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Quantitative analysis and evaluation of AVHRR and terrian data for small scale soil pattern recognition.

机译:用于小规模土壤模式识别的AVHRR和terrian数据的定量分析和评估。

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摘要

Previous research has demonstrated that data from air- and spaceborne sensors can be used effectively in identifying and delineating soil patterns and certain soil properties. However, little is known about the potential use of small scale satellite data, such as AVHRR (Advanced Very High Resolution Radiometer) in extracting soil information. The general objective of this study is to evaluate the use of coarse spatial resolution satellite imagery and digital terrain data as potential data sources for delineating meaningful soil information in support of small scale soil database development.;An effort was made to quantify and characterize the AVHRR-soil relationship. A multitemporal and multispectral database of AVHRR was used for a statistical analysis. The results show that the amount of extractable information depends on the spectral characteristics of the band, the acquisition date of the image and the environmental conditions of the observed area at the time of data acquisition. The thermal bands and the vegetation index were found to be the best for delineating soil patterns. The results suggest that the soil class identification in small scale endeavors is often more likely to be based on the environmental characteristics, the delineation of the so called soil-forming environment patterns, than on the characteristics of the soil itself.;Based on the results reported here, it was concluded, that remotely sensed data are greatly influenced by terrain variability, however, such data do not represent all the soil variability that occurs in the landscape. The integration of digital terrain data of appropriate spatial resolution has greatly improved the classification performance. A new terrain descriptor function, namely the potential drainage density function was developed and integrated to the model for the characterization of a plain landscape with the use of coarse spatial resolution digital terrain data.;With the use of integrated AVHRR and digital terrain data, numerous data processing and classification algorithms were tested and evaluated. The best result was achieved with the use of the discriminant analysis feature extraction procedure and a spatial-spectral classifier, namely the ECHO.
机译:先前的研究表明,来自空中和星载传感器的数据可以有效地用于识别和描绘土壤模式和某些土壤特性。但是,人们对于诸如AVHRR(高级超高分辨率辐射计)之类的小规模卫星数据在提取土壤信息中的潜在用途知之甚少。这项研究的总体目标是评估使用粗略的空间分辨率卫星图像和数字地形数据作为潜在数据源来描绘有意义的土壤信息,以支持小规模土壤数据库的开发。;已努力量化和表征AVHRR -土壤关系。 AVHRR的多时间和多光谱数据库用于统计分析。结果表明,可提取信息的数量取决于波段的光谱特性,图像的采集日期和数据采集时观察区域的环境条件。人们发现,热带和植被指数最适合描述土壤模式。结果表明,小规模耕作中的土壤类别识别通常更可能基于环境特征,即所谓的土壤形成环境模式的划分,而不是基于土壤本身的特征。据此得出的结论是,遥感数据受地形变化的影响很大,但是,这些数据并不代表景观中发生的所有土壤变化。具有适当空间分辨率的数字地形数据的集成大大提高了分类性能。开发了一种新的地形描述函数,即潜在的排水密度函数,并使用粗糙的空间分辨率数字地形数据将其集成到模型中,以表征平原景观。通过使用集成的AVHRR和数字地形数据,大量测试和评估了数据处理和分类算法。通过使用判别分析特征提取程序和空间光谱分类器,即ECHO,可以达到最佳结果。

著录项

  • 作者

    Dobos, Endre Zsolt.;

  • 作者单位

    Purdue University.;

  • 授予单位 Purdue University.;
  • 学科 Agriculture Agronomy.;Environmental Sciences.;Remote Sensing.
  • 学位 Ph.D.
  • 年度 1998
  • 页码 228 p.
  • 总页数 228
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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