三峡库区草堂河流域土壤pH空间分布预测制图
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摘要
以三峡库区草堂河流域为研究区,利用网格布点,共采集102个土壤样点,分析测定土壤的pH值,结合成土母质和地形等10个环境因子,以样点总数的85%作为训练集进行预测模型构建,15%作为验证集检验模型精度,利用随机森林(Random Forest, RF)模型对研究区土壤pH进行空间分布预测并制图。结果表明:土壤pH与谷深、坡长呈显著正相关,与海拔、距河网垂直距离、坡高呈显著负相关。三叠系大冶组灰岩发育的土壤pH值高于三叠系须家河组石英砂岩发育的土壤pH值。基于环境因子的RF预测模型,平均绝对误差(MAE)为0.47、均方根误差(RMSE)为0.59、决定系数(R~2)为0.85,能解释研究区土壤pH值85%的空间变异。对土壤pH值产生主要影响的环境因子为成土母质和海拔。可见,基于环境因子的RF预测模型,预测精度高,可以作为土壤pH空间分布预测的有效方法,能为流域尺度下其他土壤属性的空间分布预测提供依据和借鉴。
A total of 102 samples were collected from the topsoil at a depth of 20 cm to predict and map the spatial distribution of soil pH over the Caotang River Basin in the Three Gorges Reservoir Area. The samples were divided into calibration(85%) and validation(15%) sets. Random Forest(RF) method was applied to predict the spatial distribution of soil pH based on parent materials and terrain indicators(Elevation, Slope, Aspect, Slope Height, Valley depth, Topographical wetness index, Vertical Distance to Channel Network, Multi-resolution index of valley bottom flatness, Slope Length). The major influencing environmental factors on soil pH spatial variability were investigated by the RF model. The results showed that soil pH was significantly positively correlated to Valley depth and Slope Length, while significantly negatively correlated to Elevation, Vertical Distance to Channel Network and Slope Height. Soils developed from Limestone of Triassic Daye formation had higher values of pH than that developed from Sandstone of Triassic Xujiahe Formation. The RF model had a good performance with the mean absolute error(MAE), the root mean square error(RMSE) and the determination coefficient(R~2) of 0.47, 0.59 and 0.85, respectively. The model could explain 85% variation of soil pH in the study area. The major factors to soil pH variations were soil parent material and elevation. Therefore, RF model can serve as an effective method to predict the spatial distribution of soil pH, and can provide the basis and reference for other soil properties prediction at watershed scale.
A total of 102 samples were collected from the topsoil at a depth of 20 cm to predict and map the spatial distribution of soil pH over the Caotang River Basin in the Three Gorges Reservoir Area. The samples were divided into calibration(85%) and validation(15%) sets. Random Forest(RF) method was applied to predict the spatial distribution of soil pH based on parent materials and terrain indicators(Elevation, Slope, Aspect, Slope Height, Valley depth, Topographical wetness index, Vertical Distance to Channel Network, Multi-resolution index of valley bottom flatness, Slope Length). The major influencing environmental factors on soil pH spatial variability were investigated by the RF model. The results showed that soil pH was significantly positively correlated to Valley depth and Slope Length, while significantly negatively correlated to Elevation, Vertical Distance to Channel Network and Slope Height. Soils developed from Limestone of Triassic Daye formation had higher values of pH than that developed from Sandstone of Triassic Xujiahe Formation. The RF model had a good performance with the mean absolute error(MAE), the root mean square error(RMSE) and the determination coefficient(R~2) of 0.47, 0.59 and 0.85, respectively. The model could explain 85% variation of soil pH in the study area. The major factors to soil pH variations were soil parent material and elevation. Therefore, RF model can serve as an effective method to predict the spatial distribution of soil pH, and can provide the basis and reference for other soil properties prediction at watershed scale.
引文
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[5] FILIPPI P, CATTLE S R, BISHOP T F A, et al. Digital soil monitoring of top- and sub-soil pH with bivariate linear mixed models[J]. Geoderma, 2018, 322: 149-162.
[6] 杨忠芳,陈岳龙,钱鑂,等. 土壤pH对镉存在形态影响的模拟实验研究[J]. 地学前缘, 2005, 12(1): 252-260.YANG Z F, CHEN Y L, QIAN X, et al. A study of the effect of soil pH on chemical species of cadmium by simulated experiments[J]. Earth Science Frontiers, 2005, 12(1): 252-260.
