Hyperspectral and Full-Waveform LiDAR Improve Mapping of Tropical Dry Forest’s Successional Stages

Accurate estimation of the degree regeneration in tropical dry forest (TDF) is critical for conservation policymaking and evaluation. Hyperspectral remote sensing light detection ranging (LiDAR) have been used to characterize deterministic successional stages a TDF. These stages, classified as early, intermediate, late, are considered proxy mapping age since abandonment given area. Expanding on need more accurate mapping, our study considers attributes TDF area continuous expression relative attribute scores/levels that vary along process ecological succession. Specifically, two remote-sensing data sets: HyMap (hyperspectral) LVIS (waveform LiDAR), were acquired at Santa Rosa National Park Environmental Monitoring Super Site (SRNP-EMSS) Costa Rica, generate age-attribute metrics. metrics then entry-level variables randomized nonlinear archetypal analysis (RNAA) model select most informative from both sets. Next, learning (RAL) algorithm was adapted independent fused comparatively learn levels ages In this study, four indices five found potential map area, compared with these results, significant improvement through fusion accuracy generated maps. By linking group dynamic gradient transition patterns emerged.