Geolocation Error Research Articles

How to Find Accurate Terrain and Canopy Height GEDI Footprints in Temperate Forests and Grasslands?

AbstractFiltering approaches on Global Ecosystem Dynamics Investigation (GEDI) data differ considerably across existing studies and it is yet unclear which method is the most effective. We conducted an in‐depth analysis of GEDI's vertical accuracy in mapping terrain and canopy heights across three study sites in temperate forests and grasslands in Spain, California, and New Zealand. We started with unfiltered data (2,081,108 footprints) and describe a workflow for data filtering using Level 2A parameters and for geolocation error mitigation. We found that retaining observations with at least one detected mode eliminates noise more effectively than sensitivity. The accuracy of terrain and canopy height observations depended considerably on the number of modes, beam sensitivity, landcover, and terrain slope. In dense forests, a minimum sensitivity of 0.9 was required, while in areas with sparse vegetation, sensitivity of 0.5 sufficed. Sensitivity greater than 0.9 resulted in an overestimation of canopy height in grasslands, especially on steep slopes, where high sensitivity led to the detection of multiple modes. We suggest excluding observations with more than five modes in grasslands. We found that the most effective strategy for filtering low‐quality observations was to combine the quality flag and difference from TanDEM‐X, striking an optimal balance between eliminating poor‐quality data and preserving a maximum number of high‐quality observations. Positional shifts improved the accuracy of GEDI terrain estimates but not of vegetation height estimates. Our findings guide users to an easy way of processing of GEDI footprints, enabling the use of the most accurate data and leading to more reliable applications.

Examining the Impact of Topography and Vegetation on Existing Forest Canopy Height Products from ICESat-2 ATLAS/GEDI Data

Forest canopy height (FCH) is an important variable for estimating forest biomass and ecosystem carbon sequestration. Spaceborne LiDAR data have been used to create wall-to-wall FCH maps, such as the forest tree height map of China (FCHChina), Global Forest Canopy Height 2020 (GFCH2020), and Global Forest Canopy Height 2019 (GFCH2019). However, these products lack comprehensive assessment. This study used airborne LiDAR data from various topographies (e.g., plain, hill, and mountain) to assess the impacts of different topographical and vegetation characteristics on spaceborne LiDAR-derived FCH products. The results show that GEDI–FCH demonstrates better accuracy in plain and hill regions, while ICESat-2 ATLAS–FCH shows superior accuracy in the mountainous region. The difficulty in accurately capturing photons from sparse tree canopies by ATLAS and the geolocation errors of GEDI has led to partial underestimations of FCH products in plain areas. Spaceborne LiDAR FCH retrievals are more accurate in hilly regions, with a root mean square error (RMSE) of 4.99 m for ATLAS and 3.85 m for GEDI. GEDI–FCH is significantly affected by slope in mountainous regions, with an RMSE of 13.26 m. For wall-to-wall FCH products, the availability of FCH data is limited in plain areas. Optimal accuracy is achieved in hilly regions by FCHChina, GFCH2020, and GFCH2019, with RMSEs of 5.52 m, 5.07 m, and 4.85 m, respectively. In mountainous regions, the accuracy of wall-to-wall FCH products is influenced by factors such as tree canopy coverage, forest cover types, and slope. However, some of these errors may stem from directly using current ATL08 and GEDI L2A FCH products for mountainous FCH estimation. Introducing accurate digital elevation model (DEM) data can improve FCH retrieval from spaceborne LiDAR to some extent. This research improves our understanding of the existing FCH products and provides valuable insights into methods for more effectively extracting accurate FCH from spaceborne LiDAR data. Further research should focus on developing suitable approaches to enhance the FCH retrieval accuracy from spaceborne LiDAR data and integrating multi-source data and modeling algorithms to produce accurate wall-to-wall FCH distribution in a large area.

