scholarly journals Component-level selection and qualification for the Global Ecosystem Dynamics Investigation (GEDI) laser altimeter transmitter

2018 ◽  
Author(s):  
Aleksey A. Vasilyev ◽  
Paul R. Stysley ◽  
D. Barry Coyle ◽  
Erich Frese ◽  
Furqan Chiragh ◽  
...  
Keyword(s):  
Ecosystem Dynamics ◽  
Component Level ◽  
Laser Altimeter

scholarly journals A Structural Classification of Australian Vegetation Using ICESat/GLAS, ALOS PALSAR, and Landsat Sensor Data

2019 ◽  
Vol 11 (2) ◽  
pp. 147 ◽  
Author(s):  
Peter Scarth ◽  
John Armston ◽  
Richard Lucas ◽  
Peter Bunting
Keyword(s):  
National Level ◽  
Airborne Lidar ◽  
Ecosystem Dynamics ◽  
Sensor Data ◽  
Vegetation Density ◽  
Structural Classification ◽  
Alos Palsar ◽  
Laser Altimeter ◽  
Waveform Data ◽  
Vegetation Height

Australia has historically used structural descriptors of height and cover to characterize, differentiate, and map the distribution of woody vegetation across the continent but no national satellite-based structural classification has been available. In this study, we present a new 30-m spatial resolution reference map of Australian forest and woodland structure (height and cover), with this generated by integrating Landsat Thematic Mapper (TM) and Enhanced TM, Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) and Ice, Cloud, and land Elevation (ICESat),and Geoscience Laser Altimeter System (GLAS) data. ALOS PALSAR and Landsat-derived Foliage Projective Cover (FPC) were used to segment and classify the Australian landscape. Then, from intersecting ICESat waveform data, vertical foliage profiles and height metrics (e.g., 95% percentile height, mean height and the height to maximum vegetation density) were extracted for each of the classes generated. Within each class, and for selected areas, the variability in ICESat profiles was found to be similar with differences between segments of the same class attributed largely to clearance or disturbance events. ICESat metrics and profiles were then assigned to all remaining segments across Australia with the same class allocation. Validation against airborne LiDAR for a range of forest structural types indicated a high degree of correspondence in estimated height measures. On this basis, a map of vegetation height was generated at a national level and was combined with estimates of cover to produce a revised structural classification based on the scheme of the Australian National Vegetation Information System (NVIS). The benefits of integrating the three datasets for segmenting and classifying the landscape and retrieving biophysical attributes was highlighted with this leading the way for future mapping using ALOS-2 PALSAR-2, Landsat/Sentinel-2, Global Ecosystem Dynamics Investigation (GEDI), and ICESat-2 LiDAR data. The ability to map across large areas provides considerable benefits for quantifying carbon dynamics and informing on biodiversity metrics.

scholarly journals waveformlidar: An R Package for Waveform LiDAR Processing and Analysis

2019 ◽  
Vol 11 (21) ◽  
pp. 2552 ◽  
Author(s):  
Tan Zhou ◽  
Sorin Popescu
Keyword(s):  
Vegetation Structure ◽  
R Package ◽  
Ecosystem Dynamics ◽  
Lidar Data ◽  
Laser Altimeter ◽  
The Public ◽  
Full Waveform ◽  
Small Footprint ◽  
Waveform Lidar ◽  
Different Sources

A wealth of Full Waveform (FW) LiDAR (Light Detection and Ranging) data are available to the public from different sources, which is poised to boost extensive applications of FW LiDAR data. However, we lack a handy and open source tool that can be used by potential users for processing and analyzing FW LiDAR data. To this end, we introduce waveformlidar, an R package dedicated to FW LiDAR processing, analysis and visualization as a solution to the constraint. Specifically, this package provides several commonly used waveform processing methods such as Gaussian, Adaptive Gaussian and Weibull decompositions and deconvolution approaches (Gold and Richard-Lucy (RL)) with users’ customized settings. In addition, we also developed functions to derive commonly used waveform metrics for characterizing vegetation structure. Moreover, a new way to directly visualize FW LiDAR data is developed by converting waveforms into points to form the Hyper Point Cloud (HPC), which can be easily adopted and subsequently analyzed with existing discrete-return LiDAR processing tools such as LAStools and FUSION. Basic explorations of the HPC such as 3D voxelization of the HPC and conversion from original waveforms to composite waveforms are also available in this package. All of these functions are developed based on small-footprint FW LiDAR data but they can be easily transplanted to the large footprint FW LiDAR data such as Geoscience Laser Altimeter System (GLAS) and Global Ecosystem Dynamics Investigation (GEDI) data analysis. It is anticipated that these functions will facilitate the widespread use of FW LiDAR and be beneficial for better estimating biomass and characterizing vegetation structure at various scales.

