Relative Radiometric Calibration of Airborne LiDAR Data for Archaeological Applications

Airborne laser scanning (ALS) data can provide more than just a topographic data set for archaeological research. During data collection, laser scanning systems also record radiometric information containing object properties, and thus information about archaeological features. Being aware of the physical model of ALS scanning, the radiometric information can be used to calculate material information of the scanned object. The reflectance of an object or material states the amount of energy it reflects for a specific electromagnetic wavelength. However, the collected radiometric data are affected by several factors that cause dissimilar values to be recorded for the same object. Radiometric calibration of such data minimizes these differences in calculated reflectance values of objects, improving their usability for feature detection and visualization purposes. Previous work dealing with calibration of radiometric data in archaeological research has relied on corresponding in-field measurements to acquire calibration values or has only corrected for a limited number of variables. In this paper, we apply a desk-based approach in which radiometric calibration is conducted through the selection of homogenous areas of interest, without the use of in-field measurements. Together with flight and scan parameters, radiometric calibration allows for the estimation of reflectance values for returns of a single full-waveform ALS data collection flight. The resulting data are then processed into a raster reflectance map that approximates a monochromatic illumination-independent true orthoimage at the wavelength of the laser scanner. We apply this approach to data collected for an archaeological research project in western Sicily and discuss the relative merits of the uses of radiometric data in such locations as well as its wider applicability for present and future archaeological and environmental research. In order to make the approach more accessible, we have developed a freely available tool that allows users to apply the calibration procedure to their own data.

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CALIPSO Lidar Calibration at 532 nm: Version 4 Nighttime Algorithm

10.5194/amt-2017-365 ◽

2017 ◽

Author(s):

Jayanta Kar ◽

Mark A. Vaughan ◽

Kam-Pui Lee ◽

Jason L. Tackett ◽

Melody A. Avery ◽

...

Keyword(s):

Signal To Noise Ratio ◽

Calibration Procedure ◽

Stratospheric Aerosol ◽

Airborne Lidar ◽

High Spectral Resolution ◽

Orthogonal Polarization ◽

Radiometric Calibration ◽

Data Set ◽

Langley Research Center ◽

532 Nm

Abstract. Data products from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) were recently updated following the implementation of new (version 4.1) calibration algorithms for all of the level 1 attenuated backscatter measurements. In this work we present the motivation for and the implementation of the version 4.1 nighttime 532 nm parallel channel calibration. The nighttime 532 nm calibration is the most fundamental calibration of CALIOP data, since all of CALIOP’s other radiometric calibration procedures – i.e., the 532 nm daytime calibration and the 1064 nm calibrations during both nighttime and daytime – depend either directly or indirectly on the 532 nm nighttime calibration. The accuracy of the 532 nm nighttime calibration is significantly improved by raising the molecular normalization altitude from 30–34 km to 36–39 km to substantially reduce stratospheric aerosol contamination. Due to the greatly reduced molecular number density and consequently reduced signal-to-noise ratio at the higher altitudes used to avoid aerosols, the signal is averaged over a larger number of samples. The new calibration procedure is shown to eliminate biases introduced in earlier versions and consequently leads to an improved representation of stratospheric aerosols. Validation results using airborne lidar measurements are also presented. Biases relative to collocated measurements acquired by the Langley Research Center (LaRC) airborne High Spectral Resolution Lidar (HSRL) are reduced from 3.6 % ± 2.2 % in the version 3 data set to 1.6 % ± 2.4 % in the version 4.1 release.

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Semantic Segmentation of Airborne LiDAR Data in Maya Archaeology

Remote Sensing ◽

10.3390/rs12223685 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3685 ◽

Cited By ~ 1

Author(s):

Marek Bundzel ◽

Miroslav Jaščur ◽

Milan Kováč ◽

Tibor Lieskovský ◽

Peter Sinčák ◽

...

