scholarly journals Light Detection and Ranging (LiDAR) Assisted Detection of Rock Outcrops in Appalachian Hardwood Forests

Author(s):  
Walter Smith ◽  
Caleb Z. Mullins

The identification of small habitat features embedded within forest ecosystems is a challenge for many wildlife inventory and monitoring programs, especially for those involving rock outcrop specialist taxa. Rock outcrops are often difficult to remotely detect in dense Appalachian hardwood forests, as most outcrops remain hidden under the forest canopy and therefore invisible when relying on aerial orthoimagery to pinpoint habitat features. We investigated the ability for light detection and ranging (LiDAR) point cloud data to identify small rock outcrops during the environmental assessment phase of a proposed management project on the Jefferson National Forest in Virginia, USA. We specifically compared this approach to the visual identification of rock outcrops across the same area using aerial orthoimagery. Our LiDAR-based approach identified three times as many rock outcrop sites as aerial orthoimagery, resulting in the field-verification of four times as many previously-unknown populations of green salamanders Aneides aeneus, a rock outcrop specialist amphibian of high conservation concern, than would have been possible if relying on aerial orthoimagery alone to guide surveys. Our results indicate that LiDAR-based methods may provide an effective, efficient, and low-error approach that can remotely identify below-canopy rock outcrops embedded within Appalachian forests, especially when researchers lack pre-existing knowledge of local terrain and the location of habitat features.

Author(s):  
Yaneev Golombek ◽  
Wesley E. Marshall

This study investigates the feasibility of extracting streetscape features from high-density United States Geological Survey (USGS) quality level 1 (QL1) light detection and ranging (LiDAR) and quantifying the features into three-dimensional (3D) volumetric pixel (voxel) zones. As the USGS embarks on a national LiDAR database with the goal of collecting LiDAR across the continuous U.S.A., the USGS primarily requires QL2 or QL1 as a collection standard. The authors’ previous study thoroughly investigated the limits of extracting streetscape features with QL2 data, which was primarily limited to buildings and street trees. Recent studies published by other researchers that utilize advanced digital mapping techniques for streetscape measuring acknowledge that most features outside of buildings and street trees are too small to detect. QL1 data, however, is four times denser than QL2 data. This study divides streetscapes into Thiessen proximal polygons, sets voxel parameters, classifies QL1 LiDAR point cloud data, and computes quantitative statistics where classified point cloud data intersects voxels within the streetscape polygons. It demonstrates how most other common streetscape features are detectable in a standard urban QL1 dataset. In addition to street trees and buildings, one can also legitimately extract and statistically quantify walls, fences, landscape vegetation, light posts, traffic lights, power poles, power lines, street signs, and miscellaneous street furniture. Furthermore, as these features are processed into their appropriate voxel height zones, this study introduces a new methodology for obtaining comprehensive tabular descriptive statistics describing how streetscape features are truly represented in 3D.


2009 ◽  
Vol 24 (2) ◽  
pp. 95-102 ◽  
Author(s):  
Hans-Erik Andersen

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.


Wind Energy ◽  
2012 ◽  
Vol 16 (3) ◽  
pp. 353-366 ◽  
Author(s):  
Knud A. Kragh ◽  
Morten H. Hansen ◽  
Torben Mikkelsen

2021 ◽  
pp. 1-1
Author(s):  
Chul-Soon Im ◽  
Sung-Moon Kim ◽  
Kyeong-Pyo Lee ◽  
Seong-Hyeon Ju ◽  
Jung-Ho Hong ◽  
...  

2012 ◽  
Vol 51 (8) ◽  
pp. 083609-1 ◽  
Author(s):  
Hajin J. Kim ◽  
Charles B. Naumann ◽  
Michael C. Cornell

2009 ◽  
Vol 77 ◽  
pp. 1-27 ◽  
Author(s):  
Rachel Opitz

La città romana di Falerii Novi e quella pre-romana di Falerii Veteres vengono riviste in questo articolo attraverso la combinazione di dati da ricognizione lidar (light detection and ranging) e geofisica. La ricognizione lidar fornisce per la prima volta infomiazioni dettagliate sui bordi topograficamente complessi di questi siti e ha permesso di identificare un certo numero di nuove strutture. Osservando tali strutture nel contesto dei dati topografici e geofisici, sono state esplorate le aree urbane periferiche sia come zone per movimento sia come facciate. Tramite questi esempi vengono considerati i potenziali contributi forniti dal lidar alla comprensione generale dell'urbanismo pre-romano e romano.


Author(s):  
Vinicius Conti da Costa ◽  
Bruno Ziegler Haselein ◽  
Filipe Barbosa Veras ◽  
Manoel Kolling Dutra ◽  
Tiago Pinto

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