长江科学院院报 ›› 2016, Vol. 33 ›› Issue (2): 24-27.DOI: 10.11988/ckyyb.20140824

• 水土保持与生态建设 • 上一篇    下一篇

土壤可蚀性因子制图及其不确定性研究进展

孙佳佳a, 王志刚b   

  1. 长江科学院 a.水土保持研究所; b.水利部山洪地质灾害防治工程技术研究中心,武汉 430010
  • 收稿日期:2014-09-25 出版日期:2016-02-01 发布日期:2016-02-17
  • 作者简介:孙佳佳(1983-),女,山东泰安人,工程师,硕士,主要从事土壤侵蚀、水土保持方面的研究,(电话)027-82926365(电子信箱)hhzxinxiang@163.com。
  • 基金资助:
    国家自然科学基金项目(41201269,41101191);中央级公益性科研院所基本科研业务费资助项目(CKSF2015011/TB,CKSF2014022/TB);长江科学院创新团队项目(CKSF2012052/TB);国家科技重大专项专题(2012ZX07503-002-03)

Research Advances in Mapping of Soil Erodibility Factor and Its Uncertainty

SUN Jia-jia1, WANG Zhi-gang2   

  1. 1.Soil and Water Conservation Department, Yangtze River Scientific Research Institute, Wuhan 430010, China;
    2.Research Center on Mountain Torrent & Geologic Disaster Prevention of Ministry of Water Resources, Yangtze River Scientific Research Institute, Wuhan 430010, China
  • Received:2014-09-25 Online:2016-02-01 Published:2016-02-17

摘要: 土壤可蚀性因子是评价土壤对侵蚀敏感程度的重要指标,也是进行土壤侵蚀预报的重要参数。为促进今后相关研究的发展,较为系统地阐述了土壤可蚀性因子、土壤可蚀性因子的空间预测以及土壤可蚀性因子制图中的误差和不确定性的国内外研究现状。分析指出,准确地制作土壤可蚀性因子空间分布图具有重要的现实意义,然而,影响土壤可蚀性因子的因素相当复杂,将土壤类型、高程、遥感等辅助信息融入空间预测,量化、分析空间预测过程中的不确定性,降低制图误差、提高精度、控制风险,成为今后研究的方向。

关键词: 土壤可蚀性, 空间预测, 土壤可蚀性因子制图, 不确定性, 地统计学

Abstract: Soil erodibility is an important indicator of assessing impact of erosion on soil, and also an important parameter for predicting soil erosion. In order to promote relevant research of soil erodibility factor in the future, we present state of the art in China and abroad in terms of its spatial prediction and error and uncertainty of mapping. Analysis shows that accurately acquiring spatial distribution of soil erodibility factor is of practical significance, but it is a difficult task due to complex factors which affect soil erodibility factor. In the future, we should lay emphasis on reducing mapping error, improving accuracy and controlling risk, and quantitatively analyzing the uncertainty of spatial prediction, in association with auxiliary information such as soil types, elevation, and remote sensing images.

Key words: soil erodibility, spatial prediction, mapping of soil erodibility factor, uncertainty, geostatistics

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