Factors controlling peat soil thickness and carbon storage in temperate peatlands based on UAV high-resolution remote sensing

Abstract

• To understand peatland C dynamics we integrate multiple sensors. • We provide a multi-scale analysis of the underlying controls. • Peat depth is controlled by macrotopography, reflecting the importance of hydrology. • SOC is controlled by processes operating at finer microscales. • The current vegetation is a weak predictor for peat thickness and SOC stock. Peatlands store a large amount of carbon. However, peatlands are complex ecosystems, and acquiring reliable estimates of how much carbon is stored underneath the Earth’s surface is inherently challenging, even at small scales. Here, we aim to establish links between the above- and below-ground factors that control soil carbon status, identify the key environmental variables associated with carbon storage, as well as to explore the potential for using Unmanned Aerial Vehicle (UAV) remote sensing for spatial mapping of peatlands. We combine UAVs equipped with Red-Green-Blue (RGB), multispectral, thermal infrared, and light detection and ranging (LiDAR) sensors with ground-penetrating radar (GPR) technology and traditional field surveys to provide a comprehensive, 3-dimensional mapping of a peatland hillslope-floodplain landscape in the Belgian Hautes Fagnes. Our results indicate that both peat thickness and soil organic carbon (SOC) stock (top 1 m) are spatially heterogeneous and that the contributions from the surface topography to peat thickness and SOC stock varied from micro- to macro-scales. Peat thickness was more strongly controlled by macro-topography ( R 2 = 0.46) than SOC stock, which was more influenced by micro-topography ( R 2 = 0.21). Current vegetation had little predictive power for explaining their spatial variability. Additionally, the UAV data provided accurate estimates of both peat thickness and SOC stock, with RMSE and R 2 values of 0.16 m and 0.85 for the peat thickness, and 59.25 t/ha and 0.85 for the SOC stock. However, similar performance can already be achieved by using only topographical data from the LiDAR sensor (for peat thickness) and a combination of peat thickness and topography (for SOC stock) as predictor variables. Our study bridges the gap between surface observations and the hidden carbon reservoir below. This not only allows us to improve our ability to assess the spatial distribution of SOC stocks, but also contributes to our understanding of the environmental factors associated with SOC storage in these highly heterogeneous landscapes, providing insights for environmental science and climate projections.

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