LIDAR is an active remote sensing technology which performs range measurements from the sensor and converts them into 3D coordinates of the Earth's surface. Recent advances in LIDAR hardware make it possible to digitize full waveforms of the returned energy. LIDAR waveform decomposition involves separating the return waveform into a mixture of Gaussians which is then used to characterize the or...

We evaluate the performance of full waveform LiDAR decomposition algorithms with a high-resolution single band airborne LiDAR bathymetry system in shallow rivers. A continuous wavelet transformation (CWT) is proposed and applied in two fluvial environments, and the results are compared to existing echo retrieval methods. LiDAR water depths are also compared to independent field measurements. In...

Full waveform recording is becoming increasingly affordable and, consequently, available in today's state-of-the-art LiDAR systems. Therefore, there is no practical limitation to the complexity of pulse detection and other methods that can be applied in post-processing mode. Analyzing the entire return signal, the full waveform can provide additional geometrical and physical information about t...

Recent advances in Light Detection And Ranging (LiDAR) enable digital acquisition and storage of the entire waveform of backscattered laser pulse energy. Pre-requisites for extracting target radiometric and geometric properties via waveform analysis include defining a model for accurately determining peaks in the return energy waveform and a method for transforming waveform peak amplitudes to a...

Most commercial LIDAR systems temporarily record the entire laser pulse echo signal, called full-waveform, as a function of time to extract the return pulses at data acquisition level in real-time; typically up to 4-5 returns. The new generation of airborne laser scanners, the full-waveform LiDAR systems, are not only able to digitize but can record the entire backscattered signal of each emitt...

Species information is a key component of any forest inventory. However, when performing forest inventory from aerial scanning Lidar data, species classification can be very difficult. We investigated changes in classification accuracy while identifying five individual tree species (Douglas-fir, western redcedar, bigleaf maple, red alder, and black cottonwood) in the Pacific Northwest United St...

Estimating forest canopy height from large-footprint satellite LiDAR waveforms is challenging given the complex interaction between LiDAR waveforms, terrain, and vegetation, especially in dense tropical and equatorial forests. In this study, canopy height in French Guiana was estimated using multiple linear regression models and the Random Forest technique (RF). This analysis was either based o...

Estimation of forest structural attributes is important in biodiversity and global carbon cycle studies. These estimates can be used to predict important forest characteristics such as aboveground biomass (AGBM), which helps determine the amount of carbon in terrestrial vegetation pools. Traditional field sampling-based estimation methods are not only time consuming, expensive, and limited to l...

In the recent years, the performance of LiDAR systems was improved by acquisition of the terrain surface with steadily increasing point densities. However, the main error sources affecting the quality of LiDAR derived secondary products like DTMs and DSMs are resulting from systematic residual errors coming from insufficient calibration and strip adjustment, and from deficiencies in classificat...

ABSTRACT: The storage capability of full waveform is the state-of-the-art technology of LIDAR. In addition to the 3D point information recorded by conventional LIDAR systems, waveform LIDAR systems encode the intensity of returned signal along the time domain. This provides a user the possibility to decompose the waveform for the detection of illuminated target points. Therefore, the locations ...