Estimation of IFOV Inter-Channel Deviation for Microwave Radiation Imager Onboard FY-3G Satellite

The Microwave Radiation Imager (MWRI) onboard the FengYun satellite plays a crucial role in global change monitoring and numerical weather prediction. Estimating and correcting geolocation errors are important to retrieving accurate geophysical variables. However, the instantaneous field of view (IFOV) inter-channel deviation, which is mainly caused by the structure mounting error and measurement error of feedhorns, is less studied. In this present study, we constructed a general theoretical model to automatically estimate the IFOV inter-channel deviations suitable for conical-scanning instruments. The model can automatically detect the along-track and across-track vectors that pass through the land–sea boundary points and are perpendicular to the actual coastlines. Regarding the midpoints of the vectors as the brightness temperature (Tb) inflection points, the IFOV inter-channel deviation is the pixel offset or distance of the maximum gradients of the Tb near the inflection points for each channel relative to the 89-GHz V-pol channel. We tested the model’s operational performance using the FY-3G/MWRI-Rainfall Mission (MWRI-RM) observations. Considering that parameter uploading adjusted the IFOV inter-channel deviations, the model’s validity was verified by comparing the adjustments calculated by the model with the theoretical changes caused by parameter uploading. The result shows that the differences between them for all window channels are less than 100 m, indicating the model’s effectiveness in evaluating the IFOV inter-channel deviation for the MWRI-RM. Furthermore, the estimated on-orbit IFOV inter-channel deviations for the MWRI-RM show that all channel deviations are less than 1 km, meeting the instrument’s design requirement of 2 km. We believe this study will provide a foundation for IFOV inter-channel registration of passive microwave payloads and spatial matching of multiple payloads.

Improving extraction of forest canopy height through reprocessing ICESat-2 ATLAS and GEDI data in sparsely forested plain regions

ABSTRACT Forest canopy height (FCH) is one of the most important variables for carbon stock estimation. While many studies have focused on extracting FCH from spaceborne LiDAR in regions with spatially continuous and large patch sizes of forested lands, limited research has addressed the challenges of FCH extraction in plain regions with sparse and fragmented forest distributions. In this study, we proposed innovative processing approaches to extract FCH from ICESat-2 photons and GEDI footprints in the plain regions of Anhui Province, China. Specifically, we proposed a sectional photon denoising method for processing ICESat-2 data and a geolocation error correction method for processing GEDI data. Airborne LiDAR data were used to validate the extracted FCH products across typical plain regions. The results demonstrated the effectiveness of the proposed methods in improving FCH extraction accuracy. Evaluation indicated that the directly extracted FCH products from ATL08 and GEDI L2A had Pearson’s correlation coefficients (r) of 0.6 and 0.93, respectively. After processing with the proposed methods, the 2019 FCH products from ICESat-2 exhibited r of 0.82 and relative root mean square error (rRMSE) of 31.11% based on 3,217 ICESat-2 segments, and the products from GEDI showed r of 0.96 and rRMSE of 18.35% based on 4,862 GEDI footprints. Further application of these methods to extract FCH products from GEDI and ICESat-2 for the years 2020, 2021, and 2022 indicated their promise for addressing sparse vegetation coverage in plain regions.

Remote Sensing of Chlorophyll-a and Water Quality over Inland Lakes: How to Alleviate Geo-Location Error and Temporal Discrepancy in Model Training

Remote Sensing of Chlorophyll-a and Water Quality over Inland Lakes: How to Alleviate Geo-Location Error and Temporal Discrepancy in Model Training

