Forest stock volume inversion based on ICESat-2 and Sentinel-2A data

doi:10.6046/zrzyyg.2022478

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Abstract

Forest stock volume (FSV), a critical indicator in forestry surveys, plays a significant role in evaluating the health and carbon sequestration capacity of forests. Cooperative inversion using active and passive remote sensing data is an essential method for FSV inversion of large areas. Focusing on forests in Shangri-La, Yunnan Province, this study extracted feature variables from ICESat-2/ATLAS and Sentinel-2A images and then screened them through correlation analysis and collinearity diagnostics. Using the selected feature variables, this study constructed a Sentinel-2A variable set, an ICESat-2/ATLAS variable set, and a combined variable set. Based on the measured data of sample sites and the three feature variable sets, this study built linear and nonlinear regression models for FSV inversion using stepwise linear regression and the random forest method, respectively. Finally, this study performed accuracy verification and comparative analysis of the results: ① For the three variable sets, the random forest method yielded higher accuracy than the stepwise linear regression; ② The ICESat-2/ATLAS variable set exhibited higher inversion accuracy than the Sentinel-2A variable set under both regression methods; ③ Combining Sentinel-2A and ICESat-2/ATLAS variable sets, the random forest method yielded the highest inversion accuracy, with its coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (rRMSE) of 0.7034, 84.78 m³/hm², and 36.46%, respectively. Overall, compared to Sentinel-2A data, the inversion models based on ICESat-2/ATLAS data and multi-source remote sensing data can effectively improve the accuracy of FSV inversion and model stability.

Keywords forest stock volume feature variable random forest multiple regression ICESat-2/ATLAS Sentinel-2A

ZTFLH:	S758.57
	TP79

Issue Date: 13 March 2024

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	Meiyan LIU
	Sheng NIE
	Cheng WANG
	Xiaohuan XI
	Feng CHENG
	Baokun FENG

Cite this article:

Meiyan LIU,Sheng NIE,Cheng WANG, et al. Forest stock volume inversion based on ICESat-2 and Sentinel-2A data[J]. Remote Sensing for Natural Resources, 2024, 36(1): 210-216.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022478 OR https://www.gtzyyg.com/EN/Y2024/V36/I1/210

Fig.1 Distribution of satellite data and forest stock volume in the study area

Tab.1 Statistical information of forest stock volume

Tab.2 Data product parameters of ICESat-2/ATL08

名称	公式^①	名称	公式
归一化植被指数NDVI	$N D V I = N I R - R N I R + R$	增强型植被指数EVI	$E V I = 2.5 (N I R - R) N I R + 6 R - 7 B + 1$
比值植被指数RVI	$R V I = N I R R$	可见光抗大气指数VARI	$V A R I = G - R G + R + B$
差值植被指数DVI	$D V I = N I R - R$	大气抗阻植被指数ARVI	$A R V I = N I R - 2 (R - B) N I R + 2 (R - B)$
红绿植被指数RGVI	$R G V I = R - G R + G$	土壤修正植被指数SAVI	$S A V I = (1 + L) (N I R - R) (N I R - R + L)$ L=0.1,0.25,0.5

Tab.3 Vegetation index

Tab.4 Texture feature parameter

Tab.5 Stepwise linear regression model

Fig.2 Importance score

Tab.6 Precision comparison

Fig.3 Prediction fitting diagram of linear stepwise regression and RF model

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