ICESat Research Articles

High-accuracy bathymetric method fusing ICESAT-2 datasets and the two-media photogrammetry model

Improving the accuracy of nearshore bathymetric measurements is essential for understanding coastal environments, resource management, and navigation. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is the first laser satellite that uses the photon-counting technique. The ICESat-2 is equipped with the Advanced Topographic Laser Altimeter System (ATLAS), which enables higher-accuracy measurements of water, ice, and land elevation on Earth. Two-media photogrammetric bathymetry is a type of nearshore bathymetric technology that uses the geometrical characteristics of light rays. With this technique, the accuracy and reliability mainly depend on eliminating systematic errors and ensuring accurate spatial photogrammetric positioning relative to the object being measured. To improve the bathymetric accuracy of two-media photogrammetry, we integrated high-accuracy elevation data from photon datasets as constraining and control parameters. The improved method effectively eliminated systematic errors in two-media photogrammetry during the established joint-block adjustment model. To improve its accuracy and reliability, we employed multispectral WorldView-2 stereo images in our experiments. Furthermore, the bathymetric results were validated and assessed using in situ and photon data. The experimental results show that the highest accuracy achieved with the bathymetric measurements in our study area was a root mean square error (RMSE) of 0.96 m and a mean absolute error of 0.57 m. Using the proposed fusion method, the bathymetric accuracy (as measured using the RMSE) was 1 m higher than that of two-media photogrammetry without the photon datasets.

A Global Evaluation of Radar‐Derived Digital Elevation Models: SRTM, NASADEM, and GLO‐30

AbstractThis study evaluates global radar‐derived digital elevation models (DEMs), namely the Shuttle Radar Topography Mission (SRTM), NASADEM and GLO‐30 DEMs. We evaluate their accuracy over bare‐earth terrain and characterize elevation biases induced by forests using global Lidar measurements from the Ice, Cloud, and Land Elevation Satellite (ICESat)'s Geoscience Laser Altimeter System (GLAS), the Global Ecosystem Dynamics Investigation (GEDI) and the ICESat‐2 Advanced Topographic Laser Altimeter System (ATLAS) instruments collected on locally flat terrain. Our analysis is based on error statistics calculated for each DEM tile, which are then summarized as global error percentiles, providing a regional characterization of DEM quality. We find NASADEM to be a significant improvement upon the SRTM V3. Over bare ground areas, the mean elevation bias and root mean square error (RMSE) improved from 0.68 to 2.50 m respectively to 0.00 and 1.5 m as compared to ICESat/GLAS. GLO‐30 is more accurate with bare ground elevation bias and RMSE were below 0.05 and 0.55 m. Similar improvements were observed when compared to GEDI and ICESat‐2 measurements. The DEM biases associated with the presence of vegetation vary linearly with canopy height, and more closely follow the percentile of Lidar Relative Height (RH50). Other factors such as canopy density, radar frequency and Lidar technology also contribute to observed elevation biases. This global analysis highlights the potential of various technologies for mapping of Earth's topography, and the need for more advanced remote sensing observations that can resolve vegetation structure and sub‐canopy ground elevation.

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.

ICESat-2 single photon laser point cloud denoising algorithm based on improved DBSCAN clustering

AbstractThe Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has great potential for development due to its advantages of the use of multiple beams, low energy consumption, high repetition frequency, and high measurement sensitivity. However, the weak photon signal emitted by the photon counting lidar is susceptible to the background noise caused by the sun and the atmosphere, which can seriously affect the processing and application of laser data. This paper proposes an improved DBSCAN clustering algorithm for denoising single photon laser point clouds in mountainous areas. Firstly, a grouping method based on elevation and distance statistics is proposed to reduce the influence of terrain undulations on denoising accuracy. Finally, an automatic radius search method is put forward to determine clustering radius of each group, automatically find the optimal radius, and improve the existing DBSCAN clustering method. The method proposed in this paper is compared with the classical DBSCAN algorithm. The results show that the proposed algorithm significantly improves denoising accuracy in mountainous areas and effectively filters out most background noise. Graphical Abstract

