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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Capabilities of Lidar- and Satellite Data in Assessing the Drivers of Avian Diversity in a Fragmented Landscape
摘要： In modern landscapes, small habitat patches such as woodlands isolated in an agricultural matrix, can be important refuges for wildlife. However, their value as habitat may be compromised by their size and thus knowledge of how habitat structure influences habitat quality is vital to maximize species diversity. This study examined the factors driving avian diversity in four small woods in an agricultural landscape, and how accurately remote sensing (RS) metrics were able to quantify this. Linear mixed-effect models were used to combine annual breeding bird census data with data of habitat structure from satellite images and airborne lidar. The aims were firstly to examine the drivers of bird diversity, and secondly to reveal the strengths and weaknesses of the compared RS datasets in quantifying them. The results showed that, at first, bird diversity increased significantly towards the edges, being driven in part by vegetation structure. The amount of understorey vegetation was the most significant driver of diversity, due to which lidar-based models outperformed satellite-based ones. In general, lidar metrics correlated strongly with bird diversity, but such relationships were not discovered with satellite image metrics. The results indicate that the drivers of diversity, especially in fragmented woodlands may be too fine-scaled to be studied without sufficient consideration of the structural component of vegetation, which was proven to be attainable from lidar data.
关键词： habitat,fragmentation,lidar,bird diversity,satellite,landscape ecology
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Bias Impact Analysis and Calibration of Uav-Based Mobile Lidar System
摘要： Over the past few years, developments in mobile mapping technology, specifically Unmanned Aerial Vehicles (UAVs), have made accurate 3D mapping more feasible, thus emerging as an economical and practical mobile mapping platform. LiDAR-based UAV mapping systems are gaining widespread recognition as an efficient and cost-effective technique for rapid collection of 3D geospatial data. To derive point clouds with high positional accuracy, estimation of mounting parameters relating the laser scanners to the onboard GNSS/INS unit is the foremost and necessary step. In this paper, we first devise an optimal flight and target configuration by conducting a rigorous theoretical analysis of the potential impact of bias in mounting parameters of a LiDAR unit on the resultant point cloud. Then, we propose a LiDAR system calibration strategy that can directly estimate the mounting parameters for spinning multi-beam laser scanners onboard a UAV through an outdoor calibration procedure.
关键词： multi-beam laser scanners,UAV,GNSS/INS,mounting parameters,LiDAR
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE International Conference on Intelligent Transportation Systems (ITSC) - Maui, HI, USA (2018.11.4-2018.11.7)] 2018 21st International Conference on Intelligent Transportation Systems (ITSC) - Vehicle Detection and Localization using 3D LIDAR Point Cloud and Image Semantic Segmentation
摘要： This paper presents a real-time approach to detect and localize surrounding vehicles in urban driving scenes. We propose a multimodal fusion framework that processes both 3D LIDAR point cloud and RGB image to obtain robust vehicle position and size in a Bird's Eye View (BEV). Semantic segmentation from RGB images is obtained using our efficient Convolutional Neural Network (CNN) architecture called ERFNet. Our proposal takes advantage of accurate depth information provided by LIDAR and detailed semantic information processed from a camera. The method has been tested using the KITTI object detection benchmark. Experiments show that our approach outperforms or is on par with other state-of-the-art proposals but our CNN was trained in another dataset, showing a good generalization capability to any domain, a key point for autonomous driving.
关键词： localization,ERFNet,image semantic segmentation,KITTI,autonomous driving,vehicle detection,CNN,point cloud,multimodal fusion,3D LIDAR
更新于2025-09-23 15:22:29
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A greyscale voxel model for airborne lidar data applied to building detection
摘要： The existing binary voxel model algorithm for 3D building detection (3BD) from airborne lidar cannot distinguish between connected buildings and non-buildings. As a result, a greyscale voxel structure model, using the discretised mean intensity of lidar points, is presented to support subsequent building detection in areas where buildings are adjacent to non-buildings but with different greyscales. The resulting 3BD algorithm first detects a building roof by selecting voxels characterised by a jump in elevation as seeds, labelling them and their 3D connected regions as rooftop voxels. Then voxels which fall into buffers and possess similar greyscales to that of the corresponding building outline are assigned as building facades. The results for detected buildings are evaluated using lidar data with different densities and demonstrate a high rate of success.
