Return Waveform Research Articles

Unveiling Anomalies in Terrain Elevation Products from Spaceborne Full-Waveform LiDAR over Forested Areas

Anomalies displaying significant deviations between terrain elevation products acquired from spaceborne full-waveform LiDAR and reference elevations are frequently observed in assessment studies. While the predominant focus is on “normal” data, recognizing anomalies within datasets obtained from the Geoscience Laser Altimeter System (GLAS) and the Global Ecosystem Dynamics Investigation (GEDI) is essential for a comprehensive understanding of widely used spaceborne full-waveform data, which not only facilitates optimal data utilization but also enhances the exploration of potential applications. Nevertheless, our comprehension of anomalies remains limited as they have received scant specific attention. Diverging from prevalent practices of directly eliminating outliers, we conducted a targeted exploration of anomalies in forested areas using both transmitted and return waveforms from the GLAS and the GEDI in conjunction with airborne LiDAR point cloud data. We unveiled that elevation anomalies stem not from the transmitted pulses or product algorithms, but rather from scattering sources. We further observed similarities between the GLAS and the GEDI despite their considerable disparities in sensor parameters, with the waveforms characterized by a low signal-to-noise ratio and a near exponential decay in return energy; specifically, return signals of anomalies originated from clouds rather than the land surface. This discovery underscores the potential of deriving cloud-top height from spaceborne full-waveform LiDAR missions, particularly the GEDI, suggesting promising prospects for applying GEDI data in atmospheric science—an area that has received scant attention thus far. To mitigate the impact of abnormal return waveforms on diverse land surface studies, we strongly recommend incorporating spaceborne LiDAR-offered terrain elevation in data filtering by establishing an elevation-difference threshold against a reference elevation. This is especially vital for studies concerning forest parameters due to potential cloud interference, yet a consensus has not been reached within the community.

How useful are mispointing phase SARAL/AltiKa geophysical products for ocean applications?

SARAL/AltiKa, the first microwave altimeter operating at Ka-band frequency, recently completed nine years of operations in orbit. During these years, it has catered to many applications related to operational oceanography, climate sciences, hydrology and cryosphere. More specifically, in oceanography, SARAL has contributed immensely to operational wave and circulation modelling, eddy detection/tracking, ocean current generation and many more. However, since Feb 2019, SARAL has moved from the drifting phase (DP) to the mispointing phase (MP) due to the malfunctioning of the star sensor of the spacecraft. In this study, we analyse the instrument’s performance and its waveforms during its ongoing MP. We find out that during the MP, significant wave height (SWH) measurements are anomalously high between 18 and 24 m, and wind speed measurements are between 16 and 19 m/s. In sea surface height anomaly (SSHA), there is a steady rise in negative values during the MP. In the return waveform, ∼15% degradation in Brown-type waveforms in the open ocean region is noticed. These changes significantly impact the SARAL applications. Two important applications of wave forecast and eddy detection are discussed here as examples. Following this, we also recommend using provided quality flags so that the data can be further explored for various ocean applications.

Faint Echo Extraction from ALB Waveforms Using a Point Cloud Semantic Segmentation Model

As an active remote sensing technology, airborne LIDAR can work at all times while emitting specific wavelengths of laser light that can penetrate seawater. Airborne LIDAR bathymetry (ALB) records an object’s full return waveform, including the water surface, water column, seafloor, and the objects on it. Due to the seawater’s absorption and scattering and the seafloor’s reflectivity effect, the seafloor’s amplitude of seafloor echoes varies greatly. Seafloor echoes with low signal-to-noise ratios are not easily detected using waveform processing methods, which can lead to insufficient seafloor topography depth and incomplete seafloor topography coverage. To extract faint seafloor echoes, we proposed a depth extraction method based on the PointConv deep learning model, called FWConv. The method assumed that spatially adjacent echoes were correlated. We converted all the spatially adjacent multi-frame waveforms into a point cloud. Each point represented a bin value in the waveform, and the points’ properties contained spatial coordinates and the amplitude in the waveform. In the semantic segmentation of these point clouds using deep learning models, we considered not only each centroid’s amplitude, but also its neighboring points’ distance and amplitude. This enriched the centroids’ features and allowed the model to better discriminate between background noise and seafloor echoes. The results showed that FWConv could extract faint seafloor echoes in the experimental area and was not easily affected by noise, and that the correctness reached 99.82%. The number of point clouds increased by 158%, and the seafloor elevation accuracy reached 0.20 m concerning the multibeam echo sounder data.

