Imaging Light Detection And Ranging Research Articles

High precision rail surface obstacle detection algorithm based on 3D imaging LiDAR

Railway perimeter intrusion detection is of great significance for ensuring railway safety operations. In this paper, a Light Detection and Ranging (LiDAR) system is designed and developed based on the specific environmental requirements of railway applications. The system is capable of autonomously adjusting the imaging range and angular resolution, enabling high-precision imaging within a 50 m monitoring area. Additionally, based on registration and a series of filtering algorithms, a railway surface obstacle extraction algorithm is developed. The algorithm achieves a 100 % detection rate for obstacles with a minimum height of 15 cm at any position within the monitoring area, and a 70 % detection rate for obstacles with a height of 10 cm. In this algorithm, a coarse registration method based on global planar features combined with the Iterative Closest Point (ICP) registration algorithm achieves sub-centimeter registration accuracy for consecutive point cloud frames. The filtering algorithm based on local planar features enables precise extraction of geometric changes in the railway surface, with simple calculations and fast processing speed, making it highly valuable for extracting object edge features.

Review of Scanning and Pixel Array-Based LiDAR Point-Cloud Measurement Techniques to Capture 3D Shape or Motion

Developments in light detection and ranging (LiDAR) technology have brought innovations in three-dimensional (3D) measurement. After mechanical laser scanners were introduced in the 1990s, the speed and point density of LiDAR measurements have increased considerably with the developments in photon imagers. On the other hand, lightweight and small-size LiDAR sensors and their integrated use with other related sensors have made the use of LiDAR widespread for mapping and navigation purposes on mobile platforms. Matrix imaging LiDAR cameras and solid-state laser scanners have no or fewer moving parts for measurement, and are unaffected by vibrations. They are usually used in mobile mapping, driverless vehicle navigation, and mobile robot navigation. Pulse or phase-shift methods are used to measure the distance from the LiDAR instrument to the scan point. The measured scan point direction is determined by the orientation angles of the beam in scanners, focal length, and pixel positions in matrix viewers, and instrument-centered 3D coordinates are calculated. LiDAR tools have their own specific capabilities and limitations. Therefore, the selection of the appropriate LiDAR for any application is very important. In this study, after LiDAR principles are introduced, scanning LiDAR and pixel-based matrix imager LiDAR methods used to measure 3D point clouds are technically examined and analyzed.

Coupled adversarial learning for fusion classification of hyperspectral and LiDAR data

Hyperspectral image (HSI) provides rich spectral–spatial information and the light detection and ranging (LiDAR) data reflect the elevation information, which can be jointly exploited for better land-cover classification. However, due to different imaging mechanisms, HSI and LiDAR data always present significant image difference, current pixel-wise feature fusion classification methods relying on concatenation or weighted fusion are not effective. To achieve accurate classification result, it is important to extract and fuse similar high-order semantic information and complementary discriminative information contained in multimodal data. In this paper, we propose a novel coupled adversarial learning based classification (CALC) method for fusion classification of HSI and LiDAR data. In specific, a coupled adversarial feature learning (CAFL) sub-network is first trained, to effectively learn the high-order semantic features from HSI and LiDAR data in an unsupervised manner. On one hand, the proposed CAFL sub-network establishes an adversarial game between dual generators and discriminators, so that the learnt features can preserve detail information in HSI and LiDAR data, respectively. On the other hand, by designing weight-sharing and linear fusion structure in the dual generators, we can simultaneously extract similar high-order semantic information and modal-specific complementary information. Meanwhile, a supervised multi-level feature fusion classification (MFFC) sub-network is trained, to further improve the classification performance via adaptive probability fusion strategy. In brief, the low-level, mid-level and high-level features learnt by the CAFL sub-network lead to multiple class estimation probabilities, which are then adaptively combined to generate a final accurate classification result. Both the CAFL and MFFC sub-networks are collaboratively trained by optimizing a designed joint loss function, which consists of unsupervised adversarial loss and supervised classification loss. Overall, by optimizing the joint loss function, the proposed CALC network is pushed to learn highly discriminative fusion features from multimodal data, leading to higher classification accuracies. Extensive experiments on three well-known HSI and LiDAR data sets demonstrate the superior classification performance by the proposed CALC method than several state-of-the-art methods. The source code of the proposed method will be made publicly available at https://github.com/Ding-Kexin/CALC.

