Use of Naturally Available Reference Targets to Calibrate Airborne Laser Scanning Intensity Data

We have studied the possibility of calibrating airborne laser scanning (ALS) intensity data, using land targets typically available in urban areas. For this purpose, a test area around Espoonlahti Harbor, Espoo, Finland, for which a long time series of ALS campaigns is available, was selected. Different target samples (beach sand, concrete, asphalt, different types of gravel) were collected and measured in the laboratory. Using tarps, which have certain backscattering properties, the natural samples were calibrated and studied, taking into account the atmospheric effect, incidence angle and flying height. Using data from different flights and altitudes, a time series for the natural samples was generated. Studying the stability of the samples, we could obtain information on the most ideal types of natural targets for ALS radiometric calibration. Using the selected natural samples as reference, the ALS points of typical land targets were calibrated again and examined. Results showed the need for more accurate ground reference data, before using natural samples in ALS intensity data calibration. Also, the NIR camera-based field system was used for collecting ground reference data. This system proved to be a good means for collecting in situ reference data, especially for targets with inhomogeneous surface reflection properties

Absolute Radiometric Calibration of ALS Intensity Data: Effects on Accuracy and Target Classification

Radiometric calibration of airborne laser scanning (ALS) intensity data aims at retrieving a value related to the target scattering properties, which is independent on the instrument or flight parameters. The aim of a calibration procedure is also to be able to compare results from different flights and instruments, but practical applications are sparsely available, and the performance of calibration methods for this purpose needs to be further assessed. We have studied the radiometric calibration with data from three separate flights and two different instruments using external calibration targets. We find that the intensity data from different flights and instruments can be compared to each other only after a radiometric calibration process using separate calibration targets carefully selected for each flight. The calibration is also necessary for target classification purposes, such as separating vegetation from sand using intensity data from different flights. The classification results are meaningful only for calibrated intensity data

Multispectral terrestrial lidar : State of the Art and Challenges

The development of multispectral terrestrial laser scan-ning (TLS) is still at the very beginning, with only four instruments worldwide providing simultaneous three-dimensional (3D) point cloud and spectral measurement. Research on multiwavelength laser returns has been carried out by more groups, but there are still only about ten research instruments published and no commercial availability. This chapter summarizes the experiences from all these studies to provide an overview of the state of the art and future developments needed to bring the multispectral TLS technology into the next level. Alt-hough the current number of applications is sparse, they already show that multispectral lidar technology has po-tential to disrupt many fields of science and industry due to its robustness and the level of detail available

Moniajalliset aaltomuotolaserpiirteet metsäpuissa – fenologian, puulajien ja skannausgeometrian vaikutus