Sea Surface Height Estimation from Improved Modified, and Decontaminated Sub-Waveform Retracking Methods over Coastal Areas

Coastal zones are challenging areas for sensing by satellite altimeters because reflected signals from non-water surfaces and from calm sea surfaces in small bays and ports inside the radar footprint lead to erroneous powers in return waveforms. Accordingly, these contaminated waveforms do not follow the so-called Brown model in conventional retracking algorithms and fail to derive qualified ranges. Consequently, the estimated water level is erroneous as well. Therefore, selecting an optimized retracker for post-processing waveforms is significantly important to achieve a qualified water level estimation. To find the optimized retracker, we employed a methodology to minimize the effect of erroneous powers on retracked range corrections. To this end, two new approaches were presented, one based on a waveform decontamination method and the other based on a waveform modification method. We considered the first meaningful sub-waveforms in the decontaminated waveforms and in the modified waveforms to be processed with a threshold retracker. To assess their performance, we also retracked the decontaminated and modified full-waveforms. The first meaningful sub-waveform and full-waveform in the original waveforms were retracked to compare the performance of the modified and decontaminated waveform retracking with the original waveform retracking. To compare the results of our sub-waveform retracking algorithms with those of external sub-waveform retracking algorithms, the (Adaptive Leading Edge Sub-waveform) ALES database was also used. In our retracking scenarios, we used the Sentinel-3A SRAL Altimeter to estimate the water levels over the study area within 10 km from the coastlines in both the Persian Gulf and the Bay of Biscay from June 2016 to October 2020. The water levels from processing L2 products were estimated as well. We evaluated our retracking scenarios and L2, as well as the ALES processing results, against the tide gauges. Our analysis showed that within 0–10 km from the coast, the first meaningful sub-waveform of the decontaminated waveforms had the best performance. We reached maximum RMS improvements in this scenario of 53% and 86% over the Persian Gulf and the Bay of Biscay, respectively, in comparison with L2 processing. Over these distances from the coast, the first sub-waveform from the original waveforms and the modified waveforms stayed in the second and third order of performance. The ALES database with an RMS ranging from 13 to 51 cm had a worse performance than all of our sub-waveform retracking scenarios.

Airborne dual-wavelength waveform LiDAR improves species classification accuracy of boreal broadleaved and coniferous trees

Tree species identification constitutes a bottleneck in remote sensing applications. Waveform LiDAR has been shown to offer potential over discrete-return observations, and we assessed if the combination of two-wavelength waveform data can lead to further improvements. A total of 2532 trees representing seven living and dead conifer and deciduous species classes found in Hyytiälä forests in southern Finland were included in the experiments. LiDAR data was acquired by two single-wavelength sensors. The 1064-nm and 1550-nm data were radiometrically corrected to enable range-normalization using the radar equation. Pulses were traced through the canopy, and by applying 3D crown models, the return waveforms were assigned to individual trees. Crown models and a terrain model enabled a further split of the waveforms to strata representing the crown, understory and ground segments. Different geometric and radiometric waveform attributes were extracted per return pulse and aggregated to tree-level mean and standard deviation features. We analyzed the effect of tree size on the features, the correlation between features and the between-species differences of the waveform features. Feature importance for species classification was derived using F-test and the Random Forest algorithm. Classification tests showed significant improvement in overall accuracy (74→83% with 7 classes, 88→91% with 4 classes) when the 1064-nm and 1550-nm features were merged. Most features were not invariant to tree size, and the dependencies differed between species and LiDAR wavelength. The differences were likely driven by factors such as bark reflectance, height growth induced structural changes near the treetop as well as foliage density in old trees.