Keyword(s):

Semantic Segmentation ◽

Airborne Lidar ◽

Lidar Data ◽

Archaeological Research ◽

Ancient Maya ◽

Data Set ◽

The Road ◽

Road Map ◽

Construction Activity ◽

Airborne Lidar Data

Airborne LiDAR produced large amounts of data for archaeological research over the past decade. Labeling this type of archaeological data is a tedious process. We used a data set from Pacunam LiDAR Initiative survey of lowland Maya region in Guatemala. The data set contains ancient Maya structures that were manually labeled, and ground verified to a large extent. We have built and compared two deep learning-based models, U-Net and Mask R-CNN, for semantic segmentation. The segmentation models were used in two tasks: identification of areas of ancient construction activity, and identification of the remnants of ancient Maya buildings. The U-Net based model performed better in both tasks and was capable of correctly identifying 60–66% of all objects, and 74–81% of medium sized objects. The quality of the resulting prediction was evaluated using a variety of quantifiers. Furthermore, we discuss the problems of re-purposing the archaeological style labeling for production of valid machine learning training sets. Ultimately, we outline the value of these models for archaeological research and present the road map to produce a useful decision support system for recognition of ancient objects in LiDAR data.

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CALIPSO lidar calibration at 532 nm: version 4 nighttime algorithm

Atmospheric Measurement Techniques ◽

10.5194/amt-11-1459-2018 ◽

2018 ◽

Vol 11 (3) ◽

pp. 1459-1479 ◽

Cited By ~ 34

Author(s):

Jayanta Kar ◽

Mark A. Vaughan ◽

Kam-Pui Lee ◽

Jason L. Tackett ◽

Melody A. Avery ◽

...

Keyword(s):

High Energy ◽

Stratospheric Aerosol ◽

Airborne Lidar ◽

High Spectral Resolution ◽

Orthogonal Polarization ◽

Signal Acquisition ◽

Radiometric Calibration ◽

Data Set ◽

Meteorological Model ◽

532 Nm

Abstract. Data products from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) were recently updated following the implementation of new (version 4) calibration algorithms for all of the Level 1 attenuated backscatter measurements. In this work we present the motivation for and the implementation of the version 4 nighttime 532 nm parallel channel calibration. The nighttime 532 nm calibration is the most fundamental calibration of CALIOP data, since all of CALIOP's other radiometric calibration procedures – i.e., the 532 nm daytime calibration and the 1064 nm calibrations during both nighttime and daytime – depend either directly or indirectly on the 532 nm nighttime calibration. The accuracy of the 532 nm nighttime calibration has been significantly improved by raising the molecular normalization altitude from 30–34 km to the upper possible signal acquisition range of 36–39 km to substantially reduce stratospheric aerosol contamination. Due to the greatly reduced molecular number density and consequently reduced signal-to-noise ratio (SNR) at these higher altitudes, the signal is now averaged over a larger number of samples using data from multiple adjacent granules. Additionally, an enhanced strategy for filtering the radiation-induced noise from high-energy particles was adopted. Further, the meteorological model used in the earlier versions has been replaced by the improved Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), model. An aerosol scattering ratio of 1.01±0.01 is now explicitly used for the calibration altitude. These modifications lead to globally revised calibration coefficients which are, on average, 2–3 % lower than in previous data releases. Further, the new calibration procedure is shown to eliminate biases at high altitudes that were present in earlier versions and consequently leads to an improved representation of stratospheric aerosols. Validation results using airborne lidar measurements are also presented. Biases relative to collocated measurements acquired by the Langley Research Center (LaRC) airborne High Spectral Resolution Lidar (HSRL) are reduced from 3.6 %±2.2 % in the version 3 data set to 1.6 %±2.4 % in the version 4 release.