Ilmalaserkeilauksella ”airborne LiDAR” (Light Detection and Ranging) tuotetaan korkearesoluutioista 3D-tietoa erittäin kustannustehokkaasti. Tämänhetkiset metsien inventointimenetelmät yhdistävät sekä LiDARin että passiivisen ilmakuvauksen. Mahdollisuus pelkän LiDARin käyttöön on erittäin houkutteleva, koska se johtaisi ainakin osittain kustannusten alenemiseen. Tässä tutkimuksessa keskitytään ns. täyden aaltomuodon havaintoihin, mitkä sisältävät enemmän tietoa lähetetystä ja vastaanotetusta signaalista kuin ’tavanomaiset’ pistepilvet. Tässä tutkimuksessa tarkastellaan metsän latvuston rakenteellisten ominaisuuksien ja LiDAR-signaalien välisiä riippuvuuksia ja pyritään lisäämään ymmärrystämme LiDARin ja kasvillisuuden välisistä vuorovaikutuksista ja tekijöistä, jotka rajoittavat nykyistä kykyä käyttää LiDAR-dataa mm. puulajitulkintaan, ja sitä, kuinka erilaisin prosessointi ja laskentamenetelmin voimme parantaa LiDARin tulkintaa metsässä. Tämän tutkimuksen tarkoituksena on ymmärtää, kuinka erilaisia aaltomuotopiirteitä voidaan tulkita ja kuinka piirteet käyttäytyvät muuttuvan fenologian mukaan. Tutkimusaineisto koostuu kolmesta peräkkäisestä LiDAR- ja ilmakuva kampanjasta, jotka on tehty alueella 38 kuukauden aikana sekä tämän ajanjakson aikana mitatuista maastoreferenssipuista. Käytössä on monen ajankohdan dataa, mikä koostuu kolmesta toistetusta laserkeilauksesta, jotka kaikki käyttivät samaa sensoria, lentoratoja ja keilausasetuksia. Koska LiDAR-havainnot ovat vertailukelpoisia ja samoista puista, voidaan ns. "puutekijää" tutkia ja vaihtelua aaltomuodon ominaisuuksien välillä toistuvissa keilauksissa seurata. Fenologiset muutokset ovat havaittavissa, koska aineistot sisältävät talven (lehdetön aika), alkukesän (alhainen lehtialaindeksi (LAI) havupuilla) ja loppukesän (täyslehti, korkea LAI). Myös skannauszeniittikulman (SZA) vaikutus aaltomuodon ominaisuuksiin ja piirteisiin otettiin huomioon, koska sama puu voitiin nähdä usealta lentolinjalta. Tulokset osoittavat, että huolellisella koeasettelulla on mahdollista havaita lajien sisäisiä ja lajien välisiä fenologisia eroja ja muutoksia moniajallisista aaltomuotopiirteistä. SZA:lla ei ollut merkittävää vaikutusta tuloksiin. Puulajiluokitus onnistui hyvin vaihtelevissa fenologisissa olosuhteissa ja erirakenteellisissa metsiköissä. Fenologiset muutokset olivat hyvin ilmeisiä kausivihannoilla puilla, mutta melko pieniä ainavihannilla havupuilla. Kokonaistarkkuudet puulajiluokituksessa olivat talvella 92 %, alkukesällä 88 % ja loppukesällä 84 % kasvatusmetsässä ja talvella 84 %, alkukesällä 81 % ja loppukesällä 83 % vanhassa puustossa. "puutekijän" osoitettiin olevan merkittävä. Lajien sisäinen varianssi johtuu pääasiassa puutekijästä eli lajinsisäinen ominaisuusvarianssi edustaa luonnollista vaihtelua saman lajin puiden välillä.Airborne LiDAR (Light Detection And Ranging) produces high-resolution and cost-efficient 3D data. Currently, forest inventories combine the use of both LiDAR and passive imaging by cameras, and the possibility of using LiDAR only is very tempting as it would lead to cost reduction. Focus of this study is on the full-waveform observations that extent the information content compared to conventional point clouds and are somewhat rarer to have access to. This study explores basic dependencies between structural canopy features and LiDAR signals over time and aims at augmenting our understanding of LiDAR-vegetation interactions and factors limiting our current ability to use pulsed LiDAR data for species detection, and how possibilities to overcome those limitations. Motivation is to understand how different waveform features can be interpreted and how the features behave over time with changing vegetation phenology. The study material consists of three consecutive LiDAR campaigns and aerial imaging surveys done in the area during a 38-month period and field reference trees that have been measured during this period. I use multi-temporal data that comprise three repeated acquisitions, which all applied same sensor, trajectories, as well as sensor and acquisition settings. As I had repeated LiDAR observations of the same trees where the acquisition settings are comparable, I could study the so-called ‘tree effect’ and overall co-variation between waveform features in the repeated acquisitions. Phenological changes are available as the data comprises winter (leaf-off), early summer (low LAI in conifers) and late summer data (full leaf, high LAI). The influence of scan zenith angle (SZA) on waveform features and attributes is also considered, as the same tree can be seen from multiple strips. The results showed that by using careful experimentation it is possible to detect intra- and interspecies phenological changes from multitemporal full-waveform data, while SZA did not have markable effect on the WF features. I was also able to perform well with the tree species classification task in varying phenological conditions. The phenological changes were very apparent on deciduous trees, but rather small on evergreen conifers. In a 45-year-old stand, the overall accuracies in tree species classification were 92, 87 and 88 % for winter, early summer, and late summer, respectively. These figures were 84, 81, and 83 % for in an old growth forest. The ‘tree effect’ was shown to be significant, i.e., many of the WF features of trees were correlated over time. The intra-species feature variance that is due to the tree effect represents natural variation between trees of the same species

Geometric Calibration and Radiometric Correction of LiDAR Data and Their Impact on the Quality of Derived Products

