Scientia Silvae Sinicae ›› 2024, Vol. 60 ›› Issue (3): 65-77.doi: 10.11707/j.1001-7488.LYKX20230051
• Research papers • Previous Articles Next Articles
Chenchen Shen1(),Wenfa Xiao1,2,Jianhua Zhu1,2,Lixiong Zeng1,2,Jizhen Chen1,Zhilin Huang1,2,*
Received:
2023-02-10
Online:
2024-03-25
Published:
2024-04-08
Contact:
Zhilin Huang
E-mail:Chenchen.Shen@outlook.com
CLC Number:
Chenchen Shen,Wenfa Xiao,Jianhua Zhu,Lixiong Zeng,Jizhen Chen,Zhilin Huang. Characterization of Soil Organic Carbon and Key Influencing Factors of Natural Forests in Central China Based on Machine Learning Algorithms[J]. Scientia Silvae Sinicae, 2024, 60(3): 65-77.
Table 1
Basic information and statistics of natural forest soil samples in central China"
森林类型 Forest types | 土壤样点数量(占比) Number of soil sampling plots [percentage (%)] | 土层 Soil layers/cm | 土壤密度 Bulk density/ (g·cm?3) | 土壤有机碳含量 Soil organic carbon content/ (g·kg?1) |
常绿阔叶混交林 Evergreen broadleaved | 128(47.9) | 0~20 | 1.28±0.218 | 22.32±13.405 |
20~40 | 1.36±0.205 | 11.46±8.798 | ||
40~60 | 1.35±0.173 | 9.57±6.640 | ||
落叶阔叶混交林 Deciduous broadleaved | 101(37.9) | 0~20 | 1.18±0.229 | 27.82±17.777 |
20~40 | 1.29±0.224 | 14.56±11.652 | ||
40~60 | 1.30±0.215 | 12.01±9.396 | ||
常绿针叶混交林 Evergreen coniferous | 38(14.2) | 0~20 | 1.03±0.238 | 36.41±24.038 |
20~40 | 1.15±0.247 | 21.45±14.069 | ||
40~60 | 1.21±0.229 | 18.75±20.789 |
Table 2
Potential environmental covariates for modeling natural forest soil organic carbon content"
变量类型 Variable types | 基本变量 Variables | 变量简写与常用表达 Abbreviations and common expressions | 单位 Units |
地理地形因素 Geographic and topographical factors | 经度Longitude | Longitude | °E |
纬度Latitude | Latitude | °N | |
海拔 Elevation | Elevation | m | |
坡度 Slope | Slope | ° | |
坡向 Aspect | Aspect | ° | |
景观位置指数Landscape position index | LPI | ||
高差 Relief | Relief | ||
海拔高差比率 Elevation relief ratio | ERR | ||
成土因素 Soil forming and lithology factors | 母质 Parent material | PM | |
母质中的黏土矿物质比 Clay mineral ratio in parent material | CMR | % | |
土壤属性 Soil properties | 土壤类型 Soil subgroup | Subgroup | |
土壤质地 Soil texture | Sand, Clay, Silt | % | |
土壤侵蚀程度 Degree of soil erosion | SEr | ||
土壤密度Bulk density | BD | g·cm?3 | |
土壤孔隙度Soil porosity | Spo | % | |
表层土壤温度 Surface soil temperature | SST | ℃ | |
表层土壤湿度 Surface soil wetness | SSW | % | |
深层土壤湿度 Root zone soil wetness | RZSW | % | |
植被因素 Vegetation factors | 森林类型 Forest type | Forest | |
树种结构 Species structure | SpS | ||
样地平均树高 Mean tree height | H | m | |
叶面积指数 Leaf area index | LAI | ||
均一化植被指数的月均值与年均值 Mean normalized vegetation index | NDVI, NDVI_month | ||
植被净初级生产力多年均值 Net primary productivity of vegetation | NPP | kg·m?2a-1 | |
气候因素 Climatic factors | 太阳辐射 Solar radiation | SR | kWh·m?2 |
蒸发量 Evaporation | Evaporation | mm | |
地表温度多年均值 Land surface temperature | LST | °C | |
月度、生长季、年度气温多年均值 Mean temperature in months, growing seasons and annual mean temperature | MAT, Month_T, GST | °C | |
月度、生长季、年度降水多年均值 Monthly, growing season and annual precipitation | MAP, Month_P, GSP | mm | |
1—12月月均水分亏损Monthly hargreaves climatic moisture deficit | CMDMonth | mm |
Table 3
Advantages and important hyperparameters of soil organic carbon models via the mentioned four algorithms implemented in Python"
模型Models | 模型优点Model advantages | 重要参数代码 Code of important hyperparameters | 解释Descriptions |