关键词： lidar,greyscale,voxel,building detection,point cloud,intensity
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Dection and Health Analysis of Individual Tree in Urban Environment with Multi-Sensor Platform
摘要： With the technology enhanced, 3D mobile light detection and ranging (LiDAR) can produce more accurate 3D information for the objects. Meanwhile, hyperspectral remote sensing has more number of wavelengths and provides a higher resolution spectrum of objects. This paper proposes a multi-sensor platform to provide these two data for health detection at the individual tree level in urban environments. We firstly locate and segment the suspected tree objects by ground removal and Euclidean distance clustering. Then we take use of spectrum to remove non-tree objects, e.g., buildings, light poles. After that, we use LiDAR data to compute the geometric parameters of each tree and hyperspectral data to analyze its health situation.
关键词： point cloud,hyperspectral,spectrum,LiDAR,individual tree detection,health monitoring
更新于2025-09-23 15:22:29
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Using LiDAR to develop high-resolution reference models of forest structure and spatial pattern
摘要： Successful restoration of degraded forest landscapes requires reference models that adequately capture structural heterogeneity at multiple spatial scales and for specific landforms. Despite this need, managers often lack access to reliable reference information, in large part because field-based methods for assessing variation in forest structure are costly and inherently suffer from limited replication and spatial coverage and, therefore, yield limited insights about the ecological structure of reference forests at landscape scales. LiDAR is a cost-effective alternative that can provide high-resolution characterizations of variation in forest structure among landform types. However, managers and researchers have been reluctant to use LiDAR for characterizing structure because of low confidence in its capacity to approximate actual tree distributions. By calculating bias in LiDAR estimates for a range of tree-height cutoffs, we improved LiDAR's ability to capture structural variability in terms of individual trees. We assessed bias in the processed LiDAR data by comparing datasets of field-measured and LiDAR-detected trees of various height classes in terms of overall number of trees and estimates of structure and spatial pattern in an important contemporary reference forest, the Sierra de San Pedro Martir National Park, Baja California, Mexico. Agreement between LiDAR- and field-based estimates of tree density, as well as estimates of forest structure and spatial pattern, was maximized by removing trees less than 12 m tall. We applied this height cutoff to LiDAR-detected trees of our study landscape, and asked if forest structure and spatial pattern varied across topographic settings. We found that canyons, shallow northerly, and shallow southerly slopes were structurally similar; each had a greater number of all trees, large trees, and large tree clumps than steep southerly slopes and ridges. Steep northerly slopes supported unique structures, with taller trees than ridges and shorter trees than canyons and shallow southerly slopes. Our results show that characterizations of forest structure based on LiDAR-detected trees are reasonably accurate when the focus is narrowed to the overstory. In addition, our finding of strong variation of forest structure and spatial pattern across topographic settings demonstrates the importance of developing reference models at the landscape scale, and highlights the need for replicated sampling among stands and landforms. Methods developed here should be useful to managers interested in using LiDAR to characterize distributions of medium and large overstory trees, particularly for the development of landscape-scale reference models.
关键词： Ecological reference model,Forest structure,Sierra de San Pedro Martir,Spatial pattern,Landscape restoration,Spatial scale,LiDAR
更新于2025-09-23 15:22:29
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A Versatile Silicon-Silicon Nitride Photonics Platform for Enhanced Functionalities and Applications
摘要： Silicon photonics is one of the most prominent technology platforms for integrated photonics and can support a wide variety of applications. As we move towards a mature industrial core technology, we present the integration of silicon nitride (SiN) material to extend the capabilities of our silicon photonics platform. Depending on the application being targeted, we have developed several integration strategies for the incorporation of SiN. We present these processes, as well as key components for dedicated applications. In particular, we present the use of SiN for athermal multiplexing in optical transceivers for datacom applications, the nonlinear generation of frequency combs in SiN micro-resonators for ultra-high data rate transmission, spectroscopy or metrology applications and the use of SiN to realize optical phased arrays in the 800–1000 nm wavelength range for Light Detection And Ranging (LIDAR) applications. These functionalities are demonstrated using a 200 mm complementary metal-oxide-semiconductor (CMOS)-compatible pilot line, showing the versatility and scalability of the Si-SiN platform.
