A comprehensive approach to monitoring peatland changes using multi-temporal airborne LiDAR data

• Semi‑automated LiDAR workflow enables fuller, more accurate monitoring of peatland changes. • LiDAR data processing accurately delineates peatland boundaries and extent at local to regional scales. • Method improves boundary accuracy and detects peatlands as small as 0.05 ha. • Applying the method identified nearly 300 peatbogs (∼1,840 ha), tripling previously recorded peatland area. • LiDAR analysis quantified peat surface subsidence (avg ∼ 6 cm yr - 1 ) and accretion changes over a decade (2011/12-2022) Peatlands are unique and important ecosystems with a particular biodiversity and play key roles in the carbon balance and regulation of the water cycle. The observed climate changes, as well as anthropogenic drainage, are degrading peatlands and leading to peat mineralisation. These changes need to be monitored with spatially detailed, repeatable observations. Light Detection And Ranging (LiDAR) fulfils this need by providing centimetre‑accurate measurements of ground‑surface elevation and vegetation height; when collected at multiple dates it reveals their temporal dynamics.. In our study we investigate the possibility of determining changes in the height of peatland surfaces levels based on LiDAR data in the selected areas located in the northern and southern part of the Tuchola Pinewoods (Poland). We developed a semi‑automated change‑detection workflow that converts ALS point clouds to 1‑m rasters, normalises vertical datums, and combines grid‑difference analysis to delineate peatland boundaries and quantify surface‑elevation change. The analysis showed that the number of identified peatlands increased from ∼ 120 to nearly 300 objects with a total area of 1,839.96 ha. The average rate of peat subsidence in the analysed sites in the period 2011/12–2022 was about 6 cm yr −1 . Our method systematises multi‑temporal LiDAR processing and delivers key ecohydrological indicators LiDAR-derived surface elevation, peatland extent and vegetation structure, thereby diagnosing the environmental condition of peatlands. Data analysis allows us to track changes in time and space, which is important in estimating the balance of greenhouse gas emissions, especially from degraded peatland areas.