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: Elev\.minimum, Elev\.L3, lev\.MAD\.mode, Elev\.mode, Elev\.MAD\.median, and Elev\.skewness, 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.

翻译：根据《巴黎气候变化协定》，所有国家需在2024年前每两年提交一次温室气体排放与吸收报告。因此，森林在减少碳排放方面发挥着关键作用，这对履行协定义务至关重要。认识到森林保护在全球应对气候变化中的重要性，《巴黎协定》第五条强调需要高质量的森林数据。本研究致力于改进热带干旱森林地上生物量的制图方法。热带干旱森林被认为是认知最不足的热带森林环境之一，因此需要精确估算碳库的方法。我们采用对比分析，结合不同离散与全波形激光扫描数据集，利用普通最小二乘法和贝叶斯支持向量机方法估算地上生物量。研究以机载激光扫描、无人机激光扫描和星载激光扫描作为提取森林指标的自变量。通过变量选择、支持向量机回归调参及基于机器学习方法的交叉验证，以应对过拟合与欠拟合问题。结果表明，与树高相关的六个关键变量——Elev.minimum、Elev.L3、lev.MAD.mode、Elev.mode、Elev.MAD.median和Elev.skewness——对使用机载与无人机激光扫描数据估算地上生物量具有重要性；而叶面积指数、冠层覆盖度与高度、地形高程及全波形信号能量则成为最关键变量。对哥斯达黎加瓜纳卡斯特省十处永久热带干旱森林样地的估算显示，地上生物量介于26.02 Mg/ha至175.43 Mg/ha之间。支持向量机回归在所有激光扫描系统中均表现出17.89的误差，其中星载全波形激光扫描在估算单样地总生物量时误差最低，为17.07。