Spatially locating soil classes within complex soil polygons – Mapping soil capability for agriculture in Saskatchewan Canada

Spatially locating soil classes within complex soil polygons – Mapping soil capability for agriculture in Saskatchewan Canada
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DOI:
10.1016/j.agee.2012.02.007
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发表时间:
2012-05
期刊:
Agriculture, Ecosystems & Environment
影响因子:
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通讯作者:
Zhe Li;T. Huffman;A. Zhang;F. Zhou;B. McConkey
Zhe Li;T. Huffman;A. Zhang;F. Zhou;B. McConkey
中科院分区:
其他
文献类型:
--
作者:
Zhe Li;T. Huffman;A. Zhang;F. Zhou;B. McConkey

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本文提出了一种简化的土壤能力制图方法,该方法由加拿大土地清单(CLI)定义,基于土壤能力的主要决定因素可以由地球观测(EO)数据与其他生物物理信息相结合得出的归一化植被指数(NDVI)代替。在加拿大萨斯喀彻温省进行了一个案例研究,将决策树分类方法与增强算法用于空间定位CLI数据库复杂符号中估计的各个土壤能力类别。用于分类的输入指标包括原始NDVI图像的前四个主成分、物候参数、地形因子、土地覆盖和空间依赖性图像。验证表明,在均质土壤多边形内绘制的土壤能力等级Kappa系数较高,在非均质土壤多边形内绘制的土壤面积与气候变化指数估算的土壤面积之间的r平方系数较高。结果证实了将MODIS 250m时序归一化植被指数(NDVI)参数与辅助数据相结合可以作为土壤能力分类的综合工具的假设。
This paper proposes a simplified approach to mapping soil capability, as defined by the Canada Land Inventory (CLI), based on the hypothesis that the primary determinants of soil capability may be surrogated by Normalized Difference Vegetation Index (NDVI) derived from Earth Observation (EO) data integrated with other biophysical information. A case study in which a Decision Tree classification method with a boosting algorithm was used in spatially locating individual soil capability classes as estimated in the complex symbol of the CLI database was conducted in Saskatchewan Canada. The input metrics used for the classification include the first four principal components of the original NDVI images, phenological parameters, topographic factors, land cover and spatial dependence images. Validation showed high Kappa coefficients for the mapped soil capability classes within homogeneous soil polygons and high R-squares between the mapped soil area and CLI-estimated area within heterogeneous polygons. Results confirm the hypothesis that integrating parameters derived from the Moderate Resolution Imaging Spectro-radiometer (MODIS) 250m time-series Normalized Difference Vegetation Index (NDVI) with ancillary data may serve as a comprehensive tool for classification of soil capability.