ASSESSMENT OF BOTTOM-OF-ATMOSPHERE REFLECTANCE IN LIDAR DATA AS REFERENCE FOR HYPERSPECTRAL IMAGERY

While airborne lidar has confirmed its leading role in delivering high-resolution 3D topographic information during the last decade, its radiometric potential has not yet been fully exploited. However, with the increasing availability of commercial lidar systems which (a) make use of full-waveform information and (b) operate at several wavelengths simultaneously, this potential is increasing as well. Radiometric calibration of the full-waveform information mentioned before allows for the derivation of physical target surface parameters such as the backscatter coefficient and a diffuse reflectance value at bottom of atmosphere (BOA), i.e. the target surface. With lidar being an active remote sensing technique, these parameters can be derived from lidar data itself, accompanied by the measurement or estimation of reference data for diffuse reflectance. In contrast to this, such a radiometric calibration for passive hyperspectral imagery (HSI) requires the knowledge and/or estimation of much more unknowns. However, in case of corresponding wavelength(s) radiometrically calibrated lidar datasets can deliver an areawide reference for BOA reflectance. This paper presents criteria to check where the assumption of diffuse BOA reflectance behaviour is fulfilled and how these criteria are assessed in lidar data; the assessment is illustrated by an extended lidar dataset. Moreover, for this lidar dataset and an HSI dataset recorded over the same area, the corresponding reflectance values are compared for different surface types

Automated Classification of Airborne Laser Scanning Point Clouds

Making sense of the physical world has always been at the core of mapping. Up until recently, this has always dependent on using the human eye. Using airborne lasers, it has become possible to quickly "see" more of the world in many more dimensions. The resulting enormous point clouds serve as data sources for applications far beyond the original mapping purposes ranging from flooding protection and forestry to threat mitigation. In order to process these large quantities of data, novel methods are required. In this contribution, we develop models to automatically classify ground cover and soil types. Using the logic of machine learning, we critically review the advantages of supervised and unsupervised methods. Focusing on decision trees, we improve accuracy by including beam vector components and using a genetic algorithm. We find that our approach delivers consistently high quality classifications, surpassing classical methods

ALART: A novel lidar system for vegetation height retrieval from space

We propose a multi-kHz Single-Photon Counting (SPC) space LIDAR, exploiting low energy pulses with high repetition frequency (PRF). The high PRF allows one to overcome the low signal limitations, as many return shots can be collected from nearly the same scattering area. The ALART space instrument exhibits a multi-beam design, providing height retrieval over a wide area and terrain slope measurements. This novel technique, working with low SNRs, allows multiple beam generation with a single laser, limiting mass and power consumption. As the receiver has a certain probability to detect multiple photons from different levels of canopy, a histogram is constructed and used to retrieve the properties of the target tree, by means of a modal decomposition of the reconstructed waveform. A field demonstrator of the ALART space instrument is currently being developed by a European consortium led by cosine | measurement systems and funded by ESA under the TRP program. The demonstrator requirements have been derived to be representative of the target instrument and it will be tested in an equipped tower in woodland areas in the Netherlands. The employed detectors are state-of-the-art CMOS Single-Photon Avalanche Diode (SPAD) matrices with 1024 pixels. Each pixel is independently equipped with an integrated Time-to-Digital Converter (TDC), achieving a timing accuracy that is much lower than the SPAD dead time, resulting in a distance resolution in the centimeter range. The instrument emits nanosecond laser pulses with energy on the order of several J, at a PRF of ~ 10 kHz, and projects on ground a three-beams pattern. An extensive field measurement campaign will validate the employed technologies and algorithms for vegetation height retrieval

LASER PULSE VARIATIONS AND THEIR INFLUENCE ON RADIOMETRIC CALIBRATION OF FULL-WAVEFORM LASER SCANNER DATA

Full-waveform laser scanning extends the information content of “conventional ” laser scanning by storing the temporal profile of both the emitted laser pulse and its echoes. This allows for calculating radiometric quantities in addition to the geometric data. This radiometric information needs to be calibrated in order to enable comparison among flight strips of the same laser scanner campaign and/or different campaigns. Radiometric calibration is aimed at the determination of a calibration constant which contains the parameters of the emitted laser pulse (besides others). All of these parameters are normally treated as constants. In this paper, the sensitivity of the calibration constant to variations of the emitted laser pulse is analysed theoretically by deriving it according to the error propagation law, followed by an empirical analysis carried out on the example of two airborne full-waveform laser scanning campaigns. Both were operated with the same instrument and over the same area on two different dates.

