Carnegie Airborne Observatory: in-flight fusion of hyperspectral imaging and waveform light detection and ranging for three-dimensional studies of ecosystems

2007 ◽  
Vol 1 (1) ◽  
pp. 013536 ◽  
Author(s):  
Gregory P. Asner
Keyword(s):  
Hyperspectral Imaging ◽  
Three Dimensional ◽  
Light Detection And Ranging ◽  
Light Detection ◽  
Carnegie Airborne Observatory

scholarly journals Using Airborne Light Detection and Ranging (LIDAR) to Characterize Forest Stand Condition on the Kenai Peninsula of Alaska

2009 ◽  
Vol 24 (2) ◽  
pp. 95-102 ◽  
Author(s):  
Hans-Erik Andersen
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Grid Cell ◽  
Forest Stand ◽  
Light Detection And Ranging ◽  
Inventory Analysis ◽  
Light Detection ◽  
Individual Tree ◽  
Kenai Peninsula

Abstract Airborne laser scanning (also known as light detection and ranging or LIDAR) data were used to estimate three fundamental forest stand condition classes (forest stand size, land cover type, and canopy closure) at 32 Forest Inventory Analysis (FIA) plots distributed over the Kenai Peninsula of Alaska. Individual tree crown segment attributes (height, area, and species type) were derived from the three-dimensional LIDAR point cloud, LIDAR-based canopy height models, and LIDAR return intensity information. The LIDAR-based crown segment and canopy cover information was then used to estimate condition classes at each 10-m grid cell on a 300 × 300-m area surrounding each FIA plot. A quantitative comparison of the LIDAR- and field-based condition classifications at the subplot centers indicates that LIDAR has potential as a useful sampling tool in an operational forest inventory program.

scholarly journals AIRBORNE LIGHT DETECTION AND RANGING (LIDAR) DERIVED DEFORMATION FROM THE MW 6.0 24 AUGUST, 2014 SOUTH NAPA EARTHQUAKE ESTIMATED BY TWO AND THREE DIMENSIONAL POINT CLOUD CHANGE DETECTION TECHNIQUES

2016 ◽  
Vol XLI-B2 ◽  
pp. 35-42 ◽  
Author(s):  
A. W. Lyda ◽  
X. Zhang ◽  
C. L. Glennie ◽  
K. Hudnut ◽  
B. A. Brooks
Keyword(s):  
Change Detection ◽  
Point Cloud ◽  
Near Field ◽  
Three Dimensional ◽  
Light Detection And Ranging ◽  
Data Sets ◽  
Light Detection ◽  
Icp Algorithm ◽  
Detection Algorithms ◽  
Change Detection Algorithms

Remote sensing via LiDAR (Light Detection And Ranging) has proven extremely useful in both Earth science and hazard related studies. Surveys taken before and after an earthquake for example, can provide decimeter-level, 3D near-field estimates of land deformation that offer better spatial coverage of the near field rupture zone than other geodetic methods (e.g., InSAR, GNSS, or alignment array). In this study, we compare and contrast estimates of deformation obtained from different pre and post-event airborne laser scanning (ALS) data sets of the 2014 South Napa Earthquake using two change detection algorithms, Iterative Control Point (ICP) and Particle Image Velocimetry (PIV). The ICP algorithm is a closest point based registration algorithm that can iteratively acquire three dimensional deformations from airborne LiDAR data sets. By employing a newly proposed partition scheme, “moving window,” to handle the large spatial scale point cloud over the earthquake rupture area, the ICP process applies a rigid registration of data sets within an overlapped window to enhance the change detection results of the local, spatially varying surface deformation near-fault. The other algorithm, PIV, is a well-established, two dimensional image co-registration and correlation technique developed in fluid mechanics research and later applied to geotechnical studies. Adapted here for an earthquake with little vertical movement, the 3D point cloud is interpolated into a 2D DTM image and horizontal deformation is determined by assessing the cross-correlation of interrogation areas within the images to find the most likely deformation between two areas. Both the PIV process and the ICP algorithm are further benefited by a presented, novel use of urban geodetic markers. Analogous to the persistent scatterer technique employed with differential radar observations, this new LiDAR application exploits a classified point cloud dataset to assist the change detection algorithms. Ground deformation results and statistics from these techniques are presented and discussed here with supplementary analyses of the differences between techniques and the effects of temporal spacing between LiDAR datasets. Results show that both change detection methods provide consistent near field deformation comparable to field observed offsets. The deformation can vary in quality but estimated standard deviations are always below thirty one centimeters. This variation in quality differentiates the methods and proves that factors such as geodetic markers and temporal spacing play major roles in the outcomes of ALS change detection surveys.

scholarly journals Training a terrain traversability classifier for a planetary rover through simulation

2017 ◽  
Vol 14 (5) ◽  
pp. 172988141773540 ◽  
Author(s):  
Robert A Hewitt ◽  
Alex Ellery ◽  
Anton de Ruiter
Keyword(s):  
Three Dimensional ◽  
Point Clouds ◽  
Light Detection And Ranging ◽  
Data Sets ◽  
Light Detection ◽  
Planetary Rover ◽  
Training Methodology ◽  
Sensor Model ◽  
Random Fashion ◽  
Manual Classification

A classifier training methodology is presented for Kapvik, a micro-rover prototype. A simulated light detection and ranging scan is divided into a grid, with each cell having a variety of characteristics (such as number of points, point variance and mean height) which act as inputs to classification algorithms. The training step avoids the need for time-consuming and error-prone manual classification through the use of a simulation that provides training inputs and target outputs. This simulation generates various terrains that could be encountered by a planetary rover, including untraversable ones, in a random fashion. A sensor model for a three-dimensional light detection and ranging is used with ray tracing to generate realistic noisy three-dimensional point clouds where all points that belong to untraversable terrain are labelled explicitly. A neural network classifier and its training algorithm are presented, and the results of its output as well as other popular classifiers show high accuracy on test data sets after training. The network is then tested on outdoor data to confirm it can accurately classify real-world light detection and ranging data. The results show the network is able to identify terrain correctly, falsely classifying just 4.74% of untraversable terrain.

