Comparison of GEDI LiDAR Data Capability for Forest Canopy Height Estimation over Broadleaf and Needleleaf Forests

Abstract

The GEDI LiDAR system was specifically designed to detect vegetation structure and has proven to be a suitable tool for estimating forest biophysical parameters, especially canopy height, at a global scale. This study compares the GEDI relative height metric (RH100) over different forest types, especially deciduous broadleaf and evergreen coniferous located in Thuringia, Germany, to understand how the forest structural differences affect the GEDI height estimation. A canopy height model that was produced using digital terrain and surface models (DTM and DSM) derived from airborne laser scanning data is used as the reference data. Based on the result, GEDI canopy height over needleleaf forest is slightly more accurate (RMSE = 6.61 m) than that over broadleaf (RMSE = 8.30 m) and mixed (RMSE = 7.94 m) forest. Evaluation of the GEDI acquisition parameters shows that differences in beam type, sensitivity, and acquisition time do not significantly affect the accuracy of canopy heights, especially over needleleaf forests. Considering foliage condition impacts on canopy height estimation, the contrasting result is observed in the broadleaf and needleleaf forests. The GEDI dataset acquired during the winter when deciduous species shed their leaves (the so-called leaf-off dataset), outperforms the leaf-on dataset in the broadleaf forest but shows less accurate results for the needleleaf forest. Considering the effect of the plant area index (PAI) on the accuracy of the GEDI canopy height, the GEDI dataset is divided into two sets with low and high PAI values with a threshold of median PAI = 2. The results show that the low PAI dataset (median PAI < 2) corresponds to the non-growing season (autumn and winter) in the broadleaf forest. The results using the leaf-off/leaf-on season dataset are in line with the slightly better performance of GEDI using the non-growing dataset (RMSE = 7.40 m) compared to the growing dataset (RMSE = 8.44 m) in the deciduous broadleaf forest and vice versa as well as the slightly better result using the growing dataset (RMSE = 6.38 m) compared to the non-growing dataset (RMSE = 7.24 m) in the evergreen needleleaf forest. Although a slight improvement in canopy height estimation was observed using either the leaf-off or non-growing season dataset for broadleaf forest, and either the leaf-on or growing season dataset for needleleaf forest, the approach of filtering GEDI data based on such seasonal acquisition time is recommended when retrieving canopy height over pure stands of broadleaf or needleleaf species, and the sufficient dataset is available.

Carbon Storage Estimation of Quercus aquifolioides Based on GEDI Spaceborne LiDAR Data and Landsat 9 Images in Shangri-La

“…When the data were closer to 1, it indicated high quality. Usually, the value was set to be greater than 0.95 in dense forest areas [28]. After quality filtering, 52,366 footprints were obtained (Figure 3b).…”

Evaluation and Comparison of ICESat-2 and GEDI Data for Terrain and Canopy Height Retrievals in Short-Stature Vegetation

“…Similar to the pattern observed with terrain height being influenced by terrain slope, canopy height accuracy from GEDI decreases significantly when terrain slope is steep, while ICESat-2 maintains high canopy height accuracy. This could be attributed to the fact that increasing slope steepness results in more overlap between ground and canopy returns in GEDI data [27,28], which in turn affects the extraction of canopy height. In contrast, ICESat-2's ground and canopy-top photons are relatively less affected by terrain slope, contributing to its higher canopy height extraction accuracy in steep terrain.…”

Earth-Observation-Based Monitoring of Forests in Germany—Recent Progress and Research Frontiers: A Review

“…Plausible reasons are the novel data characteristics of GEDI (sampling mission, variety of datasets available) and the maximum latitudinal coverage of 52 • N, thus challenging Germany-wide studies. Since March 2023, several studies using GEDI data covering Germany have been published [36,[155][156][157]. A further increase in studies making use of GEDI data might be promoted through the confirmed mission extension from end of 2024 to 2031 [158] and upcoming spaceborne long-wavelength SAR (Synthetic Aperture Radar) missions (BIOMASS, NISAR).…”