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》第五条强调了获取高质量森林数据的必要性。本研究致力于改进热带干旱森林地上生物量的制图方法。热带干旱森林被视为认知最不足的热带森林环境之一，因此需要开发精确的碳库估算方法。我们采用比较分析方法，结合不同离散与全波形激光扫描数据集，运用普通最小二乘法与贝叶斯支持向量机方法进行地上生物量估算。研究将机载激光扫描、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以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，与树高密切相关的六个关键变量——高程最小值、高程L3、高程MAD众数、高程众数、高程MAD中位数和高程偏度——对于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意义；而叶面积指数、冠层覆盖度与高度、地形高程以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，地上生物量值介于26.02至175.43兆克/公顷之间。支持向量机回归在所有激光扫描系统中表现出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。