Automatic land-water classification using multispectral airborne LiDAR data for near-shore and river environments

This study investigates the use of the world’s first multispectral airborne LiDAR sensor, Optech Titan, manufactured by Teledyne Optech to serve the purpose of automatic land-water classification with a particular focus on near shore region and river environment. Although there exist recent studies utilizing airborne LiDAR data for shoreline detection and water surface mapping, the majority of them only perform experimental testing on clipped data subset or rely on data fusion with aerial/satellite image. In addition, most of the existing approaches require manual intervention or existing tidal/datum data for sample collection of training data. To tackle the drawbacks of previous approaches, we propose and develop an automatic data processing workflow for land-water classification using multispectral airborne LiDAR data. Depending on the nature of the study scene, two methods are proposed for automatic training data selection. The first method utilizes the elevation/intensity histogram fitted with Gaussian mixture model (GMM) to preliminarily split the land and water bodies. The second method mainly relies on the use of a newly developed scan line elevation intensity ratio (SLIER) to estimate the water surface data points. Regardless of the training methods being used, feature spaces can be constructed using the multispectral LiDAR intensity, elevation and other features derived from these parameters. The comprehensive workflow was tested with two datasets collected for different near shore region and river environment, where the overall accuracy yielded better than 96&thinsp;%.

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Using Multispectral Airborne LiDAR Data for Land/Water Discrimination: A Case Study at Lake Ontario, Canada

Applied Sciences ◽

10.3390/app8030349 ◽

2018 ◽

Vol 8 (3) ◽

pp. 349 ◽

Cited By ~ 8

Author(s):

Salem Morsy ◽

Ahmed Shaker ◽

Ahmed El-Rabbany

Keyword(s):

Lake Ontario ◽

Airborne Lidar ◽

Lidar Data ◽

Airborne Lidar Data ◽

Land Water

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Height Extraction and Stand Volume Estimation Based on Fusion Airborne LiDAR Data and Terrestrial Measurements for a Norway Spruce [Picea abies (L.) Karst.] Test Site in Romania

Notulae Botanicae Horti Agrobotanici Cluj-Napoca ◽

10.15835/nbha.44.1.10155 ◽

2016 ◽

Vol 44 (1) ◽

Author(s):

Bogdan APOSTOL ◽

Adrian LORENT ◽

Marius PETRILA ◽

Vladimir GANCZ ◽

Ovidiu BADEA

Keyword(s):

Picea Abies ◽

Norway Spruce ◽

Test Site ◽

Volume Estimation ◽

Airborne Lidar ◽

Lidar Data ◽

Stand Volume ◽

Airborne Lidar Data

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

Forest Ecosystems ◽

10.1186/s40663-019-0212-0 ◽

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.

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

Applied Geomatics ◽

10.1007/s12518-021-00371-6 ◽

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

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

Remote Sensing ◽

10.3390/rs13040559 ◽

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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