FBG sensor and strain induced by torsion under different twist angles. Combined with the influence of bending and torsion on strain, we establish a nonlinear matrix equation resolving three unknown parameters including maximum strain, bending direction and wavelength shift induced by torsion and temperature. The three parameters are sufficient to separate bending and torsion, and acquire two scalar functions including curvature and torsion, which could describe 3D shape of rod according to Frenet-Serret formulas. Experimental results show that the relative average error of measurement about maximum strain, bending direction is respectively 2.65% and 0.86% when shape-sensing sensor is bent into an arc with a radius of 260 mm. The separating method also applied to 2D shape and 3D shape of reconstruction, and the absolute spatial position maximum error is respectively 3.79mm and 11.10mm when shape-sensing sensor with length 500mm is bent into arc shape with a radius 260mm and helical curve. The experiment results verify the feasibility of separating method, which would provide effective parameters for precise 3D reconstruction model of shape sensing sensor.

A mobile mapping system (MMS) is usually utilized to collect environmental data on and around urban roads. Laser scanners and panoramic cameras are the main sensors of an MMS. This paper presents a new method for the registration of the point clouds and panoramic images based on sensor constellation. After the sensor constellation was analyzed, a feature point, the intersection of the connecting line between the global positioning system (GPS) antenna and the panoramic camera with a horizontal plane, was utilized to separate the point clouds into blocks. The blocks for the central and sideward laser scanners were extracted with the segmentation feature points. Then, the point clouds located in the blocks were separated from the original point clouds. Each point in the blocks was used to find the accurate corresponding pixel in the relative panoramic images via a collinear function, and the position and orientation relationship amongst different sensors. A search strategy is proposed for the correspondence of laser scanners and lenses of panoramic cameras to reduce calculation complexity and improve efficiency. Four cases of different urban road types were selected to verify the efficiency and accuracy of the proposed method. Results indicate that most of the point clouds (with an average of 99.7%) were successfully registered with the panoramic images with great efficiency. Geometric evaluation results indicate that horizontal accuracy was approximately 0.10-0.20 m, and vertical accuracy was approximately 0.01-0.02 m for all cases. Finally, the main factors that affect registration accuracy, including time synchronization amongst different sensors, system positioning and vehicle speed, are discussed.

Although map filtering-aided Pedestrian Dead Reckoning (PDR) is capable of largely improving indoor localization accuracy, it becomes less efficient when coping with highly complex indoor spaces. For instance, indoor spaces with a few close corners or neighboring passages can lead to particles entering erroneous passages, which can further cause the failure of subsequent tracking. To address this problem, we propose GridiLoc, a reliable and accurate pedestrian indoor localization method through the fusion of smartphone sensors and a grid model. The key novelty of GridiLoc is the utilization of a backtracking grid filter for improving localization accuracy and for handling dead ending issues. In order to reduce the time consumption of backtracking, a topological graph is introduced for representing candidate backtracking points, which are the expected locations at the starting time of the dead ending. Furthermore, when the dead ending is caused by the erroneous step length model of PDR, our solution can automatically calibrate the model by using the historical tracking data. Our experimental results show that GridiLoc achieves a higher localization accuracy and reliability compared with the commonly-used map filtering approach. Meanwhile, it maintains an acceptable computational complexity., Competing Interests: The authors declare no conflict of interest.

Purpose: The goal of this study was to develop a plasmid-based lux bio-reporter for use to obtain in vivo images of Brucella suis vaccine strain 2 (B.suis S2) infection with high resolution and good definition., Procedures: The pBBR-lux (pBBR1MCS-2-lxCDABE) plasmid that carries the luxCDABE operon was introduced into B. suis S2 by electroporation yielding B. suis S2-lux. The spatial and temporal transit of B. suis S2 in mice and guinea pigs was monitored by bioluminescence imaging., Results: The plasmid pBBR-lux is stable in vivo and does not appear to impact the virulence or growth of bacteria. This sensitive luciferase reporter could represent B. suis S2 survival in real time. B. suis S2 mainly colonized the lungs, liver, spleen, and uterus in mice and guinea pigs as demonstrated by bioluminescence imaging., Conclusion: The plasmid-based lux bioreporter strategy can be used to obtain high resolution in vivo images of B. suis S2 infection in mice and guinea pigs.