Improving Satellite Waveform Altimetry Measurements With a Probabilistic Relaxation Algorithm

The Geoscience Laser Altimeter System onboard the NASA Ice, Cloud, and land Elevation Satellite (ICESat/GLAS) provided elevation measurements of Earth’s surface between 2003 and 2009. The centroid and maximum-amplitude-peak (MAP) retracking methods have been designed and applied to process the returned laser waveforms for elevation measurements. Although these two methods work well in general, they may generate erroneous measurements when the returned waveform was complicated by adverse atmospheric conditions (clouds, ice fogs, blowing snow, and dust storms). The centroid retracking method is often more severely affected when compared with the MAP retracking method. In this study, we present a new retracking method that exploits the spatial contextual information from neighboring footprints along the satellite ground track, in addition to the single return waveform shape information. Our method uses a probabilistic relaxation (PR) algorithm to integrate the spatial contextual information and the waveform shape information to identify the waveform peak that most likely represents the true surface elevation, rather than simply detecting the peak with the maximum magnitude. For different types of land surfaces, such as inland lakes, polar tundra, ice sheet, and sand deserts, we demonstrate that our new PR retracking method is able to produce more reliable, consistent, and accurate elevation measurements than the standard NASA ICESat/GLAS data products. The root mean squares error (RMSE) is reduced from 0.85 to 0.17 m for inland lake, from 0.81 to 0.23 m for polar tundra, from 1.25 to 0.33 m for ice sheet, and from 2.48 to 2.34 m for sand desert.

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.

A Full-Waveform Airborne Laser Scanning Metric Extraction Tool for Forest Structure Modelling. Do Scan Angle and Radiometric Correction Matter?

In the last decade, full-waveform airborne laser scanning (ALSFW) has proven to be a promising tool for forestry applications. Compared to traditional discrete airborne laser scanning (ALSD), it is capable of registering the complete signal going through the different vertical layers of the vegetation, allowing for a better characterization of the forest structure. However, there is a lack of ALSFW software tools for taking greater advantage of these data. Additionally, most of the existing software tools do not include radiometric correction, which is essential for the use of ALSFW data, since extracted metrics depend on radiometric values. This paper describes and presents a software tool named WoLFeX for clipping, radiometrically correcting, voxelizing the waves, and extracting object-oriented metrics from ALSFW data. Moreover, extracted metrics can be used as input for generating either classification or regression models for forestry, ecology, and fire sciences applications. An example application of WoLFeX was carried out to test the influence of the relative radiometric correction and the acquisition scan angle (1) on the ALSFW metric return waveform energy (RWE) values, and (2) on the estimation of three forest fuel variables (CFL: canopy fuel load, CH: canopy height, and CBH: canopy base height). Results show that radiometric differences in RWE values computed from different scan angle intervals (0°–5° and 15°–20°) were reduced, but not removed, when the relative radiometric correction was applied. Additionally, the estimation of height variables (i.e., CH and CBH) was not strongly influenced by the relative radiometric correction, while the model obtained for CFL improved from R2 = 0.62 up to R2 = 0.79 after applying the correction. These results show the significance of the relative radiometric correction for reducing radiometric differences measured from different scan angles and for modelling some stand-level forest fuel variables.

Моделирование морской поверхности при описании формы отраженного импульса альтиметра.

Моделирование морской поверхности при описании формы отраженного импульса альтиметра.

Image processing inspired technique for enhancing performance of the underwater modulated pulse laser system

A processing technique for enhancing imagery and ranging data collected by an underwater modulated pulse laser system is presented. Laser-based sensors offer high-resolution and high-accuracy for imaging and ranging applications in the underwater environment. However, these capabilities can be degraded in turbid waters due to scattering. We present experimental results demonstrating a technique inspired by image processing which reduces the effects of both backscatter and forward scatter. By combining individual return waveforms of the modulated pulse laser system together and applying the processing approach as described, images can be formed in which an object in the scene can be distinguished from scatter using an edge detector. Results obtained by applying the technique to laboratory experimental data are presented and compared to a baseline approach. Useful three-dimensional imagery was generated out to 6.9 attenuation lengths, a 25% improvement over the baseline. In range finding experiments without a range-gate, a test object was detected at 11.1 attenuation lengths downrange, compared to 5.7 attenuation lengths for the baseline.

Method for determining the footprint center of a satellite laser altimeter based on marked waveforms by CCRs.