支持向量机 Support vector machines | 处理数据能力强 Good performance for both low and high dimensional data 兼具稳定性和灵活性 High stability and flexibility with large variety of kernel functions | ‘kernel’ | 数据类型和相对转换算法 The type of data and relative transformation |
‘C’ | 控制数据正态分布的惩罚系数 This penalty parameter controls the regularization of the data | ||
‘gamma’ | 决定边界的平滑度 It decides the smoothness of the decision boundary | ||
多层感知器 Multi-layer perceptron | 从复杂数据和模糊数据中准确提取信息 The ability to extract patterns from complex or imprecise data | ‘hidden_layer_size’ | 设定隐藏层 Hidden layer sizes |
‘activation’ | 对应隐藏层函数 Sigmoid function for the relative hidden layer | ||
‘solver’ | 权重优化器方法 Weight optimizer method | ||
‘alpha’ | 数据正则化强度 Strength of the regularization | ||
‘max_iter’ | 最大迭代次数 The maximum number of iterations | ||
随机森林 Random forests | 模拟准确性和效率高 High accuracy and efficiency 避免过渡拟合 Prevention of overfitting 适宜大数据量 Being fit for large database | ‘n_estimators’ | 森林决策树子树估算器数量 The number of estimators of subtrees |
分位数回归森林Quantile regression forests | 优点同随机森林 The same advantages like RF 可以进行不确定性评估 Allowing uncertainty evaluation | ‘max_depth’ | 拆分节点的最大回归深度 The maximum depth of regression to split a node |
‘max_feature’ | 适用要素的最大特征值 The maximum values of used features |
Table 4
Predictive performance for SOC models based on SVM along soil layers"
土层 Soil layers/ cm | 主要模型参数 Primary modeling parameters | 模型评价统计 Statistics of modeling goodness | |||||
数据转换算法 Data transformation | 惩罚系数 Penalty | EVS | R2 | RMSE | MAE | ||
0~20 | 线性Linear | 1 | 0.522 | 0.517 | 15.309 | 8.899 | |
20~40 | 多项式Poly | 1 | 0.485 | 0.466 | 8.373 | 6.088 | |
40~60 | 多项式Poly | 1 | 0.451 | 0.449 | 7.823 | 5.129 |
Table 5
Predictive performance for SOC models based on MLP along soil layers"
土层 Soil layers/cm | 主要模型参数 Primary modeling hyperparameters | 模型评价统计 Statistics of modeling goodness | |||||
模型优化器 Weight optimizer | 数据正则化强度 Strength of the regularization | EVS | R2 | RMSE | MAE | ||
0~20 | 准牛顿算法lbfgs | 0.1 | 0.530 | 0.496 | 15.975 | 9.961 | |
20~40 | 准牛顿算法lbfgs | 0.01 | 0.531 | 0.524 | 7.459 | 6.206 | |
40~60 | 准牛顿算法lbfgs | 0.1 | 0.503 | 0.469 | 7.020 | 5.943 |
Table 6
Predictive performance for SOC models based on RF along soil layers"
土层 Soil layers/cm | 主要模型参数 Primary modeling hyperparameters | 模型评价统计 Statistics of modeling goodness | ||||||
子树估算器数量 The number of estimators of subtrees | 拆分节点最大回归深度 The maximum depth of regression to split a node | 使用要素的最大特征值 The maximum values of used features | EVS | R2 | RMSE | MAE | ||
0~20 | 25 | 6 | 2 | 0.574 | 0.523 | 15.126 | 9.386 | |
20~40 | 200 | 10 | 4 | 0.621 | 0.620 | 5.959 | 5.533 | |
40~60 | 300 | 10 | 1 | 0.605 | 0.594 | 6.231 | 4.961 |
Table 7
Predictive performance for SOC models based on QRF along soil layers"
土层 Soil layers /cm | 主要模型参数 Primary modeling hyperparameters | 模型评价统计 Statistics of modeling goodness | ||||||
子树估算器数量 The number of estimators of subtrees | 拆分节点最大回归深度 The maximum depth of regression to split a node | 使用要素最大特征值 The maximum values of used features | EVS | R2 | RMSE | MAE | ||
0~20 | 25 | 2 | 11 | 0.519 | 0.497 | 15.952 | 9.044 | |
20~40 | 150 | 10 | 5 | 0.562 | 0.562 | 6.883 | 5.808 | |
40~60 | 100 | 10 | 3 | 0.569 | 0.569 | 5.695 | 4.384 |
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