LiDAR (Light Detection And Ranging) systems are capable of providing 3D positional and spectral information (in the utilized spectrum range) of the mapped surface. Due to systematic errors in the system parameters and measurements, LiDAR systems require geometric calibration and radiometric correction of the intensity data in order to maximize the benefit from the collected positional and spectral information. This paper presents a practical approach for the geometric calibration of LiDAR systems and radiometric correction of collected intensity data while investigating their impact on the quality of the derived products. The proposed approach includes the use of a quasi-rigorous geometric calibration and the radar equation for the radiometric correction of intensity data. The proposed quasi-rigorous calibration procedure requires time-tagged point cloud and trajectory position data, which are available to most of the data users. The paper presents a methodology for evaluating the impact of the geometric calibration on the relative and absolute accuracy of the LiDAR point cloud. Furthermore, the impact of the geometric calibration and radiometric correction on land cover classification accuracy is investigated. The feasibility of the proposed methods and their impact on the derived products are demonstrated through experimental results using real data

Radiometric and Geometric Calibration of an Inexpensive LED-Based Lidar Sensor

Radiometric calibration of traditional lidar sensors that employ direct time of flight or phase-based ranging is well established. However, emerging inexpensive, lightweight, short-range lidar sensors that utilize non-traditional ranging methods report measurements that are not appropriate for existing radiometric calibration techniques. One such sensor, the TeraRanger Evo 60m by Terabee is a light emitting diode (instead of laser) lidar sensor with an automatically varying collection rate. This thesis investigates the performance of a new radiometric calibration model, one based on a neural network, applied to the Evo 60m. Application of the proposed radiometric calibration model resulted in performance similar to traditional lidar sensors, with mean differences in reflectance of no more than 5% and root mean square errors of no more than 6% for non-specular targets. The radiometric calibration model provides a generic approach that may be applicable to other low-cost lidar sensors and is a potential stepping stone toward development of a low-cost, multiple wavelength (multispectral) lidar sensor. The ranging performance of the Evo 60m was also evaluated in this work. Three of the four sensors evaluated fall below the manufacturer’s stated accuracy level of ±40 millimeters while one lies just above the threshold at ±43 millimeters

Calibration of full-waveform airborne laser scanning data for 3D object segmentation

Phd ThesisAirborne Laser Scanning (ALS) is a fully commercial technology, which has seen rapid uptake from the photogrammetry and remote sensing community to classify surface features and enhance automatic object recognition and extraction processes. 3D object segmentation is considered as one of the major research topics in the field of laser scanning for feature recognition and object extraction applications. The demand for automatic segmentation has significantly increased with the emergence of full-waveform (FWF) ALS, which potentially offers an unlimited number of return echoes. FWF has shown potential to improve available segmentation and classification techniques through exploiting the additional physical observables which are provided alongside the standard geometric information. However, use of the FWF additional information is not recommended without prior radiometric calibration, taking into consideration all the parameters affecting the backscattered energy. The main focus of this research is to calibrate the additional information from FWF to develop the potential of point clouds for segmentation algorithms. Echo amplitude normalisation as a function of local incidence angle was identified as a particularly critical aspect, and a novel echo amplitude normalisation approach, termed the Robust Surface Normal (RSN) method, has been developed. Following the radar equation, a comprehensive radiometric calibration routine is introduced to account for all variables affecting the backscattered laser signal. Thereafter, a segmentation algorithm is developed, which utilises the raw 3D point clouds to estimate the normal for individual echoes based on the RSN method. The segmentation criterion is selected as the normal vector augmented by the calibrated backscatter signals. The developed segmentation routine aims to fully integrate FWF data to improve feature recognition and 3D object segmentation applications. The routine was tested over various feature types from two datasets with different properties to assess its potential. The results are compared to those delivered through utilizing only geometric information, without the additional FWF radiometric information, to assess performance over existing methods. The results approved the potential of the FWF additional observables to improve segmentation algorithms. The new approach was validated against manual segmentation results, revealing a successful automatic implementation and achieving an accuracy of 82%

Developing a dual-wavelength full-waveform terrestrial laser scanner to characterise forest canopy structure

