Full-waveform LiDAR Research Articles

Full Waveform LiDAR for Adverse Weather Conditions

We present and discuss the case for full waveform pixel and image acquisition and processing to enable LiDAR sensors to penetrate and reconstruct 3D surface maps through obscuring media. To that end, we review work on signal propagation, on scanning and arrayed sensors, on signal processing strategies for independent pixels and employing spatial context, on reducing complexity and accelerating processing by sensor design, algorithmic changes, compressed sensing, and parallel processing. We report several experimental studies on LiDAR imaging through complex media, and how these can inform the automotive LiDAR scenario. We conclude with a discussion of future development and potential for full waveform LiDAR (FWL).

Influence of voxel size on forest canopy height estimates using full-waveform airborne LiDAR data

BackgroundForest canopy height is a key forest structure parameter. Precisely estimating forest canopy height is vital to improve forest management and ecological modelling. Compared with discrete-return LiDAR (Light Detection and Ranging), small-footprint full-waveform airborne LiDAR (FWL) techniques have the capability to acquire precise forest structural information. This research mainly focused on the influence of voxel size on forest canopy height estimates.MethodsA range of voxel sizes (from 10.0 m to 40.0 m interval of 2 m) were tested to obtain estimation accuracies of forest canopy height with different voxel sizes. In this study, all the waveforms within a voxel size were aggregated into a voxel-based LiDAR waveform, and a range of waveform metrics were calculated using the voxel-based LiDAR waveforms. Then, we established estimation model of forest canopy height using the voxel-based waveform metrics through Random Forest (RF) regression method.Results and conclusionsThe results showed the voxel-based method could reliably estimate forest canopy height using FWL data. In addition, the voxel sizes had an important influence on the estimation accuracies (R2 ranged from 0.625 to 0.832) of forest canopy height. However, the R2 values did not monotonically increase or decrease with the increase of voxel size in this study. The best estimation accuracy produced when the voxel size was 18 m (R2 = 0.832, RMSE = 2.57 m, RMSE% = 20.6%). Compared with the lowest estimation accuracy, the R2 value had a significant improvement (33.1%) when using the optimal voxel size. Finally, through the optimal voxel size, we produced the forest canopy height distribution map for this study area using RF regression model. Our findings demonstrate that the optimal voxel size need to be determined for improving estimation accuracy of forest parameter using small-footprint FWL data.

Echo decomposition of full-waveform LiDAR based on a digital implicit model and a particle swarm optimization.

In the waveform decomposition of full-waveform LiDAR, the Gaussian model (GSM) and the generalized Gaussian model (GGSM) are widely used to extract echoes from return waveforms. However, those models have explicit functions that follow specific distribution shapes, so they are suitable only for decomposing echo waveforms with similar shapes. This paper introduces a digital implicit model (DIM) and presents a universal decomposition method for the full-waveform LiDAR. In this method, the decomposition model is considered to be an implicit function, associated with a digital template waveform library, whose optimization is implemented by a modified particle swarm algorithm. The template waveform library is a customized fingerprint for any special full-waveform LiDAR, so the DIM can deal effectively with infinite echoes with arbitrary shapes. A full-waveform LiDAR system with asymmetric echo distribution is designed to compare the decomposition performances among the GSM, GGSM, and DIM. Experimental results show that, when decomposing the return waveform containing a single echo, the normalized sum of squares due to fitting error (SSE) of the DIM can be 60 times lower than the GSM and the GGSM. By comparing the estimation accuracies of the amplitude, the FWHM and the location of the echo component, the DIM has the best decomposition performance and the best ranging accuracy (subcentimeter level) among the three models; when decomposing the return waveform containing three overlapping echoes, the normalized SSE of the DIM can be 28 times lower than the GSM and 12 times lower than the GGSM. By comparing the estimation accuracies of the amplitude, FWHM, and location of echoes components, the DIM has the best decomposition performance and best ranging accuracy (centimeter level) among the three models.

