Exploring carbon sequestration capacities of coastal wetland plants based on multi-parameter airborne remote sensing

doi:10.6046/zrzyyg.2024072

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Abstract

This study investigated the coastal wetland of Jiangsu Province. Using methods such as satellite remote sensing and airborne multi-parameter remote sensing, this study estimated the biomass of dominant plants and estimated their carbon sequestration capacities. Based on fine-scale classification of surface features achieved using airborne hyperspectral data, this study extracted 11 land cover types. The vegetation cover was approximately 76%, and zones with human activities accounted for about 1.5% of the study area. The model for vegetation biomass inversion using the multi-parameter airborne remote sensing demonstrated higher accuracy than that based on satellite remote sensing, with a coefficient of determination (R²) greater than 0.8 and a root mean square error (RMSE) of 0.25. As calculated using the multi-parameter airborne remote sensing, Spartina alterniflora and reed within the study area exhibited aboveground carbon sequestration capacities of 0.41 kg/m² and 0.58 kg/m², respectively. This study demonstrates that the multi-parameter airborne remote sensing method can accurately determine vegetation types in wetlands and carbon sequestration capacity, thus providing crucial assessment parameters for research on the carbon cycle of the ecosystem and the current status of habitats within wetlands and precisely serving wetland resource surveys.

Keywords airborne hyperspectral remote sensing airborne multi-parameter remote sensing carbon sequestration capacity aboveground biomass remote sensing

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Issue Date: 03 September 2025

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	Guofeng ZHAO
	Yanqi FANG
	Haofeng CHEN
	Weibing YAN
	Yan HUANG
	Wei CHEN

Cite this article:

Guofeng ZHAO,Yanqi FANG,Haofeng CHEN, et al. Exploring carbon sequestration capacities of coastal wetland plants based on multi-parameter airborne remote sensing[J]. Remote Sensing for Natural Resources, 2025, 37(4): 88-98.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024072 OR https://www.gtzyyg.com/EN/Y2025/V37/I4/88

Fig.1 Sketch map of the study area

Fig. 2 Composition diagram of MARSS

Fig.3 Workflow chart

Fig.4 Classification results of land cover types

Fig.5 Correlation coefficients of biomass between Spartina alterniflora and reed at different wavelength values

序号	植被指数	公式
X₁	归一化植被指数 NDVI	NDVI=(R₈₀₀-R₆₇₀)/(R₈₀₀+R₆₇₀)
X₂	改进叶绿素吸收指数 MCARI	MCARI=[(R₇₀₀-R₆₇₀)-0.2(R₇₀₀-R₅₀₀)](R₇₀₀/R₆₇₀)
X₃	转换叶绿素吸收指数 TCARI	TCARI=3[(R₇₀₀-R₆₇₀)-0.2(R₇₀₀-R₅₀₀)](R₇₀₀/R₆₇₀)
X₄	改进型土壤调整植被指数 MSAVI	MSAVI=0.5[2R₈₀₀+1- $(2 R 800 + 1) 2 - 8 (R 800 - R 670)$ ]
X₅	增强型植被指数 EVI	EVI=2.5(R₈₀₀-R₆₇₀)/(R₈₇₀+6R₆₇₀-7.5R₄₄₀+1)
X₆	土壤调整植被指数 SAVI	SAVI=1.5(R₈₀₀-R₆₇₀)/(R₈₀₀-R₆₇₀+0.5)
X₇	优化型土壤调整植被指数 OSAVI	OSAVI=(1+0.16)(R₈₀₀-R₆₇₀)/(R₈₀₀-R₆₇₀+0.16)
X₈	改进三角植被指数2 MTVI2	MTVI2=1.5[1.2(R₈₀₀-R₅₅₀)-2.5(R₆₇₀-R₅₅₀)]/ $(2 R 800 + 1) 2 - (6 R 800 - 5 R 670) - 0.5$
X₉	简单比值植被指数 SR	SR=R₈₀₀/R₆₈₀
X₁₀	陆地叶绿素指数 MTCI	MTCI=(R₇₅₀-R₇₁₀)/(R₇₁₀-R₆₈₀)
X₁₁	TCARI/OSAVI	—
X₁₂	红边归一化植被指数 NDVI₇₅₀	NDVI₇₅₀=(R₇₅₀-R₇₀₅)/(R₇₅₀+R₇₀₅)
X₁₃	改进红边比值植被指数 mSR₇₅₀	mSR₇₅₀=(R₇₅₀-R₄₄₅)/(R₇₀₅-R₄₄₅)
X₁₄	改进红边归一化植被指数 mNDVI₇₅₀	mNDVI₇₅₀=(R₇₅₀-R₇₀₅)/(R₇₅₀+R₇₀₅-2R₄₄₅)
X₁₅	红边指数 VOGI	VOGI=R₇₄₀/R₇₂₀
X₁₆	结构不敏感指数 SIPI	SIPI=(R₈₀₀-R₄₅₀)/(R₈₀₀+R₆₈₀)
X₁₇	生理反射植被指数 PRI	PRI=(R₅₇₀-R₅₃₁)/(R₅₇₀+R₅₃₁)
X₁₈	改进叶绿素吸收指数 MCARI2	MCARI2=1.5[2.5(R₈₀₀-R₆₇₀)-1.3(R₈₀₀-R₅₅₀)]/ $[(2 R 800 + 1) 2 - (6 R 800 - 5 R 670)] - 0.5$

Tab.1 Formula for calculating vegetation index

Tab.2 Accuracy evaluation of biomass model results for Spartina alterniflora

Fig.6 Scatter plot of the measured and predicted values of Spartina alterniflora models

Tab.3 Accuracy evaluation of biomass model results for reed

Fig.7 Scatter plot of the measured and predicted values of reed models

Fig.8 Results of aboveground biomass inversion by airborne multi-parameter remote sensing

Fig.9 Results of aboveground biomass inversion by satellite remote sensing

Fig.10 Scatter plot of inversion results by airborne multi-parameter remote sensing and satellite remote sensing

Fig.11 Statistical graph of aboveground biomass inversion results by two methods

Fig.12 Statistical chart of net carbon sequestration capacity inversion results

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