Abstract
Predictive soil mapping depends on understanding the relationships between soil properties and environmental factors. However, in a complex soil landscapes, there is a shortage of suitable approaches to establish these relationships. The main objective is to predict soil thickness in an alpine watershed relating to soil environmental factors based on an unsupervised fuzzy clustering method (fuzzy c-means, FCM) and decision tree (DT) method. In this study, FCM method was used for stratifying the landscape, and then, a representative soil thickness was assigned to each class. For each class, a number of points were randomly chosen in proportion to representative areas, and then, the environmental factors at these point locations were extracted as a training data set (3626 points). For the training data set, DT method was used to obtain the critical threshold of soil–environment relationships. Finally, soil thickness map was created by applying the results of the DT across the region. An independently collected field sampling set (31 points) was used to evaluate the effectiveness of the proposed approach. For training set, 95.48 % of the total training data were correctly predicted. For validation set, the overall accuracy and Kappa coefficient could reach 74.2 % and 0.659, respectively. Evaluation accuracy of soil map was up to 74.2 %. In conclusion, it is suggested that soil–landscape modeling using FCM and DT methods can be efficiently used as a valuable research technique for spatial soil thickness prediction in a complex soil landscape where soil characteristics and properties are not available.
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References
Ambroise B, Beven K, Freer J (1996) Toward a generalization of the TOPMODEL concepts: Topographic indices of hydrological similarity. Water Resources Research, 32(7): 2135-2145.
Bezdek JC, Ehrlich R, Full W (1984) FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences 10(2): 191-203.
Boer M, Del Barrio G, Puigdefábres J (1996) Mapping soil depth classes in dry Mediterranean areas using terrain attributes derived from a digital elevation model. Geoderma 72(1): 99-118.
Buol SW, Southard RJ, Graham RC, McDaniel PA (2011) Soil genesis and classification. John Wiley & Sons.
Cohen JA (1960) A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20(1): 37-46.
DeRose RC, Trustrum NA, Blaschke PM (1991) Geomorphic change implied by regolith-slope relationships on steepland hillslopes, Taranaki, New Zealand. Catena 18(5): 489-514.
Dietrich WE, Reiss R, Hsu ML, Montgomery DR (1995) A process-based model for colluvial soil depth and shallow landsliding using digital elevation data. Hydrological processes 9(3–4): 383-400.
Fuhlendorf SD, Smeins FE (1998) The influence of soil depth on plant species response to grazing within a semi-arid savanna. Plant Ecology 138(1): 89-96.
Gessler PE, Moore ID, McKenzie NJ, Ryan PJ (1995) Soil-landscape modelling and spatial prediction of soil attributes. International Journal of Geographical Information Systems 9(4): 421-432.
Han CT, Chen RS, Liu JF, Yang Y, Liu ZW (2013) Hydrological characteristics in non-freezing period at the alpine desert zone of Hulugou watershed, Qilian mountains. Journal of Glaciology and Geocryology 35(6): 1536-1544. (in Chinese with English abstract)
Henderson BL, Bui EN, Moran CJ, Simon DAP (2005) Australia-wide predictions of soil properties using decision trees. Geoderma 124(3): 383-398.
Huang CY (2000) Agrology. Beijing: China Agriculture Press (in Chinese).
Hudson BD (1992) The soil survey as paradigm-based science. Soil Science Society of America Journal 56(3): 836-841.
Loh WY (2011) Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1(1): 14-23.
McBratney AB, Odeh IO, Bishop TF, Dunbar MS, Shatar TM (2000) An overview of pedometric techniques for use in soil survey. Geoderma 97(3): 293-327.
McBratney AB, Santos MM, Minasny B (2003) On digital soil mapping. Geoderma 117(1): 3-52.
McIntosh PD, Lynn IH, Johnstone PD (2000) Creating and testing a geometric soil-landscape model in dry steeplands using a very low sampling density. Soil Research 38(1): 101-112.
McKenzie NJ, Gessler PE, Ryan PJ, O’Connell DA (2000) The role of terrain analysis in soil mapping. In Terrain analysis: principles and applications. New York: Wiley.