HyperScout-1 inflight calibration and product validation

ABSTRACT Earth observation aims at monitoring the Earth in increasingly more detail by improving the spatial, spectral and temporal resolution of image acquisitions. This can now be achieved in more cost-efficient ways by the miniaturization of instruments and platforms. New initiatives have emerged to accommodate hyperspectral instruments on CubeSats despite inherent platform limitations in terms of volume, power consumption, computing and downlink capacity. However, the adoption of CubeSat data for operational and scientific applications requires sufficient trust in dependability and data quality. Achieving reliable data quality from CubeSats remains challenging and requires advanced geometric, radiometric, and spectral calibration techniques. We present the successful in-orbit calibration of the HyperScout-1 mission. This In-Orbit Demonstration (IOD) mission, equipped with a miniaturized hyperspectral instrument based on Linear Variable Filter technology (LVF), was launched in 2018 by the European Space Agency (ESA). Rigorous inflight calibration has been applied to HyperScout-1 to ensure the accuracy of its data. The radiometric calibration of the HyperScout-1 instrument was performed vicariously based on modelled spectral radiances over a desert site. Independent validation using data from a CEOS RadCalNet site showed good radiometric accuracy with absolute radiometric errors <5%. Geometric errors due to inaccuracies in attitude measurements and sensor focal plane distortions were corrected using a bundle adjustment technique combined with matching to accurate Ground Control Points. This resulted in excellent geometric performance, with sub-pixel average absolute geolocation errors (~0.48 pixels across track and 0.51 pixels along track). The very limited data budget was a major challenge, forcing us to make some trade-offs in the calibration. Despite this, our robust vicarious calibration strategy allowed effective instrument calibration. As a result, the HyperScout-1 demonstrated the capability of an LVF Hyperspectral instrument on a CubeSat to deliver high-quality imagery and serve applications like land cover classification, change detection and disaster monitoring.

Assessment, Specification, and Validation of a Geolocation System's Accuracy and Predicted Accuracy

This article presents recommendations and corresponding detailed procedures for the assessment of a geolocation system's accuracy, as well as the specification of accuracy requirements and their subsequent validation when they are available. Applicable metrics and related processing are based on samples of corresponding geolocation errors. This article also presents similar recommendations for the predicted accuracy of a geolocation system, based on samples of geolocation error, as well as corresponding predicted error covariance matrices associated with the geolocations. Reliable error covariance matrices enable optimal use of a geolocation system's products, such as the optimal fusion of multiple geolocations or multiple products for higher confidence and increased accuracy. The recommendations presented in this article enable reliable estimates of accuracy and reliable predicted accuracies, both of which are critical to many geolocation-based applications. The recommendations associated with predicted accuracy are also relatively new and innovative.

Robust Building Identification from Street Views Using Deep Convolutional Neural Networks

Street view imagery (SVI) is a rich source of information for architectural and urban analysis using computer vision techniques, but its integration with other building-level data sources requires an additional step of visual building identification. This step is particularly challenging in architecturally homogeneous, dense residential streets featuring narrow buildings, due to a combination of SVI geolocation errors and occlusions that significantly increase the risk of confusing a building with its neighboring buildings. This paper introduces a robust deep learning-based method to identify buildings across multiple street views taken at different angles and times, using global optimization to correct the position and orientation of street view panoramas relative to their surrounding building footprints. Evaluating the method on a dataset of 2000 street views shows that its identification accuracy (88%) outperforms previous deep learning-based methods (79%), while methods solely relying on geometric parameters correctly show the intended building less than 50% of the time. These results indicate that previous identification methods lack robustness to panorama pose errors when buildings are narrow, densely packed, and subject to occlusions, while collecting multiple views per building can be leveraged to increase the robustness of visual identification by ensuring that building views are consistent.

Disparity Refinement for Stereo Matching of High-Resolution Remote Sensing Images Based on GIS Data