Color Restoration for Full-Waveform Multispectral LiDAR Data

The current full-waveform data at a single wavelength can mainly retrieve the geometric attributes of targets along the light path by detecting waveform components, resulting in the lack of spectral or color attribute information. This kind of device relies on a digital camera for acquiring the color information, however, which is inevitably limited by the lighting conditions and geometric registration errors. With the development of multispectral light detection and ranging (LiDAR) or even hyperspectral LiDAR that often utilize a supercontinuum laser source covering the whole visible light band, including red, green and blue bands, the simultaneous acquisition of color and spatial information becomes possible and makes passive imaging data no longer necessary. In this study, we propose a color restoration method for a full-waveform multispectral LiDAR (FWMSL) system. Additionally, we develop a multispectral lognormal function to fit the tailing echoes measured by FWMSL further accurately. Experimental data from our FWMSL system are used to evaluate the performance of the proposed method. The relative standard deviation, correlation coefficient (R2) and color difference ( Δ E ) metrics suggest that the color restoration for the full-waveform multispectral data is feasible.

Detecting Dead Standing Eucalypt Trees from Voxelised Full-Waveform Lidar Using Multi-Scale 3D-Windows for Tackling Height and Size Variations

In southern Australia, many native mammals and birds rely on hollows for sheltering, while hollows are more likely to exist on dead trees. Therefore, detection of dead trees could be useful in managing biodiversity. Detecting dead standing (snags) versus dead fallen trees (Coarse Woody Debris—CWD) is a very different task from a classification perspective. This study focuses on improving detection of dead standing eucalypt trees from full-waveform LiDAR. Eucalypt trees have irregular shapes making delineation of them challenging. Additionally, since the study area is a native forest, trees significantly vary in terms of height, density and size. Therefore, we need methods that will be resistant to those challenges. Previous study showed that detection of dead standing trees without tree delineation is possible. This was achieved by using single size 3D-windows to extract structural features from voxelised full-waveform LiDAR and characterise dead (positive samples) and live (negative samples) trees for training a classifier. This paper adds on by proposing the usage of multi-scale 3D-windows for tackling height and size variations of trees. Both the single 3D-windows approach and the new multi-scale 3D-windows approach were implemented for comparison purposes. The accuracy of the results was calculated using the precision and recall parameters and it was proven that the multi-scale 3D-windows approach performs better than the single size 3D-windows approach. This open ups possibilities for applying the proposed approach on other native forest related applications.

Evaluation of Footprint Horizontal Geolocation Accuracy of Spaceborne Full-Waveform LiDAR Based on Digital Surface Model

Spaceborne full-waveform LiDAR has shown unique advantages in measuring global surface elevation. Laser footprints generally have decimeter-level vertical accuracy, meeting the requirement of ground elevation control points. In contrast, the footprint horizontal geolocation accuracy is in the meter to ten-meter levels. Although previous researches attempted to locate the footprint horizontal coordinate based on the digital surface model (DSM), the applicability and performance of the DSM-based positioning method in evaluating the footprint geolocation accuracy should be rigorously assessed before large-scale applications. Therefore, this study practices the DSM-based footprint positioning method over several study sites with various land covers and different laser campaigns. The footprint geolocation accuracy of the ICESat/GLAS (Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System), the first Earth observation full-waveform LiDAR satellite, is evaluated by the DSM-based method. Results indicate that the DSM-based positioning method is only suitable for areas with significant height features, but not applicable in areas with high spatial correlation. The derived footprint geolocation accuracy (8.19-m horizontal shifting with 4.19-m standard deviation) is relatively reliable in urban site with relatively high spatial heterogeneity. This study helps make better use of the DSM-based footprint positioning method and design calibration experiments of full-waveform LiDAR satellites.

Optical OCDMA coding and 3D imaging technique for non-scanning full-waveform LiDAR system.

Full-waveform LiDAR systems have been developed owing to their superiority in terms of high precision and resolution. However, further improving the imaging speed and resolution continues to be a problem. In this study, we propose an optical orthogonal code division multiple access (OCDMA) coding and 3D imaging technique, and a non-scanning full-waveform LiDAR system is first demonstrated. Encoding, multiplexing, and decoding are the essential modules of the OCDMA LiDAR system, which use M detectors and N-bits code to achieve high-accuracy decomposition for ${\rm M} \times {\rm N}$M×N pixels. In this paper, a complete 3D imaging LiDAR system is introduced, and the implementation of encoding and decoding is also illustrated. To prove that the technology is scientific and effective, an imaging experiment is carried out. The experimental result indicates that a system with only four avalanche photodiodes (APDs) can achieve 256 pixels. Moreover, the vertical resolution is about 1.8 cm and the range resolution is 15 cm at the distance of 40 m.

Calibration of the Pulse Signal Decay Effect of Full-Waveform Hyperspectral LiDAR.

