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Abstract Amazonia encompasses extensive forests in areas that are periodically inundated by overflowing rivers. The inundation depth and duration vary according to the slope of the terrain and distance to major water bodies. This creates a flooding gradient from the lowest lying seasonally flooded forest up into the unflooded forest, which directly affects the biota. However, the effect of this gradient on soil organisms remains elusive. Here, we use DNA metabarcoding to estimate prokaryote and eukaryote diversity from soil and litter samples along the flooding gradient in central‐western Amazonia using 16S and 18S gene sequences, respectively. We characterize the below‐ground diversity and community composition based on amplicon sequence variants (ASVs). We examine relationships between the soil biota and the flooding gradient, soil properties, and above‐ground woody plant diversity. The flooding gradient does not explain below‐ground biodiversity, nor is below‐ground diversity explained by the above‐ground woody plant diversity. We uncover several taxonomic groups—such as Patescibacteria—not previously reported from Amazonian seasonally flooded forests. The flooding gradient and woody plant diversity partly explain the community composition of soil bacteria. Although the effects of the flooding gradient, soil properties, and above‐ground woody plant diversity are difficult to quantify, our results indicate that flood stress may influence below‐ground bacterial community composition. 

Flooding controls wetland carbon cycling and hinders accurate measurements of ecosystem structure from remotely sensed data. In wetlands, flood frequency and duration is critical to controlling carbon cycling, but high canopy cover can obscure fluctuations in inundation and increase uncertainty in measurements of ecosystem structure. Here we provide an overview of the challenges of recording accurate tree height measurements under flood conditions and the role that new digital technologies can play in characterizing sub-canopy inundation and reducing measurement uncertainty. Subsequently, we highlight the opportunities that spaceborne sensors can now provide for understanding the hydrological processes that control wetland ecosystem carbon cycling. We demonstrate this at a number of globally important high-carbon locations where changes in flooding regime impact ecosystem classification and measurement. 

Canopy heights and vertical profiles were analyzed for 12 airborne lidar tracks acquired over forests of the mid-Juruá region, Brazil. Canopy height models were classified at 1m resolution as floodplain, terrace, hillslope, or interfluvial flat; floodplains were further separated according to Horton-Strahler (HS) stream order. RH95 canopy heights, and vertical profiles at 1m intervals, were aggregated to 30m scale and compared with Copernicus DEM heights, using a DEM transform, the Relative Terrain Height (RTH). Median canopy height ranged from 15.4 m for the Juruá floodplain to 25.5 m for hillslopes; maximum canopy heights varied from 37.4 m to 60.0 m. A strong correlation between RTH and median canopy height (r = 0.75) was found for the Juruá floodplain tracks. Vertical profiles of Juruá floodplain tracks showed that the height above ground of maximum returns increased monotonically with RTH height. Our results clearly show the influence of floodplain topography on forest canopy structure. 

Accurate measurements of terrain elevation are crucial for many ecological applications. In this study, we sought to assess new global three-dimensional Earth observation data acquired by the spaceborne Light Detection and Ranging (LiDAR) missions Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI). For this, we examined the “ATLAS/ICESat-2 L3A Land and Vegetation Height”, version 5 (20 × 14 m and 100 × 14 m segments) and the “GEDI Level 2A Footprint Elevation and Height Metrics”, version 2 (25 m circle). We conducted our analysis across four land cover classes (bare soil, herbaceous, forest, savanna), and six forest types (temperate broad-leaved, temperate needle-leaved, temperate mixed, tropical upland, tropical floodplain, and tropical secondary forest). For assessment of terrain elevation estimates from spaceborne LiDAR data we used high resolution airborne data. Our results indicate that both LiDAR missions provide accurate terrain elevation estimates across different land cover classes and forest types with mean error less than 1 m, except in tropical forests. However, using a GEDI algorithm with a lower signal end threshold (e.g., algorithm 5) can improve the accuracy of terrain elevation estimates for tropical upland forests. Specific environmental parameters (terrain slope, canopy height and canopy cover) and sensor parameters (GEDI degrade flags, terrain estimation algorithm; ICESat-2 number of terrain photons, terrain uncertainty) can be applied to improve the accuracy of ICESat-2 and GEDI-based terrain estimates. Although the goodness-of-fit statistics from the two spaceborne LiDARs are not directly comparable since they possess different footprint sizes (100 × 14 m segment or 20 × 14 m segment vs. 25 m circle), we observed similar trends on the impact of terrain slope, canopy cover and canopy height for both sensors. Terrain slope strongly impacts the accuracy of both ICESat-2 and GEDI terrain elevation estimates for both forested and non-forested areas. In the case of GEDI the impact of slope is, however, partly caused by horizontal geolocation error. Moreover, dense canopies (i.e., canopy cover higher than 90%) affect the accuracy of spaceborne LiDAR terrain estimates, while canopy height does not, when considering samples over flat terrains. Our analysis of the accuracy and precision of current versions of spaceborne LiDAR products for different vegetation types and environmental conditions provides insights on parameter selection and estimated uncertainty to inform users of these key global datasets.