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Hyperspectral LiDAR Cross Analysis of Landscape Processes and Patterns

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (25 September 2021) | Viewed by 27748

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Special Issue Editors

Prof. Dr. Patrick Launeau

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Guest Editor

Laboratoire de Planétologie et Géodynamique de Nantes, UMR CNRS 6112, Université de Nantes, 2 rue de la Houssinière, 44322 Nantes, France
Interests: structural geology; soil and vegetation cover remote sensing; LiDAR hyperspectral coupling for environnemental studies in land and shorelines

Dr. Maximilian Brell

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Guest Editor

Helmholtz Center Potsdam, GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany
Interests: hyperspectral and lidar sensor fusion; hyperspectral remote sensing; lidar remote sensing; geometric and atmospheric preprocessing; sensor response adaptation; sensor cross-calibration; hyperspectral and multispectral point cloud

Special Issue Information

Dear Colleagues,

Extensive monitoring of land transformations in a context of global change are more and more requested. Coupling hyperspectral images and LiDAR data to measure land cover physical and chemical properties is stat of the art and capable to tackle these requirements. However, the fusion between the contrasting sensors and the transposition of methods between disciplines is not trivial.

Therefore, we would like to gather in one special issue a collection of applications which are based on the combined use and the fusion of LiDAR Hyperspectral data with a clear focus on landscape processes / parameters. These can be physical or empirical based fusion of heterogeneous data separated in time or synchronous acquisitions at light path level. Many combinations and fusion levels exist so we would like to facilitate the exploration of all of them from LiDAR discrete echo object segmentation of hyperspectral images to hyperspectral light path enrichment by LiDAR full waveform processing. All sort of combined classifications and quantifications approaches with the focus on landscape changes are of great interest. In general such studies also necessitate the cooperation of many disciplines allowing the extraction of comprehensive parameters for all of them which relies on a dialog between sensor designer and land mapping experts. Among them, the choice of pertinent a measurement strategy, sensor integration and scale of observation is not a trivial question and part of the process. The transposition of methods between disciplines is not trivial and increases the complexity of the measurements and analysis enormously.

We would like to gather in this special issue a large panel of applications illustrating the challenges, interests and benefits of LiDAR Hyperspectral coupling. Compilations of previous works or new methods focusing on the effective combination and impact for applications of such a coupling are very welcomed:

Advanced Pre-processing methods for LiDAR and Hyperspectral data fusion
Fusion of LiDAR and hyperspectral data
Combining 3D structural and hyperspectral imaging information for applications
Definition of suitable observation scales for certain applications
Identification of qualitative and quantitative improvements for applications
Identification of applications which are not feasible without the use of both systems

Prof. Dr. Patrick Launeau
Dr. Maximilian Brell
Guest Editors

Manuscript Submission Information

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Keywords

Multispectral LiDAR
Hyperspectral imaging
Sensor Data Fusion
Airborne
UAV
Landscape dynamics
Coastal risk
Biodiversity
City management

Published Papers (4 papers)

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Research

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30 pages, 11570 KiB

Open AccessArticle

Coastal Sand Dunes Monitoring by Low Vegetation Cover Classification and Digital Elevation Model Improvement Using Synchronized Hyperspectral and Full-Waveform LiDAR Remote Sensing

by Giovanni Frati, Patrick Launeau, Marc Robin, Manuel Giraud, Martin Juigner, Françoise Debaine and Cyril Michon

Remote Sens. 2021, 13(1), 29; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13010029 - 23 Dec 2020

Cited by 6 | Viewed by 3483

Abstract

Due to the coastal morphodynamic being impacted by climate change there is a need for systematic and large-scale monitoring. The monitoring of sandy dunes in Pays-de-la-Loire (France) requires a simultaneous mapping of (i) its morphology, allowing to assess the sedimentary stocks and (ii) its low vegetation cover, which constitutes a significant proxy of the dune dynamics. The synchronization of hyperspectral imaging (HSI) with full-waveform (FWF) LiDAR is possible with an airborne platform. For a more intimate combination, we aligned the 1064 nm laser beam of a bi-spectral Titan FWF LiDAR with 401 bands and the 15 cm range resolution on the Hyspex VNIR camera with 160 bands and a 4.2 nm spectral resolution, making both types of data follow the same emergence angle. A ray tracing procedure permits to associate the data while keeping the acquisition angles. Stacking multiple shifted FWFs, which are linked to the same pixel, enables reaching a 5 cm range resolution grid. The objectives are (i) to improve the accuracy of the digital terrain models (DTM) obtained from an FWF analysis by calibrating it on dGPS field measurements and correcting it from local deviations induced by vegetation and (ii) in combination with airborne reflectances obtained with PARGE and ATCOR-4 corrections, to implement a supervised hierarchic classification of the main foredune vegetation proxies independently of the acquisition year and the physiological state. The normalization of the FWF LiDAR range to a dry sand reference waveform and the centering on their top canopy echoes allows to isolate Ammophilia arenaria from other vegetation types using two FWF indices, without confusion with slope effects. Fourteen HSI reflectance indices and 19 HSI Spectral Angle Mapping (SAM) indices based on 2017 spectral field measurements performed with the same Hyspex VNIR camera were stacked with both FWF indices into a single co-image for each acquisition year. A simple straightforward hierarchical classification of all 35 pre-classified co-image bands was successfully applied along 20 km, out of the 250 km of coastline acquired from 2017 to 2019, prefiguring its systematic application to the whole 250 km every year. Full article