Thermodynamic and Dynamic Variations in Sea Ice Thickness of the Ross Sea, Antarctica, Driven by Atmospheric Circulation

AbstractAtmospheric circulation has significant impacts on sea ice drifting patterns and mass balance, as wind drag induces pressure ridges and leads on the sea ice surface. In this study, the spatiotemporal distributions of these dynamic sea ice deformation features in the Ross Sea are examined using ICESat‐2 (IS2) ATL10 freeboard data (2019–2022). The temporal variation of the modal sea ice thickness (SIT), caused by thermodynamic ice growth and sea ice advection, varies from 0.7–1.0 m in April to 1.0–1.6 m in July–September and decreases thereafter in the northwest (NW) and northeast (NE) sectors. This temporal variation of modal SIT agrees with the air temperature (correlation coefficients >0.5). The southwest (SW) sector shows a consistently low modal SIT (<1.0 m) because of the production of new ice in polynyas and continuous northward sea ice drift. Meanwhile, the southeast (SE) sector shows the thickest ice in Octobers 2019 and 2020 because of the advection of thick ice from the Amundsen Sea, which was reduced in 2021 and 2022. In terms of dynamic sea ice deformation, the SE sector shows the largest deformation because of the wind‐driven convergence of sea ice movement. However, such intense deformation in the SE sector diminished in 2021 and 2022 due to the dominance of strong southerly wind associated with the Amundsen Sea Low (ASL). This study emphasizes the potential of IS2 sea ice products to assess the role of atmospheric driving forces on thermodynamic and dynamic sea ice changes.

Evolution of Single Photon Lidar: From Satellite Laser Ranging to Airborne Experiments to ICESat-2

In September 2018, NASA launched the ICESat-2 satellite into a 500 km high Earth orbit. It carried a truly unique lidar system, i.e., the Advanced Topographic Laser Altimeter System or ATLAS. The ATLAS lidar is capable of detecting single photons reflected from a wide variety of terrain (land, ice, tree leaves, and underlying terrain) and even performing bathymetric measurements due to its green wavelength. The system uses a single 5-watt, Q-switched laser producing a 10 kHz train of sub-nanosecond pulses, each containing 500 microjoules of energy. The beam is then split into three “strong” and three “weak” beamlets, with the “strong” beamlets containing four times the power of the “weak” beamlets in order to satisfy a wide range of Earth science goals. Thus, ATLAS is capable of making up to 60,000 surface measurements per second compared to the 40 measurements per second made by its predecessor multiphoton instrument, the Geoscience Laser Altimeter System (GLAS) on ICESat-1, which was terminated after several years of operation in 2009. Low deadtime timing electronics are combined with highly effective noise filtering algorithms to extract the spatially correlated surface photons from the solar and/or electronic background noise. The present paper describes how the ATLAS system evolved from a series of unique and seemingly unconnected personal experiences of the author in the fields of satellite laser ranging, optical antennas and space communications, Q-switched laser theory, and airborne single photon lidars.

A Software Tool for ICESat and ICESat-2 Laser Altimetry Data Processing, Analysis, and Visualization: Description, Features, and Usage

This paper presents a web-based software tool designed to process, analyze, and visualize satellite laser altimetry data, specifically from the Ice, Cloud, and land Elevation Satellite (ICESat) mission, which collected data from 2003 to 2009, and ICESat-2, which was launched in 2018 and is currently operational. These data are crucial for studying and understanding changes in Earth’s surface and cryosphere, offering unprecedented accuracy in quantifying such changes. The software tool ICEComb provides the capability to access the available data from both missions, interactively visualize it on a geographic map, locally store the data records, and process, analyze, and explore the data in a detailed, meaningful, and efficient manner. This creates a user-friendly online platform for the analysis, exploration, and interpretation of satellite laser altimetry data. ICEComb was developed using well-known and well-documented technologies, simplifying the addition of new functionalities and extending its applicability to support data from different satellite laser altimetry missions. The tool’s use is illustrated throughout the text by its application to ICESat and ICESat-2 laser altimetry measurements over the Mirim Lagoon region in southern Brazil and Uruguay, which is part of the world’s largest complex of shallow-water coastal lagoons.

