디지털 토양도 작성 기법을 적용한 산림 토양 탄소 저장량 평가 - 편마암 지역을 대상으로 -
Assessment of Forest Soil Carbon Stocks Using Digital Soil Mapping in a Gneiss Area
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
This study aims to evaluate carbon stocks at varying soil depths in the forest soils of the Namyangju region using digital soil mapping techniques. The forest soils in the study area were classified into organic and mineral layers, and carbon stocks were assessed for each layer to analyze their correlation with environmental variables. The random forest algorithm was employed to predict the spatial distribution of forest soil carbon stocks. The analysis of forest soil carbon stocks in the study area indicated that the average carbon stock was 0.43 kg/m2 in the organic layer, 5.30 kg/m2 at a soil depth of 0-30 cm, and 6.78 kg/m2 at a depth of 0-100 cm. The evaluation of the prediction model revealed that the average RMSE for the organic layer was 0.16 with an R2 value of 0.47, and the normalized difference vegetation index (NDVI) and forest type were identified as key environmental variables for carbon stock estimation. For the 0-30 cm soil depth, the average RMSE was 1.77 with an R2 value of 0.37, and elevation and insolation were determined to be the primary variables for the prediction model. In the prediction model for the 0-100 cm depth, the average RMSE was 2.48 with an R2 value of 0.36, and NDVI was determinedas a significant variable in addition to elevation and insolation. These findings indicate that carbon stocks in the organic layer are primarily influenced by vegetation, whereas carbon stocks in the mineral layer are predominantly governed by topographic factors such as erosion, transport, deposition, and storage of soil materials.
Abstract
This study aims to evaluate carbon stocks at varying soil depths in the forest soils of the Namyangju region using digital soil mapping techniques. The forest soils in the study area were classified into organic and mineral layers, and carbon stocks were assessed for each layer to analyze their correlation with environmental variables. The random forest algorithm was employed to predict the spatial distribution of forest soil carbon stocks. The analysis of forest soil carbon stocks in the study area indicated that the average carbon stock was 0.43 kg/m2 in the organic layer, 5.30 kg/m2 at a soil depth of 0-30 cm, and 6.78 kg/m2 at a depth of 0-100 cm. The evaluation of the prediction model revealed that the average RMSE for the organic layer was 0.16 with an R2 value of 0.47, and the normalized difference vegetation index (NDVI) and forest type were identified as key environmental variables for carbon stock estimation. For the 0-30 cm soil depth, the average RMSE was 1.77 with an R2 value of 0.37, and elevation and insolation were determined to be the primary variables for the prediction model. In the prediction model for the 0-100 cm depth, the average RMSE was 2.48 with an R2 value of 0.36, and NDVI was determinedas a significant variable in addition to elevation and insolation. These findings indicate that carbon stocks in the organic layer are primarily influenced by vegetation, whereas carbon stocks in the mineral layer are predominantly governed by topographic factors such as erosion, transport, deposition, and storage of soil materials.