With the emergence of the Smart City concept, the rapid advancement of urban three-dimensional (3D) reconstruction becomes imperative. While current developments in the field of 3D reconstruction have enabled the generation of 3D products such as Digital Surface Models (DSM), challenges persist in accurately reconstructing shadows, handling occlusions, and addressing low-texture areas in very-high-resolution remote sensing images. These challenges often lead to difficulties in calculating satisfactory disparity maps using existing stereo matching methods, thereby reducing the accuracy of 3D reconstruction. This issue is particularly pronounced in urban scenes, which contain numerous super high-rise and densely distributed buildings, resulting in large disparity values and occluded regions in stereo image pairs, and further leading to a large number of mismatched points in the obtained disparity map. In response to these challenges, this paper proposes a method to refine the disparity in urban scenes based on open-source GIS data. First, we register the GIS data with the epipolar-rectified images since there always exists unignorable geolocation errors between them. Specifically, buildings with different heights present different offsets in GIS data registering; thus, we perform multi-modal matching for each building and merge them into the final building mask. Subsequently, a two-layer optimization process is applied to the initial disparity map based on the building mask, encompassing both global and local optimization. Finally, we perform a post-correction on the building facades to obtain the final refined disparity map that can be employed for high-precision 3D reconstruction. Experimental results on SuperView-1, GaoFen-7, and GeoEye satellite images show that the proposed method has the ability to correct the occluded and mismatched areas in the initial disparity map generated by both hand-crafted and deep-learning stereo matching methods. The DSM generated by the refined disparity reduces the average height error from 2.2 m to 1.6 m, which demonstrates superior performance compared with other disparity refinement methods. Furthermore, the proposed method is able to improve the integrity of the target structure and present steeper building facades and complete roofs, which are conducive to subsequent 3D model generation.

Vertical accuracy assessment of freely available global DEMs (FABDEM, Copernicus DEM, NASADEM, AW3D30 and SRTM) in flood-prone environments

ABSTRACT Flood models rely on accurate topographic data representing the bare earth ground surface. In many parts of the world, the only topographic data available are the free, satellite-derived global Digital Elevation Models (DEMs). However, these have well-known inaccuracies due to limitations of the sensors used to generate them (such as a failure to fully penetrate vegetation canopies and buildings). We assess five contemporary, 1 arc-second (≈30 m) DEMs -- FABDEM, Copernicus DEM, NASADEM, AW3D30 and SRTM -- using a diverse reference dataset comprised of 65 airborne-LiDAR surveys, selected to represent biophysical variations in flood-prone areas globally. While vertical accuracy is nuanced, contingent on the specific metrics used and the biophysical character of the site being assessed, we found that the recently-released FABDEM consistently ranked first, improving on the second-place Copernicus DEM by reducing large positive errors associated with forests and buildings. Our results suggest that land cover is the main factor explaining vertical errors (especially forests), steep slopes are associated with wider error spreads (although DEMs resampled from higher-resolution products are less sensitive), and variable error dependency on terrain aspect is likely a function of horizontal geolocation errors (especially problematic for AW3D30 and Copernicus DEM).

An Innovative Approach to Surface Deformation Estimation in Forest Road and Trail Networks Using Unmanned Aerial Vehicle Real-Time Kinematic-Derived Data for Monitoring and Maintenance

The significant increase in hiking, wood extraction, and transportation activities exerts a notable impact on the environmental balance along trails and forest roads in the form of soil degradation. The aim of this study was to develop a Deformation Classification Model for the surface of a multi-use trail, as well as to calculate sediment deposition and generate a flood hazard map in a partially forested region. The eBee X mapping Unmanned Aerial Vehicle (UAV) equipped with the senseFly S.O.D.A. 3D camera and Real-Time Kinematic (RTK) technology flew over the study area of 149 ha in Northern Greece at an altitude of 120 m and achieved a high spatial resolution of 2.6 cm. The specific constellation of fixed-wing equipment makes the use of ground control points obsolete, compared to previous, in most cases polycopter-based, terrain deformation research. Employing the same methodology, two distinct classifications were applied, utilizing the Digital Surface Model (DSM) and Digital Elevation Model (DEM) for analysis. The Geolocation Errors and Statistics for Bundle Block Adjustment exhibited a high level of accuracy in the model, with the mean values for each of the three directions (X, Y, Z) being 0.000023 m, −0.000044 m, and 0.000177 m, respectively. The standard deviation of the error in each direction was 0.022535 m, 0.019567 m, and 0.020261 m, respectively. In addition, the Root Mean Square (RMS) error was estimated to be 0.022535 m, 0.019567 m, and 0.020262 m, respectively. A total of 20 and 30 altitude categories were defined at a 4 cm spatial resolution, each assigned specific ranges of values, respectively. The area of each altitude category was quantified in square meters (m2), while the volume of each category was measured in cubic meters (m3). The development of a Deformation Classification Model for the deck of a trail or forest road, coupled with the computation of earthworks and the generation of a flood hazards map, represents an efficient approach that can provide valuable support to forest managers during the planning phase or maintenance activities of hiking trails and forest roads.

