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

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.

Carbon storage estimation of mountain forests based on deep learning and multisource remote sensing data

“…In 2021, Lefsky et al 19 conducted a comprehensive study and discussion on the aboveground biomass and carbon storage of nine forest types in Pakistan and determined that coniferous forests have a better ability to sequester carbon than other forest types. In addition, Sun et al 20 and Jie Zhang et al 21 performed similar research on forest measurement parameters at the regional scale. However, new modeling technologies and methods such as deep learning models are still lacking in the forest biomass inversion or carbon storage at the regional scale using ATLAS and optical image data, and the estimation accuracies of these models need to be further improved.…”

“…conducted a comprehensive study and discussion on the aboveground biomass and carbon storage of nine forest types in Pakistan and determined that coniferous forests have a better ability to sequester carbon than other forest types. In addition, Sun et al 20 . and Jie Zhang et al 21 .…”

Comparison of QRNN and QRF Models in Forest Biomass Estimation Based on the Screening of VIs Using an Equidistant Quantile Method

“…The field investigation of forest biomass is time-consuming and labor-intensive, and it is limited to estimating the biomass in a small region [5]. With the rapid development of remote sensing, the utilization of remote sensing data can expedite and streamline the acquisition of parameter information for forest AGB estimation [6]. Various remote sensing methods have been employed to estimate forest AGB, including optical methods, radar, and LiDAR [2,7].…”

Remote sensing data upon application of methodology for defining the adjoining lands and flooding river stripes in Bulgaria

“…25 Miller et al (2020) 26 estimated above-ground biomass of two salt marshes using a linear regression model that included both the modified soil vegetation index (MSAVI2) and the visible difference vegetation index (VDVI). 27 According to Sobrino & Raissouni (2000) 28 NDVI could give information about vegetation mixtures, however it is often confused with low vegetated areas 23 .…”