[7] 陈怀满. 土壤中化学物质的行为与环境质量[M]. 北京: 科学出版社, 2002: 46-53.CHEN H M. Behavior and environmental quality of chemical substances in soil[M]. Beijing: Science Press, 2002: 46-53.
[8] SUMFLETH K, DUTTMANN R. Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators[J]. Ecological Indicators, 2008, 8(5):485-501.
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[10] 朱阿兴,李宝林,杨琳,等. 基于GIS、模糊逻辑和专家知识的土壤制图及其在中国应用前景[J]. 土壤学报, 2005, 42(5): 142-149.ZHU A X, LI B L, YANG L, et al. Predictive soil mapping based on a GIS, expert knowledge, and fuzzy logic framework and its application prospects in China[J]. Acta Pedologica Sinica, 2005, 42(5): 142-149.
[11] 孙福军,雷秋良,刘颖,等. 数字土壤制图技术研究进展与展望[J]. 土壤通报, 2011, 42(6): 1502-1507.SUN F J, LEI Q L, LIU Y, et al. The progress and prospect of digital soil mapping research[J]. Chinese Journal of Soil Science, 2011, 42(6): 1502-1507.
[12] ZHU A X, FENG Q, MOORE A, et al. Prediction of soil properties using fuzzy membership values[J]. Geoderma, 2010, 158(3-4):199-206.
[13] 孙孝林,赵玉国,刘峰,等. 数字土壤制图及其研究进展[J]. 土壤通报, 2013, 44(3): 752-759.SUN X L, ZHAO Y G, LIU F, et al. Digital soil mapping and advance in research[J]. Chinese Journal of Soil Science, 2013, 44(3): 752-759.
[14] ZERAATPISHEH M, AYOUBI S, JAFARI A, et al. Comparing the efficiency of digital and conventional soil mapping to predict soil types in a semi-arid region in Iran[J]. Geomorphology, 2017(285): 186-204.
[15] WANG J, CUI L, GAO W, et al. Prediction of low heavy metal concentrations in agricultural soils using visible and near-infrared reflectance spectroscopy[J]. Geoderma, 2014, 216(4): 1-9.
[16] 王库. 地理权重回归在土壤pH空间预测中的应用[J]. 湖南农业大学学报(自然科学版), 2013, 39(1): 73-79.WANG K. Application of geographically weighted regression on the spatial prediction of soil pH[J]. Journal of Hunan Agricultural University (Natural Sciences), 2013, 39(1): 73-79.
[17] 董敏,王昌全,李冰,等. 基于GARBF神经网络的土壤有效锌空间插值方法研究[J]. 土壤学报, 2010, 47(1): 42-50.DONG M, WANG C Q, LI B, et al. Study on soil available zinc with GARBF Neural Network based spatial interpolation method[J]. Acta Pedologica Sinica, 2010, 47(1): 42-50.
[18] 徐丽华. 土壤养分预测方法的比较研究——以三峡库区王家沟小流域为例[D]. 西南大学, 2012.XU L H. A comparative study of soil nutrient prediction methods:A case study in Wangjiagou small watershed of Three Gorges Reservoir Area[D]. Southwest University, 2012.
[19] LIAW A, M W. Classification and regression by random forest[J]. R News, 2002, 23(23): 18-22.
[20] LI A D, GUO P T, WU W, et al. Impacts of terrain attributes and human activities on soil texture class variations in hilly areas, south-west China[J]. Environmental Monitoring & Assessment, 2017, 189(6):281.
[21] BREIMAN L. (2001). Random forests[J]. Machine Learning, 2001,45(1): 5-32.
[22] 王茵茵,齐雁冰,陈洋,等. 基于多分辨率遥感数据与随机森林算法的土壤有机质预测研究[J]. 土壤学报, 2016, 53(2): 342-354. WANG Y Y, QI Y B, CHEN Y, et al. Prediction of soil organic matter based on multi-resolution remote sensing data and random forest algorithm[J]. Acta Pedologica Sinica, 2016, 53(2): 342-354.
[23] 郭澎涛,李茂芬,罗微,等. 基于多源环境变量和随机森林的橡胶园土壤全氮含量预测[J]. 农业工程学报, 2015, 31(5): 194-200.GUO P T, LI M F, LUO W, et al. Prediction of soil total nitrogen for rubber plantation at regional scale based on environmental variables and random forest approach[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(5): 194-200.
[24] RAD M R P, AKBARIMOGHADDAM A. Spatial variability of soil texture fractions and pH in a flood plain (case study from eastern Iran)[J]. Catena, 2018: 160.
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