To evaluate the data accuracy of a satellite laser altimeter, in situ verification should be performed periodically, and the laser footprint center should be precisely extracted and positioned. To date, two methods have been derived to determine the center of the laser footprint: using waveform matching when a laser is illuminated on complex surfaces or using an energy detector array to capture the laser footprint when illuminating a flat calibration site. A corner cube retroreflector (CCR) array is never used to calculate the footprint center. In this study, a new method is derived to determine the laser center by analyzing the return waveforms, which are marked with a designed CCR array above ground. First, the parameters of a single CCR are designed to compensate for the velocity aberration of a satellite. The footprint center is positioned based on a damped steepest decent method when at least four CCRs are hit, and the corresponding return waveforms are marked. Next, the layout scheme of the CCRs is designed, and the positioning accuracy is evaluated using this new method. The simulated results indicate that the positioning accuracy of the laser footprint centers will be approximately 2m horizontal. In the future, this method will be used to determine laser footprint centers and will be integrated with the current method of energy detector array in the calibration site to provide basic data to accomplish in situ verification work for the China GF-7 satellite laser altimeter.

REFINED SIMULATION OF SATELLITE LASER ALTIMETER FULL ECHO WAVEFORM

Abstract. The return waveform of satellite laser altimeter plays vital role in the satellite parameters designation, data processing and application. In this paper, a method of refined full waveform simulation is proposed based on the reflectivity of the ground target, the true emission waveform and the Laser Profile Array (LPA). The ICESat/GLAS data is used as the validation data. Finally, we evaluated the simulation accuracy with the correlation coefficient. It was found that the accuracy of echo simulation could be significantly improved by considering the reflectivity of the ground target and the emission waveform. However, the laser intensity distribution recorded by the LPA has little effect on the echo simulation accuracy when compared with the distribution of the simulated laser energy. At last, we proposed a refinement idea by analyzing the experimental results, in the hope of providing references for the waveform data simulation and processing of GF-7 satellite in the future.

Theoretical surface type classifier based on a waveform model of a satellite laser altimeter and its performance in the north of Greenland.

Current land-cover classification methods using ICESat/GLAS's (Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System) datasets are based on empirical thresholds or machine learning by training multiple GLAS parameters, e.g., the reflectivity and elevation of the target and width, amplitude, kurtosis, and skewness of the return waveform. A theoretical classifier is derived based on a waveform model of an actual laser altimeter illuminating the sea surface. With given system parameters and the sea surface wind corresponding to the location of a laser footprint (the wind can be calculated by using the National Centers for Environmental Prediction dataset), a precise theoretical waveform can be generated as a reference. Compared with the measured waveform, a weighted total difference, which is very sensitive to small-scale sea ice within the laser footprint, can be calculated to classify the GLAS measured data as open water. In the north of Greenland, after discarding the saturated GLAS data, the new theoretical classifier performed better [overall accuracy (OA)=95.62%, Kappa coefficient=0.8959] compared to the classical support vector machine (SVM) classifier (OA=90.44%, Kappa=0.7901), but the SVM classifier showed a better result for the user's accuracy of sea ice. Benefiting from the synergies of the theoretical and SVM classifiers, the integrated theoretical and SVM classifier achieved excellent accuracy (OA=98.21%, Kappa=0.9588). In the future, the new ICESat-2 photon counting laser altimeter will also construct a "waveform" (elevation distribution) by selecting the photon cloud, and thus, this new analytical method will be potentially useful for detecting open water in the Arctic.

An Improved Quadrilateral Fitting Algorithm for the Water Column Contribution in Airborne Bathymetric Lidar Waveforms

In this paper, an improved method based on a mixture of Gaussian and quadrilateral functions is presented to process airborne bathymetric LiDAR waveforms. In the presented method, the LiDAR waveform is fitted to a combination of three functions: one Gaussian function for the water surface contribution, another Gaussian function for the water bottom contribution, and a new quadrilateral function to fit the water column contribution. The proposed method was tested on a simulated dataset and a real dataset, with the focus being mainly on the performance of retrieving bottom response and water depths. We also investigated the influence of the parameter settings on the accuracy of the bathymetry estimates. The results demonstrate that the improved quadrilateral fitting algorithm shows a superior performance in terms of low RMSE and a high detection rate in the water depth and magnitude retrieval. What’s more, compared with the use of a triangular function or the existing quadrilateral function to fit the water column contribution, the presented method retrieved the least noise and the least number of unidentified waveforms, showed the best performance in fitting the return waveforms, and had consistent fitting goodness for all different water depths.