Full-waveform hyperspectral LiDAR (FWHSL) is able to obtain spectral and spatial information by utilizing a single instrument, and it has become more and more commonly used in vertical distribution studies of structural and biochemical characteristics of vegetation. However, the pulse-echo arrival times of multiple spectral channels of the FWHSL are not consistent and this causes range ambiguity in spectral channels. In this paper, the pulse signal decay effect on range measurements was studied by measuring the varying trends of pulse signal decay between spectral channels with different material properties. The experiments were repeated at different distances. All of the spectral channels were compared for different materials. The results suggest that the channels in the red edge spectral region of vegetation have good stability and accuracy for range measurements of varied distance and materials properties. Finally, based on the geometric invariability in a specific red edge channel, a practical calibration approach for the pulse signal decay effect is also presented. The validation tests showed it could improve the pulse signal decay effect of full-waveform hyperspectral LiDAR.

Waveformlidar: An R Package for Waveform LiDAR Processing and Analysis

A wealth of Full Waveform (FW) LiDAR (Light Detection and Ranging) data are available to the public from different sources, which is poised to boost extensive applications of FW LiDAR data. However, we lack a handy and open source tool that can be used by potential users for processing and analyzing FW LiDAR data. To this end, we introduce waveformlidar, an R package dedicated to FW LiDAR processing, analysis and visualization as a solution to the constraint. Specifically, this package provides several commonly used waveform processing methods such as Gaussian, Adaptive Gaussian and Weibull decompositions and deconvolution approaches (Gold and Richard-Lucy (RL)) with users’ customized settings. In addition, we also developed functions to derive commonly used waveform metrics for characterizing vegetation structure. Moreover, a new way to directly visualize FW LiDAR data is developed by converting waveforms into points to form the Hyper Point Cloud (HPC), which can be easily adopted and subsequently analyzed with existing discrete-return LiDAR processing tools such as LAStools and FUSION. Basic explorations of the HPC such as 3D voxelization of the HPC and conversion from original waveforms to composite waveforms are also available in this package. All of these functions are developed based on small-footprint FW LiDAR data but they can be easily transplanted to the large footprint FW LiDAR data such as Geoscience Laser Altimeter System (GLAS) and Global Ecosystem Dynamics Investigation (GEDI) data analysis. It is anticipated that these functions will facilitate the widespread use of FW LiDAR and be beneficial for better estimating biomass and characterizing vegetation structure at various scales.

EXPLORING THE POTENTIAL OF FULL WAVEFORM AIRBORNE LIDAR FEATURES AND ITS FUSION WITH RGB IMAGE IN CLASSIFICATION OF A SPARSELY FORESTED AREA

Abstract. Precise measurements of forest trees is very important in environmental protection. Measuring trees parameters by use of ground- based inventories is time and cost consuming. Employing advanced remote sensing techniques to obtain forest parameters has recently made a great progress step in this research area. Among the information resources of the study field, full waveform LiDAR data have attracted the attention of researchers in the recent years. However, decomposing LiDAR waveforms is one of the challenges in the data processing. In fact, the procedure of waveform decomposition causes some of the useful information in waveforms to be lost. In this study, we aim to investigate the potential use of non-decomposed full waveform LiDAR features and its fusion with optical images in classification of a sparsely forested area. We consider three classes including i) ground, ii) Quercus wislizeni and iii) Quercus douglusii for the classification procedure. In order to compare the results, five different strategies, namely i) RGB image, ii) common LiDAR features, iii) fusion of RGB image and common LiDAR features, iv) LiDAR waveform structural features and v) fusion of RGB image and LiDAR waveform structural features have been utilized for classifying the study area. The results of our study show that classification via using fusion of LiDAR waveform features and RGB image leads to the highest classification accuracy.

Full-Waveform Airborne LiDAR Data Classification Using Convolutional Neural Networks

Point cloud classification is one of the most important and time-consuming stages of airborne LiDAR (Light Detection and Ranging) data processing, playing a key role in the generation of cartographic products. This paper describes an innovative algorithm to perform LiDAR point-cloud classification, which relies on Convolutional Neural Networks (CNNs) and takes advantage of full-waveform data registered by modern laser scanners. The proposed method consists of two steps. First, a simple CNN is used to preprocess each waveform, providing a compact representation of the data. By exploiting the coordinates of the points associated with the waveforms, output vectors generated by the first CNN are then mapped into an image that is subsequently segmented by a Fully Convolutional Network (FCN): a label is assigned to each pixel and, consequently, to the point falling in the pixel. In this way, spatial positions and geometrical relationships between neighboring data are taken into account. These particular architectures allow to accurately identify even challenging classes such as power line and transmission tower.