A COMPARISON OF LIDAR REFLECTANCE AND RADIOMETRICALLY CALIBRATED HYPERSPECTRAL IMAGERY

In order to retrieve results comparable under different flight parameters and among different flight campaigns, passive remote sensing data such as hyperspectral imagery need to undergo a radiometric calibration. While this calibration, aiming at the derivation of physically meaningful surface attributes such as a reflectance value, is quite cumbersome for passively sensed data and relies on a number of external parameters, the situation is by far less complicated for active remote sensing techniques such as lidar. This fact motivates the investigation of the suitability of full-waveform lidar as a “single-wavelength reflectometer” to support radiometric calibration of hyperspectral imagery. In this paper, this suitability was investigated by means of an airborne hyperspectral imagery campaign and an airborne lidar campaign recorded over the same area. Criteria are given to assess diffuse reflectance behaviour; the distribution of reflectance derived by the two techniques were found comparable in four test areas where these criteria were met. This is a promising result especially in the context of current developments of multi-spectral lidar systems

ASSESSMENT OF BOTTOM-OF-ATMOSPHERE REFLECTANCE IN LIDAR DATA AS REFERENCE FOR HYPERSPECTRAL IMAGERY

While airborne lidar has confirmed its leading role in delivering high-resolution 3D topographic information during the last decade, its radiometric potential has not yet been fully exploited. However, with the increasing availability of commercial lidar systems which (a) make use of full-waveform information and (b) operate at several wavelengths simultaneously, this potential is increasing as well. Radiometric calibration of the full-waveform information mentioned before allows for the derivation of physical target surface parameters such as the backscatter coefficient and a diffuse reflectance value at bottom of atmosphere (BOA), i.e. the target surface. <br><br> With lidar being an active remote sensing technique, these parameters can be derived from lidar data itself, accompanied by the measurement or estimation of reference data for diffuse reflectance. In contrast to this, such a radiometric calibration for passive hyperspectral imagery (HSI) requires the knowledge and/or estimation of much more unknowns. However, in case of corresponding wavelength(s) radiometrically calibrated lidar datasets can deliver an areawide reference for BOA reflectance. <br><br> This paper presents criteria to check where the assumption of diffuse BOA reflectance behaviour is fulfilled and how these criteria are assessed in lidar data; the assessment is illustrated by an extended lidar dataset. Moreover, for this lidar dataset and an HSI dataset recorded over the same area, the corresponding reflectance values are compared for different surface types

Analysing the suitability of radiometrically calibrated full-waveform lidar data for delineating Alpine rock glaciers

With full-waveform (FWF) lidar systems becoming increasingly available from different commercial manufacturers, the possibility for extracting physical parameters of the scanned surfaces in an area-wide sense, as addendum to their geometric representation, has risen as well. The mentioned FWF systems digitize the temporal profiles of the transmitted laser pulse and of its backscattered echoes, allowing for a reliable determination of the target distance to the instrument and of physical target quantities by means of radiometric calibration, one of such quantities being the diffuse Lambertian reflectance. The delineation of glaciers is a time-consuming task, commonly performed manually by experts and involving field trips as well as image interpretation of orthophotos, digital terrain models and shaded reliefs. In this study, the diffuse Lambertian reflectance was compared to the glacier outlines mapped by experts. We start the presentation with the workflow for analysis of FWF data, their direct georeferencing and the calculation of the diffuse Lambertian reflectance by radiometric calibration; this workflow is illustrated for a large FWF lidar campaign in the Ötztal Alps (Tyrol, Austria), operated with an Optech ALTM 3100 system. The geometric performance of the presented procedure was evaluated by means of a relative and an absolute accuracy assessment using strip differences and orthophotos, resp. The diffuse Lambertian reflectance was evaluated at two rock glaciers within the mentioned lidar campaign. This feature showed good performance for the delineation of the rock glacier boundaries, especially at their lower parts