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Optimization of Sample Points for Monitoring Arable Land Quality by Simulated Annealing while Considering Spatial Variations

Author

Listed:
  • Junxiao Wang

    (School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210046, China)

  • Xiaorui Wang

    (School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210046, China)

  • Shenglu Zhou

    (School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210046, China)

  • Shaohua Wu

    (School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210046, China)

  • Yan Zhu

    (Nanjing Nanyuan Land Development and Utilization Consulting Co. Ltd., Nanjing 210008, China)

  • Chunfeng Lu

    (Nanjing Nanyuan Land Development and Utilization Consulting Co. Ltd., Nanjing 210008, China)

Abstract

With China’s rapid economic development, the reduction in arable land has emerged as one of the most prominent problems in the nation. The long-term dynamic monitoring of arable land quality is important for protecting arable land resources. An efficient practice is to select optimal sample points while obtaining accurate predictions. To this end, the selection of effective points from a dense set of soil sample points is an urgent problem. In this study, data were collected from Donghai County, Jiangsu Province, China. The number and layout of soil sample points are optimized by considering the spatial variations in soil properties and by using an improved simulated annealing (SA) algorithm. The conclusions are as follows: (1) Optimization results in the retention of more sample points in the moderate- and high-variation partitions of the study area; (2) The number of optimal sample points obtained with the improved SA algorithm is markedly reduced, while the accuracy of the predicted soil properties is improved by approximately 5% compared with the raw data; (3) With regard to the monitoring of arable land quality, a dense distribution of sample points is needed to monitor the granularity.

Suggested Citation

  • Junxiao Wang & Xiaorui Wang & Shenglu Zhou & Shaohua Wu & Yan Zhu & Chunfeng Lu, 2016. "Optimization of Sample Points for Monitoring Arable Land Quality by Simulated Annealing while Considering Spatial Variations," IJERPH, MDPI, vol. 13(10), pages 1-12, September.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:10:p:980-:d:79682
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    References listed on IDEAS

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    Cited by:

    1. Jamal Jokar Arsanjani, 2017. "Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health: An Editorial," IJERPH, MDPI, vol. 14(4), pages 1-3, April.

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