A Photon-Counting Full-Waveform Lidar**Supported by the National Natural Science Foundation of China under Grant Nos 11774095, 11804099 and 11621404, the Shanghai Basic Research Project under Grant No 18JC1412200, and the Program of Introducing Talents of Discipline to Universities under Grant No B12024.

We present the results of using a photon-counting full-waveform lidar to obtain detailed target information with high accuracy. The parameters of the waveforms (i.e., vertical structure, peak position, peak amplitude, peak width and backscatter cross section) are derived with a high resolution limit of 31 mm to establish the vertical structure and scattering properties of targets, which contribute to the recognition and classification of various scatterers. The photon-counting full-waveform lidar has higher resolution than linear-mode full-waveform lidar, and it can obtain more specific target information compared to photon-counting discrete-point lidar, which can provide a potential alternative technique for tomographic surveying and mapping.

Estimating leaf chlorophyll and nitrogen contents using active hyperspectral LiDAR and partial least square regression method

As hyperspectral LiDAR (HSL) combines the advantages of hyperspectral remote sensing and LiDAR, it has the potential of estimating vegetation biochemical contents at any three-dimensional (3-D) location. We investigate the capability of HSL to monitor leaf chlorophyll and nitrogen contents at a distance of 7.5 m. Using full-waveform LiDAR data obtained by HSL, the performance of the partial least square regression (PLSR) model using three strategies (only wavelength bands, only vegetation indices, and both wavelength bands and vegetation indices) is explored based on the correlation analysis of 16 wavelength bands and 16 vegetation indices. The result shows that the PLSR model can make full use of various wavelength bands and vegetation indices, having a strong ability to monitor leaf chlorophyll and nitrogen contents using full-waveform LiDAR data. The analysis can be applied for predicting leaf biochemical components of other vegetation and provides the basis for monitoring 3-D distribution of biochemical contents using HSL in the future.

Full-Waveform LiDAR Point Clouds Classification Based on Wavelet Support Vector Machine and Ensemble Learning.

Light Detection and Ranging (LiDAR) produces 3D point clouds that describe ground objects, and has been used to make object interpretation in many cases. However, traditional LiDAR only records discrete echo signals and provides limited feature parameters of point clouds, while full-waveform LiDAR (FWL) records the backscattered echo in the form of a waveform, which provides more echo information. With the development of machine learning, support vector machine (SVM) is one of the commonly used classifiers to deal with high dimensional data via small amount of samples. Ensemble learning, which combines a set of base classifiers to determine the output result, is presented and SVM ensemble is used to improve the discrimination ability, owing to small differences in features between different types of data. In addition, previous kernel functions of SVM usually cause under-fitting or over-fitting that decreases the generalization performance. Hence, a series of kernel functions based on wavelet analysis are used to construct different wavelet SVMs (WSVMs) that improve the heterogeneity of ensemble system. Meanwhile, the parameters of SVM have a significant influence on the classification result. Therefore, in this paper, FWL point clouds are classified by WSVM ensemble and particle swarm optimization is used to find the optimal parameters of WSVM. Experimental results illustrate that the proposed method is robust and effective, and it is applicable to some practical work.

Characterizing relative degrees of clumping structure in vegetation canopy using waveform LiDAR

With the advances in complexity and resolution in ecological modeling, there is an increasing demand for more detailed characterization of vegetation canopy architecture. Clumping, a characteristic of foliage distribution, is one of the important aspects of canopy structure and significantly influences light penetration through the canopy. Airborne full-waveform LiDAR data contains large amounts of structural information, and presents a powerful avenue for providing detailed foliage distribution information over large areas. In this study, we present a method for describing physical canopy clumping structure for individual trees that can resolve fine scale variations in foliage clumping. We first utilize K-means clustering algorithm to extract structure found within full-waveform LiDAR data. Then we use representative traits for each data cluster to classify them into their natural groups. Based on these traits, we draw conclusions about physical representations of each group and identify groups as containing clumps in vegetation ranging from spares to dense. We formulate a clumping index multiplier based on cluster traits that inform relative degrees of vegetation clumping at the sub-canopy scale. We use the clumping index multiplier to introduce heterogeneity to a modeled plot level clumping index and generate a vertical clumping index profile for a forested plot. This study demonstrates that large amounts of canopy structural information can be extracted from waveform LiDAR data. The fine resolution canopy clumping structure found by the method described in this work can be used as valuable input for ecological models.