Optimized estimation of marine deflection of the vertical from multibeam laser altimeter data of ICESat-2

SUMMARY Satellite altimetry data, with its increasing density and quality, has become the primary source for marine deflection of the vertical (DOV) and gravity anomaly modelling. Limited by orbital inclinations, the precision of the meridian component of the gridded deflection of the vertical (GDOV) calculated by traditional altimetry satellites is significantly better than that of the prime vertical component, and the excessive precision difference between these two components restricts the inversion precision of marine gravity anomaly model. The study of cross-track deflection of the vertical (CTDOV) is enabled by the multibeam synchronous observation mode of the new laser altimetry satellite, Ice, Cloud and Land Elevation Satellite-2 (ICESat-2). Based on the remove-restore method, residual geoid gradients are first calculated in this paper using three approaches: along-track (A-T), cross-track (C-T) and an integration of along-track and cross-track. Vertical deflections are then computed on a 1′ × 1′ grid using the least squares collocation (LSC) method, and the precision is verified against the SIO V32.1_DOV model. An optimized combination is proposed to address the issue of precision differences between the meridian and prime vertical components, and to enhance the precision of DOV inversion. A new DOV combination is formed by combining the meridian component from along-track deflection of the vertical (ATDOV) with the prime vertical component from cross-track deflection of the vertical (CTDOV) based on the remove-restore method. The Philippine Sea (0°–35°N, 120°–150°E) is selected as the test area to verify the feasibility of the optimized combination. The results indicate that the optimized combination of the meridian and prime vertical components achieved test precision of 2.63 and 3.33 μrad, respectively, when compared against the SIO V32.1_DOV model. The precision gap between the components is effectively narrowed by this approach, which maintains the precision of the meridian component and enhances that of the prime vertical component, thereby achieving optimal inversion precision for gravity anomalies.

ICESat-2 Shows Sea Ice Leads Have Little Overall Effects on the Arctic Cloudiness in Cold Months

Abstract The effect of leads in Arctic sea ice on clouds is a potentially important climate feedback. We use observations of clouds and leads from the Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) to study the effects of leads on clouds. Both leads and clouds are strongly forced by synoptic weather conditions, with more clouds over both leads and sea ice at lower sea level pressure. Contrary to previous studies, we find that the overall lead effect on low-level cloud cover is −0.02, a weak cloud dissipating effect in cold months, after the synoptic forcing influence is removed. This is due to compensating contributions from the cloud dissipating effect by newly frozen leads under high pressure systems and the cloud enhancing effect by newly open leads under low pressure system. The lack of proper representation of lead effect on clouds in current climate models and reanalyses may impact their performance in winter months, such as in sea ice growth and Arctic cyclone development.

An Optimal Denoising Method for Spaceborne Photon-Counting LiDAR Based on a Multiscale Quadtree

Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has excellent potential for obtaining water depth information around islands and reefs. Combining the density-based spatial clustering of applications with noise algorithm (DBSCAN) and multiscale quadtree analysis, we propose a new photon-counting lidar denoising method to discard the large amount of noise in ICESat-2 data. First, the kernel density estimation (KDE) is used to preprocess the point cloud data, and a threshold is set to remove the noise photons on the sea surface. Next, the DBSCAN algorithm is used to preliminarily remove underwater noise photons. Then, the quadtree segmentation and Otsu algorithm are used for fine denoising to extract accurate bottom signal photons. Based on ICESat-2 pho-ton-counting data from six typical islands and reefs worldwide, the proposed method outperforms other algorithms in terms of denoising effect. Compared to in situ data, the determination coefficient (R2) reaches 94.59%, and the root mean square error (RMSE) is 1.01 m. The proposed method can extract accurate underwater terrain information, laying a foundation for offshore bathymetry.