Quantifying and correcting geolocation error in spaceborne LiDAR forest canopy observations using high spatial accuracy data: A Bayesian model approach

AbstractGeolocation error in spaceborne sampling light detection and ranging (LiDAR) measurements of forest structure can compromise forest attribute estimates and degrade integration with georeferenced field measurements or other remotely sensed data. Data integration is especially problematic when geolocation error is not well quantified. We propose a general model that uses airborne laser scanning data to quantify and correct geolocation error in spaceborne sampling LiDAR. To illustrate the model, LiDAR data from NASA Goddard's LiDAR Hyperspectral and Thermal Imager (G‐LiHT) was used with a subset of LiDAR data from NASA's Global Ecosystem Dynamics Investigation (GEDI). The model accommodates multiple canopy height metrics derived from a simulated GEDI footprint kernel using spatially coincident G‐LiHT, and incorporates both additive and multiplicative mapping between the canopy height metrics generated from both datasets. A Bayesian implementation provides probabilistic uncertainty quantification in both parameter and geolocation error estimates. Results show a systematic geolocation error of 9.62 m in the southwest direction. In addition, estimated geolocation errors within GEDI footprints were highly variable, with results showing a 0.45 probability the true footprint center is within 20 m. Estimating and correcting geolocation error via the model outlined here can help inform subsequent efforts to integrate spaceborne LiDAR data, like GEDI, with other georeferenced data.

Oceanic Validation of IMERG-GMI Version 6 Precipitation Using the GPM Validation Network

Abstract NASA’s multisatellite precipitation product from the Global Precipitation Measurement (GPM) mission, the Integrated Multi-satellitE Retrievals for GPM (IMERG) product, is validated over tropical and high-latitude oceans from June 2014 to August 2021. This oceanic study uses the GPM Validation Network’s island-based radars to assess IMERG when the GPM Core Observatory’s Microwave Imager (GMI) observes precipitation at these sites (i.e., IMERG-GMI). Error tracing from the Level 3 (gridded) IMERG V06B product back through to the input Level 2 (satellite footprint) Goddard Profiling Algorithm GMI V05 climate (GPROF-CLIM) product quantifies the errors separately associated with each step in the gridding and calibration of the estimates from GPROF-CLIM to IMERG-GMI. Mean relative bias results indicate that IMERG-GMI V06B overestimates Alaskan high-latitude oceanic precipitation by +147% and tropical oceanic precipitation by +12% with respect to surface radars. GPROF-CLIM V05 overestimates Alaskan oceanic precipitation by +15%, showing that the IMERG algorithm’s calibration adjustments to the input GPROF-CLIM precipitation estimates increase the mean relative bias in this region. In contrast, IMERG adjustments are minimal over tropical waters with GPROF-CLIM overestimating oceanic precipitation by +14%. This study discovered that the IMERG V06B gridding process incorrectly geolocated GPROF-CLIM V05 precipitation estimates by 0.1° eastward in the latitude band 75°N–75°S, which has been rectified in the IMERG V07 algorithm. Correcting for the geolocation error in IMERG-GMI V06B improved oceanic statistics, with improvements greater in tropical waters than Alaskan waters. This error tracing approach enables a high-precision diagnosis of how different IMERG algorithm steps contribute to and mitigate errors, demonstrating the importance of collaboration between evaluation studies and algorithm developers. Significance Statement Evaluation of IMERG’s oceanic performance is very limited to date. This study uses the GPM Validation Network to conduct the first extensive assessment of IMERG V06B at its native resolution over both high-latitude and tropical oceans, and traces errors in IMERG-GMI back through to the input GPROF-CLIM GMI product. IMERG-GMI overestimates tropical oceanic precipitation (+12%) and strongly overestimates Alaskan oceanic precipitation (+147%) with respect to the island-based radars studied. IMERG’s GMI estimates are assessed as these should be the optimal estimates within the multisatellite product due to the GMI’s status as calibrator of the GPM passive microwave constellation.