A Linear-Array Receiver AFE Circuit Embedded 8-to-1 Multiplexer for Direct ToF Imaging LiDAR Applications

To increase the light detection and ranging (LiDAR) system spatial resolution, the multiple photodetectors are typically deployed in the receiver. An equivalent linear-array receiver with multiplexing operation scheme is presented for the direct time of flight (dToF) LiDAR applications. In particular, the analog front-end (AFE) circuit of the proposed receiver mainly consists of eight transimpedance preamplifiers (preTIAs), a dummy preTIA, a post amplifier (PA) with an 8-to-1 multiplexer, an analog output buffer and a timing discriminator. The proposed AFE circuit, which achieves a transimpedance gain of ~106 dB <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> , a bandwidth of ~220 MHz, an equivalent input-referred noise current of ~5.1 pA/Hz0.5 and a channel switchover time of 6 ns, was fabricated in a 65 nm standard CMOS technology. The associated DC power consumption is 137 mW with a power supply of 2.5 V. The total area of the proposed AFE circuit, which includes the circuit core, bias circuits and I/O PADs, is approximately equal to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.2\times 1.2$ </tex-math></inline-formula> mm2.

Building roof extraction from ASTIL echo images applying OSA-YOLOv5s.

Light detection and ranging (LiDAR) is a type of essential tool for urban planning and geoinformation extraction. Airborne streak tube imaging LiDAR (ASTIL) is a new system with great advantages in the rapid collection of remote sensing data. To the best of our knowledge, a new method to extract a building roof from the echo images of ASTIL is proposed. We improve YOLOv5s with a one-shot aggregation (OSA) module to improve efficiency. The experimental results show that the mean average precision of the OSA-YOLOv5s algorithm can reach 95.2%, and the frames per second can reach 11.74 using a CPU and 39.39 using a GPU. The method proposed can extract building objects efficiently from the echo images of ASTIL and acquire the building roof point cloud.

Research on Key Technologies of MEMS-based 3D Imaging LIDAR Vision System

In this paper, a new type of 3D imaging light detection and ranging (LIDAR) vision system based on Micro-Electro-Mechanical System (MEMS) mirror and novel optical system is proposed. The co-aperture transceiver optical system offers a compact architecture and wide Field-of-View (FOV), which also solves the problem of stray light affecting the system. Thanks to the MEMS and the optical system, the detecting FOV is greatly enlarged and the system volume is much smaller compared to the traditional system. The whole system is designed and verified by simulations and experiments.

Discrimination of Canopy Structural Types in the Sierra Nevada Mountains in Central California

Accurate information about ecosystem structure and biogeochemical properties is essential to providing better estimates ecosystem functioning. Airborne LiDAR (light detection and ranging) is the most accurate way to retrieve canopy structure. However, accurately obtaining both biogeochemical traits and structure parameters requires concurrent measurements from imaging spectrometers and LiDARs. Our main objective was to evaluate the use of imaging spectroscopy (IS) to provide vegetation structural information. We developed models to estimate structural variables (i.e., biomass, height, vegetation heterogeneity and clumping) using IS data with a random forests model from three forest ecosystems (i.e., an oak-pine low elevation savanna, a mixed conifer/broadleaf mid-elevation forest, and a high-elevation montane conifer forest) in the Sierra Nevada Mountains, California. We developed and tested general models to estimate the four structural variables with accuracies greater than 75%, for the structurally and ecologically different forest sites, demonstrating their applicability to a diverse range of forest ecosystems. The model R2 for each structural variable was least in the conifer/broadleaf forest than either the low elevation savanna or the montane conifer forest. We then used the structural variables we derived to discriminate site-specific, ecologically meaningful descriptions of canopy structural types (CST). Our CST results demonstrate how IS data can be used to create comprehensive and easily interpretable maps of forest structural types that capture their major structural features and trends across different vegetation types in the Sierra Nevada Mountains. The mixed conifer/broadleaf forest and montane conifer forest had the most complex structures, containing six and five CSTs respectively. The identification of CSTs within a site allowed us to better identify the main drivers of structural variability in each ecosystem. CSTs in open savanna were driven mainly by differences in vegetation cover; in the mid-elevation mixed forest, by the combination of biomass and canopy height; and in the montane conifer forest, by vegetation heterogeneity and clumping.

