Open source Global Digital Elevation Models (GDEMs) serve as an important base for studies in geosciences. However, these models contain vertical errors due to various reasons. In this study, data from two Satellite LiDAR altimetry systems, GEDI and ICESat-2, were used to improve the vertical accuracy of GDEMs. Three different machine learning methods, namely an Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), and a Convolutional Neural Network (CNN), were employed to improve existing DEM data with satellite LiDAR data. The methodology was tested in five areas with varying characteristics. Ground control data were selected from high accuracy DEMs generated from Airborne LiDAR and GNSS data. The use of ANN method improved the vertical accuracy of SRTM data from 6.45 to 3.72 m in Test area-4. Similarly, the CNN method demonstrated an improvement in the vertical accuracy of bare ground SRTM data increasing from 3.4 to 0.6 m in Test area-4. In Test area-5, the ANN method improved the vertical accuracy of SRTM data with slopes between 30 and 60%, increasing from 3.8 to 0.5 m. Notably, the results underscore the successful improvement of GDEMs across all test areas.@en

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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Global Ecosystem Dynamics Investigate (GEDI) is a spaceborne laser altimeter system used for earth observation in many areas such as forest canopy, water level and terrain height estimation. GEDI data is affected by atmospheric effects due to the sensor used while observing. In this study, we propose a 7-step, multi-variable strategy for determining the elevation of the terrain with GEDI. These steps involve both different geoid models, GEDI ancillary data, and topographic features. We evaluated the effect of each step using high quality DEM data obtained by Airborne LiDAR over the central part of Puerto Rico, where building areas and forests are dominant, while the terrain has an average slope of 24%. The GEDI data of the test area consists of 3 different orbits (O06225, O07933, O08061) with different solar elevation and cloudiness rates. While the raw data of orbit O06225, obtained during a solar elevation of 8.4 and cloudy conditions, has a Root Mean Square Error (RMSE) of 418.67 m., the RMSE is reduced to 4.59 m. after applying all seven filtering steps. The raw data of orbit O07933, obtained with a solar elevation of 50.5 during cloud free conditions, has a RMSE of 10.04 m., and is reduced to a similar value of 4.8 m. as a result of the filtering steps. On the other hand, orbit O08061 was obtained with little clouds during a near-dawn solar elevation of -0.7. Its raw RMSE of 50,34 m could only be reduced to 12.41 m. by the proposed filtering procedure. It is concluded that although there are many outliers in data acquired during cloudy conditions, the accuracy of the data remaining after applying our filtering strategy can be as high as the accuracy obtained during cloud free conditions. Better results than 5 m were obtained according to the RMSE in areas with low solar elevation. In addition, it is observed that accuracy decreases strongly when the solar elevation is close to 0. Overall, it is concluded that appropriate filtering is required when determining terrain height with GEDI data.

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Key message: Despite showing a cost-effective potential for quantifying vertical forest structure, the GEDI and ICESat-2 satellite LiDAR missions fall short of the data accuracy standards required by tree- and stand-level forest inventories. Abstract: Tree and stand heights are key inventory variables in forestry, but measuring them manually is time-consuming for large forestlands. For that reason, researchers have traditionally used terrestrial and aerial remote sensing systems to retrieve forest height information. Recent developments in sensor technology have made it possible for spaceborne LiDAR systems to collect height data. However, there is still a knowledge gap regarding the utility and reliability of these data in varying forest structures. The present study aims to assess the accuracies of dominant stand heights retrieved by GEDI and ICESat-2 satellites. To that end, we used stand-type maps and field-measured inventory data from forest management plans as references. Additionally, we developed convolutional neural network (CNN) models to improve the data accuracy of raw LiDAR metrics. The results showed that GEDI generally underestimated dominant heights (RMSE = 3.06 m, %RMSE = 21.80%), whereas ICESat-2 overestimated them (RMSE = 4.02 m, %RMSE = 30.76%). Accuracy decreased further as the slope increased, particularly for ICESat-2 data. Nonetheless, using CNN models, we improved estimation accuracies to some extent (%RMSEs = 20.12% and 19.75% for GEDI and ICESat-2). In terms of forest structure, GEDI performed better in fully-covered stands than in sparsely-covered forests. This is attributable to the smaller height differences between canopy tops in dense forest conditions. ICESat-2, on the other hand, performed better in thin forests (DBH < 20 cm) than in large-girth and mature stands of Crimean pine. We conclude that GEDI and ICESat-2 missions, particularly in hilly landscapes, rarely achieve the standards needed in stand-level forest inventories when used alone.

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