Quantification of GEDI Geolocation Error and Its Influence on Elevation and Canopy Height

Quantification of GEDI Geolocation Error and Its Influence on Elevation and Canopy Height

Tree View Assessment: Survey of Two Municipalities Located in the Brussels Capital Region

Abstract Background Recent literature has highlighted the importance of visual accessibility to nature to reduce stress, anxiety, or depression amongst others. However, green visual accessibility is yet rarely considered in urban policy implementations. Reasons behind this are manifold, and include the challenges associated with the measurability of green views which require data-intensive pedestrian view computations, and assessment methods are yet to be agreed upon. Methods Two methods, Street View Images (SVI) and semantic classification, and geospatial viewshed analysis, were used to compute street level tree views. All street views contained within 2 municipalities from the Brussels Capital Region (BCR) have been studied. Using the SVI method, 15 green view indicators have been proposed. Using the viewshed analysis, the tree view area ratio (TVar) from each SVI geo-location has been computed. The independence between the indicators was evaluated, and using a random forest model, the principal SVI indicators to describe the TVarhave been studied. Results The variability explained by the random forest model was approximately 60% to 70%. The SVI indicators related to the horizontality of green infrastructure and tree canopy explained most of TVar. The results also reveal the tree canopy differences between both municipalities. Conclusions SVI tree view indicators provide acceptable predictions of the TVarwhich could be particularly useful for municipalities with no access to detailed geospatial data. The 30% to 40% of the unexplained variability, could be related to errors derived from the tree canopy geospatial layer, differences in the data collection dates, or geolocation errors of the SVIs.

Soil Organic Carbon Estimation in Ferrara (Northern Italy) Combining In Situ Geochemical Analyses and Hyperspectral Remote Sensing

This study investigated whether surface soil organic carbon (SOC) content could be estimated using hyperspectral data provided by the Italian Space Agency PRISMA satellite. We collected 100 representative topsoil samples in an area of 30 × 30 Km2 in the province of Ferrara (Northern Italy), estimated their SOC content and other soil properties through thermo-gravimetric analysis, and matched these to the spectra of the sampled areas that were measured by PRISMA on 7 April 2020. A tentative model was created for SOC estimation using ordinary least-squares (OLS) regression and an artificial neural network (ANN). Repeated k-fold cross-validation of the OLS and ANN models yielded R2 values of 0.64 and 0.49, respectively. The performance of the models was inferior to that obtained from the literature using similar modeling techniques in relatively small areas (up to 3 × 3 Km2) and characterized by restricted SOC variability (0.2–2.1 wt%). However, our data were collected over a wider area with high SOC content variability (0.7–9.3 wt%); consequently, significant variations were observed over a spatial scale of just a few meters. Therefore, this work shows the importance of testing remote sensing techniques for SOC measurements in more complex areas than those reported in the existing literature. Furthermore, our study sheds light on the geolocation errors and missing data of PRISMA.

