RESEARCH PAPER
Application of land use modes in the spatial prediction of soil organic carbon in urban green spaces
1
Key Laboratory of Forest Management and Growth Modelling, National State Forestry and Grassland Administration, Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
2
Ecological Construction Investment Company Limited, Xiong'an Group, Xiong'an 071699, China
3
Industry Development and Planning Institute, NFGA, Beijing 100010, China
Final revision date: 2022-10-09
Acceptance date: 2022-10-24
Publication date: 2022-12-30
Corresponding author
Xianzhao Liu
Key Laboratory of Forest Management and Growth Modelling, State Forestry and Grassland Administration, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, China
Key Laboratory of Forest Management and Growth Modelling, State Forestry and Grassland Administration, Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, China
Int. Agrophys. 2023, 37(1): 1-14
HIGHLIGHTS
- The spatial distribution pattern of soil organic carbon in Xiong'an New Area was predicted using four interpolation methods.
- Land use modes are important factors affecting the spatial distribution of urban soil organic carbon.
- Using land use type as an auxiliary variable can effectively improve the prediction accuracy of soil spatial distribution.
KEYWORDS
TOPICS
ABSTRACT
The challenge of predicting soil organic carbon distribution accurately has received great attention in order to support urban green space soil management during climate change. This study compared four geostatistical methods: kriging combined with land use, ordinary kriging, inverse distance weighting and radial basis function, to predict the spatial distribution patterns of soil organic carbon content and soil organic carbon density in the Xiong'an New Area, estimate organic carbon stocks, and assess the role of land use types in the spatial prediction of soil organic carbon stocks. The results showed that the soil organic carbon content decreased with increasing soil depth, and was significantly affected by different land use types (p<0.05). The correlation coefficient values of kriging combined with land use were on average 0.229 higher than those of other methods. The root mean squared error and the mean absolute error of kriging combined with land use were on average 0.148 and 0.139 lower than those of the other methods. Kriging combined with land use has a greater advantage over other methods in predicting the spatial distribution of soil organic carbon content, and also the spatial distribution of soil organic carbon density and the spatial distribution of soil organic carbon, the prediction results of the four interpolation methods were similar. The average soil organic carbon density was 2085 Gg (0-30 cm) and 1363 Gg (30-60 cm). In conclusion, land use type clearly influences the spatial distribution of soil organic carbon in urban areas, and by using land use type as auxiliary data, we can obtain a more accurate spatial distribution of soil organic carbon and predict the total storage capacity of the soil. This study may result in significant advances in the spatial prediction of soil organic carbon for urban areas.
ACKNOWLEDGEMENTS
The authors are grateful to the Fundamental Research Funds of CAF (CAFYBB2019ZB005).
FUNDING
This work was supported by the Chinese Academy of Forestry (CAFYBB2019ZB005; 2019-2022).
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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