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Exploitation of Countrywide Airborne Lidar Dataset for Documentation of Historical Human Activities in Countryside

Geoinformatics FCE CTU ◽

10.14311/gi.6.13 ◽

2011 ◽

Vol 6 ◽

pp. 97-102

Author(s):

Petr Dušánek

Keyword(s):

Czech Republic ◽

Human Activities ◽

Laser Scanning ◽

Cultural Landscape ◽

Digital Terrain Model ◽

Airborne Lidar ◽

Mining Activities ◽

Data Set ◽

Entire Area ◽

The Czech Republic

During three years (2010 – 12) The Czech Office for Surveying, Mapping and Cadastre in cooperation with The Ministry of Defense of the Czech Republic and The Ministry of Agriculture of the Czech Republic are providing mapping of the entire area of the Czech Republic by Airborne laser scanning (ALS) technology. The goal of this project is to derive a highly accurate Digital Terrain Model (DTM) for purposes of administration like detection of flooded areas, orthorectification of areal images etc. Such data set also seems to be an interesting da ta source for mapping of human activities in countryside. Human settlements, agriculture or mining activities left significant scars on natural landscape. These significant man-made structures are a part of so called cultural landscape. Man-made structures include ancient settlements, remains of medieval mining activities or remains of settlements abandoned during 20th century. This article generally presents how to derive information about the man-made structures from raw LiDAR. Examples of significant findings of man-made imprints in countryside are also presented. Goal of this article is not to describe a certain archeological site but to inform about strengths of ALS data to map human activities in countryside, mainly in forested areas.

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A New Method for Rapid Measurement of Canal Water Table Depth Using Airborne LiDAR, with Application to Drained Peatlands in Indonesia

Water ◽

10.3390/w12051486 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1486

Author(s):

Ronald Vernimmen ◽

Aljosja Hooijer ◽

Dedi Mulyadi ◽

Iwan Setiawan ◽

Maarten Pronk ◽

...

Keyword(s):

Water Management ◽

Data Collection ◽

Water Table ◽

Grid Cell ◽

Soil Surface ◽

Field Measurements ◽

Water Table Depth ◽

Airborne Lidar ◽

Lidar Data ◽

Canal Water

Water management in lowland areas usually aims to keep water tables within a narrow range to avoid flooding and drought conditions. A common water management target parameter is the depth of the canal water table below the surrounding soil surface. We demonstrated a method that rapidly determines canal water table depth (CWD) from airborne LiDAR data. The water table elevation was measured as the minimum value determined in a grid of 100 m × 100 m applied to a 1 m × 1 m digital terrain model (DTM), and the soil surface was calculated as the median value of values in each grid cell. Results for areas in eastern Sumatra and West Kalimantan, Indonesia, were validated against 145 field measurements at the time of LiDAR data collection. LiDAR-derived CWD was found to be accurate within 0.25 m and 0.5 m for 86% and 99% of field measurements, respectively, with an R2 value of 0.74. We demonstrated the method for CWD conditions in a drained peatland area in Central Kalimantan, where we found CWD in the dry season of 2011 to be generally below −1.5 and often below −2.5 m indicating severely overdrained conditions. We concluded that airborne LiDAR can provide an efficient and rapid mapping tool of CWD at the time of LiDAR data collection, which can be cost-effective especially where LiDAR data or derived DTMs are already available. The method can be applied to any LiDAR-based DTM that represents a flat landscape that has open water bodies.

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AUTOMATIC FEATURE DETECTION, DESCRIPTION AND MATCHING FROM MOBILE LASER SCANNING DATA AND AERIAL IMAGERY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b1-609-2016 ◽

2016 ◽

Vol XLI-B1 ◽

pp. 609-616

Author(s):

Zille Hussnain ◽

Sander Oude Elberink ◽

George Vosselman

Keyword(s):

