比较三种激光扫描技术估算热带干旱林生物量，提升碳汇监测精度。
Estimation of aboveground biomass in a tropical dry forest: An intercomparison of airborne, unmanned, and space laser scanning
- 融合多种激光扫描数据与机器学习模型，优化生物量估算方法。
- 六项树高相关变量和叶面积指数等为关键影响因素，误差最低17.07。
- 适用于需要高精度碳储量评估的森林监测与气候应对研究。
根据巴黎气候协定要求，各国需每两年提交一次温室气体排放与吸收报告，至2024年实施。森林在减少碳排放中起关键作用，而第5条强调高质量森林数据的重要性。本研究聚焦热带干旱森林地上生物量（AGB）的估算方法改进。该类森林是热带生态系统中最少被理解的环境之一，亟需精准的碳库估算手段。我们对比分析了机载（ALS）、无人机（ULS）和星载（SLSF）激光扫描数据，结合普通最小二乘法与贝叶斯支持向量机（SVM）模型，提取森林结构指标作为输入变量。通过变量选择、模型调参与交叉验证，有效防止过拟合与欠拟合。结果显示，六项与树高相关的变量——Elevminimum、ElevL3、levMADmode、Elevmode、ElevMADmedian 和 Elevskewness，在ALS和ULS数据中对AGB估算尤为重要；而叶面积指数、冠层覆盖与高度、地形高程及全波形信号能量在空间激光扫描中表现突出。基于哥斯达黎加瓜纳卡斯特省10个永久样地的数据，生物量范围为26.02至175.43 Mg/ha。所有扫描系统下SVM回归平均误差为17.89，其中SLSF W系统误差最低，仅为17.07，表现出最优性能。
原文摘要 · Abstract (English)
According to the Paris Climate Change Agreement, all nations are required to submit reports on their greenhouse gas emissions and absorption every two years by 2024. Consequently, forests play a crucial role in reducing carbon emissions, which is essential for meeting these obligations. Recognizing the significance of forest conservation in the global battle against climate change, Article 5 of the Paris Agreement emphasizes the need for high-quality forest data. This study focuses on enhancing methods for mapping aboveground biomass in tropical dry forests. Tropical dry forests are considered one of the least understood tropical forest environments; therefore, there is a need for accurate approaches to estimate carbon pools. We employ a comparative analysis of AGB estimates, utilizing different discrete and full-waveform laser scanning datasets in conjunction with Ordinary Least Squares and Bayesian approaches SVM. Airborne Laser Scanning, Unmanned Laser Scanning, and Space Laser Scanning were used as independent variables for extracting forest metrics. Variable selection, SVM regression tuning, and cross-validation via a machine-learning approach were applied to account for overfitting and underfitting. The results indicate that six key variables primarily related to tree height: Elevminimum, ElevL3, levMADmode, Elevmode, ElevMADmedian, and Elevskewness, are important for AGB estimation using ALSD and ULSD, while Leaf Area Index, canopy coverage and height, terrain elevation, and full-waveform signal energy emerged as the most vital variables. AGB values estimated from ten permanent tropical dry forest plots in Costa Rica Guanacaste province ranged from 26.02 Mg/ha to 175.43 Mg/ha. The SVM regressions demonstrated a 17.89 error across all laser scanning systems, with SLSF W exhibiting the lowest error 17.07 in estimating total biomass per plot.
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