Small Imaging Depth LIDAR and DCNN-Based Localization for Automated Guided Vehicle.

We present our third prototype sensor and a localization method for Automated Guided Vehicles (AGVs), for which small imaging LIght Detection and Ranging (LIDAR) and fusion-based localization are fundamentally important. Our small imaging LIDAR, named the Single-Photon Avalanche Diode (SPAD) LIDAR, uses a time-of-flight method and SPAD arrays. A SPAD is a highly sensitive photodetector capable of detecting at the single-photon level, and the SPAD LIDAR has two SPAD arrays on the same chip for detection of laser light and environmental light. Therefore, the SPAD LIDAR simultaneously outputs range image data and monocular image data with the same coordinate system and does not require external calibration among outputs. As AGVs travel both indoors and outdoors with vibration, this calibration-less structure is particularly useful for AGV applications. We also introduce a fusion-based localization method, named SPAD DCNN, which uses the SPAD LIDAR and employs a Deep Convolutional Neural Network (DCNN). SPAD DCNN can fuse the outputs of the SPAD LIDAR: range image data, monocular image data and peak intensity image data. The SPAD DCNN has two outputs: the regression result of the position of the SPAD LIDAR and the classification result of the existence of a target to be approached. Our third prototype sensor and the localization method are evaluated in an indoor environment by assuming various AGV trajectories. The results show that the sensor and localization method improve the localization accuracy.

Development of a Regional Coral Observation Method by a Fluorescence Imaging LIDAR Installed in a Towable Buoy

Coral bleaching and mortality is predicted to increase under global climate change. A new observation technique is required to monitor regional coral conditions. To this end, we developed a light detection and ranging (LIDAR) system installed in a towable buoy for boat observations, which acquires continuous fluorescent images of the seabed during day-time. Most corals have innate fluorescent proteins in their tissue, and they emit fluorescence by ultraviolet excitation. This fluorescence distinguishes living coral from dead coral skeleton, crustose coralline algae, and sea algae. This paper provides a proof of concept for using the LIDAR system and fluorescence to map coral distribution within 1 km scale and coral cover within 100 m scale for a single reef in Japan.

An Improved Dual-axis Mirror Control Scheme for Imaging LIDAR Application

An improved dual-axis mirror control scheme for the imaging LIDAR (Light Detection And Ranging) is proposed in this paper. The scheme features that a direct drive mechanism is developed utilizing PMSMs (Permanent Magnet Synchronous Motor) and rotary resolvers to control the angular positions of x-y mirrors. The controller is composed of the standard d-q current loops and a specifically designed position loop. Thus, the capabilities of accurate scanning and high accuracy pointing and tracking are enabled, together with the enhanced laser generator, resulting in the extension of the range measurement to be as far as 25 kilometres, as well as the near-zone imaging capability. In addition, a safety mode is developed to prevent the LIDAR from interior damage. Then, the implementation of the mirror subsystem is introduced and the experimental results have validated the agreement with the requirement.

Prediction of Macronutrients at the Canopy Level Using Spaceborne Imaging Spectroscopy and LiDAR Data in a Mixedwood Boreal Forest

Information on foliar macronutrients is required in order to understand plant physiological and ecosystem processes such as photosynthesis, nutrient cycling, respiration and cell wall formation. The ability to measure, model and map foliar macronutrients (nitrogen (N), phosphorus (P), potassium (K), calcium (Ca) and magnesium (Mg)) at the forest canopy level provides information on the spatial patterns of ecosystem processes (e.g., carbon exchange) and provides insight on forest condition and stress. Imaging spectroscopy (IS) has been used particularly for modeling N, using airborne and satellite imagery mostly in temperate and tropical forests. However, there has been very little research conducted at these scales to model P, K, Ca, and Mg and few studies have focused on boreal forests. We report results of a study of macronutrient modeling using spaceborne IS and airborne light detection and ranging (LiDAR) data for a mixedwood boreal forest canopy in northern Ontario, Canada. Models incorporating Hyperion data explained approximately 90% of the variation in canopy concentrations of N, P, and Mg; whereas the inclusion of LiDAR data significantly improved the prediction of canopy concentration of Ca (R2 = 0.80). The combined used of IS and LiDAR data significantly improved the prediction accuracy of canopy Ca and K concentration but decreased the prediction accuracy of canopy P concentration. The results indicate that the variability of macronutrient concentration due to interspecific and functional type differences at the site provides the basis for the relationship observed between the remote sensing measurements (i.e., IS and LiDAR) and macronutrient concentration. Crown closure and canopy height are the structural metrics that establish the connection between macronutrient concentration and IS and LiDAR data, respectively. The spatial distribution of macronutrient concentration at the canopy scale mimics functional type distribution at the site. The ability to predict canopy N, P, K, Ca and Mg in this study using only IS, only LiDAR or their combination demonstrates the excellent potential for mapping these macronutrients at canopy scales across larger geographic areas into the next decade with the launch of new IS satellite missions and by using spaceborne LiDAR data.

