Assessing Amazon rainforest regrowth with GEDI and ICESat-2 data

Two novel satellite LiDAR missions -GEDI and ICESat-2- are currently operational and combined provide nearglobal measurements of forest height and structure. Such data underpin a new era of large-area approaches for measuring forest height in regrowing forests of different ages and assessing associated regrowth rates. Two LiDAR missions further allow for comparing independently derived forest heights and regrowth rates. This study utilized both GEDI and ICESat-2 measurements to assess regrowth rates in regrowing forests of different ages for the Brazilian state Rondo<SIC>nia. We considered 19 data subgroups stratified by beam strength, light condition, beam sensitivity, and waveform processing algorithm to assess the retrieval uncertainty and identify data subgroups associated with the most reliable regrowth estimates. The quality assessment of GEDI and ICESat-2 forest heights over four 50 km long airborne LiDAR strips determined a root mean square error of 4.14 m (CV = 17%) and 5.91 m (CV = 19%) and a mean error of 0.04 m and -2.81 m, respectively. A linear calibration model between satellite- and airborne-LiDAR heights was then derived for each data subgroup and used to calibrate satellite heights. Forest regrowth rates were subsequently estimated for each satellite mission using a space-for-time imputation with forest heights' medians per stand age class. The total growth of GEDI and ICESat-2 median forest heights after 33 years was 20.17 m (SE = 1.3 m) and 20.13 m (SE = 2.8 m), respectively. However, when growth was approximated with different non-linear models, the total growth differed by up to 6%, and the average regrowth rate even by up to 23%. The study revealed that omitting either the calibration step or the removal of secondary-forest-border pixels would result in an underestimation of the regrowth rate by more than 20%. Furthermore, the ICESat-2 weak beams were found unreliable for regrowth retrieval. The study showed that the novel satellite LiDAR data and the proposed methods could assess median forest height growth over large areas. However, forest age errors should also be accounted for in the retrieval uncertainty before comparing the growth estimates across different regions. Code and data necessary to reproduce the results are freely available on GitHub and Zenodo.