CAJ | 학술논문

复杂景观环境下，土壤-环境关系知识的获取是预测性土壤制图的基础。为了探究复杂景观下土壤厚度分布与环境条件的关系，该文以黑河上游祁连山区典型小流域为研究区，应用模糊c均值聚类（fuzzy C-means cluster，FCM）和决策树（decision Tree，DT）方法，建立了一套获取土壤厚度分布与环境间关系知识的方法。利用2种方法结合获得流域内土壤厚度各分布等级的环境要素关键阈值与土壤-环境关系知识集，将所得环境阈值和知识集进行预测性制图，并通过野外独立样点对制图结果进行精度评价。结果表明：土壤厚度图的总体精度为74.2%，Kappa系数为0.659。该研究将2种方法结合获得了土壤厚度分布对应的土壤环境关键阈值和土壤-环境关系知识集，为复杂景观环境下土壤厚度的预测性制图提供了一种有效的解决方案。
복잡경관배경하，토양-배경관계지식적획취시예측성토양제도적기출。위료탐구복잡경관하토양후도분포여배경조건적관계，해문이흑하상유기련산구전형소류역위연구구，응용모호c균치취류（fuzzy C-means cluster，FCM）화결책수（decision Tree，DT）방법，건립료일투획취토양후도분포여배경간관계지식적방법。이용2충방법결합획득류역내토양후도각분포등급적배경요소관건역치여토양-배경관계지식집，장소득배경역치화지식집진행예측성제도，병통과야외독립양점대제도결과진행정도평개。결과표명：토양후도도적총체정도위74.2%，Kappa계수위0.659。해연구장2충방법결합획득료토양후도분포대응적토양배경관건역치화토양-배경관계지식집，위복잡경관배경하토양후도적예측성제도제공료일충유효적해결방안。
Soil depth is one of the most important input parameters for hydroecological models in arid and semiarid regions. However, soil depth is highly variable spatially and traditional measures of soil depth are laborious, time consuming and even difficult to practically perform, especially in the complex landscape areas. In these areas, the mapping based on the relationships between soil properties and environmental factors may be useful. However, the approach used to establish their relationships is limited. Therefore, this study proposed an efficient method for obtaining and establishing the soil-environment relationships in complex landscape environments. The method was based on an fuzzy clustering method (fuzzy C-means, FCM) and decision tree (DT). Using this method, the relationships between soil depth distribution and environmental factors in a typical alpine watershed in the Qilian Mountains, northwestern China with easy-to-obtain environmental covariates data was established. The method was based on the assumption that soil was the production of the interaction among its formative environmental factors with time. The environment variables, such as altitude, slope, aspect, plan curvature, profile curvature, topographic wetness index and normalized differential vegetation index, were extracted as auxiliary variables for data analysis. A total of 3626 points obtained by FCM and DT methods was as training sample set, and 31 points collecting from field survey through representative sampling strategy was used as validation sample set. The method consisted of 4 step 1) to define the environmental factors playing dominant roles in formation and development of soil depth, then to obtain the environmental niches by running FCM analysis (after correlation analyses altitude, profile curvature and terrain wetness index were selected to carry out FCM analysis); 2) to assign the ranked distribution of soil depth based on the field investigation data and pedogenesis principles; 3) to select the typical areas of fuzzy membership threshold greater than 0.5, and to randomly choose a certain number of points which were proportional to area extent, and to possess an approximate quantity of points, then to extract the locating information of environmental factors so as to build up the training sample set; 4) to obtain the critical thresholds of soil environmental factors and the knowledge about soil-environment relationships by running training sample set through the DT arithmetic. The method was applied in a typical alpine watershed of the Qilian Mountain, the Heihe River basin, and the soil depth distribution map was created. In addition, an independently field sample set was used to validate the effectiveness of the method in establishing the relationships between soil depth and environmental factors. Its overall accuracy and Kappa coefficient reached 74.2% and 0.659 respectively. Therefore, the proposed method is an optional efficient solution for predictive soil depth mapping in the complex landscape environment.