Linking imaging spectroscopy and LiDAR with floristic composition and forest structure in Panama

Landsat and Shuttle Radar Topography Mission (SRTM) imagery have recently been used to identify broad-scale floristic units in Neotropical rain forests, corresponding to geological formations and their edaphic properties. Little is known about the structural and functional variation between these floristic units, however, and Landsat and SRTM data lack the spectral and spatial resolution needed to provide this information. Imaging spectroscopy and LiDAR (Light Detection and Ranging) have been used to measure canopy structure and function in a variety of ecosystems, but the ability of these technologies to measure differences between compositionally-distinct but otherwise uniform tropical forest types remains unknown. We combined 16 tree inventories from central Panama with imaging spectroscopy and LiDAR elevation data from the Carnegie Airborne Observatory to test our ability to identify patterns in plant species composition, and to measure the spectral and structural differences between adjacent closed-canopy tropical forest types. We found that variations in spectroscopic imagery and LiDAR data were strong predictors of spatial turnover in plant species composition. We also found that these compositional, chemical, and structural patterns corresponded to underlying geological formations and their geomorphological properties. We conclude that imaging spectroscopy and LiDAR data can be used to interpret patterns identified in lower resolution sensors, to provide new information on forest function and structure, and to identify underlying determinants of these patterns.

High-peak-power, single-mode, nanosecond pulsed, all-fiber laser for high resolution 3D imaging LIDAR system

This study presents an eye-safe, single-mode, nanosecond-pulsed, and all-fiber laser source with master-oscillator-power-amplifier configuration at 1 550 nm that is suitable for high-resolution three-dimensional (3D) imaging light detection and ranging (LIDAR) system. The output peak power of 7.6 kW is obtained at the 1.2-ns pulse width and 50-kHz repetition rate. The single-mode pulse laser output ensures the range precision and imaging results of the LIDAR system. The laser is used as a transmitter for the 3D imaging LIDAR system. The detailed characteristics of the LIDAR system and the results of the 3D imaging are presented.

Aerodynamic roughness length estimation from very high-resolution imaging LIDAR observations over the Heihe basin in China

Abstract. Roughness length of land surfaces is an essential variable for the parameterisation of momentum and heat exchanges. The growing interest in the estimation of the surface turbulent flux parameterisation from passive remote sensing leads to an increasing development of models, and the common use of simple semi-empirical formulations to estimate surface roughness. Over complex surface land cover, these approaches would benefit from the combined use of passive remote sensing and land surface structure measurements from Light Detection And Ranging (LIDAR) techniques. Following early studies based on LIDAR profile data, this paper explores the use of imaging LIDAR measurements for the estimation of the aerodynamic roughness length over a heterogeneous landscape of the Heihe river basin, a typical inland river basin in the northwest of China. The point cloud obtained from multiple flight passes over an irrigated farmland area were used to separate the land surface topography and the vegetation canopy into a Digital Elevation Model (DEM) and a Digital Surface Model (DSM) respectively. These two models were then incorporated in two approaches: (i) a strictly geometrical approach based on the calculation of the plan surface density and the frontal surface density to derive a geometrical surface roughness; (ii) a more aerodynamic approach where both the DEM and DSM are introduced in a Computational Fluid Dynamics model (CFD). The inversion of the resulting 3-D wind field leads to a fine representation of the aerodynamic surface roughness. Examples of the use of these three approaches are presented for various wind directions together with a cross-comparison of results on heterogeneous land cover and complex roughness element structures.

