Monitoring Aboveground Biomass in Forest Conservation and Restoration Areas Using GEDI and Optical Data Fusion

Forests play a critical role in the global carbon cycle by sequestering carbon in the form of aboveground biomass. Area-based conservation measures, such as protected areas (PAs), are a cornerstone conservation strategy for preserving some of the world's most at-risk forest ecosystems. Beyond PAs, tree planting and forest restoration have been lauded as solutions to combat climate change and criticized as ways for polluters to offset carbon emissions. Consistent monitoring and quantification of forest restoration can impact decisions on future restoration activities. In this dissertation, I utilized a fusion of remote sensing assets and a combination of remote sensing with impact assessment techniques, to obtain objective baseline information for reconstructing past forest biomass conditions, and for monitoring and quantifying the patterns and success of forest regrowth in areas that underwent different forest management interventions. This overarching research goal is approached in three studies corresponding to chapters 2-4. In chapter 2, PAs’ effectiveness in storing biomass carbon and preserving forest structure is assessed on a regional scale using Global Ecosystem Dynamics Investigation (GEDI) lidar data in combination with a counterfactual analysis using statistical matching. This chapter provides an assessment of the reference condition of the biomass carbon storage capacity by one of the most stringent forest management means. The study finds that analyzed PAs in Tanzania possess 24.4% higher biomass densities than their unprotected counterparts and highlights that community-governed PAs are the most effective category of PAs at preserving forest structure and aboveground biomass density (AGBD). In chapter 3, empirical models are developed to link current (2019-2020) AGBD estimates from the GEDI with Landsat (2007-2019) at a regional scale. This will allow both current wall-to-wall biomass mapping and estimation of biomass dynamics across time. We demonstrate the utility of the method by applying it to quantify the AGBD dynamics associated with forest degradation for charcoal production. In chapter 4, the same modeling framework laid out in chapter 3 will be used to derive AGBD trajectories for 27 forest restoration sites across three biomes in East Africa. To assess the effectiveness of and compare Assisted Natural Regeneration (ANR) and Active Restoration (AR) in enhancing forest AGBD growth compared to natural regeneration (NR), we used staggered difference-in-difference (staggered DiD) to analyze the average annual AGBD change. We controlled for pre-intervention AGBD change rate between AR/ANR and NR and estimated the effectiveness with explicit consideration of intervention duration. This study finds that AR and ANR outperform NR during long-term restoration. Using the most suitable restoration interventions in each biome and timeframe, 4% suitable areas could enhance 2.40 ± 0.78 Gt (billion metric tons) forest carbon uptake over 30 years, equivalent to 3.6 years of African-wide emissions. Overall, this dissertation develops remote sensing methodological frameworks for using GEDI data and its fusion with Landsat time series to quantify and monitor forest AGBD. Moreover, by combining remote sensing-derived AGBD dynamics with impact assessment techniques, such as statistical matching and staggered DiD, the dissertation further assesses and compares different conservation and restoration means’ effectiveness in increasing AGBD and carbon uptake in forests. The dissertation therefore advances the applications of state-of-the-art remote sensing data and techniques for sustainably managing forests towards climate mitigation targets.