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 R² = 0.681 (95% CI: 0.626–0.729) for DEF and R² = 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 R² = 0.342 (DEF) and 0.473 (DDF). Multitemporal phenological features and per-forest stratification jointly outperformed single-date baselines by 3.4× in DEF and 2.0× 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 ~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.

Per-tree geolocation in closed tropical canopies has typical uncertainties of 5–15 m with GNSS receivers, preventing automated linking of field inventories to point-cloud stem data. We propose an anchor-based matching framework that does not require per-tree GNSS. A handheld SLAM LiDAR scanner maps stems and girths within ≈40 min; field crews record species, girth, and serial numbers without physical markers or tools. Dataset linkage uses a small subset of reflective-tape anchor trees (35 and 43 per hectare, roughly one per 400–500 m²) with approximate GNSS locations. Species identity is transferred using median-based GNSS bias correction and quadrant-partitioned Hungarian matching with global deduplication; accuracy is validated by leave-one-anchor-out (LOAO) tests and exact binomial statistics. Tested in two 1-ha plots of open Dry Dipterocarp Forest (DDF; 280 trees/ha) and dense Dry Evergreen Forest (DEF; ~1054 trees/ha) at the Sakaerat Biosphere Reserve, Thailand, SLAM girth matched tape data with R² = 0.997, RMSE = 1.82 cm in DDF; in DEF, after correcting a 6.38 m GNSS bias, R² = 0.986 and RMSE = 7.01 cm, with ≥99% detection for stems ≥30 cm girth (99.2% DDF; 100% DEF). LOAO accuracy was 35/35 in DDF and 40/43 in DEF. Retroreflective-tape anchors were additionally detected automatically from the SLAM intensity channel in 71.4% of DDF anchors (95% CI 53.7–85.4) and 76.7% of DEF anchors (95% CI 61.4–88.2) at intensity ≥ 150 DN, with up to 59-fold enrichment over matched non-anchor controls at I ≥ 250 in DEF (Fisher’s exact p < 1 × 10⁻¹⁵), enabling a fully automated anchor-detection pipeline.

Exploiting the complementary views of ground-based and aerial laser scanning requires co-registering their point clouds, which is difficult in closed tropical forest where Global Navigation Satellite System (GNSS) reception fails. We co-registered handheld Simultaneous Localization and Mapping (SLAM) LiDAR and UAV LiDAR without per-tree GNSS, using girth-identity anchors inherited from a companion inventory, over an open dry dipterocarp forest (DDF) and a closed dry evergreen forest (DEF) at the SERS, northeastern Thailand. Coarse-to-fine registration reduced the residual to a mean absolute distance of 0.17–0.18 m, and the clouds sampled complementary strata-stems and understory to about 25–30 m, emergent canopy to about 37 m. The co-registered data give each tree a ground-measured stem diameter and an aerially measured height, which we used to test whether diameter can instead be inferred from the canopy. It cannot: both a height-based and a best-case crown-and-height model saturated near 50 cm while measured stems reached about 100 cm, so UAV-only individual-tree carbon did not track the field reference; supplying the ground-measured diameter restored agreement (R² = 0.91), validated against reflective-tape anchor trees. The horizontal offset between a stem base and its crown apex further explains why position-only stem-to-crown matching fails and why girth-identity linkage is needed. We conclude that credible individual-tree carbon in closed tropical forest requires ground measurement of stem diameter, made possible by GNSS-independent SLAM-UAV co-registration; the aerial platform contributes height and coverage, not diameter.

Dry dipterocarp forest (DDF) in mainland Southeast Asia burns frequently in surface fires, yet the vertical fuel strata observable from airborne and spaceborne sensors remain uncertain. We compared handheld SLAM LiDAR, UAV LiDAR, GEDI, ICESat-2, PlanetScope, and Sentinel-1/2 at Sakaerat, Thailand, using spatial block-bootstrap validation. SLAM and UAV LiDAR converged on the 1–2 m and 2–5 m strata (r = 0.53 for both, n = 212) but not below 0.5 m, identifying the 2–5 m ladder fuel fraction as the strongest cross-platform target. Optical models explained at most 32% of understory and 37% of ladder fuel variance, with no benefit from finer pixels or Sentinel-1. GEDI relative-height metrics failed below 2 m because the ground return was broad (mean FWHM 4.2 m) and widened with slope. Subtracting a fitted ground Gaussian recovered a reproducible 1–5 m waveform signal correlated with UAV ladder fuel in both 2025 (ρ = 0.47) and 2026 (ρ = 0.42), robust across detection thresholds and fitting windows. Only 22% of regional GEDI footprints supported two-mode canopy-base detection, while ICESat-2 showed the same height-ordered sensitivity. In this open DDF stand, remote sensing was therefore best suited to ladder fuel and vertical continuity; near-surface fuel requires ground-based observation.

Individual-tree segmentation of dense point clouds increasingly supplies the crown dimensions from which crown–stem allometry is fitted, yet no study has measured how much the fitted crown–stem exponent changes with what is done to material a segment holds but may not own. In two 1 ha plots spanning the open-to-closed canopy gradient of a Southeast Asian tropical dry forest, scanned with handheld SLAM LiDAR against a field stem map of 1320 trees, we voxelized each of 340 segments at 5 cm, split it into 26-connected components and computed crown projected area three times with identical code: on the delivered segment, after removing every component not physically continuous with the tree’s own bole (strict) and after removing only those components in which a field-mapped stem other than this tree falls inside the component’s own footprint (conservative). In the closed evergreen stand the strict treatment moved the exponent of crown area on stem diameter from 1.54 to 1.04 (paired-bootstrap difference −0.49, 95% CI −0.65 to −0.34). The conservative treatment moved it by −0.003 (−0.005 to −0.001), and its median removal was 0.000 of a segment’s points in both stands. Removal based on stem-footprint overlap therefore moves the exponent by two orders of magnitude less than removal of every disconnected component, a contrast that bounds the sensitivity without establishing the ownership of either group. The result holds with the crown base fixed at its delivered value, with the largest segments removed and on the segments whose crown base came from the primary detection rule (−0.372, −0.587 to −0.147). Its direction also holds across bole gates, crown-base thresholds, an alpha-shape crown area and connectivity voxels of 3 to 10 cm, although its magnitude scales with the voxel (−0.89 to −0.24). The crown–stem exponent obtained from segmented closed-canopy point clouds is therefore not determined by the point cloud alone, and we quantify how much of it the analyst decides.