Effects of Morella faya tree invasion on aboveground carbon storage in Hawaii

The negative effects of biological invasion are often the focus of ecological studies, but few have considered potential positive impacts, such as increased carbon storage, resulting from invasion. We combined airborne imaging spectrometer and LiDAR (light detection and ranging) observations with field measurements to assess if the highly invasive nitrogen-fixing tree Morella faya alters canopy 3-D structure and aboveground biomass (AGB) along a 1,500 mm precipitation gradient in Hawaii. Airborne analysis of canopy water content, leaf nitrogen concentration, fractional canopy cover, and vegetation height facilitated mapping of native- and Morella-dominated canopies in rainforest, woodland–savanna and shrubland ecosystems, with Morella detection errors ranging from 0 to 13.4%. Allometric equations were developed to relate the combined LiDAR and spectral data to field-based AGB estimates (r2 = 0.97, P < 0.01), and to produce a map of biomass stocks throughout native and invaded ecosystems. The structure of the invasive Morella canopies varied by ecosystem type, and the invader shaded out native understory plants in rainforest zones. Despite a 350% increase in AGB going from shrubland to rainforest, Morella did not increase average AGB in any ecosystem it invaded. Furthermore, spatial distributions of biomass indicated that Morella decreased maximum AGB in the woodland–savanna ecosystems. We conclude that Morella tree invasion does not enhance aboveground carbon stocks in any ecosystem it invades in Hawaii, thereby minimizing its contribution to this potentially important ecosystem service. We also found that the fusion of spectral and LiDAR remote sensing provided canopy chemical and structural data facilitating a landscape assessment of how biological invasion alters on carbon stocks and other ecosystem properties.

Multi-source land cover classification for forest fire management based on imaging spectrometry and LiDAR data

Forest fire management practices are highly dependent on the proper monitoring of the spatial distribution of the natural and man-made fuel complexes at landscape level. Spatial patterns of fuel types as well as the three-dimensional structure and state of the vegetation are essential for the assessment and prediction of forest fire risk and fire behaviour. A combination of the two remote sensing systems, imaging spectrometry and light detection and ranging (LiDAR), is well suited to map fuel types and properties, especially within the complex wildland–urban interface. LiDAR observations sample the spatial information dimension providing explicit geometric information about the structure of the Earth's surface and super-imposed objects. Imaging spectrometry on the other hand samples the spectral dimension, which is sensitive for discrimination of surface types. As a non-parametric classifier support vector machines (SVM) are particularly well adapted to classify data of high dimensionality and from multiple sources as proposed in this work. The presented approach achieves an improved land cover mapping adapted to forest fire management needs. The map is based on a single SVM classifier combining the spectral and spatial information dimensions provided by imaging spectrometry and LiDAR.

Invasive species detection in Hawaiian rainforests using airborne imaging spectroscopy and LiDAR

Remote sensing of invasive species is a critical component of conservation and management efforts, but reliable methods for the detection of invaders have not been widely established. In Hawaiian forests, we recently found that invasive trees often have hyperspectral signatures unique from that of native trees, but mapping based on spectral reflectance properties alone is confounded by issues of canopy senescence and mortality, intra- and inter-canopy gaps and shadowing, and terrain variability. We deployed a new hybrid airborne system combining the Carnegie Airborne Observatory (CAO) small-footprint light detection and ranging (LiDAR) system with the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) to map the three-dimensional spectral and structural properties of Hawaiian forests. The CAO-AVIRIS systems and data were fully integrated using in-flight and post-flight fusion techniques, facilitating an analysis of forest canopy properties to determine the presence and abundance of three highly invasive tree species in Hawaiian rainforests.The LiDAR sub-system was used to model forest canopy height and top-of-canopy surfaces; these structural data allowed for automated masking of forest gaps, intra- and inter-canopy shadows, and minimum vegetation height in the AVIRIS images. The remaining sunlit canopy spectra were analyzed using spatially-constrained spectral mixture analysis. The results of the combined LiDAR-spectroscopic analysis highlighted the location and fractional abundance of each invasive tree species throughout the rainforest sites. Field validation studies demonstrated <6.8% and <18.6% error rates in the detection of invasive tree species at ∼7 m2 and ∼2 m2 minimum canopy cover thresholds. Our results show that full integration of imaging spectroscopy and LiDAR measurements provides enormous flexibility and analytical potential for studies of terrestrial ecosystems and the species contained within them.