The development of a dual-wavelength full-waveform terrestrial laser scanner to measure the three-dimensional structure of forest canopies is described, and field measurements used to evaluate and test the instrument measurement characteristics. The Salford Advanced Laser Canopy Analyser (SALCA) measures the full-waveform of backscattered radiation at two laser wavelengths, one in the near-infrared (1063 nm) and one in the shortwave infrared (1545 nm). The instrument is field-portable and measures up to nine million waveforms, at the two wavelengths, across a complete hemisphere above the instrument. SALCA was purpose-built to measure structural characteristics of forest canopies and this paper reports the first results of field-based data collection using the instrument. Characteristics of the waveforms, and waveform data processing are outlined, applications of dual wavelength measurements are evaluated, and field deployment of the instrument at a forest test site described. Preliminary instrument calibration results are presented and challenges in extracting useful information on forest structure are highlighted. Full-waveform multiple-wavelength terrestrial laser scanners are likely to provide more detailed and more accurate forest structural measurement in the future. This research demonstrates how SALCA provides a key step to develop, test and apply this new technology in a range of forest-related problems

Potential of ILRIS3D Intensity Data for Planar Surfaces Segmentation

Intensity value based point cloud segmentation has received less attention because the intensity value of the terrestrial laser scanner is usually altered by receiving optics/hardware or the internal propriety software, which is unavailable to the end user. We offer a solution by assuming the terrestrial laser scanners are stable and the behavior of the intensity value can be characterized. Then, it is possible to use the intensity value for segmentation by observing its behavior, i.e., intensity value variation, pattern and presence of location of intensity values, etc. In this study, experiment results for characterizing the intensity data of planar surfaces collected by ILRIS3D, a terrestrial laser scanner, are reported. Two intensity formats, grey and raw, are employed by ILRIS3D. It is found from the experiment results that the grey intensity has less variation; hence it is preferable for point cloud segmentation. A warm-up time of approximate 1.5 hours is suggested for more stable intensity data. A segmentation method based on the visual cues of the intensity images sequence, which contains consecutive intensity images, is proposed in order to segment the 3D laser points of ILRIS3D. This method is unique to ILRIS3D data and does not require radiometric calibration

Advances in measuring forest structure by terrestrial laser scanning with the Dual Wavelength ECHIDNA® LIDAR (DWEL)

Leaves in forests assimilate carbon from the atmosphere and woody components store the net production of that assimilation. Separate structure measurements of leaves and woody components advance the monitoring and modeling of forest ecosystem functions. This dissertation provides a method to determine, for the first time, the 3-D spatial arrangement and the amount of leafy and woody materials separately in a forest by classification of lidar returns from a new, innovative, lidar scanner, the Dual-Wavelength Echidna® Lidar (DWEL). The DWEL uses two lasers pulsing simultaneously and coaxially at near-infrared (1064 nm) and shortwave-infrared (1548 nm) wavelengths to locate scattering targets in 3-D space, associated with their reflectance at the two wavelengths. The instrument produces 3-D bispectral "clouds" of scattering points that reveal new details of forest structure and open doors to three-dimensional mapping of biophysical and biochemical properties of forests. The three parts of this dissertation concern calibration of bispectral lidar returns; retrieval of height profiles of leafy and woody materials within a forest canopy; and virtual reconstruction of forest trees from multiple scans to estimate their aboveground woody biomass. The test area was a midlatitude forest stand within the Harvard Forest, Petersham, Massachusetts, scanned at five locations in a 1-ha site in leaf-off and leaf-on conditions in 2014. The model for radiometric calibration assigned accurate values of spectral apparent reflectance, a range-independent and instrument-independent property, to scattering points derived from the scans. The classification of leafy and woody points, using both spectral and spatial context information, achieved an overall accuracy of 79±1% and 75±2% for leaf-off and leaf-on scans, respectively. Between-scan variation in leaf profiles was larger than wood profiles in leaf-off seasons but relatively similar to wood profiles in leaf-on seasons, reflecting the changing spatial heterogeneity within the stand over seasons. A 3-D structure-fitting algorithm estimated wood volume by modeling stems and branches from point clouds of five individual trees with cylinders. The algorithm showed the least variance for leaf-off, woody-points-only data, validating the value of separating leafy and woody points to the direct biomass estimates through the structure modeling of individual trees