Hyperspectral LiDAR Research Articles

Evaluation of the FluorWPS Model and Study of the Parameter Sensitivity for Simulating Solar-Induced Chlorophyll Fluorescence

Solar-induced chlorophyll fluorescence (SIF) has been used as an indicator for the photosynthetic activity of vegetation at regional and global scales. Canopy structure affects the radiative transfer process of SIF within canopy and causes the angular-dependencies of SIF. A common solution for interpreting these effects is the use of physically-based radiative transfer models. As a first step, a comprehensive evaluation of the three-dimensional (3D) radiative transfers is needed using ground truth biological and hyperspectral remote sensing measurements. Due to the complexity of forest modeling, few studies have systematically investigated the effect of canopy structural factors and sun-target-viewing geometry on SIF. In this study, we evaluated the capability of the Fluorescence model with the Weighted Photon Spread method (FluorWPS) to simulate at-sensor radiance and SIF at the top of canopy, and identified the influence of the canopy structural factors and sun-target-viewing geometry on the magnitude and directional response of SIF in deciduous forests. To evaluate the model, a 3D forest scene was first constructed from Goddard’s LiDAR Hyperspectral and Thermal (G-LiHT) LiDAR data. The reliability of the reconstructed scene was confirmed by comparing the calculated leaf area index with the measured ones from the scene, which resulted in a relative error of 3.5%. Then, the performance of FluorWPS was evaluated by comparing the simulated at-sensor radiance spectra with the spectra measured from the DUAL and FLUO spectrometer of HyPlant. The radiance spectra simulated by FluorWPS agreed well with the measured spectra by the two high-performance imaging spectrometers, with a coefficient of determination (R2) of 0.998 and 0.926, respectively. SIF simulated by the FluorWPS model agreed well with the values of the DART model. Furthermore, a sensitivity analysis was conducted to assess the effect of the canopy structural parameters and sun-target-viewing geometry on SIF. The maximum difference of the total SIF can be as large as 45% and 47% at the wavelengths of 685 nm and 740 nm for different foliage area volume densities (FAVDs), and 48% and 46% for fractional vegetation covers (FVCs), respectively. Leaf angle distribution has a markedly influence on the magnitude of SIF, with a ratio of emission part to SIF range from 0.48 to 0.72. SIF from the grass layer under the tree contributed 10%+ more to the top of canopy SIF even for a dense forest canopy (FAVD = 3.5 m−1, FVC = 76%). The red SIF at the wavelength of 685 nm had a similar shape to the far-red SIF at a wavelength of 740 nm but with higher variability in varying illumination conditions. The integration of the FluorWPS model and LiDAR modeling can greatly improve the interpretation of SIF at different scales and angular configurations.

Design of supercontinuum laser hyperspectral light detection and ranging (LiDAR) (SCLaHS LiDAR)

ABSTRACT Traditional Light Detection and Rangings (LiDARs) can quickly collect high-accuracy of three-dimensional (3D) point cloud data at a designated wavelength (i.e., cannot obtain hyperspectral data), while the passive hyperspectral imager can collect rich spectral data of ground objects, but are lack of 3D spatial data. This paper presents one innovative study on the design of airborne-oriented supercontinuum laser hyperspectral (SCLaHS) LiDAR with 50 bands covering 400 nm to 900 nm at a spectral resolution of 10 nm and ground sampling distance (GSD) of 0.5 m. The major innovations include (1) development of the high-power narrow-pulse supercontinuum laser source covering 400 nm to 900 nm with 50 bands using multi-core microstructure fibre, all-polarization maintaining fibre and ultra-long cavity structure, (2) a miniaturized aberration correction holographic concave grating spectroscopic and streak tube technique are developed for 50 bands laser echoes detection at high spectral-spatial-temporal resolution and dynamic airborne platform, and (3) the algorithm theoretic basis for SCLaHS LiDAR point cloud data 3D geodetic coordination calculation, including in-flight airborne calibration algorithm. The initial experimental results demonstrated that the designed SCLaHS LiDAR is doable, and a prototype of the (SCLaHS) LiDAR intends to be implemented.

