Paper
14 November 2007 Simplified desertification monitoring approach based on K-TTCT: a case study on Guyuan County, Heibei Province, China
Bao Cao, Qiming Qin, Lin Zhu, Baishou Li
Author Affiliations +
Proceedings Volume 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications; 67900E (2007) https://doi.org/10.1117/12.741703
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China
Abstract
Desertification is severely threatening the agricultural production and social stability in the 21st century. Traditionally, desertification assessment is indicated by Vegetation Coverage (VC), which can be derived from remote sensing data. However, vegetation indices are inefficient when VC is less than 15%. A simplified desertification monitoring approach based on Kauth-Thomas Tasseled Cap Transformation (K-TTCT) is proposed in this paper: First, brightness, greenness and wetness information was produced using landsat5 TM images by K-TTCT. The non density model was used for the reversion of VC. And the brightness, greenness, wetness and VC were plotted in n-visualization. They plotted nearly in a linear shape when the data was rotated to a certain view angle. Then their characteristics in n-D visualization were analyzed and training samples were selected with the help of n-D visualizer. Finally, a case study was carried out in Guyuan county, Heibei province, China using the approach proposed in this paper. It shows that this approach can overcome the deficiency of traditional desertification assessment approaches and produce a better desertification assessment outputs with an overall accuracy higher than 85%.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bao Cao, Qiming Qin, Lin Zhu, and Baishou Li "Simplified desertification monitoring approach based on K-TTCT: a case study on Guyuan County, Heibei Province, China", Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 67900E (14 November 2007); https://doi.org/10.1117/12.741703
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Cited by 2 scholarly publications.
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KEYWORDS
Vegetation

Visualization

Earth observing sensors

Landsat

Remote sensing

Virtual colonoscopy

Data modeling

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