Developing accurate methods to map vegetation structure in tropical forests is essential to protect their biodiversity and improve their carbon stock estimation. We integrated LIDAR (Light Detection and Ranging), multispectral and SAR (Synthetic Aperture Radar) data to improve the prediction and mapping of canopy height (CH) at high spatial resolution (30 m) in tropical forests in South America. We modeled and mapped CH estimated from aircraft LiDAR surveys as a ground reference, using annual metrics derived from multispectral and SAR satellite imagery in a dry forest, a moist forest, and a rainforest of tropical South America. We examined the effect of the three forest types, five regression algorithms, and three predictor groups on the modelling and mapping of CH. Our CH models reached errors ranging from 1.2-3.4 m in the dry forest and 5.1-7.4 m in the rainforest and explained variances from 94-60% in the dry forest and 58-12% in the rainforest. Our best models show higher accuracies than previous works in tropical forests. The average accuracy of the five regression algorithms decreased from dry forests (2.6 m +/- 0.7) to moist (5.7 m +/- 0.4) and rainforests (6.6 m +/- 0.7). Random Forest regressions produced the most accurate models in the three forest types (1.2 m +/- 0.05 in the dry, 4.9 m +/- 0.14 in the moist, and 5.5 m +/- 0.3 the rainforest). Model performance varied considerably across the three predictor groups. Our results are useful for CH spatial prediction when GEDI (Global Ecosystem Dynamics Investigation lidar) data become available.
- ALOS-PALSAR (Phased Array type L-band Synthetic Aperture Radar)
- Dry tropical forest
- Learning algorithms
- Moist tropical forest
- Spatial modelling
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)