ICESat‐2 Onboard Flight Receiver Algorithms: On‐Orbit Parameter Updates the Impact on Science Driven Observations

AbstractThe ICESat‐2 (Ice, Cloud and Land Elevation Satellite‐2) photon‐counting laser altimeter technology required the design and development of very sophisticated onboard algorithms to collect, store and downlink the observations. These algorithms utilize both software and hardware solutions for meeting data volume requirements and optimizing the science achievable via ICESat‐2 measurements. Careful planning and dedicated development were accomplished during the pre‐launch phase of the mission in preparation for the 2018 launch. Once on‐orbit all of the systems and subsystems were evaluated for performance, including the receiver algorithms, to ensure compliance with mission standards and satisfy the mission science objectives. As the mission has progressed and the instrument performance and data volumes were better understood, there have been several opportunities to enhance ICESat‐2's contributions to Earth observation science initiated by NASA and the ICESat‐2 science community. We highlight multiple updates to the flight receiver algorithms, the onboard software for signal processing, that have extended ICESat‐2's data capabilities and allowed for advanced science applications beyond the original mission objectives.

Error-Reduced Digital Elevation Model of the Qinghai-Tibet Plateau using ICESat-2 and Fusion Model

The Qinghai-Tibet Plateau (QTP) holds significance for investigating Earth’s surface processes. However, due to rugged terrain, forest canopy, and snow accumulation, open-access Digital Elevation Models (DEMs) exhibit considerable noise, resulting in low accuracy and pronounced data inconsistency. Furthermore, the glacier regions within the QTP undergo substantial changes, necessitating updates. This study employs a fusion of open-access DEMs and high-accuracy photons from the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2). Additionally, snow cover and canopy heights are considered, and an ensemble learning fusion model is presented to harness the complementary information in the multi-sensor elevation observations. This innovative approach results in the creation of HQTP30, the most accurate representation of the 2021 QTP terrain. Comparative analysis with high-resolution imagery, UAV-derived DEMs, control points, and ICESat-2 highlights the advantages of HQTP30. Notably, in non-glacier regions, HQTP30 achieved a Mean Absolute Error (MAE) of 0.71 m, while in glacier regions, it reduced the MAE by 4.35 m compared to the state-of-the-art Copernicus DEM (COPDEM), demonstrating its versatile applicability.

Quantifying Volumetric Scattering Bias in ICESat‐2 and Operation IceBridge Altimetry Over Greenland Firn and Aged Snow

AbstractThe Ice, Cloud, and Land Elevation Satellite‐2 (ICESat‐2) mission has collected surface elevation measurements for over 5 years. ICESat‐2 carries an instrument that emits laser light at 532 nm, and ice and snow absorb weakly at this wavelength. Previous modeling studies found that melting snow could induce significant bias to altimetry signals, but there is no formal assessment on ICESat‐2 acquisitions during the melting season. We performed two case studies over the Greenland Ice Sheet to quantify bias in ICESat‐2 signals over snow: one to validate Airborne Topographic Mapper (ATM) data against Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS‐NG) grain sizes, and a second to estimate ICESat‐2 bias relative to ATM. We used snow optical grain sizes derived from ATM and AVIRIS‐NG to attribute altimetry bias to snowpack properties. For the first case study, the mean and standard deviation of optical grain sizes were 340 ± 65 µm (AVIRIS‐NG) and 670 ± 420 µm (ATM). A mean altimetry bias of 4.81 ± 1.76 cm was found for ATM, with larger biases linked to increases in grain size. In the second case study, we found a mean grain size of 910 ± 381 µm and biases of 6.42 ± 1.77 cm (ICESat‐2) and 9.82 ± 0.97 cm (ATM). The grain sizes and densities needed to recreate biases with a model are uncommon in nature, so we propose that additional surface attributes must be considered to characterize ICESat‐2 bias over snow. The altimetry biases are within the accuracy requirements of the ICESat‐2 mission, but we cannot rule out more significant errors over coarse‐grained snow.

