Integration and analysis of remote sensing, airborne geophysics, and geochemical data of Sar Cheshmeh area by using directed principal component analysis

2003 ◽  
Author(s):  
Hojjatollah Ranjbar ◽  
Mehdi Honarmand ◽  
Zohre Moezifar ◽  
Morteza Shakoori
Keyword(s):  
Remote Sensing ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Geochemical Data ◽  
Airborne Geophysics ◽  
Sar Cheshmeh

Application of the principal component analysis on geochemical data: a case study in the basement complex of Southern Ilesa area, Nigeria

2010 ◽  
Vol 4 (1-2) ◽  
pp. 239-247 ◽  
Author(s):  
Emmanuel A. Ariyibi ◽  
Samuel L. Folami ◽  
Bankole D. Ako ◽  
Taye R. Ajayi ◽  
Adebowale O. Adelusi
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Geochemical Data ◽  
Basement Complex

scholarly journals SVD-based principal component analysis of geochemical data

2005 ◽  
Vol 3 (4) ◽  
pp. 731-741 ◽  
Author(s):  
Petr Praus
Keyword(s):  
Principal Component Analysis ◽  
Euclidean Distance ◽  
Principal Component ◽  
Component Analysis ◽  
Data Matrix ◽  
Geochemical Data ◽  
Average Group ◽  
Testing Data ◽  
Value Decomposition ◽  
Geochemical Variables

AbstractPrincipal Component Analysis (PCA) was used for the mapping of geochemical data. A testing data matrix was prepared from the chemical and physical analyses of the coals altered by thermal and oxidation effects. PCA based on Singular Value Decomposition (SVD) of the standardized (centered and scaled by the standard deviation) data matrix revealed three principal components explaining 85.2% of the variance. Combining the scatter and components weights plots with knowledge of the composition of tested samples, the coal samples were divided into seven groups depending on the degree of their oxidation and thermal alteration.The PCA findings were verified by other multivariate methods. The relationships among geochemical variables were successfully confirmed by Factor Analysis (FA). The data structure was also described by the Average Group dendrogram using Euclidean distance. The found sample clusters were not defined so clearly as in the case of PCA. It can be explained by the PCA filtration of the data noise.

scholarly journals Identification of catchment functional units by time series of thermal remote sensing images

2014 ◽  
Vol 18 (12) ◽  
pp. 5345-5359 ◽  
Author(s):  
B. Müller ◽  
M. Bernhardt ◽  
K. Schulz
Keyword(s):  
Remote Sensing ◽  
Principal Component Analysis ◽  
Time Series ◽  
Land Surface ◽  
Temporal Dynamics ◽  
Principal Component ◽  
Component Analysis ◽  
Land Surface Models ◽  
Surface Models ◽  
Functional Units

Abstract. The identification of catchment functional behavior with regards to water and energy balance is an important step during the parameterization of land surface models. An approach based on time series of thermal infrared (TIR) data from remote sensing is developed and investigated to identify land surface functioning as is represented in the temporal dynamics of land surface temperature (LST). For the mesoscale Attert catchment in midwestern Luxembourg, a time series of 28 TIR images from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) was extracted and analyzed, applying a novel process chain. First, the application of mathematical–statistical pattern analysis techniques demonstrated a strong degree of pattern persistency in the data. Dominant LST patterns over a period of 12 years were then extracted by a principal component analysis. Component values of the two most dominant components could be related for each land surface pixel to land use data and geology, respectively. The application of a data condensation technique ("binary words") extracting distinct differences in the LST dynamics allowed the separation into landscape units that show similar behavior under radiation-driven conditions. It is further outlined that both information component values from principal component analysis (PCA), as well as the functional units from the binary words classification, will highly improve the conceptualization and parameterization of land surface models and the planning of observational networks within a catchment.

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