Phenology-Informed Multitemporal PlanetScope and UAV-LiDAR Fusion for Above-Ground Carbon Mapping in Tropical Dry Forests of Sakaerat Biosphere Reserve, Thailand

초록

Tropical dry forests of mainland Southeast Asia contain considerable above-ground carbon (AGC) but present challenges for precise satellite-based AGC quantification because seasonal leaf phenology alters canopy reflectance throughout the year. To address this, we propose a phenology-informed approach that fuses multitemporal satellite imagery with airborne LiDAR. Using 17 PlanetScope images acquired between February 2024 and April 2026 over the Sakaerat Biosphere Reserve, together with UAV-LiDAR data, we extracted 128 phenological features and 12 canopy metrics at 10, 20 and 30 m. Machine learning models (Random Forest, XGBoost and LightGBM) were trained separately for dry evergreen forest (DEF) and dry dipterocarp forest (DDF). Under random five-fold cross-validation at 30 m, the best Random Forest models yielded R-2 = 0.681 (95% CI: 0.626-0.729) for DEF and R-2 = 0.661 (95% CI: 0.615-0.705) for DDF, with RMSE of 11.85 and 7.40 Mg C ha(-1), respectively. Because the AGC reference labels are themselves back-calculated from LiDAR canopy height, these Combined values partly reflect allometric circularity between predictors and labels and should be read as an upper bound rather than an independent accuracy; the spectral-only PlanetScope models, which are free of this circularity, give a more conservative R-2 = 0.342 (DEF) and 0.473 (DDF). Multitemporal phenological features and per-forest stratification jointly outperformed single-date baselines by 3.4x in DEF and 2.0x in DDF. We produced a 30 m AGC map of the reserve (total = 0.217 Tg C) and a higher resolution 3 m layer comprising similar to 8.7 million pixels. The results demonstrate the value of phenology-informed features and forest-type stratification for accurate AGC mapping in seasonally dry tropical forests, marking a step forward for remote sensing carbon assessment in phenologically dynamic landscapes.

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