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Machine learning-based estimates of aboveground biomass of subalpine forests using Landsat 8 OLI and Sentinel-2B images in the Jiuzhaigou National Nature Reserve, Eastern Tibet Plateau

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Abstract

Accurate estimates of forest aboveground biomass (AGB) are critical for supporting strategies of ecosystem conservation and climate change mitigation. The Jiuzhaigou National Nature Reserve, located in Eastern Tibet Plateau, has rich forest resources on steep slopes and is very sensitive to climate change but plays an important role in the regulation of regional carbon cycles. However, an estimation of AGB of subalpine forests in the Nature Reserve has not been carried out and whether a global biomass model is available has not been determined. To provide this information, Landsat 8 OLI and Sentinel-2B data were combined to estimate subalpine forest AGB using linear regression, and two machine learning approaches–random forest and extreme gradient boosting, with 54 inventory plots. Regardless of forest type, Observed AGB of the Reserve varied from 61.7 to 475.1 Mg ha−1 with an average of 180.6 Mg ha−1. Results indicate that integrating the Landsat 8 OLI and Sentinel-2B imagery significantly improved model efficiency regardless of modelling approaches. The results highlight a potential way to improve the prediction of forest AGB in mountainous regions. Modelled AGB indicated a strong spatial variability. However, the modelled biomass varied greatly with global biomass products, indicating that global biomass products should be evaluated in regional AGB estimates and more field observations are required, particularly for areas with complex terrain to improve model accuracy.

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Authors and Affiliations

  1. College of Earth Science, Chengdu University of Technology, Chengdu, 610059, People’s Republic of China

    Ke Luo, Liang Liu, Xinrui Luo & Yuehong Shi

  2. State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, 610059, People’s Republic of China

    Yufeng Wei & Xiangjun Pei

  3. Jiuzhaigou Nature Reserve Administration, Aba Tibetan and Qiang Autonomous Prefecture, Jiuzhai, 623402, People’s Republic of China

    Jie Du

  4. College of Ecology and Environment, Chengdu University of Technology, Chengdu, 610059, People’s Republic of China

    Ningfei Lei, Jingji Li & Xiaolu Tang

  5. State Environmental Protection Key Laboratory of Synergetic Control and Joint Remediation for Soil & Water Pollution, Chengdu Univer-Sity of Technology, Chengdu, 610059, People’s Republic of China

    Ningfei Lei, Jingji Li & Xiaolu Tang

  6. China Railway, Eryuan Engineering Group Co. Ltd, Chengdu, 610031, People’s Republic of China

    Ci Song

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  1. Ke Luo
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Correspondence to Yufeng Wei.

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Project funding: This project was supported financially by the Specialized Fund for the Post-Disaster Reconstruction and Heritage Protec-tion in Sichuan Province (5132202019000128); the Everest Scientific Research Program of Chengdu University of Technology (80000-2021ZF11410); the Second Tibetan Plateau Scientific Expedition and Research Program (STEP, 2019QZKK0307); the State Key Laborato-ry of Geohazard Prevention and Geoenvironment Protection Independent Research Project (SKLGP2018Z004); the key technologies of Mountain rail transit green construction in ecologically sensitive region based on Mountain rail transit from Dujiangyan to Mt. Siguniang anti-poverty project (2018-zl-08); and, Study on risk identification and countermeasures of Sichuan-Tibet Railway Major Projects (2019YFG0460).

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Corresponding editor: Zhu Hong.

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Luo, K., Wei, Y., Du, J. et al. Machine learning-based estimates of aboveground biomass of subalpine forests using Landsat 8 OLI and Sentinel-2B images in the Jiuzhaigou National Nature Reserve, Eastern Tibet Plateau. J. For. Res. 33, 1329–1340 (2022). https://doi.org/10.1007/s11676-021-01421-w

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