Pedosphere 15(1): 103--109, 2005
ISSN 1002-0160/CN 32-1315/P
©2005 Soil Science Society of China
Published by Elsevier B.V. and Science Press
| Soil organic matter mapping by decision tree modeling |
ZHOU Bin , ZHANG Xing-Gang, WANG Fan and WANG Ren-Chao |
| Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, Hangzhou 310029 (China). E-mail: zhoubin@zju.edu.cn |
| ABSTRACT |
| Based on a case study of Longyou County, Zhejiang Province, the decision tree, a data mining method, was used to analyze the relationships between soil organic matter (SOM) and other environmental and satellite sensing spatial data. The decision tree associated SOM content with some extensive easily observable landscape attributes, such as landform, geology, land use, and remote sensing images, thus transforming the SOM-related information into a clear, quantitative, landscape factor-associated regular system. This system could be used to predict continuous SOM spatial distribution. By analyzing factors such as elevation, geological unit, soil type, land use, remotely sensed data, upslope contributing area, slope, aspect, planform curvature, and profile curvature, the decision tree could predict distribution of soil organic matter levels. Among these factors, elevation, land use, aspect, soil type, the first principle component of bitemporal Landsat TM, and upslope contributing area were considered the most important variables for predicting SOM. Results of the prediction between SOM content and landscape types sorted by the decision tree showed a close relationship with an accuracy of 81.1%. |
| Key Words: decision tree, SOM, spatial prediction |
| Citation: Zhou, B., Zhang, X. G., Wang, F. and Wang, R. C. 2005. Soil organic matter mapping by decision tree modeling. Pedosphere. 15(1): 103-109. |
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