Combining GEDI and Sentinel data for structural forest parameter estimation
Authors: Manuela Hirschmugl, Florian Lippl, Hannah Scheicher
Forests have a major impact on the carbon cycle (Mitchard 2018). The majority of the stored carbon dioxide in the biosphere emitted by fossil fuels and industry is absorbed by forests (Pugh et al. 2019). However, the magnitude of its contribution and its distribution as carbon sink is not yet fully understood and remains highly uncertain (Pan et al. 2011). Due to human induced climate change, biodiversity is rapidly declining and habitat is being destructed (Turner et al. 2003; Jetz et al. 2007). In order to mitigate and understand the effects on the ecosystem, continuous spatial measurement frameworks for land cover and vegetation are needed (Bergen et al. 2009). Forest variables such as canopy height, canopy vertical height profiles and biomass have to be analyzed. Pre-launch calibration and validation studies employing simulated GEDI waveforms processed from Airborne LiDAR Instruments (ALS) show promising results and suggest real GEDI data as well suited for capturing vegetation patterns and biomass products and hence being used as a reference data (Rishmawi et al. 2021; Qi et al. 2019; Schneider et al. 2020; Duncanson et al. 2020). Since the release of version 1 GEDI data, various studies have been published assessing the accuracy of GEDI data by evaluating ground elevation and canopy height estimates against airborne laser scanning height data (Adam et al. 2020; Spracklen and Spracklen 2021; Lang et al. 2021; Potapov et al. 2021). These studies are in good agreement to each other and highlight the applicability of GEDI data to forest structure investigations. Furthermore, the ability of the spaceborne laser to analyze complex forest structures with dense and multilayered canopies enables not only AGB estimations but gives also new valuable insights into biodiversity (Guerra-Hernández and Pascual 2021; Spracklen and Spracklen 2021). This could help to a better understanding of the carbon cycle and ecological forecasting (Schneider et al. 2020).
In our project “GEDI-Sens”, we investigate the relations and combination options between forest parameters provided by GEDI and data from the Copernicus Sentinel-1 (S-1) and Sentinel-2 (S-2) satellites. Previous works show varying levels of agreement between GEDI and S-1 (Verhelst et al., 2021) or S-2 data (Lang et al., 2019; Pereira-Pires et al., 2021). The works had different foci, mainly targeting canopy height and/or above ground biomass (AGB). Some authors also integrated both S-1 and S-2 data to improve the relationship (Chen et al., 2021; Debastiani et al., 2019).
In the first step of the project, we investigated the quality of the GEDI data compared to ALS data for a mountainous forest area in the National Park Kalkalpen, Austria. We found the accuracy of the DTM height from GEDI to decreases with increasing slope inclination from an RMSE of 2.71 m for slopes < 10° up to 10.6 m for slopes > 50°. The mean RMSE is 7.6 m. This error is also visible in the evaluation of the canopy heights. The RH100 from GEDI compared to the maximum height of the ALS data shows an RMSE of 7.92 m and a low R² of only 0.38, even if the winter data is excluded (mainly deciduous forests). When excluding all changed areas in the forest cover such as storm damages between the ALS acquisition (2018) and the GEDI data (2019-2020), the RMSE only slightly improves to 7.91 m. In the next step, we used the correct ALS-based terrain height instead of the GEDI inherent terrain height to calculate a “corrected” vegetation height. The resulting R² improved slightly to 0.39, but with an RMSE of 8.01 m. These results suggest that the usability of GEDI for canopy height measurements in mountainous areas is limited. A similar analysis will be done in our second test site in the tropical forests of Uganda, where flat to hilly terrain prevails.
The vertical structure of the vegetation however should be independent of height errors and thus we expect better correlation. This remains to be analysed in the next step. Given a positive result, we will use time series data from both S-1 and S-2, their reflectance/backscatter as well as indices and textural features. We expect the results compared to both ALS derived vertical structure as well as compared to field plots by the time of the symposium. We are also investigating the use of the joint NASA-ESA Multi-Mission Algorithm and Analysis Platform (MAAP) platform for this purpose.
This study is supported by the Austrian Research Agency FFG under the Austrian Space Application Programme (ASAP) No. 38308664.
Fig. 1: Relation of ALS-based vegetation height with (a) GEDI RH100 and (b) with the canopy height deducted from GEDI top-of-canopy height and ALS-based terrain height.
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