Abstract
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.