An Advanced Terrain Vegetation Signal Detection Approach for Forest Structural Parameters Estimation Using ICESat-2 Data

Accurate forest structural parameters (such as forest height and canopy cover) support forest carbon monitoring, sustainable forest management, and the implementation of silvicultural practices. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), which is a spaceborne Light Detection and Ranging (LiDAR) satellite, offers significant potential for acquiring precise and extensive information on forest structural parameters. However, the ICESat-2 ATL08 product is significantly influenced by the geographical environment and forest characteristics, maintaining considerable potential for enhancing the accuracy of forest height estimation. Meanwhile, it does not focus on providing canopy cover data. To acquire accurate forest structural parameters, the Terrain Signal Neural Network (TSNN) framework was proposed, integrating Computer Vision (CV), Ordering Points to Identify the Clustering Structure (OPTICS), and deep learning. It encompassed an advanced approach for detecting terrain vegetation signals and constructing deep learning models for estimating forest structural parameters using ICESat-2 ATL03 raw data. First, the ATL03 footprints were visualized as Profile Raster Images of Footprints (PRIF), implementing image binarization through adaptive thresholding and median filtering denoising to detect the terrain. Second, the rough denoising buffers were created based on the terrain, combining with the OPTICS clustering and Gaussian denoising algorithms to recognize the terrain vegetation signal footprints. Finally, deep learning models (convolutional neural network (CNN), ResNet50, and EfficientNetB3) were constructed, training standardized PRIF to estimate forest structural parameters (including forest height and canopy cover). The results indicated that the TSNN achieved high accuracy in terrain detection (coefficient of determination (R2) = 0.97) and terrain vegetation signal recognition (F-score = 0.72). The EfficientNetB3 model achieved the highest accuracy in forest height estimation (R2 = 0.88, relative Root Mean Squared Error (rRMSE) = 13.5%), while the CNN model achieved the highest accuracy in canopy cover estimation (R2 = 0.80, rRMSE = 18.5%). Our results have significantly enhanced the accuracy of acquiring ICESat-2 forest structural parameters, while also proposing an original approach combining CV and deep learning for utilizing spaceborne LiDAR data.

Long-term Satellite-derived Bathymetry of Arctic Supraglacial Lake from ICESat-2 and Sentinel-2

Abstract. The polar ice sheets serve as natural thermostats, regulating Earth’s temperatures. The Greenland Ice Sheet (GrIS), the second-largest ice sheet, is a critical indicator of climate change and global warming. Estimating the volume of supraglacial lakes on the GrIS, which is directly linked to the extent of melting in the Arctic ice sheet, requires information on both lake area and water depth. Conventional bathymetric methods (i.e., airborne bathymetric LiDAR, shipborne echo-sounder) are commonly used for accurate water depth measurement. However, polar supraglacial lakes face challenging conditions, leading to uncertainties in their spatial and temporal distribution. To overcome the limitations, this study combines Sentinel-2 and ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) to estimate bathymetry and detect changes in lake volume on the GrIS from 2019 to 2023. Firstly, Sentinel-2 images were pre-processed, and ICESat-2 single-photon LiDAR points were extracted using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method, followed by the bathymetric corrections as the training data. Subsequently, three bathymetry models (i.e., log-linear, log-ratio, and BP (Back Propagation) neural network) were constructed using Sentinel-2 images and ICESat-2 data. Lastly, the highresolution ArcticDEM (Arctic Digital Elevation Model) was used as the validation data to assess the satellite-derived bathymetry accuracy. In this study, the log-ratio model yielded the best results with the R2, RMSE, and MAE of 0.92, 0.79 m (lower than 10% of the maximum depth), and 0.62 m. The results demonstrate the feasibility of the integrated active and passive remote sensing approach for bathymetry in Arctic supraglacial lakes.

