지형분류를 이용한 산지 토양 예측가능성
Predictability of soil properties in mountain regions using landform classification
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초록
This study aims to propose an improved approach for soil prediction in mountain regions using landform classification. Four representative landform classification methodologies(process based, statistically based, and object based techniques) of soil prediction were selected and each method has been assessed using the obtained soil data. The landform classification methods explained 15~28% of the total variance of the soil properties in our study area. The process-based method estimated 15~28% of the variation in the exchangeable cations (Ca2+, Mg2+, and K+) at significant levels. The statistically-based approach showed statistical significances in fine particles and Na+(17~21%). The object-based one, however, failed to separate soil properties at significant levels. In summary, the result showed that the predictive values of landform classification turn out to be lower than expected. The causes of this gap can be twofold; first, the inattention to consider optimizations of window sizes in the landform analysis process, second, the fundamental limitation of the method that it cannot perfectly reflect the true variation of spatially continuous soil properties within the selected landform unit.
Abstract
This study aims to propose an improved approach for soil prediction in mountain regions using landform classification. Four representative landform classification methodologies(process based, statistically based, and object based techniques) of soil prediction were selected and each method has been assessed using the obtained soil data. The landform classification methods explained 15~28% of the total variance of the soil properties in our study area. The process-based method estimated 15~28% of the variation in the exchangeable cations (Ca2+, Mg2+, and K+) at significant levels. The statistically-based approach showed statistical significances in fine particles and Na+(17~21%). The object-based one, however, failed to separate soil properties at significant levels. In summary, the result showed that the predictive values of landform classification turn out to be lower than expected. The causes of this gap can be twofold; first, the inattention to consider optimizations of window sizes in the landform analysis process, second, the fundamental limitation of the method that it cannot perfectly reflect the true variation of spatially continuous soil properties within the selected landform unit.
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