海南岛橡胶林大尺度生物量估算的不同重要预测因子与模型比较

Remote. Sens. Pub Date : 2023-07-07 DOI:10.3390/rs15133447
X. Li, Xincheng Wang, Yuanfeng Gao, Jiuhao Wu, Renxi Cheng, Donghao Ren, Qing Bao, Tin Yun, Zhixiang Wu, Guishui Xie, Bangqian Chen
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引用次数: 2

摘要

橡胶林(Hevea brasiliensis Muell.)是热带地区最重要的农业生态系统之一,对区域碳平衡起着至关重要的作用。由于严重的信号饱和问题,对这些人工林进行精确的大尺度生物量估算仍然是一项具有挑战性的任务。遥感大数据、云平台和机器学习的最新进展促进了关键生理变量的精确获取,如林龄(A)和冠层高度(H),这些参数是生物量估算的关键参数,但在以往的研究中未得到充分利用。本文以中国第二大橡胶种植基地海南岛为研究对象,结合大量地面调查、林龄和冠层高度图、遥感指标和地理气候指标,确定了估算橡胶林生物量的最佳方法。采用随机森林算法比较了直接和间接两种不同的输入和估算方法,分析了海南岛橡胶林生物量的时空特征。结果表明,传统模型(RSIs + ECIs)准确度较低,估计偏差显著(R2 = 0.24, RMSE = 38.36 mg/ha)。林龄和冠层高度的增加显著提高了模型的精度(R2 = 0.77, RMSE≈21.12 mg/ha)。此外,结合通过间接反演获得的胸径可以获得更高的预测精度(R2 = 0.97, RMSE = 7.73 mg/ha),优于异速生长方程模型输入的胸径(R2 = 0.67, RMSE = 25.43 mg/ha)。然而,在rsi、eci和DBH的基础上增加林龄、冠层高度或它们的组合只能略微提高模型的精度。因此,不建议在数据和计算资源有限的场景中使用。利用优化后的模型,绘制了2016年和2020年海南岛橡胶林生物量分布图,结果表明,海南岛橡胶林生物量的时空分布格局与橡胶林建立年份密切相关。虽然少数地区的平均生物量略有下降,但总生物量呈现显著增长,到2020年底达到5.46 × 107 mg,凸显了其作为碳汇的巨大价值。
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Comparison of Different Important Predictors and Models for Estimating Large-Scale Biomass of Rubber Plantations in Hainan Island, China
Rubber (Hevea brasiliensis Muell.) plantations are among the most critical agricultural ecosystems in tropical regions, playing a vital role in regional carbon balance. Accurate large-scale biomass estimation for these plantations remains a challenging task due to the severe signal saturation problem. Recent advances in remote sensing big data, cloud platforms, and machine learning have facilitated the precise acquisition of key physiological variables, such as stand age (A) and canopy height (H), which are critical parameters for biomass estimation but have been underutilized in prior studies. Using Hainan Island—the second-largest rubber planting base in China—as a case study, we integrated extensive ground surveys, maps of stand age and canopy height, remote sensing indicators (RSIs), and geographical and climate indicators (ECIs) to ascertain the optimal method for estimating rubber plantation biomass. We compared different inputs and estimation approaches (direct and indirect) using the random forest algorithm and analyzed the spatiotemporal characteristics of rubber plantation biomass on Hainan Island. The results indicated that the traditional model (RSIs + ECIs) had low accuracy and significant estimation bias (R2 = 0.24, RMSE = 38.36 mg/ha). The addition of either stand age or canopy height considerably enhance model accuracy (R2 = 0.77, RMSE ≈ 21.12 mg/ha). Moreover, incorporating the DBH obtained through indirect inversion yielded even greater predictive accuracy (R2 = 0.97, RMSE = 7.73 mg/ha), outperforming estimates derived from an allometric equation model input with the DBH (R2 = 0.67, RMSE = 25.43 mg/ha). However, augmenting the model with stand age, canopy height, or their combination based on RSIs, ECIs, and DBH only marginally improved the accuracy. Consequently, it is not recommended in scenarios with limited data and computing resources. Employing the optimal model, we generated biomass maps of rubber plantations on Hainan Island for 2016 and 2020, revealing that the spatiotemporal distribution pattern of the biomass is closely associated with the establishment year of the rubber plantations. While average biomass in a few areas has undergone slight decreases, total biomass has exhibited significant growth, reaching 5.46 × 107 mg by the end of 2020, underscoring its considerable value as a carbon sink.
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