(This article belongs to the Special Issue Hyperspectral LiDAR Cross Analysis of Landscape Processes and Patterns)

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22 pages, 8899 KiB

Open AccessArticle

Automatic Extraction of Grasses and Individual Trees in Urban Areas Based on Airborne Hyperspectral and LiDAR Data

by Qixia Man, Pinliang Dong, Xinming Yang, Quanyuan Wu and Rongqing Han

Remote Sens. 2020, 12(17), 2725; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12172725 - 23 Aug 2020

Cited by 21 | Viewed by 4318

Abstract

Urban vegetation extraction is very important for urban biodiversity assessment and protection. However, due to the diversity of vegetation types and vertical structure, it is still challenging to extract vertical information of urban vegetation accurately with single remotely sensed data. Airborne light detection and ranging (LiDAR) can provide elevation information with high-precision, whereas hyperspectral data can provide abundant spectral information on ground objects. The complementary advantages of LiDAR and hyperspectral data could extract urban vegetation much more accurately. Therefore, a three-dimensional (3D) vegetation extraction workflow is proposed to extract urban grasses and trees at individual tree level in urban areas using airborne LiDAR and hyperspectral data. The specific steps are as follows: (1) airborne hyperspectral and LiDAR data were processed to extract spectral and elevation parameters, (2) random forest classification method and object-based classification method were used to extract the two-dimensional distribution map of urban vegetation, (3) individual tree segmentation was conducted on a canopy height model (CHM) and point cloud data separately to obtain three-dimensional characteristics of urban trees, and (4) the spatial distribution of urban vegetation and the individual tree delineation were assessed by validation samples and manual delineation results. The results showed that (1) both the random forest classification method and object-based classification method could extract urban vegetation accurately, with accuracies above 99%; (2) the watershed segmentation method based on the CHM could extract individual trees correctly, except for the small trees and the large tree groups; and (3) the individual tree segmentation based on point cloud data could delineate individual trees in three-dimensional space, which is much better than CHM segmentation as it can preserve the understory trees. All the results suggest that two- and three-dimensional urban vegetation extraction could play a significant role in spatial layout optimization and scientific management of urban vegetation. Full article

(This article belongs to the Special Issue Hyperspectral LiDAR Cross Analysis of Landscape Processes and Patterns)

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14 pages, 4985 KiB

Open AccessArticle

High-Resolution Reef Bathymetry and Coral Habitat Complexity from Airborne Imaging Spectroscopy

by Gregory P. Asner, Nicholas R. Vaughn, Christopher Balzotti, Philip G. Brodrick and Joseph Heckler

Remote Sens. 2020, 12(2), 310; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12020310 - 17 Jan 2020

Cited by 26 | Viewed by 4663

Abstract

Coral reef ecosystems are rapidly changing, and a persistent problem with monitoring changes in reef habitat complexity rests in the spatial resolution and repeatability of measurement techniques. We developed a new approach for high spatial resolution (<1 m) mapping of nearshore bathymetry and three-dimensional habitat complexity (rugosity) using airborne high-fidelity imaging spectroscopy. Using this new method, we mapped coral reef habitat throughout two bays to a maximum depth of 25 m and compared the results to the laser-based SHOALS bathymetry standard. We also compared the results derived from imaging spectroscopy to a more conventional 4-band multispectral dataset. The spectroscopic approach yielded consistent results on repeat flights, despite variability in viewing and solar geometries and sea state conditions. We found that the spectroscopy-based results were comparable to those derived from SHOALS, and they were a major improvement over the multispectral approach. Yet, spectroscopy provided much finer spatial information than that which is available with SHOALS, which is valuable for analyzing changes in benthic composition at the scale of individual coral colonies. Monitoring temporal changes in reef 3D complexity at high spatial resolution will provide an improved means to assess the impacts of climate change and coastal processes that affect reef complexity. Full article

(This article belongs to the Special Issue Hyperspectral LiDAR Cross Analysis of Landscape Processes and Patterns)

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Review

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39 pages, 3199 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Hyperspectral and Lidar Data Applied to the Urban Land Cover Machine Learning and Neural-Network-Based Classification: A Review

by Agnieszka Kuras, Maximilian Brell, Jonathan Rizzi and Ingunn Burud

Remote Sens. 2021, 13(17), 3393; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173393 - 26 Aug 2021

Cited by 59 | Viewed by 10746

Abstract

Rapid technological advances in airborne hyperspectral and lidar systems paved the way for using machine learning algorithms to map urban environments. Both hyperspectral and lidar systems can discriminate among many significant urban structures and materials properties, which are not recognizable by applying conventional RGB cameras. In most recent years, the fusion of hyperspectral and lidar sensors has overcome challenges related to the limits of active and passive remote sensing systems, providing promising results in urban land cover classification. This paper presents principles and key features for airborne hyperspectral imaging, lidar, and the fusion of those, as well as applications of these for urban land cover classification. In addition, machine learning and deep learning classification algorithms suitable for classifying individual urban classes such as buildings, vegetation, and roads have been reviewed, focusing on extracted features critical for classification of urban surfaces, transferability, dimensionality, and computational expense. Full article

(This article belongs to the Special Issue Hyperspectral LiDAR Cross Analysis of Landscape Processes and Patterns)

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