IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i7p1344-d1186977.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/7/1344/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/12/7/1344/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rina Wu & Ruinan Wang & Leting Lv & Junchao Jiang, 2024. "The Past, Present and Future of Land Use and Land Cover Changes: A Case Study of Lower Liaohe River Plain, China," Sustainability, MDPI, vol. 16(14), pages 1-21, July.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yingqiang Song & Zeao Zhang & Yan Li & Runyan Zou & Lu Wang & Hao Yang & Yueming Hu, 2023. "The Role of High Nature Value Farmland for Landscape and Soil Pollution Assessment in a Coastal Delta in China Based on High-Resolution Indicators," Sustainability, MDPI, vol. 15(8), pages 1-19, April.
    2. Chen, Hang & Meng, Fei & Yu, Zhenning & Tan, Yongzhong, 2022. "Spatial–temporal characteristics and influencing factors of farmland expansion in different agricultural regions of Heilongjiang Province, China," Land Use Policy, Elsevier, vol. 115(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:12:y:2023:i:7:p:1344-:d:1186977. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.