머신러닝 기반 산림 토양 pH의 디지털 토양도 작성 연구 = A Study on Digital Soil Mapping of Forest Soil pH Based on Machine Learning

다국어 초록 (Multilingual Abstract)

The geology and climate of the Korean Peninsula contribute to a high prevalence of acidic soil, and this situation is exacerbated by the recent increase in acid rain resulting from air pollution, further worsening the acidification of forest soils. In this research, through digital soil mapping to predict the spatial distribution of forest soil properties, we developed digital soil maps of forest soil pH across South Korea. Upon employing various machine learning methods, the random forest algorithm emerged as the most accurate. Key environmental factors influencing the distribution of forest soil pH were identified, including geographic, topographic, and parent material variables. The spatial distribution of forest soil pH revealed higher pH values in areas with alkaline bedrock (limestone and basalt) and lower pH values in areas dominated by gneiss parent material. Notably, highly acidic soils (pH < 4.5) were found in the metropolitan area, southwestern Chungcheong region, and the southern coast. Strongly acidic soil can negatively affect vegetation growth environment and carbon sequestration capacity, so anthropogenic factors that accelerate acidification should be reduced and continuously managed through periodic monitoring.

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