Studies of a scanning light detection and ranging for three-dimensional detection of plume emission

2015 ◽  
Vol 54 (4) ◽  
pp. 044106
Author(s):  
Lingbing Bu ◽  
Zujing Qiu ◽  
Haiyang Gao ◽  
Aizhen Gao ◽  
Xingyou Huang
Keyword(s):  
Three Dimensional ◽  
Light Detection And Ranging ◽  
Light Detection ◽  
Plume Emission

scholarly journals Analyzing the Angle Effect of Leaf Reflectance Measured by Indoor Hyperspectral Light Detection and Ranging (LiDAR)

2020 ◽  
Vol 12 (6) ◽  
pp. 919 ◽  
Author(s):  
Peilun Hu ◽  
Huaguo Huang ◽  
Yuwei Chen ◽  
Jianbo Qi ◽  
Wei Li ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Specular Reflection ◽  
Incident Angle ◽  
Light Detection And Ranging ◽  
Light Detection ◽  
Leaf Reflectance ◽  
Data Registration ◽  
Angle Effect ◽  
Laser Pulse Intensity

Hyperspectral light detection and ranging (LiDAR) (HSL) combines the characteristics of hyperspectral imaging and LiDAR techniques into a single instrument without any data registration. It provides more information than hyperspectral imaging or LiDAR alone in the extraction of vegetation physiological and biochemical parameters. However, the laser pulse intensity is affected by the incident angle, and its effect on HSL has not yet been fully explored. It is important for employing HSL to investigate vegetation properties. The aim of this paper is to study the incident angle effect of leaf reflectance with HSL and build a model about this impact. In this paper, we studied the angle effect of leaf reflectance from indoor HSL measurements of individual leaves from four typical tree species in Beijing. We observed that (a) the increasing of incident angle decreases the leaf reflectance; (b) the leaf spectrum observed by HSL from 650 to 1000 nm with 10 nm spectral resolution (36 channels) are consistent with those that measured by Analytica Spectra Devices (ASD) spectrometer (R2 = 0.9472 ~ 0.9897); (c) the specular reflection is significant in the red bands, and clear non-Lambertian characteristics are observed. In the near-infrared, there is little specular reflection, but it follows the Lambert-scattering law. We divided the whole band (650–1000 nm) into six bands and established an empirical model to correct the influence of angle effect on the reflectance of the leaf for HSL applications. In the future, the calibration of HSL measurements applied for other targets will be studied by rigorous experiments and modelling.

Applying the bicoded spatial model to nonhuman primates in an arboreal multilayer environment

2013 ◽  
Vol 36 (5) ◽  
pp. 552-553 ◽  
Author(s):  
Allison M. Howard ◽  
Dorothy M. Fragaszy
Keyword(s):  
Spatial Model ◽  
Nonhuman Primates ◽  
Three Dimensional ◽  
Light Detection And Ranging ◽  
Natural Environments ◽  
Light Detection ◽  
Terrestrial Environments

AbstractApplying the framework proposed by Jeffery et al. to nonhuman primates moving in multilayer arboreal and terrestrial environments, we see that these animals must generate a mosaic of many bicoded spaces in order to move efficiently and safely through their habitat. Terrestrial light detection and ranging (LiDAR) technology and three-dimensional modelling of canopy movement may permit testing of Jeffery et al.'s framework in natural environments.

scholarly journals Independent Biaxial Scanning Light Detection and Ranging System Based on Coded Laser Pulses without Idle Listening Time

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2943 ◽  
Author(s):  
Gunzung Kim ◽  
Yongwan Park
Keyword(s):  
Direct Detection ◽  
Three Dimensional ◽  
Laser Pulses ◽  
White Paper ◽  
Light Detection And Ranging ◽  
Prototype System ◽  
Light Detection ◽  
Scanning Lidar ◽  
Idle Listening ◽  
Code Division Multiple

The goal of light detection and ranging (LIDAR) systems is to achieve high-resolution three-dimensional distance images with high refresh rates and long distances. In scanning LIDAR systems, an idle listening time between pulse transmission and reception is a significant obstacle to accomplishing this goal. We apply intensity-modulated direct detection (IM/DD) optical code division multiple access (OCDMA) using nonreturn-to-zero on-off keying to eliminate the idle listening time in scanning LIDAR systems. The transmitter records time information while emitting a coded laser pulse in the measurement angle derived from the pixel information as the measurement direction. The receiver extracts and decodes the reflected laser pulses and estimates the distance to the target using time-of-flight until the pulse is received after being transmitted. Also, we rely on a series of pulses and eliminate alien pulses via several detection decision steps to enhance the robustness of the decision result. We built a prototype system and evaluated its performance by measuring black matte and white paper walls and assessing object detection by measuring a watering can in front of the black matte paper wall. This LIDAR system eliminated both shot and background noises in the reception process and measured greater distances with improvements in accuracy and precision.

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