Spatial Distribution Prediction of the Depth Class of Fe, Mn Concretion and Grey Mottle in Soils Using Indicator Kriging Combined with Discriminant Analysis

• Main Authors: Chiang, Tien-Yin, 江天因
• Other Authors: Lee Dar-Yuan, Chen Zueng-Sang
• Format: Others
• Language: zh-TW
• Published: 1997
• Online Access: http://ndltd.ncl.edu.tw/handle/73050479733584646032

碩士 === 國立臺灣大學 === 農業化學系 === 85 === Spatial Distribution Prediction of the Depth Class of Fe, Mn Concretion and Grey Mottle Using Indicator Kriging Combined with Discriminant Analysis Chiang, Tien-Yin ABSTRACT Spatial variability of basic soil properties are mainly dependent on soil pedogenic processes and the environmental factors in soil survey, especially caused by the different factors of landscape. In this study, the variables of soil- landscapes including geography, vegetation, and parent material obtained from geographic information system (GIS) were collected. These variables include elevation, kinds of parent materials, distance to local stream, distance to local irrigation channel, distance to local irrigation pond, distance to local road, and distance to living area. Then, the soil- landscape model were established by multivariate discriminant analysis (MDA) based on the variables used in soil-landscape model and basic soil properties, and to predict the spatial variability of soil properties distributed in another area which was not sampled. The spatial distribution of soil properties can be predicted by indicator kriging method of geostatistics attributed to the spatial dependent of variations of soil properties in the study area. Two hundred and twelve soil pedons were sampled from 1774 hectares rural soils located at Nankang, Taoyuan county in northern Taiwan. The grid sampling method was used to sample soils from soil surface to 150 cm depth by auger to identify the depth of redoximorphic features including iron concretion, manganese concretion and grey mottle in soil pedons. Indicator kriging method and it was combined with soil-landscape model were used to predict the spatial distribution of Fe concretion, Mn concretion and grey mottle under different sampling strategies including different densities (50 and 100 pedon numbers) and different methods (grid and random methods), respectively. The accuracy of prediction on the depth class of Fe concretion, Mn concretion and grey mottle by indicator kriging method under the sampling intervals of distance from 250 to 500 meters (n=100) are much better than those of the sampling distance of 500 meters (n=50). The accuracy of prediction by grid sampling method under low sampling density is better than that of random sampling method, but the accuracy of prediction by random sampling method under high sampling density is better than that of grid sampling method. On the boundary area of different depth classes of concretions or mottles, better accuracy of prediction can be got by grid method. There are no significant differences on the prediction of depth classes of grey mottles in the soil pedon between grid and random sampling methods. Soil-landscape model combined with indicator kriging method can increase the accuracy of prediction on the depth classes of Fe-Mn concretions and grey mottles in the study area compared that predicted only by indicator kriging method. This model combined with indicator kriging method can effectively identify the boundary area of different depth classes of concretion and mottles and also increase the accuracy of prediction on the spatial variability of soil properties. This combined technique can be effectively predict the spatial distribution of basic soil properties caused by pedogenic factors, especially on the application of the geostatistics in soil science. Key Words: Indicator Kriging, Multivariate discriminant analysis, Soil-landscape model, Sampling strategy, Pedogenic processes, Fe concretion, Mn concretion, Grey mottle, Depth classes, GIS, Geostatistics.