scholarly journals Regional Scale Rain-Forest Height Mapping Using Regression-Kriging of Spaceborne and Airborne LiDAR Data: Application on French Guiana

2016 ◽  
Vol 8 (3) ◽  
pp. 240 ◽  
Author(s):  
Ibrahim Fayad ◽  
Nicolas Baghdadi ◽  
Jean-Stéphane Bailly ◽  
Nicolas Barbier ◽  
Valéry Gond ◽  
...  
Keyword(s):  
Rain Forest ◽  
French Guiana ◽  
Regional Scale ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Regression Kriging ◽  
Forest Height ◽  
Data Application ◽  
Airborne Lidar Data

scholarly journals Estimation of Voxel-Based Above-Ground Biomass Using Airborne LiDAR Data in an Intact Tropical Rain Forest, Brunei

Forests ◽  
2016 ◽  
Vol 7 (12) ◽  
pp. 259 ◽  
Author(s):  
Eunji Kim ◽  
Woo-Kyun Lee ◽  
Mihae Yoon ◽  
Jong-Yeol Lee ◽  
Yowhan Son ◽  
...  
Keyword(s):  
Rain Forest ◽  
Tropical Rain Forest ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Airborne Lidar Data

scholarly journals Characterizing a New England Saltmarsh with NASA G-LiHT Airborne Lidar

2019 ◽  
Vol 11 (5) ◽  
pp. 509 ◽  
Author(s):  
Ian Paynter ◽  
Crystal Schaaf ◽  
Jennifer Bowen ◽  
Linda Deegan ◽  
Francesco Peri ◽  
...  
Keyword(s):  
New England ◽  
Spartina Alterniflora ◽  
Regional Scale ◽  
Satellite Observations ◽  
Airborne Lidar ◽  
Aerial Imagery ◽  
Lidar Data ◽  
Terrestrial Lidar ◽  
Airborne Lidar Data ◽  
Lidar Observations

Airborne lidar can observe saltmarshes on a regional scale, targeting phenological and tidal states to provide the information to more effectively utilize frequent multispectral satellite observations to monitor change. Airborne lidar observations from NASA Goddard Lidar Hyperspectral and Thermal (G-LiHT) of a well-studied region of saltmarsh (Plum Island, Massachusetts, United States) were acquired in multiple years (2014, 2015 and 2016). These airborne lidar data provide characterizations of important saltmarsh components, as well as specifications for effective surveys. The invasive Phragmites australis was observed to increase in extent from 8374 m2 in 2014, to 8882 m2 in 2015 (+6.1%), and again to 13,819 m2 in 2016 (+55.6%). Validation with terrestrial lidar supported this increase, but suggested the total extent was still underestimated. Estimates of Spartina alterniflora extent from airborne lidar were within 7% of those from terrestrial lidar, but overestimation of height of Spartina alterniflora was found to occur at the edges of creeks (+83.9%). Capturing algae was found to require observations within ±15° of nadir, and capturing creek structure required observations within ±10° of nadir. In addition, 90.33% of creeks and ditches were successfully captured in the airborne lidar data (8206.3 m out of 9084.3 m found in aerial imagery).

scholarly journals Pluriannual beach-dune evolutions at regional scale: Erosion and recovery sequences analysis along the aquitaine coast based on airborne LiDAR data

2019 ◽  
Vol 189 ◽  
pp. 103974 ◽  
Author(s):  
Alexandre Nicolae Lerma ◽  
Bruce Ayache ◽  
Beatrice Ulvoas ◽  
François Paris ◽  
Nicolas Bernon ◽  
...  
Keyword(s):  
Regional Scale ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Sequences Analysis ◽  
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.

The use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space

2021 ◽  
Author(s):  
Renato César dos Santos ◽  
Mauricio Galo ◽  
André Caceres Carrilho ◽  
Guilherme Gomes Pessoa
Keyword(s):  
Urban Space ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Multi Temporal ◽  
Airborne Lidar Data

scholarly journals A Comparative Study about Data Structures Used for Efficient Management of Voxelised Full-Waveform Airborne LiDAR Data during 3D Polygonal Model Creation

2021 ◽  
Vol 13 (4) ◽  
pp. 559
Author(s):  
Milto Miltiadou ◽  
Neill D. F. Campbell ◽  
Darren Cosker ◽  
Michael G. Grant
Keyword(s):  
Data Structure ◽  
Data Structures ◽  
Airborne Lidar ◽  
Memory Allocation ◽  
Lidar Data ◽  
Full Waveform ◽  
Waveform Lidar ◽  
Airborne Lidar Data ◽  
Full Waveform Lidar ◽  
Polygonal Model

In this paper, we investigate the performance of six data structures for managing voxelised full-waveform airborne LiDAR data during 3D polygonal model creation. While full-waveform LiDAR data has been available for over a decade, extraction of peak points is the most widely used approach of interpreting them. The increased information stored within the waveform data makes interpretation and handling difficult. It is, therefore, important to research which data structures are more appropriate for storing and interpreting the data. In this paper, we investigate the performance of six data structures while voxelising and interpreting full-waveform LiDAR data for 3D polygonal model creation. The data structures are tested in terms of time efficiency and memory consumption during run-time and are the following: (1) 1D-Array that guarantees coherent memory allocation, (2) Voxel Hashing, which uses a hash table for storing the intensity values (3) Octree (4) Integral Volumes that allows finding the sum of any cuboid area in constant time, (5) Octree Max/Min, which is an upgraded octree and (6) Integral Octree, which is proposed here and it is an attempt to combine the benefits of octrees and Integral Volumes. In this paper, it is shown that Integral Volumes is the more time efficient data structure but it requires the most memory allocation. Furthermore, 1D-Array and Integral Volumes require the allocation of coherent space in memory including the empty voxels, while Voxel Hashing and the octree related data structures do not require to allocate memory for empty voxels. These data structures, therefore, and as shown in the test conducted, allocate less memory. To sum up, there is a need to investigate how the LiDAR data are stored in memory. Each tested data structure has different benefits and downsides; therefore, each application should be examined individually.

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