关键词： transceiver,silicon photonics,Kerr nonlinearity,optical phased array,Coarse Wavelength Division Multiplexing (CWDM),multiplexing,LIDAR,silicon nitride,beam steering,frequency comb,grating coupler
更新于2025-09-23 15:22:29
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Development of Raman Lidar for Remote Sensing of CO2 Leakage at an Artificial Carbon Capture and Storage Site
摘要： We developed a Raman lidar system that can remotely detect CO2 leakage and its volume mixing ratio (VMR). The system consists of a laser, a telescope, an optical receiver, and detectors. Indoor CO2 cell measurements show that the accuracy of the Raman lidar is 99.89%. Field measurements were carried out over a four-day period in November 2017 at the Eumsong Environmental Impact Evaluation Test Facility (EIT), Korea, where a CO2 leak was located 0.2 km from the Raman lidar. The results show good agreement between CO2 VMR measured by the Raman lidar system (CO2 VMRRaman LIDAR) and that measured by in situ instruments (CO2 VMRIn-situ). The correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), and percentage difference between CO2 VMRIn-situ and CO2 VMRRaman LIDAR are 0.81, 0.27%, 0.37%, and 4.92%, respectively. The results indicate that Raman lidar is an effective tool in detecting CO2 leakage and in measuring CO2 VMR remotely.
关键词： CO2,CO2 leakage remote sensing,Carbon capture and storage,Raman lidar
更新于2025-09-23 15:22:29
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An Improved DBSCAN Method for LiDAR Data Segmentation with Automatic Eps Estimation
摘要： Point cloud data segmentation, ?ltering, classi?cation, and feature extraction are the main focus of point cloud data processing. DBSCAN (density-based spatial clustering of applications with noise) is capable of detecting arbitrary shapes of clusters in spaces of any dimension, and this method is very suitable for LiDAR (Light Detection and Ranging) data segmentation. The DBSCAN method needs at least two parameters: The minimum number of points minPts, and the searching radius ε. However, the parameter ε is often harder to determine, which hinders the application of the DBSCAN method in point cloud segmentation. Therefore, a segmentation algorithm based on DBSCAN is proposed with a novel automatic parameter ε estimation method—Estimation Method based on the average of k nearest neighbors’ maximum distance—with which parameter ε can be calculated on the intrinsic properties of the point cloud data. The method is based on the ?tting curve of k and the mean maximum distance. The method was evaluated on different types of point cloud data: Airborne, and mobile point cloud data with and without color information. The results show that the accuracy values using ε estimated by the proposed method are 75%, 74%, and 71%, which are higher than those using parameters that are smaller or greater than the estimated one. The results demonstrate that the proposed algorithm can segment different types of LiDAR point clouds with higher accuracy in a robust manner. The algorithm can be applied to airborne and mobile LiDAR point cloud data processing systems, which can reduce manual work and improve the automation of data processing.
关键词： parameter estimation,segmentation,DBSCAN,LiDAR
更新于2025-09-23 15:22:29
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CALIPSO lidar level?3 aerosol profile product: version?3 algorithm design
摘要： The CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) level 3 aerosol profile product reports globally gridded, quality-screened, monthly mean aerosol extinction profiles retrieved by CALIOP (the Cloud-Aerosol Lidar with Orthogonal Polarization). This paper describes the quality screening and averaging methods used to generate the version 3 product. The fundamental input data are CALIOP level 2 aerosol extinction profiles and layer classification information (aerosol, cloud, and clear-air). Prior to aggregation, the extinction profiles are quality-screened by a series of filters to reduce the impact of layer detection errors, layer classification errors, extinction retrieval errors, and biases due to an intermittent signal anomaly at the surface. The relative influence of these filters are compared in terms of sample rejection frequency, mean extinction, and mean aerosol optical depth (AOD). The 'extinction QC flag' filter is the most influential in preventing high-biases in level 3 mean extinction, while the 'misclassified cirrus fringe' filter is most aggressive at rejecting cirrus misclassified as aerosol. The impact of quality screening on monthly mean aerosol extinction is investigated globally and regionally. After applying quality filters, the level 3 algorithm calculates monthly mean AOD by vertically integrating the monthly mean quality-screened aerosol extinction profile. Calculating monthly mean AOD by integrating the monthly mean extinction profile prevents a low bias that would result from alternately integrating the set of extinction profiles first and then averaging the resultant AOD values together. Ultimately, the quality filters reduce level 3 mean AOD by -24 and -31 % for global ocean and global land, respectively, indicating the importance of quality screening.
关键词： version 3,aerosol,lidar,optical depth,CALIPSO,quality screening,extinction
更新于2025-09-23 15:22:29