Estimating forest aboveground biomass using small-footprint full-waveform airborne LiDAR data

Forest biomass is a key biophysical parameter for climate change, ecological modeling and forest management. Compared with discrete-return LiDAR data, full-waveform LiDAR data can provide more accurate and abundant vertical structure information on vegetation and thus have been increasingly applied to the estimation of forest aboveground biomass (AGB). The main objective of this research is to estimate forest AGB using full-waveform airborne LiDAR data. In this study, we constructed voxel-based waveforms (0.5 × 0.5 m) using small-footprint full-waveform LiDAR data, and then aggregated voxel-based waveforms into pseudo-large-footprint waveforms with a plot size of 20 × 20 m. We extracted a range of waveform metrics from voxel-based waveforms and pseudo-large-footprint waveforms (FWm), respectively, and then calculated the mean of the voxel-based waveform metrics within a plot (FWμ). Based on the Random Forest (RF) regression, the forest biomasses were estimated using two types of waveform metrics: FWm (R2 = 0.84, RMSE% = 21.4%, bias = -0.11 Mg ha−1) and FWμ (R2 = 0.81, RMSE% = 23.3%, bias = 0.13 Mg ha−1). We found that slightly higher biomass estimation accuracy was obtained with FWm than with FWμ. In addition, a comparison between the biomasses predicted by the waveform metrics and by the traditional discrete-return metrics (R2 = 0.80, RMSE% = 23.4%, bias = 0.20 Mg ha−1) was performed to explore the potential to improve biomass estimates using the waveform metrics, and the results showed that both waveform metrics and discrete-return metrics could accurately predict forest biomass. However, the biomass estimations from the waveform metrics were more accurate than those from the traditional discrete-return metrics. We concluded that the method proposed in this study has the potential to estimate vegetation structure parameters using full-waveform LiDAR data.

Three dimensional (3D) spatial metrics for objects

The increasing availability of lidar data and structure from motion processing techniques is moving pattern metric research toward the development of three-dimensional (3D) analysis. There is a need to develop spatial pattern metrics that leverage 3D datasets, such as those derived from lidar or unmanned aircraft systems technology, that are meaningful and interpretable across landscape contexts. We introduce a suite of 3D spatial pattern metrics that can be computed on gradient surfaces such as digital surface models, but are rooted in traditional, patch-based landscape metrics that are familiar and interpretable across landscape contexts. We compute a suite of 3D metrics and demonstrate their use by analyzing a landscape pattern in the built environment of New Orleans in 2002 and 2008—pre- and post- Hurricane Katrina. Lidar data are used to segment individual buildings and calculate 3D patterns at the equivalent of the patch-, class- and landscape-levels for traditional landscape metrics. 3D spatial metrics can characterize landscape patterns at multiple spatial scales. These metrics capture aspect of pattern that traditional patch-mosaic and surface metrics cannot. Future research can build from these measures to develop other measures of 3D spatial patterns that are applicable for different ecological contexts. Continuing advances in full waveform lidar may contribute to the development of more complex metrics.

Mapping tropical dry forest age using airborne waveform LiDAR and hyperspectral metrics

Tropical dry forest (TDF) regeneration has been extensively characterized as three deterministic successional stages, i.e., early, intermediate, and late, for the past few decades. This deterministic definition, however, ignores many biophysical and biochemical processes in the forest regeneration. This study mapped a second TDF as a function of regeneration age at the Santa Rosa National Park (SRNP) Environmental Monitoring Super Site, Costa Rica, using an airborne full-waveform LiDAR (Laser Vegetation Imaging Sensor or LVIS), a hyperspectral dataset (Hyperspectral MAPper or HyMap) and three advanced machine learning methods. We defined five age groups (0–10 years, 10–20 years, 20–30 years, 30–50 years, 50 + years) based on historical forest cover maps, and analyzed their effective LiDAR waveforms and cumulative return energy curves (derived from LVIS) and their reflectance (derived from the HyMap). Then, nine LVIS metrics and eleven HyMap indices were calculated and their abilities to differentiate the age groups were evaluated using Multiple Comparison Analysis (MCA). We found that six of the LVIS metrics which describe the vertical structure of the forests can significantly differentiate all age groups. None of HyMap metrics can differentiate all age groups, but some of them can identify certain age groups. Selected LVIS metrics and HyMap indices were used to map TDF age, through Support Vector Machine, Artificial Neural Network and Random Forest (RF). We found LVIS plus HyMap metrics generally produced more accurate forest age maps than either LVIS metrics or HyMap indices and RF better performed than other two classifiers. We finally proposed a method to synthesize different forest age maps into one age map which had the highest accuracy for all age groups. Our study highlighted the importance to consider the forest regeneration as a continuous stochastic phenomenon, and also highlighted the advantages to incorporate multiple remote sensing techniques to describe the forest regeneration. Our method to synthesize the forest age map can also benefit other researchers who need to take advantage of multiple mapping results.