Assessment of CryoSat-2 SAR mode wind and wave data

Significant wave height (SWH) and surface wind speed (WS) products from the CryoSat-2 Delay-Doppler, which is commonly known as Synthetic Aperture Radar (SAR), Mode are validated against operational ECMWF atmospheric and wave model results in addition to available observations from buoys, platforms and Jason-2 altimeter. The CryoSat-2 SAR Mode data are processed from Level 1A (also known as Full Bit Rate, FBR, in the CryoSat-2 terminology) up to L1B in accordance to the Delay-Doppler algorithm, and then retracked using SAMOSA (SAR Altimetry MOde Studies and Applications) SAR return waveform model, as implemented in the Grid Processing on Demand (G-POD) service called SAR Versatile Altimetric Toolkit for Ocean Research and Exploitation (SARvatore). The data cover two geographic boxes: one in the northeast Atlantic Ocean (NE Atlantic Box) for the period from 6 September 2010 to 30 June 2014 and the other is in the eastern Pacific (Pacific Box) for the period from 7 May 2012 to 30 June 2014. The amount of data is limited by the CryoSat-2 SAR Mode acquisition mask over ocean but is large enough to ensure robustness and significance of the results. The results show that the quality of both CryoSat-2 SAR SWH and WS products is very high when compared to typical altimetry mission requirements. When compared against model and in-situ data, the correlation coefficients are as high as 0.98 for SWH and 0.95 for WS while the bias and standard deviation of the difference is less than 5% and 0.3 m, respectively, for SWH and less than 0.3 m/s and 1.3 m/s, respectively for WS. The results show that the quality of both CryoSat-2 SAR SWH and WS products is in line with Jason-2 performances and satisfies the typical altimetry mission requirements.

A revised calibration of the interferometric mode of the CryoSat-2 radar altimeter improves ice height and height change measurements in western Greenland

Abstract. We compare geocoded heights derived from the interferometric mode (SARIn) of CryoSat to surface heights from calibration–validation sites on Devon Ice Cap and western Greenland. Comparisons are included for both the heights derived from the first return (the point-of-closest-approach or POCA) and heights derived from delayed waveform returns (swath processing). While swath-processed heights are normally less precise than edited POCA heights, e.g. standard deviations of ∼ 3 and ∼ 1.5 m respectively for the western Greenland site, the increased coverage possible with swath data complements the POCA data and provides useful information for both system calibration and improving digital elevation models (DEMs). We show that the pre-launch interferometric baseline coupled with an additional roll correction ( ∼ 0.0075° ± 0.0025°), or equivalent phase correction ( ∼ 0.0435 ± 0.0145 radians), provides an improved calibration of the interferometric SARIn mode. We extend the potential use of SARIn data by showing the influence of surface conditions, especially melt, on the return waveforms and that it is possible to detect and measure the height of summer supraglacial lakes in western Greenland. A supraglacial lake can provide a strong radar target in the waveform, stronger than the initial POCA return, if viewed at near-normal incidence. This provides an ideal situation for swath processing and we demonstrate a height precision of ∼ 0.5 m for two lake sites, one in the accumulation zone and one in the ablation zone, which were measured every year from 2010 or 2011 to 2016. Each year the lake in the ablation zone was viewed in June by ascending passes and then 5.5 days later by descending passes, which allows an approximate estimate of the filling rate. The results suggest that CryoSat waveform data and measurements of supraglacial lake height change could complement the use of optical satellite imagery and be helpful as proxy indicators for surface melt around Greenland.

지형 기울기에 의한 항공 수심 라이다 수심 측정 오차 보정

Light detection and ranging (LiDAR) is one of the most efficient technologies to obtain the topographic and bathymetric map of coastal zones, superior to other technologies, such as sound navigation and ranging (SONAR) and synthetic aperture radar (SAR). However, the measurement results using LiDAR are vulnerable to environmental factors. To achieve a correspondence between the acquired LiDAR data and reality, error sources must be considered, such as the water surface slope, water turbidity, and seafloor slope. Based on the knowledge of those factors’ effects, error corrections can be applied. We concentrated on the effect of the seafloor slope on LiDAR waveforms while restricting other error sources. A simulation regarding in-water beam scattering was conducted, followed by an investigation of the correlation between the seafloor slope and peak timing of return waveforms. As a result, an equation was derived to correct the depth error caused by the seafloor slope.

