Received: 08-01-2016
Accepted: 29-04-2016
DOI:
Views
Downloads
How to Cite:
Evaluating the Potential of Digital Soil Mapping Method to Map Soil Typesin BacNinh Province
,
Le Thi Giang
1
Keywords
Diversity index, multinomial logistic regression, soil map, soil map purity
Abstract
The multinomial logistic regression (MLR) model has been being widely used for soil mapping because it could help save time and costs for collecting and analyzing soil data points compared to conventional methods. This research aimed to assess the potential of mapping soil types in Bac Ninh by assessing the performance of Multinomial Logistic Regression (MLR) model in predicting soil types. Nine predictive variables were derived from the ancillary data including land use, altitude, slope, NDVI, PVI, RVI, Topographic Wetness Index and SAGA Wetness Index. MLR model was constructed to predict soil classes at 2 levels: WRB-Reference Soil Group and intermediate level of Soil Group between Reference Soil Group and the full WRB soil name. The map quality was evaluated by the soil map purity estimated with an independent validation dataset. The diversity indices were calculated to assess the information content of the resultant maps. Assessment of the model’s prediction was based on the soil map purity, the Shannon’s entropy and a combined index. MLR yielded high map purity at the level of Reference Soil Group. When the taxonomic level changed from Reference Soil Group level to intermediate level, the map purity decreased while the value of the diversity indices increased. Therefore, soil mapping using MLR in predicting Reference Soil Group might be more efficient. However, at intermediate level, the model predicted higher diversity of soil map and thus the informative value estimated by the combined index was higher.
References
A.P.D.Turetta, M. L. Mendoca Santos, L.H.C.Anjos and R.L.L.Berbara (2006). Chapter 22: Spatial-Temporal Changes in Land Cover
Carré, F. and M. C. Girard (2002). "Quantitative mapping of soil types based on regression kringing of taxonomics distances with landform and land cover attributes". Geoderma, 111: 241-263.
Fagerland M. W., D. W. Hosmer and A. M. Bonfi. 2008. ‘Multinomial goodness-of-fit tests for logistic regression models.’ Statistics in Medicine. 27(21).
FAO (2001). Lecture notes on the Major Soils of the World.
Finke, P. (2011). Syllabus for the course Soil prospection and classification in the Physical Land Resources program.
Kempen, B., D. J. Brus, Heuvelink GBM, Stoorvogel JJ (2009). "Updating the 1:50,000 Dutch soil map using legacy soil data: A multinomial logistic regression approach". Geoderma, 151: 311-326
Lagacherie, P. and A. B. McBratney (2007). Chapter 1. Spatial soil information system and spatial soil inference systems: perspective for Diagital Soil Mapping. Diagital Soil Mapping: An Introductory Perspective. P. Lagacherie, A. B. McBratney and M. Voltz. Amsterdam, Elsevier. Development in Soil Science, pp. 3-24.
Martin, M. A. and J. M. Rey (2000). "On the role of Shannon's entropy as a measure of heterogeneity". Geoderma, 98: 1-3.
Olaya, V. F. (Ed.) (2004). A gentle introduction to Saga GIS Gottingen, Germany.
WRB, I. W. G. (2006). World Reference Base for soil resources 2006. Rome, FAO.
Yang, L., Y. Jiao, S. Fahmy, A-Xing Zhu, S. Hann, J. E. Burt, and Feng Qi. (2011). "Updating Conventional Soil Maps through Digital Soil Mapping". Soil Science Society of America, 75(3): 1044-1053.