Surface Elevation Change Monitoring Based on ICESat-2 Satellite Altimetry Data

The ICESat-2 (Ice, Cloud and Land Elevation Satellite-2), launched by NASA on September 18, 2018, offers higher precision in surface elevation monitoring. This study aims to investigate the surface elevation changes over different time periods in the Delong and Bulianta open-pit mines in Wulanmuren Township, Ordos City, using ICESat-2 laser data. The study utilizes the spatial distribution information of each photon from ATL03 (terrain elevation data) and the classification information of each photon from ATL08 (vegetation canopy height and surface elevation data) to extract surface photon elevation data for different periods in the same area. The precision of ICESat-2 satellite elevation data is validated using the on-board radar data from Henan Polytechnic University's Feima unmanned aerial vehicle D2000 in Jiaozuo City, Henan Province.Results indicate that in the No.1 area of the Delong open-pit mine, the surface subsided by approximately 16.3 meters between November 14, 2019, and November 10, 2021, due to soil removal operations. In the No.2 and No.3 areas of the Bulianta open-pit mine, the surface was uplifted by 3.1 meters and 13.6 meters, respectively, between May 16, 2019, and August 13, 2020, due to backfilling after mining. Additionally, the No.4 area was uplifted by about 10.5 meters between November 14, 2019, and November 10, 2021.Furthermore, the accuracy assessment yielded the following results: R2=0.956, RMSE=0.324, and MAE=0.105 meters, indicating that ICESat-2 data can be used as scientific data for inverting surface elevation changes.

Potential and performance for classifying Earth surface only with ICESat-2 altimetric data

The huge volume of data from the Ice, Cloud and land Elevation Satellite-2 (ICESat-2), designed for mapping polar ice, sea ice, and continental vegetation, requires a highly automated data analysis and reliable terrain classification. In particular, we have developed a method to identify 4 distinct terrain categories in observed terrain, namely ocean, land, sea ice, and ice sheets. This study performed the following efforts: first, the spatial distribution characteristics for each of the 4 categories within individual ICESat-2 “major frames” along the orbit were extracted; second, these features were fed into Classification and Regression Tree (CART) and Random Forest (RF) for training; and lastly, post-processing enhancement was used to improve the classification results. Based on the 76,891 major frame samples (10,764,740 m along track) acquired via various ICESat-2 datasets, the accuracy of the two model were calculated using ten-fold cross-validation. The results indicate that the RF algorithm obtained higher classification accuracy (average accuracy [AA] = 0.9353, overall accuracy [OA] = 0.9342, and Cohen’s Kappa coefficient [kappa] = 0.9122) when compared with the CART algorithm (AA = 0.9066, OA = 0.9057, and kappa = 0.8743). Overall, our approach can effectively reduce the workload of human field investigation or visual inspection of altimetry data, improve the accuracy for Earth surface classification, and add to the variety of ways to obtain global surface information from ICESat-2 data.

Deforestation detection from spaceborne full-waveform laser altimetry, incorporating terrain effects: A case study in Porto Velho, Brazil

Forest structure, notably the canopy height above the ground, can be obtained from the full-waveform data acquired by satellite laser altimetry. On sloped terrain, the laser pulse can simultaneously reflect from both the vegetation and the ground within their intersecting vertical extents, causing their signals to overlap. This overlap complicates the separation of canopy and ground in the analysis. Consequently, accurately obtaining canopy height and its variation, while considering the influence of terrain factors, poses a significant challenge.In this paper, to counter this, we introduce an innovative approach to remove terrain effects for more reliable identification of canopy and ground. Overlapping footprints from the Ice, Cloud, and land Elevation Satellite (ICESat)/Geoscience Laser Altimeter System (GLAS) and Global Ecosystem Dynamics Investigation (GEDI) campaigns are extracted as coincidental observations. Utilizing the different footprint sizes and by minimizing the center and width of each decomposed Gaussian component pair, optimized ground returns can be obtained. Validation using airborne LiDAR data from the Bartlett Experimental Forest, New Hampshire, USA, indicate that the proposed approach can reduce the ground elevation root-mean-square error (RMSE) by approximately 3 m, compared to the prevalent methods and standard products. While the GEDI product algorithms exhibit a certain accuracy in canopy height retrieval, the proposed approach presents a more constrained error band, signifying less impact from ground elevation discrepancies.While the proposed method enhances canopy height precision, its reliance on overlapping footprints means sacrificing some observations. However, the proposed method is effective in scenarios such as change detection that require repeat observations. As a case study, we applied the proposed method in Porto Velho, Rondônia, Brazil, which is a deforestation hotspot. Using the GlobeLand30 dataset, we investigated the land-cover dynamics from co-located laser footprints, identifying significant forest loss areas and validating our findings with the Hansen Global Forest Change dataset, achieving an accuracy of 83.81 %.In conclusion, this research emphasizes the significance of terrain topography effects in vegetation structure analysis, and positions satellite laser altimetry as instrumental for forest monitoring.

