Cross-Platform Mapping of Aboveground Carbon Storage Using MLS, UAV LiDAR, GEDI, and Sentinel-2 in a Romanian Mountain Forest

Abstract

Forest landscapes used for recreation also provide climate-regulating services that are often absent from spatial planning. We mapped aboveground biomass density (AGBD) and aboveground carbon storage in the Postavaru-Piatra Mare mountain landscape near Brașov, Romania, using a cross-platform remote-sensing workflow. MLS point clouds from 16 plots served as an independent plot-level reference, UAV LiDAR acquired at 70, 120, and 150 m provided airborne comparisons, and GEDI L4A samples, Sentinel-2 predictors, and terrain variables were modelled in Google Earth Engine using Random Forest (RF) and Gradient Boosted Trees (GBT). GBT achieved R2 = 0.34 and RMSE = 106.42 Mg ha−1, whereas RF achieved R2 = 0.33 and RMSE = 107.45 Mg ha−1. These values indicate limited explanatory power; accordingly, the resulting 25 m maps are interpreted as landscape-scale screening products rather than operational stand inventories. Against MLS, RF produced an RMSE of 79.05 Mg ha−1 and GBT 82.58 Mg ha−1, while UAV estimates were positively biased at all tested flight heights. Mean carbon density peaked at 1200–1400 m and was highest in coniferous forest, whereas broadleaved and tree-cover classes contributed the largest total stock because of their larger area. The RF and GBT estimates differed by 2.3% in aggregated study-area carbon stock. Aboveground carbon storage was the only ecosystem service quantified. The resulting maps should be interpreted as landscape-scale screening layers and as ecological inputs for future studies integrating trail, visitor-use, participatory-mapping or perception data; they do not quantify cultural ecosystem services.