Assessment of terrain elevation estimates from ICESat-2 and GEDI spaceborne LiDAR missions across different land cover and forest types

Digital elevation models (DEM) are an essential data source in many professional disciplines, with the help of gridded height information and values such as slope and aspect produced from that information. In this study, Ice, Cloud and land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI) satellite-altimetry data, and SRTM, ASTER-GDEM, and ALOS World3D data were used as Global DEMs (GDEMs) data in three different areas (U.S.A., New Zealand and Puerto Rico). We used kriging methods for interpolation to create the new rasters. Point-based accuracies were compared with the GDEMs from satellite-altimetry systems and raster-based comparisons were made by deriving DEMs with satellite-altimetry data in three different areas. It was seen that the ICESat-2 data in point-based results had similar accuracy with other GDEMs. DEMs produced by using ICESat-2 and GEDI data together gave relatively better results than using alone. In particular, the correlation was found to be highly correlated with 99%.

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“…2. The statistical analysis and visualization were done using the statistical software R 4.1.0 (Team RC 2013).…”

Section: Methodology and Accuracy Assessmentmentioning

confidence: 99%

A comparison of vertical accuracy of global DEMs and DEMs produced by GEDI, ICESat-2

Narin

Güllü

2023

Earth Sci Inform

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“…However, the RMSE of sub-canopy topography remained larger in areas of tall forests than in areas of short forests. This is because the ICESat-2 system cannot detect the real surface in dense and tall forests [54,55]. For the Lope and Mabounie test sites, the RMSE of sub-canopy topography exhibited a trend of increase-decrease-increase, compared with the Krycklan and Remningstorp test sites, showing slightly worse accuracy in the forest height ranges of 0 m to 30 m. This was because there are few low-vegetation areas in the Lope and Mabounie test sites, and the SPC height in the low-vegetation area was significantly overestimated because of the influence of the adjacent area of high vegetation.…”

Section: Correlation Between Forest Height and The Accuracy Of Sub-ca...mentioning

confidence: 99%

Section: Correlation Between Forest Height and The Accuracy Of Sub-ca...mentioning

confidence: 99%

Automated Estimation of Sub-Canopy Topography Combined with Single-Baseline Single-Polarization TanDEM-X InSAR and ICESat-2 Data

Hu,

Zhu,

et al. 2024

Remote Sensing

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TanDEM-X bistatic interferometric system successfully generated a high-precision, high-resolution global digital elevation model (DEM). However, in forested areas, two core problems make it difficult to obtain sub-canopy topography: (1) the penetrability of short-wave signals is limited, and the DEM obtained in dense forest areas contains a significant forest signal, that is, the scattering phase center (SPC) height; and (2) the single-baseline and single-polarization TanDEM-X interferometric synthetic aperture radar (InSAR) data cannot provide sufficient observations to make the existing physical model reversible for estimating the real surface phase, whereas the introduction of optical data makes it difficult to ensure data synchronization and availability of cloud-free data. To overcome these problems in accurately estimating sub-canopy topography from TanDEM-X InSAR data, this study proposes a practical method of sub-canopy topography estimation based on the following innovations: (1) An orthogonal polynomial model was established using TanDEM-X interferometric coherence and slope to estimate the SPC height. Interferometric coherence records forest height and dielectric property information from an InSAR perspective and has spatiotemporal consistency with the InSAR-derived DEM. (2) Introduce Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data to provide more observational information and automatically screen ICESat-2 control points with similar forest and slope conditions in the local area to suppress forest spatial heterogeneity. (3) A weighted least squares criterion was used to solve this model to estimate the SPC height. The results were validated at four test sites using high-precision airborne light detection and ranging (LiDAR) data as a reference. Compared to the InSAR-derived DEM, the accuracy of the sub-canopy topography was improved by nearly 60%, on average. Furthermore, we investigated the necessity of local modeling, confirming the potential of the proposed method for estimating sub-canopy topography by relying only on TanDEM-X and ICESat-2 data.

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“…GEDI was found to provide accurate ground elevation and canopy top heights measurements, although errors can reach up to several meters [14], [15], [16], [17], [18]. A significant part of errors was attributed to low horizontal accuracy [1], [2], [8], [12], [14], [18]. Based on GEDI data simulations, [19] showed that AGB estimation errors increase with increasing geolocation error.…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies assessed GEDI data quality to estimate ground elevation, canopy height and above-ground biomass (AGB) through comparison with aerial lidar system (ALS) data [10], [11], [12], [13]. GEDI was found to provide accurate ground elevation and canopy top heights measurements, although errors can reach up to several meters [14], [15], [16], [17], [18]. A significant part of errors was attributed to low horizontal accuracy [1], [2], [8], [12], [14], [18].…”

Section: Introductionmentioning

confidence: 99%

Improving GEDI Footprint Geolocation Using a High-Resolution Digital Elevation Model

Schleich

Durrieu

Vega

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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GEDI (Global Ecosystem Dynamics Investigation) is a lidar system on-board the International Space Station designed to study forest ecosystems. However, GEDI footprint low accuracy geolocation is a major impediment to the optimal benefit of the data. We thus proposed a geolocation correction method, GeoGEDI, only based on high-resolution digital elevation models (DEMs) and GEDI derived ground elevations. For each footprint, an error map between GEDI ground estimates and reference DEM was computed, and a flow accumulation algorithm was used to retrieve the optimal footprint position. GeoGEDI was tested on 150 000 footprints in Landes and Vosges, two French forests with various stands and topographic conditions. It was applied to GEDI versions 1 (v1) and 2 (v2), by either a single or four full-power laser beam tracks. GeoGEDI output accuracy was evaluated by analyzing shift distributions and comparing GEDI ground elevations and surface heights to reference data. GeoGEDI corrections were greater for v1 than for v2 and agreed with errors published by NASA. Within forests, GeoGEDI improved the RMSE of ground elevation in Landes by 26.8 % (0.34 m) and by 13.3 % (0.14 m) for v1 and v2, respectively. For Vosges, ground elevation RMSE improved by 59.6 % (3.82 m) and 36.2 % (1.41 m), for v1 and v2, respectively. Regarding surface heights, except for v2 in Landes, where insufficient variations in topography combined to GEDI ground detection issues might have penalized the adjustment, GeoGEDI improved GEDI estimates. Using GeoGEDI showed efficient to improve positioning bias and precision.

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Assessment of terrain elevation estimates from ICESat-2 and GEDI spaceborne LiDAR missions across different land cover and forest types

Cited by 23 publications

References 55 publications

A comparison of vertical accuracy of global DEMs and DEMs produced by GEDI, ICESat-2

A comparison of vertical accuracy of global DEMs and DEMs produced by GEDI, ICESat-2

Automated Estimation of Sub-Canopy Topography Combined with Single-Baseline Single-Polarization TanDEM-X InSAR and ICESat-2 Data

Improving GEDI Footprint Geolocation Using a High-Resolution Digital Elevation Model

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