Monitoring of Oil Spill Using Ultra-Violet Laser

We have developed the helicopter-based fluorescence imaging LIDAR (Light Detection and Ranging) for the monitoring of oil spills. This is an active, remote sensing system which shoots a UV pulsed laser beam to an oil spill on the sea's surface, the fluorescence image is then observed. Compared to other techniques, this monitoring method has the merit of a high reliability of oil spill identification. And also high observability, even in nighttime, or rainfall and in high-wave conditions.In the helicopter flight experiments, the detection and identification of sea water and light fuel oil has been successful. In addition, the area of light fuel oil film was estimated by the fluorescence 2-D image observations.In this technical report, we describe the details of this system and the results of the performance evaluation so far and by experimentation using helicopters.

Fusion of imaging spectrometer and LIDAR data over combined radiative transfer models for forest canopy characterization

A comprehensive canopy characterization of forests is derived from the combined remote sensing signal of imaging spectrometry and large footprint LIDAR. The inversion of two linked physically based Radiative Transfer Models (RTM) provided the platform for synergistically exploiting the specific and independent information dimensions obtained by the two earth observation systems. Due to its measurement principle, LIght Detection And Ranging (LIDAR) is particularly suited to assess the horizontal and vertical canopy structure of forests, while the spectral measurements of imaging spectrometry are specifically rich on information for biophysical and -chemical canopy properties. In the presented approach, the specific information content inherent to the observations of the respective sensor was not only able to complement the canopy characterization, but also helped to solve the ill-posed problem of the RTM inversion. The theoretical feasibility of the proposed earth observation concept has been tested on a synthetic data set generated by a forest growth model for a wide range of forest stands. Robust estimates on forest canopy characteristics were achieved, ranging from maximal tree height, fractional cover (fcover), Leaf Area Index (LAI) to the foliage chlorophyll and water content. The introduction of prior information on the canopy structure derived from large footprint LIDAR observations significantly improved the retrieval performance relative to estimates based solely on spectral information.

Remote sensing of forest pigments using airborne imaging spectrometer and LIDAR imagery

This study created and tested predictive models developed using airborne imaging spectrometer and light detection and ranging (LIDAR) instruments for estimating the concentrations of photosynthetic pigments in broad-leaved and coniferous forest plantations. Data were acquired using a Compact Airborne Spectrographic Imager (CASI) and an Airborne Laser Terrain Mapping (ALTM) 1020 instrument in midsummer for study sites in the New Forest, England, along with concomitant in situ measurements of canopy properties. The stands used displayed a wide variation in the biophysical and biochemical properties of interest. When employing the imaging spectrometer data alone, there were no relationships between any spectral variables (band reflectance, band ratios, or first derivatives of reflectance) and canopy biophysical and biochemical properties when both broad-leaved and coniferous stands were analysed as a combined data set. However, for the broad-leaved stands alone, curvilinear relationships were found between the wavelength position of the red edge ( λ RE) and pigment concentrations per unit ground area (e.g., R 2=0.88** for chlorophyll a [Chl a]) and per unit leaf mass (e.g., R 2=0.76** for Chl a). The predictive value of these models was somewhat limited; for example, the root mean squared error (RMSE) was 300 mg m −2 (27% of the mean) for Chl a concentration per unit ground area and 1.17 mg g −1 (24% of the mean) for Chl a concentration per unit leaf mass. A ratio of a near-infrared and a green band (865 nm/553 nm) was linearly related to leaf area index (LAI) of the broad-leaved stands ( R 2=0.71**) and the regression model was a reasonable predictor of the LAI for the independent test sites (RMSE=0.88; 18.6% of the mean). Canopy height information derived from the ALTM data was used to mask out canopy gap areas from the CASI imagery of each stand. This process had limited impact on the relationships between spectral and canopy variables for the broad-leaved stands, and λ RE remained unrelated to pigment concentrations per unit ground area for the coniferous stands. However, the masking process substantially improved the strength of the relationship between λ RE and pigment concentrations per unit leaf mass for the coniferous stands (e.g., for Chl a R 2=0.85**; RMSE of prediction=0.84 mg g −1 [22% of the mean]). Therefore, the study demonstrates that for broad-leaved stands, spectral models can be applied to imaging spectrometer data to quantify forest pigments and LAI with moderate accuracy. For coniferous stands, the use of LIDAR data to remove canopy gap areas from the CASI imagery considerably increases the accuracy of spectral predictive models for quantifying pigment concentrations per unit leaf mass.