ESTIMATING FOREST ATTRIBUTES IN INDUSTRIAL Pinus taeda L. FOREST PLANTATIONS IN BRAZIL USING SIMULATED NASA’S GEDI SPACEBORNE LIDAR DATA

Remote sensing technologies can dramatically increase the efficiency of plantation management by reducing or replacing time-consuming field sampling. In this study, we evaluated the capability of the NASA’s Global Ecosystem Dynamic Investigation (GEDI) spaceborne lidar system for estimating forest attributes at footprint level in industrial Pinus teada L. forest plantations in Southern Brazil. In the field, 100 field plots were measured and top canopy height (HMAX; m) and timber volume (V; m3/ha) were computed. GEDI-derived metrics were simulated using airborne lidar (ALS) data. We used multiple linear regression for modeling HMAX and V from GEDI-like metrics, and we found that models defined as a function of only three GEDI-like metrics (RH98: canopy height at 98 percentiles of energy, COV: canopy cover; FHD: foliage height diversity) had a very strong and unbiased predictive power. The promising results presented herein show that GEDI, during its lifetime time of two years, may provide an appropriate technology to assist forest managers towards more cost-effective and efficient forest inventory in industrial pine forest plantations.