참고문헌 (42)
- Behrens, T., "Multi-scale digital terrain analysis and feature selection for digital soil mapping", Geoderma, 155(3-4), pp.175~185 (2010)
- Blanco-Canqui, H., "Principles of soil conservation and management" (2008), Springer
- Böhner, J., "Land-surface parameters specific to topo-climatology", Developments in Soil Science, 33, pp.195~226 (2009)
- Böhner, J., "SAGA – Analysis and Modelling Applications" (2006), Goettinger Geographische Abhandlungen
- Conrad, O., "System for automated geoscientific analyses(SAGA)v. 2. 1. 4", Geoscientific Model Development, 8(7), pp.1991~2007 (2015)
- Dharumarajan, S., "The need for digital soil mapping in India", Geoderma Regional, 16, pp.e00204~ (2019)
- Entry, J. A., "Influence of irrigated agriculture on soil carbon and microbial community structure", Environmental Management, 33, pp.363~373 (2004)
- Global Soil Organic Carbon Database(at 30arcsec), "FAO", FAO, ~ (2017)
- Genuer, R., "Random Forests with R" (2020), Springer
- The National Atlas of Korea, "NGII", NGII, ~ (2020)
- Florinsky, I. V., "Prediction of soil properties by digital terrain modelling", Environmental Modelling & Software, 17(3), pp.295~311 (2002)
- Friedlingstein, P., "Global carbon budget 2020", Earth System Science Data Discussions, 2020, pp.1~3 (2020)
- Hastie, T., "Random forests, The elements of statistical learning:Data mining, inference, and prediction" (2009), Springer
- Hengl, T., "Predictive soil mapping with R" (2019), OpenGeoHub foundation
- Hoyle, F. C., "Capacity for increasing soil organic carbon stocks in dryland agricultural systems", Soil Research, 51(8), pp.657~667 (2013)
- Jenny, H., "Factors of soil formation: a system of quantitative pedology" (1941), Dover publications
- Jeong, G., "Spatial soil nutrients prediction using three supervised learning methods for assessment of land potentials in complex terrain", Catena, 154, pp.73~84 (2017)
- Jeong, G., "Effective Sampling Design for Assessing Forest Soil Carbon Stocks and Environmental Correlations", Journal of the Korean Geographical Society, 59(4), pp.471~486 (2024)
- Keshavarzi, A., "Digital mapping of soil texture classes for efficient land management in the Piedmont plain of Iran", Soil Use and Management, 38(4), pp.1705~1735 (2022)
- Kofpi, "Development of Forest Soil Carbon Survey and Analysis Techniques (Request for Proposal)", ~ (2023)
- Kuhn, M., "Applied predictive modeling" (2013), Springer
- Lal, R., "Soil carbon sequestration to mitigate climate change", Geoderma, 123(1-2), pp.1~22 (2004)
- McBratney, A. B., "On digital soil mapping", Geoderma, 117(1-2), pp.3~52 (2003)
- Minasny, B., "A conditioned Latin hypercube method for sampling in the presence of ancillary information", Computers &Geosciences, 32(9), pp.1378~1388 (2006)
- Minasny, B., "Digital mapping of soil carbon", Advances in Agronomy, 118, pp.1~47 (2013)
- Monger, H. C., "Sequestration of inorganic carbon in soil and groundwater", Geology, 43(4), pp.375~378 (2015)
- Mulder, V. L., "The use of remote sensing in soil and terrain mapping – A review", Geoderma, 162(1-2), pp.1~19 (2011)
- Pan, Y., "A large and persistent carbon sink in the world’s forests", Science, 333(6045), pp.988~993 (2011)
- Ren, W., "Global pattern and change of cropland soil organic carbon during 1901-2010 : Roles of climate, atmospheric chemistry, land use and management", Geography and Sustainability, 1(1), pp.59~69 (2020)
- Riley, S. J., "Index that quantifies topographic heterogeneity", Intermountain Journal of sciences, 5(1-4), pp.23~27 (1999)
- Schlesinger, W. H., "Biogeochemistry: An Analysis of Global Change" (2020), Academic Press
- Sedjo, R. A., "The carbon cycle and global forest ecosystem", Water, Air, and Soil Pollution, 70, pp.295~307 (1993)
- Streck, C., "International Yearbook of Soil Law and Policy 2016", pp.105~128 (2017), Springer International Publishing AG
- Tautges, N. E., "Deep soil inventories reveal that impacts of cover crops and compost on soil carbon sequestration differ in surface and subsurface soils", Global Change Biology, 25(11), pp.3753~3766 (2019)
- Tucker, C. J., "Satellite Remote Sensing of Primary Production", International Journal of Remote Sensing, 7, pp.1395~1416 (1986)
- Weiss, A. D., "Topographic Positions and Landforms Analysis (poster)", ESRI International User Conference (2001), ESRI
- Whitmore, A. P., "Technologies for increasing carbon storage in soil to mitigate climate change", Soil Use and Management, 31, pp.62~71 (2015)
- Yang, Y., "Storage, patterns and environmental controls of soil organic carbon in China", Biogeochemistry, 84(2), pp.131~141 (2007)
- Zenvenbergen, L. W., "Quantitative analysis of land surface topography", Earth Surface Processes and Landforms, 12(1), pp.47~56 (1987)
- Zeraatpisheh, M., "Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran", Geoderma, 338, pp.445~452 (2019)
- "Namyangju"[URL]
- "UNFCCC"[URL]