ABSTRACT Commercial forest plantations are increasing globally, absorbing a large amount of carbon valuable for climate change mitigation. Whereas most carbon assimilation studies have mainly focused on natural forests, understanding the spatial distribution of carbon in commercial forests is central to determining their role in the global carbon cycle. Forest soils are the largest carbon reservoir; hence soils under commercial forests could store a significant amount of carbon. However, the variability of soil organic carbon (SOC) within forest landscapes is still poorly understood. Due to limitations encountered in traditional systems of SOC determination, especially at large spatial extents, remote sensing approaches have recently emerged as a suitable option in mapping soil characteristics. Therefore, this study aimed at predicting soil organic carbon (SOC) stocks in commercial forests using Landsat 8 data. Eighty-one soil samples were processed for SOC concentration and fifteen Landsat 8 derived variables, including vegetation indices and bands were used as predictors to SOC variability. The random forest (RF) was adopted for variable selection and regression method for SOC prediction. Variable selection was done using RF backward elimination to derive three best subset predictors and improve prediction accuracy. These variables were then used to build the RF final model for SOC prediction. The RF model yielded good accuracies with root mean square error of prediction (RMSE) of 0.704 t/ha (16.50% of measured mean SOC) and 10-fold cross-validation of 0.729 t/ha (17.09% of measured mean SOC). The results demonstrate the effectiveness of Landsat 8 bands and derived vegetation indices and RF algorithm in predicting SOC stocks in commercial forests. This study provides an effective framework for local, national or global carbon accounting as well as helps forest managers constantly evaluate the status of SOC in commercial forest compartments.

中文翻译：

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使用遥感数据预测南非夸祖鲁-纳塔尔省商业林人工林下土壤有机碳储量

摘要全球商业林种植园正在增加，吸收了大量对减缓气候变化有价值的碳。尽管大多数碳同化研究主要集中在天然林上，但了解商品林中碳的空间分布对于确定其在全球碳循环中的作用至关重要。森林土壤是最大的碳库；因此，商业林下的土壤可以储存大量的碳。然而，森林景观中土壤有机碳（SOC）的变异性仍然知之甚少。由于传统 SOC 测定系统遇到的限制，特别是在大空间范围内，遥感方法最近已成为绘制土壤特征的合适选择。所以，本研究旨在使用 Landsat 8 数据预测商品林中的土壤有机碳 (SOC) 储量。针对 SOC 浓度处理了 81 个土壤样本，并使用了 15 个 Landsat 8 衍生变量（包括植被指数和波段）作为 SOC 变异性的预测因子。SOC 预测的变量选择和回归方法采用随机森林 (RF)。变量选择是使用 RF 后向消除来完成的，以推导出三个最佳子集预测器并提高预测精度。然后使用这些变量来构建用于 SOC 预测的 RF 最终模型。RF 模型产生了良好的准确性，预测均方根误差 (RMSE) 为 0.704 t/ha（实测平均 SOC 的 16.50%）和 0.729 t/ha（实测平均 SOC 的 17.09%）的 10 倍交叉验证。结果证明了 Landsat 8 波段和派生的植被指数和 RF 算法在预测商品林 SOC 储量方面的有效性。这项研究为地方、国家或全球碳核算提供了一个有效的框架，并帮助森林管理者不断评估商业森林隔间中 SOC 的状态。