Forest Biomass Estimation of Linpan in Western Sichuan Using Multi-Source Remote Sensing

Jiaming Lai, Yuxuan Lin, Yan Lu, Mingdi Yue and Gang Chen ()
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Jiaming Lai: College of Forestry, Sichuan Agricultural University, Chengdu 611130, China
Yuxuan Lin: College of Forestry, Sichuan Agricultural University, Chengdu 611130, China
Yan Lu: College of Forestry, Sichuan Agricultural University, Chengdu 611130, China
Mingdi Yue: Sichuan Forestry and Grassland Survey and Planning Institute (Sichuan Forestry and Grassland Ecological Environment Monitoring Center), Chengdu 610084, China
Gang Chen: College of Forestry, Sichuan Agricultural University, Chengdu 611130, China

Sustainability, 2025, vol. 17, issue 17, 1-18

Abstract: Linpan ecosystems, distinct to western Sichuan, China, are integral to regional biodiversity and carbon cycling. However, comprehensive biomass estimation for these systems has not been thoroughly investigated. This study seeks to fill this gap by enhancing the accuracy and precision of biomass estimation in these ecologically vital landscapes through the application of multi-source remote sensing techniques, specifically by integrating the strengths of optical and radar remote sensing data. The focus of this research is on the forest biomass of Linpan, encompassing the tree layer, which includes the trunk, branches, leaves, and underground roots. Specifically, the research focused on the Linpan ecosystems in the Wenjiang District of western Sichuan, utilizing an integration of Sentinel-1 SAR, Sentinel-2 multispectral, and GF-2 high-resolution data for multi-source remote sensing-based biomass estimation. Through the preprocessing of these data, Pearson correlation analysis was conducted to identify variables significantly correlated with the forest biomass as determined by field surveys. Ultimately, 19 key modeling factors were selected, including band information, vegetation indices, texture features, and phenological characteristics. Subsequently, three algorithms—multiple stepwise regression (MSR), support vector machine (SVM), and random forest (RF)—were employed to model biomass across mixed-type, deciduous broadleaved, evergreen broadleaved, and bamboo Linpan. The key findings include the following: (1) Sentinel-2 spectral data and Sentinel-1 VH backscatter coefficients during the summer, combined with vegetation indices and texture features, were critical predictors, while phenological indices exhibited unique correlations with biomass. (2) Biomass displayed a marked north–south gradient, characterized by higher values in the south and lower values in the north, with a mean value of 161.97 t ha −1 , driven by dominant tree species distribution and management intensity. (3) The RF model demonstrated optimal performance in mixed-type Linpan (R 2 = 0.768), whereas the SVM was more suitable for bamboo Linpan (R 2 = 0.892). The research suggests that integrating multi-source remote sensing data significantly enhances Linpan biomass estimation accuracy, offering a robust framework to improve estimation precision.

Keywords: Linpan in western Sichuan; biomass estimation; multi-source remote sensing data; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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