scholarly journals The accuracy and relative efficiency of Landsat data and orthophotos for determining area and volume of spruce.

1985 ◽  
Vol 33 (2) ◽  
pp. 141-150
Author(s):  
D.A. Stellingwerf ◽  
S. Lwin
Keyword(s):  
Principal Components ◽  
Relative Efficiency ◽  
Field Work ◽  
Data Sets ◽  
Landsat Data ◽  
Age Classes ◽  
Upper Austria ◽  
Components Analysis ◽  
Total Forest Area ◽  
Area And Volume

Comparative estimates were made for a 12 841-ha area of Upper Austria comprising areas of pure or mixed Norway spruce and beech, young stands and non-forest. The Landsat data, classified by principal components analysis, gave very inaccurate differentiation of species, age classes and smaller non-forest areas, although the total forest area was reasonably accurate. Stand vol. of spruce was estimated by 2-stage sampling of both data sets followed by field work on sample plots. The Landsat method required 53% more primary (first-stage sampling) units, 23% more man-days and higher extra costs than the orthophoto method for the same accuracy. (Abstract retrieved from CAB Abstracts by CABI’s permission)

scholarly journals Principal Components Analysis of Multispectral Image Data

2004 ◽  
Vol 12 (5) ◽  
pp. 36-39 ◽  
Author(s):  
Brent Neal ◽  
John C. Russ
Keyword(s):  
Principal Components Analysis ◽  
20Th Century ◽  
Principal Components ◽  
Principal Axis ◽  
Image Data ◽  
Data Sets ◽  
Principal Axes ◽  
Source Data ◽  
Components Analysis ◽  
The One

Principal components analysis of multivariate data sets is a standard statistical method that was developed in the early halt or the 20th century. It provides researchers with a method for transforming their source data axes into a set of orthogonal principal axes and ranks. The rank for each axis in the principal set represents the significance of that axis as defined by the variance in the data along that axis. Thus, the first principal axis is the one with the greatest amount of scatter in the data and consequently the greatest amount of contrast and information, while the last principal axis represents the least amount of information.

scholarly journals Haplotype Classification Using Copy Number Variation and Principal Components Analysis

2013 ◽  
Vol 7 (1) ◽  
pp. 19-24
Author(s):  
Kevin Blighe
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Large Scale ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Set ◽  
Reduction Techniques ◽  
Number Variation ◽  
Components Analysis

Elaborate downstream methods are required to analyze large microarray data-sets. At times, where the end goal is to look for relationships between (or patterns within) different subgroups or even just individual samples, large data-sets must first be filtered using statistical thresholds in order to reduce their overall volume. As an example, in anthropological microarray studies, such ‘dimension reduction’ techniques are essential to elucidate any links between polymorphisms and phenotypes for given populations. In such large data-sets, a subset can first be taken to represent the larger data-set. For example, polling results taken during elections are used to infer the opinions of the population at large. However, what is the best and easiest method of capturing a sub-set of variation in a data-set that can represent the overall portrait of variation? In this article, principal components analysis (PCA) is discussed in detail, including its history, the mathematics behind the process, and in which ways it can be applied to modern large-scale biological datasets. New methods of analysis using PCA are also suggested, with tentative results outlined.

scholarly journals Relationships between Association of Research Libraries (ARL) Statistics and Bibliometric Indicators: A Principal Components Analysis

2017 ◽  
Author(s):  
Dean Hendrix
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Quantitative Research ◽  
Bibliometric Indicator ◽  
Data Sets ◽  
Research Libraries ◽  
Association Of Research Libraries ◽  
Size Dependent ◽  
Dependent Variables ◽  
Components Analysis

This study analyzed 2005–2006 Web of Science bibliometric data from institutions belonging to the Association of Research Libraries (ARL) and corresponding ARL statistics to find any associations between indicators from the two data sets. Principal components analysis on 36 variables from 103 universities revealed obvious associations between size-dependent variables, such as institution size, gross totals of library measures, and gross totals of articles and citations. However, size-independent library measures did not associate positively or negatively with any bibliometric indicator. More quantitative research must be done to authentically assess academic libraries’ influence on research outcomes.

scholarly journals Relationships between Association of Research Libraries (ARL) Statistics and Bibliometric Indicators: A Principal Components Analysis

2010 ◽  
Vol 71 (1) ◽  
pp. 32-41
Author(s):  
Dean Hendrix
Keyword(s):  
Principal Components Analysis ◽  
Principal Components ◽  
Quantitative Research ◽  
Bibliometric Indicator ◽  
Data Sets ◽  
Research Libraries ◽  
Association Of Research Libraries ◽  
Size Dependent ◽  
Dependent Variables ◽  
Components Analysis

This study analyzed 2005–2006 Web of Science bibliometric data from institutions belonging to the Association of Research Libraries (ARL) and corresponding ARL statistics to find any associations between indicators from the two data sets. Principal components analysis on 36 variables from 103 universities revealed obvious associations between size-dependent variables, such as institution size, gross totals of library measures, and gross totals of articles and citations. However, size-independent library measures did not associate positively or negatively with any bibliometric indicator. More quantitative research must be done to authentically assess academic libraries’ influence on research outcomes.

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