scholarly journals Erratum: Wang, Q.; Ni-Meister, W. Forest Canopy Height and Gaps from Multiangular BRDF, Assessed with Airborne LiDAR Data (Short Title: Vegetation Structure from Lidar and Multiangular Data). Remote Sens. 2019, 11, 2566

2020 ◽  
Vol 13 (1) ◽  
pp. 17
Author(s):  
Qiang Wang ◽  
Wenge Ni-Meister
Keyword(s):  
Vegetation Structure ◽  
Forest Canopy ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Airborne Lidar Data

There is an error in the title of one of our publications [...]

scholarly journals Regionalization of an Existing Global Forest Canopy Height Model for Forests of the Southern United States

2021 ◽  
Vol 13 (9) ◽  
pp. 1722
Author(s):  
Nian-Wei Ku ◽  
Sorin Popescu ◽  
Marian Eriksson
Keyword(s):  
United States ◽  
Large Scale ◽  
Forest Canopy ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Southern United States ◽  
Biomass Estimation ◽  
Lidar Data ◽  
Local Data ◽  
Airborne Lidar Data

A large-scale global canopy height map (GCHM) is essential for global forest aboveground biomass estimation. Four GCHMs have recently been built using data from the Geoscience Laser Altimeter System (GLAS) sensor aboard the Ice, Cloud, and land Elevation Satellite (ICESat), along with auxiliary spatial and climate information. The main objectives of this research were to find out how well a selected GCHM agrees with airborne lidar data from locations in the southern United States and to recalibrate that GCHM to more closely match the forest canopy heights found in the region. The airborne lidar resource was built from data collected between 2010 and 2012, available from in-house and publicly available sources, for sites that included a variety of vegetation types across the southern United States. EPA ecoregions were used to provide ecosystem information for the southern United States. The airborne lidar data were pre-processed to provide lidar-derived metrics, and assigned to four height categories—namely, returns from above 0 m, 1 m, 3 m, and 5 m. The assessment phase results indicated that the 90th and 95th percentiles of the airborne lidar height values were well-suited for use in the recalibration phase of the study. Simple linear regression was used to generate a new, recalibrated GCHM. It was concluded that the characterization of the agreement of a selected GCHM with local data, followed by the recalibration of the existing GCHM to the local region, is both viable and essential for future GCHMs in studies conducted at large scales.

scholarly journals Influence of voxel size on forest canopy height estimates using full-waveform airborne LiDAR data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Cheng Wang ◽  
Shezhou Luo ◽  
Xiaohuan Xi ◽  
Sheng Nie ◽  
Dan Ma ◽  
...  
Keyword(s):  
Forest Canopy ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Voxel Size ◽  
Height Estimates ◽  
Full Waveform ◽  
Airborne Lidar Data

scholarly journals Assessing and Correcting Topographic Effects on Forest Canopy Height Retrieval Using Airborne LiDAR Data

Sensors ◽  
2015 ◽  
Vol 15 (6) ◽  
pp. 12133-12155 ◽  
Author(s):  
Zhugeng Duan ◽  
Dan Zhao ◽  
Yuan Zeng ◽  
Yujin Zhao ◽  
Bingfang Wu ◽  
...  
Keyword(s):  
Forest Canopy ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Topographic Effects ◽  
Airborne Lidar Data

scholarly journals Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wuming Zhang ◽  
Shangshu Cai ◽  
Xinlian Liang ◽  
Jie Shao ◽  
Ronghai Hu ◽  
...  
Keyword(s):  
Tree Height ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Simulation Based ◽  
Maximum Tree Height ◽  
Airborne Lidar Data ◽  
Better Than

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

scholarly journals Modelling relationships between birds and vegetation structure using airborne LiDAR data: a review with case studies from agricultural and woodland environments

Ibis ◽  
2005 ◽  
Vol 147 (3) ◽  
pp. 443-452 ◽  
Author(s):  
RICHARD B. BRADBURY ◽  
ROSS A. HILL ◽  
DAVID C. MASON ◽  
SHELLEY A. HINSLEY ◽  
JEREMY D. WILSON ◽  
...  
Keyword(s):  
Case Studies ◽  
Vegetation Structure ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Airborne Lidar Data

Tropical Forest Canopy Structure and Change Assessment Using Landsat, GEDI, and Airborne Lidar Data

2021 ◽  
Author(s):  
Peter Potapov ◽  
Xinyuan Li ◽  
Andres Hernandez-Serna ◽  
Svetlana Turubanova ◽  
Alexandra Tyukavina ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Forest Canopy ◽  
Canopy Structure ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Change Assessment ◽  
Airborne Lidar Data ◽  
Tropical Forest Canopy

scholarly journals Validation of Canopy Height Profile methodology for small-footprint full-waveform airborne LiDAR data in a discontinuous canopy environment

2015 ◽  
Vol 104 ◽  
pp. 144-157 ◽  
Author(s):  
Karolina D. Fieber ◽  
Ian J. Davenport ◽  
Mihai A. Tanase ◽  
James M. Ferryman ◽  
Robert J. Gurney ◽  
...  
Keyword(s):  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Height Profile ◽  
Full Waveform ◽  
Small Footprint ◽  
Airborne Lidar Data
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