Science of Remote Sensing (Dec 2023)
Computational tools for assessing forest recovery with GEDI shots and forest change maps
Abstract
Tropical secondary forests are ecosystems of critical importance for protecting biodiversity, buffering primary forest loss, and sequestering atmospheric carbon. Monitoring growth and carbon sequestration in secondary forests is difficult, with inventory plots sampling <0.001% of secondary forests. The Global Ecosystem Dynamics Investigation (GEDI), a space-borne LiDAR sampler, provides billions of aboveground carbon density (ACD) estimates across the tropics. We fuse these carbon density estimates with a time series of forest change maps to identify their age since last deforestation and thus estimate the average rate of carbon sequestration in secondary forests across the Amazon biome. To our knowledge, this is the first estimate of these rates made using the new GEDI dataset. Moreover, this paper addresses key statistical and computational challenges of GEDI data fusion and analysis. We propagate both GEDI ACD and geolocation uncertainty to the regrowth rate estimate through a Monte Carlo approach, and we handle heteroskedasticity, outliers, and spatial autocorrelation using robust statistical methods. The large size of the GEDI dataset combined with the proposed Monte Carlo bootstrap can be highly computationally intensive, with a naive implementation taking over a month to complete. Nevertheless, we demonstrate the feasibility of our method by developing optimized open-source code that performs this computation on the 151 million quality-filtered GEDI shots available for the Amazon biome from April 2019–August 2021 in under 25 min in benchmark tests. By resolving these statistical and computational challenges with an efficient open-source pipeline, we create a standard approach that can be used more broadly in any work seeking to combine the GEDI dataset with high-resolution classification maps. Using this approach, we identify approximately 23, 000 GEDI samples of regrowing forest at least 60 m × 60 m wide across the Amazon biome and estimate a carbon sequestration rate of 1.86 MgC/ha/yr with a 95% empirical confidence interval of 1.75–1.97 MgC/ha/yr, with rates varying from 1.27 to 1.99 MgC/ha/yr across smaller subregions.
