Airborne laser scanning (ALS) is an active remote sensing technique providing range data as 3D point clouds. This paper aims at presenting a survey of the literature related to such techniques, with emphasis on the new sensors called full-waveform lidar systems. Indeed, an emitted laser pulse interacts with complex natural and man-made objects leading to a temporal distortion of the returned energy profile. The new technology of full-waveform laser scanning systems permits one to digitize the complete waveform of each backscattered pulse. Full-waveform lidar data give more control to an end user in the interpretation process of the physical measurement and provide additional information about the structure and the physical backscattering characteristics of the illuminated surfaces. In this paper, the theoretical principles of full-waveform airborne laser scanning are first described. Afterwards, a review of the main sensors as well as signal processing techniques are presented. We then discuss the interpretation of full-waveform measures with special interest on vegetated and urban areas.

Full-waveform topographic lidar : State-of-the-art

https://cfs.nrcan.gc.ca/pubwarehouse/pdfs/33377.pdf

Lidar sampling for large-area forest characterization: A review

Airborne lidar systems have become a source for the acquisition of elevation data. They provide georeferenced, irregularly distributed 3D point clouds of high altimetric accuracy. Moreover, these systems can provide for a single laser pulse, multiple returns or echoes, which correspond to different illuminated objects. In addition to multi-echo laser scanners, full-waveform systems are able to record 1D signals representing a train of echoes caused by reflections at different targets. These systems provide more information about the structure and the physical characteristics of the targets. Many approaches have been developed, for urban mapping, based on aerial lidar solely or combined with multispectral image data. However, they have not assessed the importance of input features. In this paper, we focus on a multi-source framework using aerial lidar (multi-echo and full waveform) and aerial multispectral image data. We aim to study the feature relevance for dense urban scenes. The Random Forests algorithm is chosen as a classifier: it runs efficiently on large datasets, and provides measures of feature importance for each class. The margin theory is used as a confidence measure of the classifier, and to confirm the relevance of input features for urban classification. The quantitative results confirm the importance of the joint use of optical multispectral and lidar data. Moreover, the relevance of full-waveform lidar features is demonstrated for building and vegetation area discrimination.

http://recherche.ign.fr/labos/matis/pdf/articles_revues/chehata_etal_IJPRS10.pdf

Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests

In this paper, a literature overview is presented on the use of laser rangefinder techniques for the retrieval of forest inventory parameters and structural characteristics. The existing techniques are ordered with respect to their scale of application (i.e. spaceborne, airborne, and terrestrial laser scanning) and a discussion is provided on the efficiency, precision, and accuracy with which the retrieval of structural parameters at the respective scales has been attained. The paper further elaborates on the potential of LiDAR (Light Detection and Ranging) data to be fused with other types of remote sensing data and it concludes with recommendations for future research and potential gains in the application of LiDAR for the characterization of forests.

Retrieval of forest structural parameters using LiDAR remote sensing

Various multi-echo and Full-waveform (FW) lidar features can be processed. In this paper, multiple classifers are applied to lidar feature selection for urban scene classification. Random forests are used since they provide an accurate classification and run efficiently on large datasets. Moreover, they return measures of variable importance for each class. The feature selection is obtained by backward elimination of features depending on their importance. This is crucial to analyze the relevance of each lidar feature for the classification of urban scenes. The Random Forests classification using selected variables provide an overall accuracy of 94.35%.

/pdf/airborne-lidar-feature-selection-for-urban-classification-4k06nvjjfv.pdf

Airborne lidar feature selection for urban classification using random forests

Small-footprint full-waveform airborne laser scanning (ALS) is a remote sensing technique capable of mapping vegetation in three dimensions with a spatial sampling of about 0.5-2 m in all directions. This is achieved by scanning the laser beam across the Earth's surface and by emitting nanosecond-long infrared pulses with a high frequency of typically 50-150 kHz. The echo signals are digitized during data acquisition for subsequent off-line waveform analysis. In addition to delivering the three-dimensional (3D) coordinates of scattering objects such as leaves or branches, full-waveform laser scanners can be calibrated for measuring the scattering properties of vegetation and terrain surfaces in a quantitative way. As a result, a number of physical observables are obtained, such as the width of the echo pulse and the backscatter cross-section, which is a measure of the electromagnetic energy intercepted and re-radiated by objects. The main aim of this study was to build up an understanding of the scattering characteristics of vegetation and the underlying terrain. It was found that vegetation typically causes a broadening of the backscattered pulse, while the backscatter cross-section is usually smaller for canopy echoes than for terrain echoes. These scattering properties allowed classification of the 3D point cloud into vegetation and non-vegetation echoes with an overall accuracy of 89.9% for a dense natural forest and 93.7% for a baroque garden area. In addition, by removing the vegetation echoes before the filtering process, the quality of the digital terrain model could be improved.

https://www.tandfonline.com/doi/pdf/10.1080/01431160701736398

V. Ducic

Papers

3D vegetation mapping using small-footprint full-waveform airborne laser scanners