Estimation of aboveground biomass of natural secondary forests based on optical-ALS variable combination and non-parametric models

ZHAO Yinghui, GUO Xinlong, ZHEN Zhen

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2021, Vol. 45 ›› Issue (4) : 49-57.

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南京林业大学学报（自然科学版）
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JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2021, Vol. 45 ›› Issue (4) : 49-57. DOI: 10.12302/j.issn.1000-2006.202010004

Estimation of aboveground biomass of natural secondary forests based on optical-ALS variable combination and non-parametric models

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Abstract

【Objective】 We explored feature variables extracted through a combination of airborne laser scanning (ALS) and Sentinel-2A data, and investigated with the aim of identifying the best variable combination mode and estimation method for estimating the forest aboveground biomass (AGB) of natural secondary forests. 【Method】 Based on ALS data from 2015, Sentinel-2A data, and the fixed sample plots of the continuous inventory of secondary forest resources from Maoershan Forest Farm in 2016, this study extracted height features from ALS data (all the LiDAR variables, AL), several vegetation indices from Sentinel-2A (all the optical variables, AO), and then combined the two kinds of variables into new variables (COLI1 and COLI2). Finally, five models were constructed, including the stepwise multiple linear regression (SMLR), K-nearest neighbor (K-NN), support vector regression (SVR), random forest (RF), and stack sparse auto-encoder (SSAE) of AGB for natural secondary forests using six feature combinations (AO+AL, COLI1, COLI2, COLI1+AO+AL, COLI2+AO+AL and COLI1+ COLI2+AO+AL). The influence of the COLIs variable, and that of different models, on the accuracy of the AGB was investigated. 【Result】The COLIs variable could efficiently improve the accuracy of AGB estimates and, compared to the other four models, SSAE had the highest accuracy regardless of the variables. The SSAE model with the combination of optical and ALS features (COLI1+ COLI2+AO+AL) had the best model performance of R2 which was 0.83, RMSE was 11.06 t/hm2, rRMSE was 8.23%. 【Conclusion】 The combined variable COLIs can effectively improve the estimation accuracy of natural secondary forest AGB, and one way of the deep learning model (SSAE) is superior to the other prediction models in estimating the AGB of a natural secondary forest. This conclusion will help to further apply the deep learning model to draw a large-area AGB spatial distribution map and estimate the other forest parameters. In general, the SSAE model with the combination of ALS and Sentinel-2A data could estimate AGB more accurately than the other models. This finding provides a technical support for AGB estimation and carbon evaluation of natural secondary forests.

Key words

airborne laser radar (ALS) / Sentinel-2A / combined optical and LiDNR index (COLIs) / stack sparse auto-encoder (SSAE) / natural secondary forest / aboveground biomass

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ZHAO Yinghui , GUO Xinlong , ZHEN Zhen. Estimation of aboveground biomass of natural secondary forests based on optical-ALS variable combination and non-parametric models[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2021, 45(4): 49-57 https://doi.org/10.12302/j.issn.1000-2006.202010004

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Abstract
快速、定量、精确地估算区域森林生物量一直是森林生态功能评价以及碳储量研究的重要问题。该研究基于机载激光雷达(LiDAR)点云与Landsat 8 OLI多光谱数据, 借助江苏省常熟市虞山地区55块调查样地数据, 首先提取并分析了87个特征变量(53个OLI特征变量, 34个LiDAR特征变量)与森林地上、地下生物量的Pearson&#x02019;s相关系数以进行变量优选, 然后利用多元逐步回归法建立森林生物量估算模型(OLI生物量估算模型和LiDAR生物量估算模型), 并与基于两种数据建立的综合生物量估算模型的结果进行比较, 讨论预测结果及其精确性。结果表明: 3种模型(OLI模型、LiDAR模型和综合模型)在所有样地无区分分析时, 地上和地下生物量的估算精度均达到0.4以上, 基于不同森林类型(针叶林、阔叶林、混交林)分析时地上和地下生物量的估算精度均有明显提高, 达到0.67及以上。利用分森林类型模型估算生物量, 综合生物量估算模型精度(地上生物量: R<sup>2</sup>为0.88; 地下生物量: R<sup>2</sup>为0.92)优于OLI生物量估算模型(地上生物量: R<sup>2</sup>为0.73; 地下生物量: R<sup>2</sup>为0.81)和LiDAR生物量估算模型(地上生物量: R<sup>2</sup>为0.86; 地下生物量: R<sup>2</sup>为0.83)。
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