Assessing canopy height measurements from ICESat-2 and GEDI orbiting LiDAR across six different biomes with G-LiHT LiDAR

The height of woody plants is a defining characteristic of forest and shrubland ecosystems because height responds to climate, soil and disturbance history. Orbiting LiDAR instruments, Ice, Cloud and land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation LiDAR (GEDI), can provide near-global datasets of plant height at plot-level resolution. We evaluate canopy height measurements from ICESat-2 and GEDI with high resolution airborne LiDAR in six study sites in different biomes from dryland shrub to tall forests, with mean canopy height across sites of 0.5–40 m. ICESat-2 and GEDI provide reliable estimates for the relative height with RMSE and mean absolute error (MAE) of 7.49 and 4.64 m (all measurements ICESat-2) and 6.52 and 4.08 m (all measurements GEDI) for 98th percentile relative heights. Both datasets slightly overestimate the height of short shrubs (1–2 m at 5 m reference height), underestimate that of tall trees (by 6–7 m at 40 m reference height) and are highly biased (>3 m) for reference height <5 m, perhaps because of the difficulty of distinguishing canopy from ground signals. Both ICESat-2 and GEDI height estimates were only weakly sensitive to canopy cover and terrain slope (R 2 < 0.06) and had lower error for night compared to day samples (ICESat-2 RMSE night: 5.57 m, day: 6.82 m; GEDI RMSE night: 5.94 m, day: 7.03 m). For GEDI, the day versus night differences varied with differences in mean sample heights for the day and night samples and had little effect on bias. Accuracy of ICESat-2 and GEDI canopy heights varies among biomes, and the highest MAE was observed in the tallest, densest forest (GEDI: 7.85 m; ICESat-2: 7.84 m (night) and 12.83 m (day)). Improvements in canopy height estimation would come from better discrimination of canopy photons from background noise for ICESat-2 and improvements in the algorithm for decomposing ground and canopy returns for GEDI. Both would benefit from methods to distinguish outlier samples.

Signal Photon Extraction and Classification for ICESat-2 Photon-Counting Lidar in Coastal Areas

The highly accurate data of topography and bathymetry are fundamental to ecological studies and policy decisions for coastal zones. Currently, the automatic extraction and classification of signal photons in coastal zones is a challenging problem, especially the surface type classification without auxiliary data. The lack of classification information limits large-scale bathymetric applications of ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2). In this study, we propose a photon extraction–classification method to process geolocated photons in coastal areas from the ICESat-2 ATL03 product. The basic idea is to extract the signal photons using an adaptive photon clustering algorithm, and the extracted signal photons are classified based on the accumulated histogram and triangular grid. We also generate the bottom profile using the weighted interpolation. In four typical coastal areas (artificial coast, natural coast, island, and reefs), the extraction accuracy of a signal photons exceeds 0.90, and the Kappa coefficients of four surface types exceed 0.75. This method independently extracts and classifies signal photons without relying on auxiliary data, which can greatly improve the efficiency of obtaining bathymetric points in all kinds of coastal areas and provide technical support for other coastal studies using ICESat-2 data.