Modelling Aboveground Biomass Carbon Stock of the Bohai Rim Coastal Wetlands by Integrating Remote Sensing, Terrain, and Climate Data

Shaobo Sun,Xiangbin Ran,Qiang Li,Zhaoliang Song,Wei Chen,Yonggen Zhang,Yidong Wang,Xi Chen,Chu Chen,Yafei Wang,Wenping Yuan,Lele Wu,Xiuchen Wu

doi:10.3390/rs13214321

Abstract

Remotely sensed vegetation indices (VIs) have been widely used to estimate the aboveground biomass (AGB) carbon stock of coastal wetlands by establishing Vis-related linear models. However, these models always have high uncertainties due to the large spatial variation and fragmentation of coastal wetlands. In this paper, an efficient coastal wetland AGB model for the Bohami Rim coastal wetlands was presented based on multiple data sets. The model was developed statistically with 7 independent variables from 23 metrics derived from remote sensing, topography, and climate data. Compared to previous models, it had better performance, with a root mean square error and r value of 188.32 g m−2 and 0.74, respectively. Using the model, we firstly generated a regional coastal wetland AGB map with a 10 m spatial resolution. Based on the AGB map, the AGB carbon stock of the Bohai Rim coastal wetland was 2.11 Tg C in 2019. The study demonstrated that integrating emerging high spatial resolution multi-remote sensing data and several auxiliary metrics can effectively improve VIs-based coastal wetland AGB models. Such models with emerging freely available data sets will allow for the rapid monitoring and better understanding of the special role that “blue carbon” plays in global carbon cycle.

Highlights

Coastal wetlands, including salt marshes, tidal marshes, estuaries, mangroves, and seagrasses, demonstrate higher atmospheric CO2 sequestration rates and contribute significantly to the mitigation of climate warming when compared to most other terrestrial ecosystems despite their small global coverage [1]
The results showed that 9 variables including enhanced vegetation index (EVI), normalized difference salinity index (NDSI), polarization index (POL), blue NDVI (BNDVI), vertical transmit-vertical channel backscattering (VV), B8, digital elevation model (DEM), topographic position index (TPI), and mean annual precipitation (MAP) had small collinearity effects for fitting a statistical model and had variance inflation factors (VIFs) values that were less than 4
The BNDVI was

Summary

Introduction

Coastal wetlands, including salt marshes, tidal marshes, estuaries, mangroves, and seagrasses, demonstrate higher atmospheric CO2 sequestration rates and contribute significantly to the mitigation of climate warming when compared to most other terrestrial ecosystems despite their small global coverage [1]. Carbon storage in these coastal wetlands is referred to as “coastal wetland blue carbon” [2], which is accumulated in biomass on a Remote Sens. The natural coastal wetlands were reduced by more than 53% in China during the period 1957–2017 [5,6] These reductions in coastal wetlands largely decrease the blue carbon sink through reducing aboveground biomass (AGB). To evaluate carbon sequestration and storage in coastal wetlands and to mitigate the reduction in blue carbon sink, it is essential to quantify the AGB of coastal wetlands at a large spatial scale [10]

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