Modeling of Airborne Bathymetric Lidar Waveforms

ABSTRACT Kim, M.; Kopilevich, Y.; Feygels, V.; Park, J.Y., and Wozencraft, J., 2016. Modeling of airborne bathymetric lidar waveforms. In: Brock, J.C.; Gesch, D.B.; Parrish, C.E.; Rogers, J.N., and Wright, C.W. (eds.), Advances in Topobathymetric Mapping, Models, and Applications. Journal of Coastal Research, Special Issue, No. 76, pp. 18–30. Coconut Creek (Florida), ISSN 0749-0208. Modeling the optical power of the lidar return waveform is performed for the radiometrically calibrated CZMIL (Coastal Zone Mapping and Imaging Lidar, Optech, Inc.) data. For this purpose, a lidar waveform simulator was developed. The theory is described based on the receiver sensitivity function, the radiative transfer equation (RTE) via the Greens function, the optical reciprocity theorem, and the small angle approximation (SAA). The SAA-based RTE is solved for the radiance distribution using the Fourier transform method. Along with the numerical algorithms, the contribution was made on the air-water and water-bottom interfa...

ANALYSIS OF THE SIDE-LAP EFFECT ON FULL-WAVEFORM LIDAR DATA ACQUISITION FOR THE ESTIMATION OF FOREST STRUCTURE VARIABLES

Abstract. LiDAR full-waveform provides a better description of the physical and forest vertical structure properties than discrete LiDAR since it registers the full wave that interacts with the canopy. In this paper, the effect of flight line side-lap is analysed on forest structure and canopy fuel variables estimations. Differences are related to pulse density changes between flight stripe side-lap areas, varying the point density between 2.65 m−2 and 33.77 m−2 in our study area. These differences modify metrics extracted from data and therefore variable values estimated from these metrics such as forest stand variables. In order to assess this effect, 64 pairwise samples were selected in adjacent areas with similar canopy structure, but having different point densities. Two parameters were tested and evaluated to minimise this effect: voxel size and voxel value assignation testing maximum, mean, median, mode, percentiles 90 and 95. Student’s t-test or Wilcoxon test were used for the comparison of paired samples. Moreover, the absolute value of standardised paired samples was calculated to quantify dissimilarities. It was concluded that optimizing voxel size and voxel value assignation minimised the effect of point density variations and homogenised full-waveform metrics. Height/median ratio (HTMR) and Vertical distribution ratio (VDR) had the lowest variability between different densities, and Return waveform energy (RWE) reached the best improvement with respect to initial data, being the difference between standardised paired samples 1.28 before and 0.69 after modification.

ANALYSIS OF THE SIDE-LAP EFFECT ON FULL-WAVEFORM LIDAR DATA ACQUISITION FOR THE ESTIMATION OF FOREST STRUCTURE VARIABLES

LiDAR full-waveform provides a better description of the physical and forest vertical structure properties than discrete LiDAR since it registers the full wave that interacts with the canopy. In this paper, the effect of flight line side-lap is analysed on forest structure and canopy fuel variables estimations. Differences are related to pulse density changes between flight stripe side-lap areas, varying the point density between 2.65 m<sup>−2</sup> and 33.77 m<sup>−2</sup> in our study area. These differences modify metrics extracted from data and therefore variable values estimated from these metrics such as forest stand variables. In order to assess this effect, 64 pairwise samples were selected in adjacent areas with similar canopy structure, but having different point densities. Two parameters were tested and evaluated to minimise this effect: voxel size and voxel value assignation testing maximum, mean, median, mode, percentiles 90 and 95. <br><br> Student’s t-test or Wilcoxon test were used for the comparison of paired samples. Moreover, the absolute value of standardised paired samples was calculated to quantify dissimilarities. It was concluded that optimizing voxel size and voxel value assignation minimised the effect of point density variations and homogenised full-waveform metrics. Height/median ratio (HTMR) and Vertical distribution ratio (VDR) had the lowest variability between different densities, and Return waveform energy (RWE) reached the best improvement with respect to initial data, being the difference between standardised paired samples 1.28 before and 0.69 after modification.