scholarly journals waveformlidar: An R Package for Waveform LiDAR Processing and Analysis

2019 ◽  
Author(s):  
Tan Zhou ◽  
Sorin Popescu
Keyword(s):  
Vegetation Structure ◽  
R Package ◽  
Ecosystem Dynamics ◽  
Lidar Data ◽  
Laser Altimeter ◽  
The Public ◽  
Full Waveform ◽  
Small Footprint ◽  
Waveform Lidar ◽  
Different Sources

A wealth of Full Waveform (FW) LiDAR data are available to the public from different sources, which is poised to boost the extensive application of FW LiDAR data. However, we lack a handy and open source tool that can be used by potential users for processing and analyzing FW LiDAR data. To this end, we introduce waveformlidar, an R package dedicated to FW LiDAR processing, analysis and visualization as a solution to the constraint. Specifically, this package provides several commonly used waveform processing methods such as Gaussian, adaptive Gaussian and Weibull decompositions, and deconvolution approaches (Gold and Richard-Lucy (RL)) with users’ customized settings. In addition, we also developed functions to derive commonly used waveform metrics for characterizing vegetation structure. Moreover, a new way to directly visualize FW LiDAR data is developed through converting waveforms into points to form the Hyper Point cloud (HPC), which can be easily adopted and subsequently analyzed with existing discrete-return LiDAR processing tools such as LAStools and FUSION. Basic explorations of the HPC such as 3D voxelization of the HPC and conversion from original waveforms to composite waveforms are also available in this package. All of these functions are developed based on small-footprint FW LiDAR data, but they can be easily transplanted to the large footprint FW LiDAR data such as Geoscience Laser Altimeter System (GLAS) and Global Ecosystem Dynamics Investigation (GEDI) data analysis. It is anticipated that these functions will facilitate the widespread use of FW LiDAR and be beneficial for better estimating biomass and characterizing vegetation structure at various scales. The package and code examples can be found at https://github.com/tankwin08/waveformlidar.

Dynamic Infrared System Level Fault Isolation

2003 ◽  
Author(s):  
John A. Naoum ◽  
Johan Rahardjo ◽  
Yitages Taffese ◽  
Marie Chagny ◽  
Jeff Birdsley ◽  
...  
Keyword(s):  
Fault Isolation ◽  
System Level ◽  
System Component ◽  
Component Level ◽  
Ir Imaging ◽  
Non Destructive

Abstract The use of Dynamic Infrared (IR) Imaging is presented as a novel, valuable and non-destructive approach for the analysis and isolation of failures at a system/component level.

Cloud-integrated wireless-optical broadband access network with survivability

2020 ◽  
Vol 10 ◽  
Author(s):  
Sangita Solanki ◽  
Raksha Upadhyay ◽  
Uma Rathore Bhatt
Keyword(s):  
Access Network ◽  
Access Networks ◽  
Manhattan Distance ◽  
Broadband Access ◽  
Optical Access Networks ◽  
Component Level ◽  
Backup Path ◽  
Wireless Router ◽  
Path Computation ◽  
Broadband Access Network

Cloud-integrated wireless optical broadband (CIW) access networks inheriting advantages of cloud computing, wireless and optical access networks have a broad prospect in the future. Due to failure of components like OLT level, ONU level, link or path failure and cloud component level in CIW, survivability is becoming one of the important issues. In this paper, we have presented cloud-integrated wireless-optical broadband access network with survivability using integer linear programming (ILP) model, to minimize the number of cloud components while providing maximum backup paths. Hence, we have proposed protection through cloud-integrated wireless router to available ONUs (PCIWRAO). So, evaluated the backup path computation. We have considered ONU level failure in which the affected traffic is transferred through wireless routers and cloud component to the available ONUs using Manhattan distance algorithm. Simulation results show different configurations for different number of routers and cloud components illustrating available backup path when ONU fails.

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