Urban Areas ◽

Laser Scanning ◽

Feature Detection ◽

Image Resolution ◽

Aerial Images ◽

Image Feature ◽

Aerial Image ◽

Feature Points ◽

Data Set ◽

Feature Extraction Method

In mobile laser scanning systems, the platform’s position is measured by GNSS and IMU, which is often not reliable in urban areas. Consequently, derived Mobile Laser Scanning Point Cloud (MLSPC) lacks expected positioning reliability and accuracy. Many of the current solutions are either semi-automatic or unable to achieve pixel level accuracy. We propose an automatic feature extraction method which involves utilizing corresponding aerial images as a reference data set. The proposed method comprise three steps; image feature detection, description and matching between corresponding patches of nadir aerial and MLSPC ortho images. In the data pre-processing step the MLSPC is patch-wise cropped and converted to ortho images. Furthermore, each aerial image patch covering the area of the corresponding MLSPC patch is also cropped from the aerial image. For feature detection, we implemented an adaptive variant of Harris-operator to automatically detect corner feature points on the vertices of road markings. In feature description phase, we used the LATCH binary descriptor, which is robust to data from different sensors. For descriptor matching, we developed an outlier filtering technique, which exploits the arrangements of relative Euclidean-distances and angles between corresponding sets of feature points. We found that the positioning accuracy of the computed correspondence has achieved the pixel level accuracy, where the image resolution is 12cm. Furthermore, the developed approach is reliable when enough road markings are available in the data sets. We conclude that, in urban areas, the developed approach can reliably extract features necessary to improve the MLSPC accuracy to pixel level.

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AUTOMATIC FEATURE DETECTION, DESCRIPTION AND MATCHING FROM MOBILE LASER SCANNING DATA AND AERIAL IMAGERY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b1-609-2016 ◽

2016 ◽

Vol XLI-B1 ◽

pp. 609-616 ◽

Cited By ~ 7

Author(s):

Zille Hussnain ◽

Sander Oude Elberink ◽

George Vosselman

Keyword(s):

Urban Areas ◽

Laser Scanning ◽

Feature Detection ◽

Image Resolution ◽

Aerial Images ◽

Image Feature ◽

Aerial Image ◽

Feature Points ◽

Data Set ◽

Feature Extraction Method

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A Remote Field Course Implementing High-Resolution Topography Acquisition and Applications Applied to Geomorphology

10.5194/gc-2021-26 ◽

2021 ◽

Author(s):

Sharon Bywater-Reyes ◽

Beth Pratt-Sitaula

Keyword(s):

High Resolution ◽

Data Collection ◽

Structure From Motion ◽

Laser Scanning ◽

Earth Science ◽

Terrestrial Laser Scanning ◽

Airborne Lidar ◽

Earth Surface ◽

University Of Northern Colorado ◽

The University

Abstract. The course "Geoscience Field Issues Using High-Resolution Topography to Understand Earth Surface Processes" was originally intended to be conducted in-person with both field data collection and analysis to meet the "field" component of the University of Northern Colorado's Earth Science degrees (Environmental and Geology). With the 2020 world pandemic and cancellation of most in-person activities for 2020, the course was adapted for 100 % online implementation with an optional one-day field campaign. To prepare for the changed delivery model, UNAVCO and the University of Northern Colorado collected GNSS data, drone imagery for use in structure from motion, and terrestrial laser scanning from a site near Greeley, Colorado USA on the Cache la Poudre River. These data were used in mock field campaigns and real analyses implemented by students virtually through Zoom and Canvas. The objective of the course is to train students in manual and remote sensing methods of topographic data collection, including 1) GPS/GNSS surveys, 2) structure from motion (SfM), and 3) ground-based (terrestrial laser scanning, TLS) and airborne LiDAR. Course content focused on earth-surface process applications, but could be adapted to other applications. This was taught workshop style with the bulk of the instruction and application occurring within a 2-week period during the summer. Students from throughout North America attended the course, most meeting Field Camp requirements required for graduation. Despite the challenging conditions, students met the majority of the National Association of Geoscience Teachers’ Field Capstone Learning Outcomes.