Application of Hyperspectral LiDAR on 3-D Chlorophyll-Nitrogen Mapping of Rohdea Japonica in Laboratory

Biochemicals, such as chlorophyll (Chl) and nitrogen (N), are closely related to photosynthesis process of vegetation. Their accurate estimation is an important topic in remote sensing of vegetation. Previous studies mainly focused on Chl-N content inversion in leaf and canopy level, and few cared about their 3-D distributions, which was also an important indicator for the growth status of vegetation (GSV). Hyperspectral LiDAR (HSL) is a novel active remote sensing technology, which has target-sensitive band with hyperspectra resolution. Its 3-D point cloud data simultaneously contains rich spectral and precise geometrical characteristics of the target. This work aims to apply HSL data on 3-D Chl-N content mapping in vegetation through constructing HSL-based spectral indices (SIs). Except for following the SI forms of previous works, the normalized differential vegetation index and ratio index (RI) with four broadbands in an HSL spectral space were successively proposed to invert Chl-N content for the whole vegetation based on the artificial neural network (ANN) method. These four broadbands were transformed based on the relative spectral response curve of detector and the feature weights (FWs) of multiwavelength, respectively. Results show that most HSL-based ANN models can accurately invert Chl-N content with a mean R2 of >0.75, and some that fusing broadband data with convolution transformation, namely the FW-based RI, can even obtain a model R2 of 0.84 for N content inversion. Thus, HSL can be efficiently applied to 3-D Chl-N content mapping of vegetation and has great potential in GSV monitoring.

Research on Classification of Pest and Disease Tree Samples Based on Hyperspectral Lidar

Research on Classification of Pest and Disease Tree Samples Based on Hyperspectral Lidar

Analysis and Design of Hyperspectral Imaging LiDAR Scanning Mirror

Analysis and Design of Hyperspectral Imaging LiDAR Scanning Mirror

Application of hyperspectral imager and lidar in marine biological detection

Application of hyperspectral imager and lidar in marine biological detection

Development and prospect of hyperspectral LiDAR for earth observation

Development and prospect of hyperspectral LiDAR for earth observation

Classification of coal/rock based on Hyperspectral LiDAR calibration-free signals

Classification of coal/rock based on Hyperspectral LiDAR calibration-free signals

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

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.

Remote colorimetric measurements by hyperspectral lidar compared to contact conventional colorimetry

AbstractLidars have many applications in different fields, including the field of Cultural Heritage. The purpose of this study is to evaluate a scanning hyperspectral lidar prototype developed at ENEA Center of Frascati as a useful tool for colorimetric analysis when samples cannot be studied with standard contact instruments. A certified X‐Rite Color‐checker sample, consisting of 24 colored blocks with a coordinated colorimetric certificate, was analyzed. In order to obtain colorimetric data from the reflectance spectra, the precepts of the Commission de l'Éclairage were followed and an algorithm was developed to calculate the color difference between the certified values of the Color‐checker and commercial and non‐commercial spectrophotometers and those obtained by the lidar, confirming this latter as a useful tool for remote colorimetry.

A practical method utilizing multi-spectral LiDAR to aid points cloud matching in SLAM

