| 基于地形因子和随机森林的丘陵区农田土壤有效铁空间分布预测 |
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| 引用本文： | 杨其坡,武伟,刘洪斌.基于地形因子和随机森林的丘陵区农田土壤有效铁空间分布预测[J].中国生态农业学报,2018,26(3):422-431. |
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| 作者姓名： | 杨其坡 武伟 刘洪斌 |
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| 作者单位： | 西南大学资源环境学院 重庆 400716;重庆市数字农业重点实验室 重庆 400716,西南大学计算机与信息科学学院 重庆 400715;重庆市数字农业重点实验室 重庆 400716,西南大学资源环境学院 重庆 400716;重庆市数字农业重点实验室 重庆 400716 |
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| 基金项目： | 中央高校基本科研业务费专项（XDJK2016D041）资助 |
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| 摘 要： | 为了掌握丘陵地区农田土壤有效铁含量及其空间分布,本文以重庆市江津区永兴镇内同源成土母质的典型丘陵(2 km2)为研究区,采集309个土壤样点,利用普通克里格(Ordinary Kriging,OK)、多元线性回归(Multiple Linear Regression,MLR)、随机森林(Random Forest,RF)模型,结合高程、坡度、坡向、谷深、平面曲率、剖面曲率、汇聚指数、相对坡位指数、地形湿度指数等地形因子对土壤有效铁进行空间分布预测,并通过85个验证点评价、筛选预测模型。结果表明:1)土壤有效铁与谷深、地形湿度指数存在极显著水平正相关关系,与坡度、平面曲率、剖面曲率、汇聚指数、相对坡位指数存在极显著水平负相关关系。2)随机森林模型的预测精度明显高于多元线性回归和普通克里格插值,其平均绝对误差为22.33 mg·kg-1、均方根误差为27.98 mg·kg-1、决定系数为0.76,是研究区土壤有效铁含量空间分布的最适预测模型。3)地形湿度指数和坡度是影响该区域土壤有效铁含量空间分布的主要地形因子。土壤有效铁与坡度、谷深、平面曲率、剖面曲率、汇聚指数、相对坡位指数、地形湿度指数均达到极显著水平相关关系。4)研究区土壤有效铁含量范围为3.00~276.97 mg?kg-1,水田有效铁含量大于旱地;土壤有效铁具有较强的空间相关性,土壤有效铁含量空间变异主要受到结构性因素的影响。可见,基于地形因子的随机森林预测模型可以较好地解释丘陵区农田土壤有效铁含量的空间变异,研究结果为丘陵区土壤中、微量元素含量及空间分布预测提供方法借鉴和理论依据。
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| 关 键 词： | 地形因子 随机森林模型 土壤有效铁 空间分布 丘陵区农田 |
| 收稿时间： | 2017/5/17 0:00:00 |
| 修稿时间： | 2017/12/5 0:00:00 |
Prediction of spatial distribution of soil available iron in a typical hilly farm-land using terrain attributes and random forest model |
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| YANG Qipo,WU Wei and LIU Hongbin.Prediction of spatial distribution of soil available iron in a typical hilly farm-land using terrain attributes and random forest model[J].Chinese Journal of Eco-Agriculture,2018,26(3):422-431. |
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| Authors: | YANG Qipo WU Wei and LIU Hongbin |
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| Institution: | College of Resources and Environment, Southwest University, Chongqing 400716, China;Chongqing Key Laboratory of Digital Agriculture, Chongqing 400716, China,College of Computer and Information Science, Southwest University, Chongqing 400715, China;Chongqing Key Laboratory of Digital Agriculture, Chongqing 400716, China and College of Resources and Environment, Southwest University, Chongqing 400716, China;Chongqing Key Laboratory of Digital Agriculture, Chongqing 400716, China |
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| Abstract: | Soil available iron is essential for plant growth. Detailed information on the spatial distribution of soil available iron is critical for effective management of soil fertility. To date, published works on soil available iron have mainly focused on the spatial variability and little has been done on predicting the spatial distribution of soil available iron. To understand the spatial distribution of soil available iron in hilly areas of Southwest China, we conducted a study in 2014 at a 2-km2 typical hilly region with uniform soil parent materials in Yongxing Town, Jiangjin County, Chongqing City. A total of 309 soil samples were collected from cultivated lands at the depth of 0-20 cm. The samples were randomly divided into calibration (224) and validation (85) samples. Nine terrain attributes (including elevation, slope, aspect, valley depth, horizontal curvature, profile curvature, convergence index, relation position index and topographic wetness index) were extracted from a digital elevation model of spatial resolution of 2.0 m. Ordinary Kriging (OK), Multiple Linear Regression (MLR) and Random Forest (RF) analyses were used to predict the content of soil available iron based on the terrain attributes. Accuracy indicators, including mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2), were used to evaluate model performance based on validation data. Correlation analysis showed that topographic wetness index and valley depth were significantly positively correlated with soil available iron content. Slope, horizontal curvature, profile curvature, convergence index and relative position index were on the other hand significantly negatively correlated with soil available iron content. Compared with OK and MLR, RF model performed best, with MAE=22.33 mg·kg-1, RMSE=27.98 mg·kg-1 and R2=0.76. Additionally, RF analysis indicated that topographic wetness and slope were the main factors controlling the spatial distribution of soil available iron. Soil available iron content in the study area was 3.00-276.97 mg·kg-1, which was higher for paddy field than for dryland. Semivariance model showed strong spatial autocorrelation of soil available iron, indicating that structural factors were the main driving force of spatial variation of soil available iron. Therefore it was concluded that the RF model together with terrain attributes well explained the spatial variability of soil available iron in the area. The result of the study provided valuable information for studies on predicting the spatial distribution of trace elements in soils in hilly areas. |
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| Keywords: | Terrain attribute Random forest model Soil available iron Spatial distribution Hilly farmland |
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