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Laser scanning as a tool for the analysis of the greenery on the example of Poremba district in Lublin

Ecology and Noospherology ◽

10.15421/031527 ◽

2015 ◽

Vol 26 (3-4) ◽

pp. 132-140

Author(s):

P. G. Kotsyuba ◽

I. D. Semko ◽

I. I. Kozak ◽

T. V. Parpan ◽

G. G. Kozak ◽

...

Keyword(s):

Laser Scanning ◽

Green Space ◽

Three Dimensional ◽

Tree Height ◽

Field Measurements ◽

Airborne Lidar ◽

Dimensional Structure ◽

Lidar Data ◽

Definition Of ◽

Airborne Lidar Data

World experience shows that the survey of green spaces by traditional methods is very time consuming, costly and does not always get all the information you need to make of adequate management decisions by municipal authorities. The aim of this article was to show the main stages of analysis and prospects of urban green space using aerial lidar data and submit the effect of three-dimensional visualization of the study area. There were presented the possibilities and perspectives of using the data obtained from airborne laser scanning (ALS) for the analysis of greenery on the example of Poremba district in Lublin (Poland). Research conducted in Poremba district in the Polish city of Lublin (district was built from 1988 to 2005 and is located in the western part of the city). Analysis of green space conducted using quantitative analytical methods. By detailed analysis of the study area were used aerial lidar data from the year 2015. To classify aerial lidar data such software were used: LP360, ArcMap 10.3, Toolbox LAStools. The process of analysis begins with the definition of points, belonging to ground (Ground - GR), and the classification was realized using «lasground» with tools LAStools. The article is dedicated to development the method of estimation the tree height based on airborne LiDAR data. Method applies more information about the three-dimensional structure of natural objects derived from the processing of airborne LiDAR data compared with known methods. Furthermore, the method is adapted to determine and calculate characteristics of stand which using for tree inventory in cities. Methodological and algorithmic instructions to determine the tree parameters in city were proposed. These instructions allow automatically calculating the characteristics of the tree parameters, such as the allocation of each tree and tree height. The study area was analyzed in terms of the distribution of vegetation (separately individual growing trees and groups of trees). For that purpose there was applied an available ALS data. Based on the ALS data there were separated the tops of the trees and their height. In order to verify the ALS data there were used the results of field measurements (coordinates for the tree trunks, the diameter at breast height of trees, their height, crown projection). The analysis of the greenery within the Poremba district using the ALS data after verification with the field measurements proved to be an effective tool for the characterization of the greenery areas in particular city. This research may be important in terms of planning the planting of greenery areas and spatial development of the Lublin.

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Algorithms for automated montage synthesis of images from laser-scanning confocal microscopes

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100139627 ◽

1995 ◽

Vol 53 ◽

pp. 650-651

Author(s):

D. E. Becker

Keyword(s):

Image Analysis ◽

High Resolution ◽

Laser Scanning ◽

Cell Counting ◽

Wide Area ◽

Data Set ◽

Computational Synthesis ◽

Automated Cell Counting ◽

Partial View ◽

The Individual

An efficient, robust, and widely-applicable technique is presented for computational synthesis of high-resolution, wide-area images of a specimen from a series of overlapping partial views. This technique can also be used to combine the results of various forms of image analysis, such as segmentation, automated cell counting, deblurring, and neuron tracing, to generate representations that are equivalent to processing the large wide-area image, rather than the individual partial views. This can be a first step towards quantitation of the higher-level tissue architecture. The computational approach overcomes mechanical limitations, such as hysterisis and backlash, of microscope stages. It also automates a procedure that is currently done manually. One application is the high-resolution visualization and/or quantitation of large batches of specimens that are much wider than the field of view of the microscope.The automated montage synthesis begins by computing a concise set of landmark points for each partial view. The type of landmarks used can vary greatly depending on the images of interest. In many cases, image analysis performed on each data set can provide useful landmarks. Even when no such “natural” landmarks are available, image processing can often provide useful landmarks.

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