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Integrating Landscape Pattern Metrics to Map Spatial Distribution of Farmland Soil Organic Carbon on Lower Liaohe Plain of Northeast China

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  • Xiaochen Liu

    (College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, China
    Key Laboratory of Cultivated Land System Protection, Department of Natural Resources of Liaoning Province, Shenyang 110866, China)

  • Zhenxing Bian

    (College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, China
    Key Laboratory of Cultivated Land System Protection, Department of Natural Resources of Liaoning Province, Shenyang 110866, China)

  • Zhentao Sun

    (College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, China)

  • Chuqiao Wang

    (College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, China
    Key Laboratory of Cultivated Land System Protection, Department of Natural Resources of Liaoning Province, Shenyang 110866, China)

  • Zhiquan Sun

    (College of Land and Environment, Shenyang Agricultural University, Shenyang 110866, China
    Key Laboratory of Cultivated Land System Protection, Department of Natural Resources of Liaoning Province, Shenyang 110866, China)

  • Shuang Wang

    (Natural Resources Affairs Service Center, Tieling 112608, China)

  • Guoli Wang

    (Shanshui Planning and Design Limited Liability Company, Shenyang 110032, China)

Abstract

Accurate digital mapping of farmland soil organic carbon (SOC) contributes to sustainable agricultural development and climate change mitigation. Farmland landscape pattern has changed greatly under anthropogenic influence, which should be considered an environmental variable to characterize the impact of human activities on SOC. In this study, we verified the feasibility of integrating landscape patterns in SOC prediction on Lower Liaohe Plain. Specifically, ten variables (climate, topographic, and landscape pattern variables) were selected for prediction with Random Forest (RF) and Support Vector Machines (SVMs). The effectiveness of landscape metrics was verified by establishing different variable combinations: (1) natural variables, and (2) natural and landscape pattern variables. The results confirmed that landscape variables improved mapping accuracy compared with natural variables. R 2 of RF and SVM increased by 20.63% and 20.75%, respectively. RF performed better than SVM with smaller prediction error. Ranking of importance of variables showed that temperature and precipitation were the most important variables. The Aggregation Index (AI) contributed more than elevation, becoming the most important landscape variable. The Mean Contiguity Index (CONTIG-MN) and Landscape Contagion Index (CONTAG) also contributed more than other topographic variables. We conclude that landscape patterns can improve mapping accuracy and support SOC sequestration by optimizing farmland landscape management policies.

Suggested Citation

  • Xiaochen Liu & Zhenxing Bian & Zhentao Sun & Chuqiao Wang & Zhiquan Sun & Shuang Wang & Guoli Wang, 2023. "Integrating Landscape Pattern Metrics to Map Spatial Distribution of Farmland Soil Organic Carbon on Lower Liaohe Plain of Northeast China," Land, MDPI, vol. 12(7), pages 1-19, July.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:7:p:1344-:d:1186977
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    References listed on IDEAS

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    1. An, Yi & Liu, Shiliang & Sun, Yongxiu & Shi, Fangning & Zhao, Shuang, 2020. "Negative effects of farmland expansion on multi-species landscape connectivity in a tropical region in Southwest China," Agricultural Systems, Elsevier, vol. 179(C).
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    Cited by:

    1. Odunayo David Adeniyi & Hauwa Bature & Michael Mearker, 2024. "A Systematic Review on Digital Soil Mapping Approaches in Lowland Areas," Land, MDPI, vol. 13(3), pages 1-22, March.

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