Canopy Height Layering Biomass Estimation Model (CHL-BEM) with Full-Waveform LiDAR

Forest biomass is an important descriptor for studying carbon storage, carbon cycles, and global change science. The full-waveform spaceborne Light Detection And Ranging (LiDAR) Geoscience Laser Altimeter System (GLAS) provides great possibilities for large-scale and long-term biomass estimation. To the best of our knowledge, most of the existing research has utilized average tree height (or height metrics) within a GLAS footprint as the key parameter for biomass estimation. However, the vertical distribution of tree height is usually not as homogeneous as we would expect within such a large footprint of more than 2000 m2, which would limit the biomass estimation accuracy vastly. Therefore, we aim to develop a novel canopy height layering biomass estimation model (CHL-BEM) with GLAS data in this study. First, all the trees with similar height were regarded as one canopy layer within each GLAS footprint. Second, the canopy height and canopy cover of each layer were derived from GLAS waveform parameters. These parameters were extracted using a waveform decomposition algorithm (refined Levenberg–Marquardt—RLM), which assumed that each decomposed vegetation signal corresponded to a particular canopy height layer. Third, the biomass estimation model (CHL-BEM) was established by using the canopy height and canopy cover of each height layer. Finally, the CHL-BEM was compared with two typical biomass estimation models of GLAS in the study site located in Ejina, China, where the dominant species was Populus euphratica. The results showed that the CHL-BEM presented good agreement with the field measurement biomass (R2 = 0.741, RMSE = 0.487, %RMSE = 24.192) and achieved a significantly higher accuracy than the other two models. As a whole, we expect our method to advance all the full-waveform LiDAR development and applications, e.g., the newly launched Global Ecosystem Dynamics Investigation (GEDI).

Estimating tree height from TanDEM-X data at the northwestern Canadian treeline

The circum-Arctic transitional zone between forest and tundra, i.e. the treeline zone, is shifting northward due to current Arctic warming and, therefore, requires systematic monitoring. Up to now, radar remote sensing was hardly possible in the treeline zone due to spatial resolution and/or temporal decorrelation constraints of preceding satellite missions. The unique constellation of the TanDEM-X satellites with its bistatic mode and very high spatial resolution opens up opportunities for monitoring small (≥0.01 km2) and isolated patches of very sparse forest which are typical for the transitional zone. We focused on an area at the northern edge of the treeline zone in the Northwest Territories, Canada, and evaluated the potential of TanDEM-X bistatic data to characterize the tree height in the forest patches in this region. TanDEM-X data were acquired during the TanDEM-X Science Phase in 2015, when the perpendicular baseline was large (corresponding to the height of ambiguity of approximately 14.6 m) and kept constant. We employed TanDEM-X backscatter, bistatic coherence, and interferometric height from the stack of seven multitemporal bistatic pairs and compared them to maximum vegetation height obtained from full-waveform airborne LiDAR data. We found strong linear relationships between all TanDEM-X metrics and LiDAR vegetation height within the forest patches with r = 0.67, r = −0.69, and r = 0.78 for the backscatter, coherence, and interferometric height, respectively. Furthermore, we extracted the position of individual trees from the LiDAR data and estimated tree density as the number of trees per unit area. The linear relationships between all TanDEM-X metrics and the tree density were very weak. The relationships between all TanDEM-X metrics and tree height differentiated for three tree density classes (low, medium, and high) remained strong. Random forests regression using all three TanDEM-X metrics predicted the tree height with a mean absolute error of 0.7 m (mean forest height in the study area was 2.5 m). CoSSC pairs were generally consistent with each other and the multitemporal averaging slightly improved the performance compared to single pairs. Taking into account the global coverage of bistatic TanDEM-X data acquired for the global digital elevation model, our results show a potential for quantifying the tree height in small forest patches along the circum-Arctic treeline zone.