Coupling multi-sensory earth observation datasets, in-situ measurements, and machine learning algorithms for total blue C stock estimation of an estuarine mangrove forest

Abstract

In recent years, research on blue carbon (C) has garnered substantial attention worldwide. Nevertheless, we observed a lack of holistic approach, in terms of measurement of total blue C (TBC) potentials. This study focuses on developing a novel approach toward blue C accounting by spatially explicit modelling and estimation of TBC stock in a mangrove wetland of eastern India. A hybrid methodology has been adopted incorporating destructive and non-destructive sampling, allometric and predictive modelling, laboratory-based elemental analysis, and multi-sensory remote sensing (RS) based datasets. Predicted TBC density has been mapped within the wetland influence zone (WIZ) of the study site. Point-specific sample data (n = 250) has been used for the determination of the soil organic C (SOC) prediction model. Spline interpolation, displaying highest R2 value (R2 = 0.74) has been chosen for spatially explicit modelling of total SOC stock. Above ground biomass (AGB) was determined using the relationship between remotely sensed data (ALOS PALSAR-2 and Pleiades-1B) and in-situ dendrometric variables (viz. wood density, tree height, and girth at breast height). Here, among the different parametric and nonparametric models to estimate AGB, the BP-ANN models, specifically model number 22 (adjusted R2 = 0.84, MSE = 1.28, AIC = 3.67, BIC = 1.60), has been identified as the best-fit one with higher adjusted R2 and lesser AIC and BIC values. Indirect allometric equations involving modelled AGB values had been used to generate spatially explicit community-specific below ground biomass values at per pixel basis (∼2 m). Above and below ground C were estimated from these raster data. Integrating all these datasets in a GIS platform, the overall TBC stock of the mangrove was recorded at 246710.91 Mg. The TBC density of mangrove WIZ had revealed considerable variations, ranging from 0.34 Mg ha−1 to 881.50 Mg ha−1. Cumulatively, the study attempted to amalgamate all facets of blue C pools with satisfactory accuracy. This holistic methodology may further aid in regional C stock inventorization, management, and policy formulation, thereby strengthening the socio-economic resilience of coastal communities through carbon trading, reduce emissions from ecosystem degradation as well as support ongoing conservation efforts.