Light Detection and Ranging (LiDAR) sensors are popular in Simultaneous Localization and Mapping (SLAM) owing to their capability of obtaining ranging information actively. Researchers have attempted to use the intensity information that accompanies each range measurement to enhance LiDAR SLAM positioning accuracy. However, before employing LiDAR intensities in SLAM, a calibration operation is usually carried out so that the intensity is independent of the incident angle and range. The range is determined from the laser beam transmitting time. Therefore, the key to using LiDAR intensities in SLAM is to obtain the incident angle between the laser beam and target surface. In a complex environment, it is difficult to obtain the incident angle robustly. This procedure also complicates the data processing in SLAM and as a result, further application of the LiDAR intensity in SLAM is hampered. Motivated by this problem, in the present study, we propose a Hyperspectral LiDAR (HSL)-based-intensity calibration-free method to aid point cloud matching in SLAM. HSL employed in this study can obtain an eight-channel range accompanied by corresponding intensity measurements. Owing to the design of the laser, the eight-channel range and intensity were collected with the same incident angle and range. According to the laser beam radiation model, the ratio values between two randomly selected channels’ intensities at an identical target are independent of the range information and incident angle. To test the proposed method, the HSL was employed to scan a wall with different coloured papers pasted on it (white, red, yellow, pink, and green) at four distinct positions along a corridor (with an interval of 60 cm in between two consecutive positions). Then, a ratio value vector was constructed for each scan. The ratio value vectors between consecutive laser scans were employed to match the point cloud. A classic Iterative Closest Point (ICP) algorithm was employed to estimate the HSL motion using the range information from the matched point clouds. According to the test results, we found that pink and green papers were distinctive at 650, 690, and 720 nm. A ratio value vector was constructed using 650-nm spectral information against the reference channel. Furthermore, compared with the classic ICP using range information only, the proposed method that matched ratio value vectors presented an improved performance in heading angle estimation. For the best case in the field test, the proposed method enhanced the heading angle estimation by 72%, and showed an average 25.5% improvement in a featureless spatial testing environment. The results of the primary test indicated that the proposed method has the potential to aid point cloud matching in typical SLAM of real scenarios.

Joint Classification of Hyperspectral and LiDAR Data Using Hierarchical Random Walk and Deep CNN Architecture

Earth observation using multisensor data is drawing increasing attention. Fusing remotely sensed hyperspectral imagery and light detection and ranging (LiDAR) data helps to increase application performance. In this article, joint classification of hyperspectral imagery and LiDAR data is investigated using an effective hierarchical random walk network (HRWN). In the proposed HRWN, a dual-tunnel convolutional neural network (CNN) architecture is first developed to capture spectral and spatial features. A pixelwise affinity branch is proposed to capture the relationships between classes with different elevation information from LiDAR data and confirm the spatial contrast of classification. Then in the designed hierarchical random walk layer, the predicted distribution of dual-tunnel CNN serves as global prior while pixelwise affinity reflects the local similarity of pixel pairs, which enforce spatial consistency in the deeper layers of networks. Finally, a classification map is obtained by calculating the probability distribution. Experimental results validated with three real multisensor remote sensing data demonstrate that the proposed HRWN significantly outperforms other state-of-the-art methods. For example, the two branches CNN classifier achieves an accuracy of 88.91% on the University of Houston campus data set, while the proposed HRWN classifier obtains an accuracy of 93.61%, resulting in an improvement of approximately 5%.

Analysis of Oil Spill Pollution in the Sea Based on Hyperspectral Laser Radar

Li, J.; Che, Y.; Song, N.; Chen, D., and Li, J., 2020. Analysis of oil spill pollution in the sea based on hyperspectral laser radar. In: Yang, D.F. and Wang, H. (eds.), Recent Advances in Marine Geology and Environmental Oceanography. Journal of Coastal Research, Special Issue No. 108, pp. 156–160. Coconut Creek (Florida), ISSN 0749-0208.The oceans, which account for 71% of the earth's surface area, play a very important role in the development of human society and provide rich resources for mankind. The pollution from oil spills on the sea surface will cause great harm to marine natural ecology and marine living resources. In this paper, according to the characteristics and harms of petroleum pollutants, combined with the development trend of domestic and foreign super-continuous spectrum LIDAR technology, a set of hyperspectral LIDAR prototypes for monitoring oil spill pollution in the sea is designed. The LIDAR can cover the band range of 400 nm∼1100 nm and measure the spectral irradiance of oil spills on the surface of water with different thicknesses at daytime and at night, respectively. According to the experimental data, the super-continuous spectrum LIDAR prototype can be well-qualified for the monitoring of oil spills on the sea surface at night. The experimental data show that the LIDAR prototype can be applied to the monitoring of oil spill pollution.