GraphNEI: A GNN-based network entity identification method for IP geolocation

Network entity geolocation technology is a technique for inferring geographic location through features such as network measurements or IP address searchable information, also known as IP geolocation. By obtaining the device type of the target can assist in target IP geolocation or IP landmark mining. Most traditional rule-based or learning-based methods identify network entities. However, due to the existence of firewalls and the limitations of feature detection, the features of individual targets are prone to be missing or spoofed, which can lead to a decrease in the accuracy of network entity identification (NEI). This paper propose a graph neural network-based network entity identification method, GraphNEI model, which embeds network entities into the graph structure and uses graph neural networks to improve the current problem of missing or spoofed features in order to improve the accuracy of current network entity identification. It mainly includes five steps: data processing, subgraph partition, weight calculation, node update and classification. First, the acquired dataset is subjected to feature extraction and anonymization; second, the target nodes are subjected to network topology graph construction and community division; third, the nodes’ self-attention, structural attention and similarity of neighbors are combined and used to calculate the nodes’ combined attention; fourth, node aggregation and update, and update the node representation based on the combined attention results; finally, the nodes are classified. We successfully identified 7 different network entities on the publicly collected dataset, and the identification accuracy of network entities is above 95.49%, improved 0.51%–10.42% compared to typical rule-based or learning-based methods. It effectively improves the effective NEI in the absence of network entity features. In addition, we conduct IP geolocation research based on NEI, and the experimental results show that the method is effective in reducing IP geolocation errors distance.

Orthorectification of Data from the AHI Aboard the Himawari-8 Geostationary Satellite

The use of geostationary meteorological satellites for land remote sensing has attracted much attention after the launch of the Himawari-8 satellite equipped with a sensor with enhanced land observation capabilities. In the context of land remote sensing, geolocation errors are often a critical issue, especially in mountainous regions, where a precise orthorectification process is required to maintain high geometric accuracy. The present work addresses the issues related to orthorectification of the new-generation geostationary Earth orbit (GEO) satellites by applying an algorithm known as the ray-tracing indirect method to the data acquired by the Advanced Himawari Imager (AHI) aboard the Himawari-8 satellite. The orthorectified images of the AHI were compared with data from the Sentinel-2 Multispectral Instrument (MSI). The comparison shows a clear improvement of the geometric accuracy, especially in high-elevation regions located far from the subsatellite point. The results indicate that approximately 7.3% of the land pixels are shifted more than 3 pixels during the orthorectification process. Furthermore, the maximum displacement after the orthorectification is up to 7.2 pixels relative to the location in the original image, which is of the Tibetan Plateau. Moreover, serious problems caused by occlusions in the images of GEO sensors are clearly indicated. It is concluded that special caution is needed when using data from GEO satellites for land remote sensing in cases where the target is in a mountainous region and the pixels are located far from the subsatellite point.

Evaluating and mitigating the impact of systematic geolocation error on canopy height measurement performance of GEDI

NASA's Global Ecosystem Dynamics Investigation (GEDI) is designed to provide high-resolution measurements of forest structure and topography between 52° N and S. However, current geolocation accuracy may limit further science applications of footprint-level products as early adopters have found it difficult to align with in-situ forestry inventory data and high-resolution imagery for calibration and validation purpose. Here we developed a new means to rapidly evaluate and mitigate the impact of systematic geolocation error on the performance of GEDI's forest height estimates in the US. By integrating nationwide high-resolution airborne lidar data collected through the 3D Elevation Program of the USGS, we provided optimal geolocation adjustments of GEDI at per beam level and tracked their performances over the first 18-mo. Our results suggest that the first release of GEDI product (R01) can have large systematic geolocation errors at beam level (i.e., 50.5% of beams with an error > 20 m). Its impact on canopy height measurement could drastically vary in space and time, which in turn also offers a separate indirect method to evaluate and track geolocation performance. The second release of GEDI data (R02) has achieved a much-improved systematic geolocation accuracy which is shown to meet the mission requirement (0.2% beams >20 m and 80.8% beams <10 m) and should be able to meet requirements from many practical science applications tolerant to moderate geolocation errors. In sum, our approach has provided a short-term solution for an enhanced Cal/Val strategy for GEDI. While further improvements will certainly be made in future releases, it can potentially create an alternative pathway to generate and validate biomass products by linking GEDI footprint samples directly with in-situ data collections.

Canopy structure from space using GEDI lidar

Canopy structure from space using <scp>GEDI</scp> lidar