Using Bayesian Regression Tree Models and remotely sensed data to characterize recent environmental change in Alaska, USA

Remotely sensed Advanced Very High Resolution Radiometer (AVHRR) images, collected between 1995 and 2007, and Bayesian Regression Tree Modeling were brought together to characterize growing season environmental (vegetation, temperature, precipitable water, and cloudiness) change in Alaska. This meth...

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Main Author: Harvey, Joann W., 1955-
Other Authors: Harvey, Joann W., 1955- (author), Green, Edwin J. (chair), Smouse, Peter E. (internal member), Morin, Peter J. (internal member), Lathrop, Richard G. (internal member), Strawderman, William E. (outside member), Rutgers University, Graduate School - New Brunswick
Format: Thesis
Language:English
Published: 2011
Subjects:
Online Access:http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000063452
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spelling ftrutgersuniv:oai:example.org:rutgers-lib:36038 2023-05-15T15:08:45+02:00 Using Bayesian Regression Tree Models and remotely sensed data to characterize recent environmental change in Alaska, USA Harvey, Joann W., 1955- Harvey, Joann W., 1955- (author) Green, Edwin J. (chair) Smouse, Peter E. (internal member) Morin, Peter J. (internal member) Lathrop, Richard G. (internal member) Strawderman, William E. (outside member) Rutgers University Graduate School - New Brunswick 2011 xxii, 248 p. : ill. electronic resource application/pdf http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000063452 eng eng Rutgers University Electronic Theses and Dissertations Graduate School - New Brunswick Electronic Theses and Dissertations rucore19991600001 http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000063452 Ecology and Evolution Climatic changes--Alaska Bayesian statistical decision theory Text theses 2011 ftrutgersuniv 2022-05-30T13:43:56Z Remotely sensed Advanced Very High Resolution Radiometer (AVHRR) images, collected between 1995 and 2007, and Bayesian Regression Tree Modeling were brought together to characterize growing season environmental (vegetation, temperature, precipitable water, and cloudiness) change in Alaska. This method highlighted general trends and local variation. The method was applied in two stages to reduce the effects of cloudiness upon the results and reveal the temporal distribution of cloudiness conditions. A reversible form of tree model “subtree replacement” was included in the Reversible Jump MCMC algorithm. A sensitivity analysis showed that larger values of some hyperprior parameters could increase the number of subsets delineated by the method. For data collected during 1995 – 2002, the analyses showed local variation and subtle changes. In 2003, conditions of higher precipitable water, higher Normalized Difference Vegetation Index (NDVI), and/or greater cloudiness were highlighted. In 2004, the analyses detected a shift to lower precipitable water and/or lower cloudiness, often accompanied or followed by lower NDVI and higher land surface temperature. In 2007, continued warming was highlighted in the Arctic and northern interior regions, in contrast with a return to earlier conditions and increased cloudiness revealed in regions near the Bering Sea. Ph. D. Includes bibliographical references by Joann W. Harvey Thesis Arctic Bering Sea Alaska RUcore - Rutgers University Community Repository Arctic Bering Sea
institution Open Polar
collection RUcore - Rutgers University Community Repository
op_collection_id ftrutgersuniv
language English
topic Ecology and Evolution
Climatic changes--Alaska
Bayesian statistical decision theory
spellingShingle Ecology and Evolution
Climatic changes--Alaska
Bayesian statistical decision theory
Harvey, Joann W., 1955-
Using Bayesian Regression Tree Models and remotely sensed data to characterize recent environmental change in Alaska, USA
topic_facet Ecology and Evolution
Climatic changes--Alaska
Bayesian statistical decision theory
description Remotely sensed Advanced Very High Resolution Radiometer (AVHRR) images, collected between 1995 and 2007, and Bayesian Regression Tree Modeling were brought together to characterize growing season environmental (vegetation, temperature, precipitable water, and cloudiness) change in Alaska. This method highlighted general trends and local variation. The method was applied in two stages to reduce the effects of cloudiness upon the results and reveal the temporal distribution of cloudiness conditions. A reversible form of tree model “subtree replacement” was included in the Reversible Jump MCMC algorithm. A sensitivity analysis showed that larger values of some hyperprior parameters could increase the number of subsets delineated by the method. For data collected during 1995 – 2002, the analyses showed local variation and subtle changes. In 2003, conditions of higher precipitable water, higher Normalized Difference Vegetation Index (NDVI), and/or greater cloudiness were highlighted. In 2004, the analyses detected a shift to lower precipitable water and/or lower cloudiness, often accompanied or followed by lower NDVI and higher land surface temperature. In 2007, continued warming was highlighted in the Arctic and northern interior regions, in contrast with a return to earlier conditions and increased cloudiness revealed in regions near the Bering Sea. Ph. D. Includes bibliographical references by Joann W. Harvey
author2 Harvey, Joann W., 1955- (author)
Green, Edwin J. (chair)
Smouse, Peter E. (internal member)
Morin, Peter J. (internal member)
Lathrop, Richard G. (internal member)
Strawderman, William E. (outside member)
Rutgers University
Graduate School - New Brunswick
format Thesis
author Harvey, Joann W., 1955-
author_facet Harvey, Joann W., 1955-
author_sort Harvey, Joann W., 1955-
title Using Bayesian Regression Tree Models and remotely sensed data to characterize recent environmental change in Alaska, USA
title_short Using Bayesian Regression Tree Models and remotely sensed data to characterize recent environmental change in Alaska, USA
title_full Using Bayesian Regression Tree Models and remotely sensed data to characterize recent environmental change in Alaska, USA
title_fullStr Using Bayesian Regression Tree Models and remotely sensed data to characterize recent environmental change in Alaska, USA
title_full_unstemmed Using Bayesian Regression Tree Models and remotely sensed data to characterize recent environmental change in Alaska, USA
title_sort using bayesian regression tree models and remotely sensed data to characterize recent environmental change in alaska, usa
publishDate 2011
url http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000063452
geographic Arctic
Bering Sea
geographic_facet Arctic
Bering Sea
genre Arctic
Bering Sea
Alaska
genre_facet Arctic
Bering Sea
Alaska
op_relation Rutgers University Electronic Theses and Dissertations
Graduate School - New Brunswick Electronic Theses and Dissertations
rucore19991600001
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000063452
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