McKenzie NJ, Ryan PJ (1999) Spatial prediction of soil properties using environmental correlation. Geoderma 89(1): 67-94.
Meyer MD, North MP, Gray AN, Zald HS (2007) Influence of soil thickness on stand characteristics in a Sierra Nevada mixed-conifer forest. Plant and Soil 294(1–2): 113-123.
Moore ID, Gessler PE, Nielsen GA, Peterson GA (1993) Soil attribute prediction using terrain analysis. Soil Science Society of America Journal 57(2): 443-452.
Odeh IOA, Chittleborough DJ, McBratney AB (1992) Soil pattern recognition with fuzzy-c-means: application to classification and soil-landform interrelationships. Soil Science Society of America Journal 56(2): 505-516.
Park SJ, McSweeney K, Lowery B (2001) Identification of the spatial distribution of soils using a process-based terrain characterization. Geoderma 103(3): 249-272.
Rouse Jr JW (1973) Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. MD: NASA/GSFC Type III Final Report.
Santos MM, Guenat C, Bouzelboudjen M, Golay F (2000) Three-dimensional GIS cartography applied to the study of the spatial variation of soil horizons in a Swiss floodplain. Geoderma 97(3): 351-366.
Scull P, Franklin J, Chadwick OA, McArthur D (2003) Predictive soil mapping: a review. Progress in Physical Geography 27(2): 171-197.
Scull P, Franklin J, Chadwick OA (2005). The application of classification tree analysis to soil type prediction in a desert landscape. Ecological Modelling 181(1), 1-15.
Sun XL, Zhao YG, Zhang GL, Li DC (2008) Optimization of clustering parameters in predictive mapping of soil organic matter. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE) 24(9): 31-37. (in Chinese with English abstract)
Taghizadeh-Mehrjardi R, Sarmadian F, Minasny B, Trianta filis J, Omid M (2014) Digital mapping of soil classes using decision tree and auxiliary data in the Ardakan region, Iran. Arid Land Research and Management 28(2): 147-168.
Xu WN, Wang PX, Han P, Yan TL, Zhang SY (2011) Application of Kappa coefficient to accuracy assessments of drought forecasting model: a case study of Guanzhong Plain. Journal of Natural Disasters 20(6): 81-86. (in Chinese with English abstract)
Yang L, Zhu AX, Li, BL, Qin, CZ, Pei T, Liu BY, Li RK, Cai QG (2007) Extraction of knowledge about soil-environment relationship for soil mapping using Fuzzy c-means (FCM) clustering. Acta Pedologica Sinica 44(5): 784-791. (in Chinese with English abstract)
Zheng ZP, Liu ZX (2003) Soil quality and its evaluation. Chinese Journal of Applied Ecology 14(1): 131-134. (in Chinese with English abstract)
Zhou MX (2012) The Inversion of Lunar Regolith Layer Thickness By Using the Data Obtained from Microwave Radiometer On CE-1 Satellite. Nanjing: Nanjing University of Aeronautics and Astronautics, College of Electronic and Information Engineering. (in Chinese with English abstract)
Zhu AX, Band LE, Dutton B, Nimlos TJ (1996) Automated soil inference under fuzzy logic. Ecological Modelling, 90(2): 123-145.
Acknowledgements
Funding for this study was provided by National Natural Science Foundation of China (Project No. 41130530 and 91325301). The authors are grateful to Cold and Arid Regions Science Data Center at Lanzhou (CARD) for providing us with the basic geographic data. Furthermore, the authors would like to acknowledge the teachers and students in one team for their help and support in field investigation and soil samples collection and physical and chemical analysis.
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Lu, Y., Zhang, G., Zhao, Y., Li, D., Yang, J., Liu, F. (2016). Mapping Soil Thickness by Integrating Fuzzy C-Means with Decision Tree Approaches in a Complex Landscape Environment. In: Zhang, GL., Brus, D., Liu, F., Song, XD., Lagacherie, P. (eds) Digital Soil Mapping Across Paradigms, Scales and Boundaries. Springer Environmental Science and Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-10-0415-5_6
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DOI: https://doi.org/10.1007/978-981-10-0415-5_6
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