Vegetation canopy height estimation in dynamic tropical landscapes with TanDEM‐X supported by GEDI data

Abstract

1. Vegetation canopy height is a relevant proxy for aboveground biomass, carbon stock, and biodiversity. Wall-to-wall information of canopy height with high spatial resolution and accuracy is not yet available on large scales. For the globally consistent TanDEM-X data, simplifications are necessary to estimate canopy height with semi-empirical models based on polarimetric synthetic aperture radar interferometry (PolInSAR).2. We trained the semi-empirical models with sampled GEDI data, because the assumptions behind the application of such simplifications are not always valid for TanDEM-X. General linear as well as sinc models and empirical parameterizations of these models were applied to estimate the canopy height in tropical landscapes of Sumatra, Indonesia. Airborne laser scanning (ALS) data were consistently used as an independent reference. The general simplified models were compared with the trained empirical versions to assess the potential improvement of the empirical parameterization of the models. The residuals of the different canopy height models were further evaluated in relation to land use and structural information of the vegetation. 3. Our results indicated that the empirical parameters substantially improved the estimation from a root-mean-square-error (RMSE) of 10.3 m (55.8%) to 8.8 m (47.7%), when using the linear model. In contrast, the improvement of the sinc model with empirical parameters was not substantial compared to the general sinc model (7.4 m [40.4%] vs. 6.9 m [37.5%]). A consistent improvement was observed in the linear model, whereas the improvement of the sinc model was dependent on the land-use type. Structural attributes like the canopy height itself and vegetation cover had a significant effect on the accuracies, with higher and denser vegetation generally resulting in higher residuals.4. We demonstrate the potential of the combined exploitation of the TanDEM-X and GEDI missions for a wall-to-wall canopy height estimation in a tropical region. This study provides relevant findings for a consistent mapping of vegetation

Large-Scale Forest Height Mapping by Combining TanDEM-X and GEDI Data

“…GEDI provides dense and well distributed lidar waveform measurements across Earths tropical and temperate forests and defines an ideal framework to explore the synergetic use of waveform lidar and interferometric X-band SAR measurements at local to global scales. Indeed, the synergies between TanDEM-X and waveform lidar data [34] have been confirmed by a number of studies using the TanDEM-X / GEDI framework [30][31], [37][38].…”

Towards complex applications of active remote sensing for ecology and conservation

“…Spaceborne RADAR data offer a complementary source of data, particularly across the tropics. Schlund et al (2022) suggested an approach to combine GEDI data with TanDEM‐X polarimetric SAR data via a semi‐empirical model for canopy height estimations over tropical forests and then validated the results with airborne LiDAR data. A higher and denser vegetation cover was shown to result in larger errors in the estimation of canopy height by the applied linear models.…”

“…1. Stand-, canopy-and tree-level structural analysis by active RS data (Atkins et al, 2023;Blanchard et al, 2023;Coverdale & Davies, 2023;Hardenbol et al, 2022;Schlund et al, 2022;Singh et al, 2023;Tatsumi et al, 2022;Zhang & Liu, 2023).…”

“…Of the accepted papers, four are published in Journal of Animal Ecology , two in Journal of Applied Ecology , four in Journal of Ecology and eight in Methods in Ecology and Evolution . When assessing the general trends being covered by the contributions, a few general (and partly overlapping) trends can be distinguished, including: Stand‐, canopy‐ and tree‐level structural analysis by active RS data (Atkins et al, 2023; Blanchard et al, 2023; Boucher et al, 2023; Coverdale & Davies, 2023; Hardenbol et al, 2022; Schlund et al, 2022; Singh et al, 2023; Tatsumi et al, 2022; Zhang & Liu, 2023). Climate‐mediated response of animal populations (from insects to ungulates) as investigated by active RS data (Brlík et al, 2022; Hockridge et al, 2022).…”

“…photon-counting, large footprint and transect pattern for data acquisition has offered precious sources of 3D information, though often insufficient for spatially explicit and consistent estimates or upscaling over large areas. Spaceborne RADAR data offer a complementary source of data, particularly across the tropics Schlund et al (2022). suggested an approach to combine GEDI data with TanDEM-X polarimetric SAR data via a semi-empirical model for canopy height estimations over tropical forests and then validated the results with airborne LiDAR data.…”