Radiometric Calibration for Incidence Angle, Range and Sub-Footprint Effects on Hyperspectral LiDAR Backscatter Intensity

Terrestrial hyperspectral LiDAR (HSL) sensors could provide not only spatial information of the measured targets but also the backscattered spectral intensity signal of the laser pulse. The raw intensity collected by HSL is influenced by several factors, among which the range, incidence angle and sub-footprint play a significant role. Further studies on the influence of the range, incidence angle and sub-footprint are needed to improve the accuracy of backscatter intensity data as it is important for vegetation structural and biochemical information estimation. In this paper, we investigated the effects on the laser backscatter intensity and developed a practical correction method for HSL data. We established a laser ratio calibration method and a reference target-based method for HSL and investigated the calibration procedures for the mixed measurements of the effects of the incident angle, range and sub-footprint. Results showed that the laser ratio at the red-edge and near-infrared laser wavelengths has higher accuracy and simplicity in eliminating range, incident angle and sub-footprint effects and can significantly improve the backscatter intensity discrepancy caused by these effects.

A practical method for employing multi-spectral LiDAR intensities in points cloud classification

ABSTRACT Light Detection and Ranging (LiDAR) intensity is associated with the target surface material, which could help the points cloud classification. However, the intensity is also associated with the laser beam incident angle and the transmitting distance, which obstructs its further application in points cloud classification. Motivated by this problem, this paper proposed a practical method for employing the LiDAR intensities in points cloud classification without distance and incident angle calibration, specifically, ratio values between different spectral channels from a newly invented Hyper-spectral LiDAR (HSL) were defined and calculated for generating robust spectral features. Since the HSL different channels had the same transmitting distance and incident angle, therefore, the ratio values were independent on the laser pulse transmitting distance and laser beam incident angle. An indoor experiment was conducted for fully assessing the proposed method. The HSL had eight different spectral channels with spectral wavelength covering from 650 nm to 1000 nm. In the experiments, papers with different colours were pasted on a flat glass; the HSL scanned them at four distinctive positions with 60 cm displacement. The spectral ratio values between different channels at each position were calculated using the obtained multiple spectral profiles from the HSL. The results showed that the points cloud scanned at different incident and distance could be classified though the spectral ratio values without complex distance and incident angle calibration.

Daytime measurements of bioaerosol simulants using a hyperspectral laser-induced fluorescence LIDAR for biosphere research

A new compact home-built hyper-spectral laser-induced fluorescence (LIF) light detection and ranging (LIDAR) system is presented that can detect and identify bioaerosol clouds in an open atmosphere in daylight up to a distance of 3−4 km. The system is based on a 266 nm laser source and a dual-stage micro-channel plate intensified charge-coupled device sensor, which provides a high-resolution fluorescence spectrum with the sensitivity of a single photon counting imaging detection at a 10 Hz repetition rate. Controlled remote sensing experiments on biological simulants in the aerosol-phase within an enclosed chamber (ΔR=1.2 m) were conducted from a short distance of ∼170 m. A meticulous comparison between the remote fluorescence signal detected by the system and the instantaneous concentration and particle size distribution of the bioaerosol inside the chamber detected by an aerodynamic particle sizer yielded a threshold of ∼20 and 137 μgr s m-3 for the spectral identification of tryptophan and ovalbumin, respectively. This showed that the LIDAR can measure environmental airborne biological aerosol remotely in the daytime and can be utilized as a real-time biosphere research tool. Next, the system was tested in open atmospheric field campaigns in the Israeli desert during the day. It successfully detected bioaerosol clouds at up to 2.5 km in strong wind and sunny conditions. These empirical detection and identification results provide robust estimates that a biological cloud can be sensed at a distance of ∼ 4 km.

