scholarly journals Investigating Climate Change by Digital Analysis of Blue Ice Extent on Satellite Images of Antarctica

1990 ◽  
Vol 14 ◽  
pp. 211-215 ◽  
Author(s):  
Olav Orheim ◽  
Baerbel Lucchitta
Keyword(s):  
Climate Change ◽  
Principal Component Analysis ◽  
Satellite Images ◽  
Accumulation Rate ◽  
Principal Component ◽  
Thematic Mapper ◽  
Digital Analysis ◽  
Near Ir ◽  
Band Ratios ◽  
Rate Comparison

Landsat-5 Thematic Mapper (TM) and SPOT data collected two years apart from an identical area of Dronning (Queen) Maud Land, Antarctica, have been analyzed to detect variations in surface features that may signal climatic change, and to establish a technique that readily identifies such changes. We found that selective principal component analysis (Chavez and Kwarteng 1989), on band ratios of near-IR/green, highlights changes in blue ice areas. The formation and preservation of blue ice is poorly understood, but we suggest that it generally takes longer to increase a blue ice area than to decrease it, and that blue ice extent is most sensitive to changes in accumulation rate. The investigated blue ice area shows a decrease in extent over the two-year period caused by incursion of snow that probably resulted from an increase in accumulation rate. Comparison of two TM images collected 18 days apart shows that transitory snow drifts have little effect on blue ice extent.

scholarly journals Investigating Climate Change by Digital Analysis of Blue Ice Extent on Satellite Images of Antarctica

1990 ◽  
Vol 14 ◽  
pp. 211-215 ◽  
Author(s):  
Olav Orheim ◽  
Baerbel Lucchitta
Keyword(s):  
Climate Change ◽  
Principal Component Analysis ◽  
Satellite Images ◽  
Accumulation Rate ◽  
Principal Component ◽  
Thematic Mapper ◽  
Digital Analysis ◽  
Near Ir ◽  
Band Ratios ◽  
Rate Comparison

Landsat-5 Thematic Mapper (TM) and SPOT data collected two years apart from an identical area of Dronning (Queen) Maud Land, Antarctica, have been analyzed to detect variations in surface features that may signal climatic change, and to establish a technique that readily identifies such changes. We found that selective principal component analysis (Chavez and Kwarteng 1989), on band ratios of near-IR/green, highlights changes in blue ice areas. The formation and preservation of blue ice is poorly understood, but we suggest that it generally takes longer to increase a blue ice area than to decrease it, and that blue ice extent is most sensitive to changes in accumulation rate. The investigated blue ice area shows a decrease in extent over the two-year period caused by incursion of snow that probably resulted from an increase in accumulation rate. Comparison of two TM images collected 18 days apart shows that transitory snow drifts have little effect on blue ice extent.

The Quantitative Evaluation on the Response of Climate Change upon the Sustainability of the Agricultural Production

2010 ◽  
Vol 129-131 ◽  
pp. 1161-1165
Author(s):  
Lin Chun Hou ◽  
Hui Qin Li
Keyword(s):  
Climate Change ◽  
Principal Component Analysis ◽  
Linear Regression ◽  
Agricultural Production ◽  
Agricultural Productivity ◽  
Principal Component ◽  
Environmental Condition ◽  
Land Utilization ◽  
Agricultural Society ◽  
Per Capita

The aim: quantitatively evaluate the response of climate change upon the sustainability of the agricultural production. The method: the paper selected two regions (Hubei and shan’xi province) which represented different climate environment, utilized modern statistic data, Principal Component Analysis and multivariate linear regression to quantitatively evaluate the influence of climate change upon agricultural production through isolating climate environment from arable area, land utilization and management and landform and so on. The conclusion: The study indicated that when environmental condition turned good to agriculture, the function of environmental condition to agriculture relatively decreased; the capability of agricultural society and production decreased too, and people could select the land to cultivate, where agricultural productivity is higher. And that when environmental condition turned bad to agriculture, the function of environmental condition to agriculture relatively increased; the capability of agricultural society and production increased, too; people could not put emphasis on the land where agricultural productivity is higher, whereas focused on productivity per capita.

scholarly journals A Data-driven Study of RR Lyrae Near-IR Light Curves: Principal Component Analysis, Robust Fits, and Metallicity Estimates

2018 ◽  
Vol 857 (1) ◽  
pp. 55 ◽  
Author(s):  
Gergely Hajdu ◽  
István Dékány ◽  
Márcio Catelan ◽  
Eva K. Grebel ◽  
Johanna Jurcsik
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Light Curves ◽  
Data Driven ◽  
Rr Lyrae ◽  
Near Ir ◽  
Ir Light

scholarly journals Digital analysis of multispectral space imagery Landsat 7 ETM+ of the area in central part of Ukrainian Carpathians

2013 ◽  
pp. 205-212 ◽  
Author(s):  
Olexandr Mkrtchian
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Vegetation Index ◽  
Principal Component ◽  
Digital Analysis ◽  
Vegetation Types ◽  
Space Imagery ◽  
Ndvi Index ◽  
Landsat 7 ◽  
Spectral Channels

The methodology and results of the digital analysis of multispectral space imagery LANDSAT 7 ETM+ are considered, namely the calculation of NDVI index and isolation of principal components. The meaning of the latter has been analyzed by the calculation of their correlations with spectral channels and relations to the main vegetation types. Key words: multispectral geoimagery, vegetation index, principal component analysis, geoinformation analysis.

scholarly journals A Comparative Analysis of Countries' Performance According to SDG Indicators based on Machine Learning

2021 ◽  
Author(s):  
Guilherme Souza ◽  
Julian Santos ◽  
Gabriel SantClair ◽  
Janaina Gomide ◽  
Luan Santos
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Economic Growth ◽  
Principal Component Analysis ◽  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Principal Component ◽  
The Sustainable Development ◽  
Development Goals ◽  
Impacts Of Climate Change

The Sustainable Development Goals (SDGs) are part of a global effort to reduce the impacts of climate change, promoting social justice and economic growth. The United Nations provides a database with hundreds of indicators to track the SDGs since 2016 for a total of 302 regions. This work aims to assess which countries are in a similar situation regarding sustainable development. Principal Component Analysis was used to reduce the dimension of the dataset and k-means algorithm was used to cluster countries according to their SDGs indicators. For the years of 2016, 2017 and 2018 were obtained 11, 13 and 11 groups, respectively. This paper also analyses clusters changes throughout the years.

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