HIGH-PRECISION RANGING BASED ON MULTISPECTRAL FULL-WAVEFORM LIDAR

Abstract. Full-waveform light detection and ranging (LiDAR) has become a well-established technique for remote sensing of surface topography and target characterization, offering numerous opportunities in investigation and interpretation of the structural diversity of surface coverage. However, most prevailing waveform decomposition methods employ the Gaussian function or some other probability distributions to model LiDAR waveforms of specific shapes, which cannot be universally used. Moreover, most of these waveform decomposition methods operate at a single laser wavelength and cannot be applied well to multispectral LiDAR (MSL) or hyperspectral LiDAR (HSL), which can simultaneously collect the spectral and geometric attributes with multiple transmitting laser wavelengths. In this paper, we propose a new multispectral B-spline waveform decomposition model to achieve high precision multi-target ranging. Considering both the spatial consistency of each channel and the irregular shape of the received waveform, LiDAR waveforms are modelled by B-splines rather than any other specific probability distribution. Thus, the proposed method can be extended to other FWMSL data benefiting from the flexibility of B-splines on fitting arbitrary curves. Both simulated MSL echoes and a measured dataset from a FWMSL system with three wavelengths of 556, 670, and 780 nm were used in this study. Compared with two single wavelength waveform decomposition models and a multispectral waveform decomposition model based on Gaussian function, the proposed method has excellent robustness for processing different shapes of waveforms and can improve ranging accuracy significantly for irregular waveforms.

Spectral observation and classification of typical tree species leaves based on indoor hyperspectral lidar

Spectral observation and classification of typical tree species leaves based on indoor hyperspectral lidar

Integration of hyperspectral and LiDAR data for mapping small water bodies

Inland water bodies are globally threatened by environmental degradation and climate change. On the other hand, new water bodies can be designed during landscape restoration (e.g. after coal mining). Effective management of new water resources requires continuous monitoring; in situ surveys are, however, extremely time-demanding. Remote sensing has been widely used for identifying water bodies. However, the use of optical imagery is constrained by accuracy problems related to the difficulty in distinguishing water features from other surfaces with low albedo, such as tree shadows. This is especially true when mapping water bodies of different sizes. To address these problems, we evaluated the potential of integrating hyperspectral data with LiDAR (hereinafter “integrative approach”). The study area consisted of several spoil heaps containing heterogeneous water bodies with a high variability of shape and size. We utilized object-based classification (Support Vector Machine) based on: (i) hyperspectral data; (ii) LiDAR variables; (iii) integration of both datasets. Besides, we classified hyperspectral data using pixel-based approaches (K-mean, spectral angle mapper). Individual approaches (hyperspectral data, LiDAR data and integrative approach) resulted in 2–22.4 % underestimation of the water surface area (i.e, omission error) and 0.4–1.5 % overestimation (i.e., commission error).The integrative approach yielded an improved discrimination of open water surface compared to other approaches (omission error of 2 % and commission error of 0.4 %). We also evaluated the success of detecting individual ponds; the integrative approach was the only one capable of detecting the water bodies with both omission and commission errors below 10 %. Finally, the assessment of misclassification reasons showed a successful elimination of shadows in the integrative approach. Our findings demonstrate that the integration of hyperspectral and LiDAR data can greatly improve the identification of small water bodies and can be applied in practice to support mapping of restoration process.

Analyzing the Angle Effect of Leaf Reflectance Measured by Indoor Hyperspectral Light Detection and Ranging (LiDAR)

Hyperspectral light detection and ranging (LiDAR) (HSL) combines the characteristics of hyperspectral imaging and LiDAR techniques into a single instrument without any data registration. It provides more information than hyperspectral imaging or LiDAR alone in the extraction of vegetation physiological and biochemical parameters. However, the laser pulse intensity is affected by the incident angle, and its effect on HSL has not yet been fully explored. It is important for employing HSL to investigate vegetation properties. The aim of this paper is to study the incident angle effect of leaf reflectance with HSL and build a model about this impact. In this paper, we studied the angle effect of leaf reflectance from indoor HSL measurements of individual leaves from four typical tree species in Beijing. We observed that (a) the increasing of incident angle decreases the leaf reflectance; (b) the leaf spectrum observed by HSL from 650 to 1000 nm with 10 nm spectral resolution (36 channels) are consistent with those that measured by Analytica Spectra Devices (ASD) spectrometer (R2 = 0.9472 ~ 0.9897); (c) the specular reflection is significant in the red bands, and clear non-Lambertian characteristics are observed. In the near-infrared, there is little specular reflection, but it follows the Lambert-scattering law. We divided the whole band (650–1000 nm) into six bands and established an empirical model to correct the influence of angle effect on the reflectance of the leaf for HSL applications. In the future, the calibration of HSL measurements applied for other targets will be studied by rigorous experiments and modelling.