<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-19T04:48:16Z</responseDate><request verb="GetRecord" identifier="oai:dspace.mit.edu:1721.1/114113" metadataPrefix="dim">https://dspace.mit.edu/server/oai/request</request><GetRecord><record><header><identifier>oai:dspace.mit.edu:1721.1/114113</identifier><datestamp>2022-01-13T07:53:59Z</datestamp><setSpec>com_1721.1_7582</setSpec><setSpec>com_1721.1_7581</setSpec><setSpec>col_1721.1_131024</setSpec></header><metadata><dim:dim xmlns:dim="http://www.dspace.org/xmlns/dspace/dim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.dspace.org/xmlns/dspace/dim http://www.dspace.org/schema/dim.xsd">
   <dim:field mdschema="dc" element="contributor" qualifier="advisor" lang="en_US">Dennis McLaughlin.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="author" lang="en_US">Hutchison, Leah (Leah Ellen Ann)</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="other" lang="en_US">Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department">Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences</dim:field>
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   <dim:field mdschema="dc" element="date" qualifier="accessioned">2018-03-12T19:30:25Z</dim:field>
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   <dim:field mdschema="dc" element="date" qualifier="copyright" lang="en_US">2004</dim:field>
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   <dim:field mdschema="dc" element="identifier" qualifier="oclc" lang="en_US">1027704370</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Thesis: S.B. in Geosciences, Massachusetts Institute of Technology, Department of Earth, Atmospheric, and Planetary Sciences, 2004.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Cataloged from PDF version of thesis. Some pages in original thesis contain text that run off the edge of the page.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Includes bibliographical references (pages 61-64).</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract" lang="en_US">Desertification, the spread of desert-like conditions in arid or semiarid areas due to human influence or to climatic change, affects most arable land in arid and semi-arid China. This project provides an analysis of desertification in northeastern arid and semi-arid China to determine its spatial distribution, severity, and causes. It locates areas of desertification and identifies and ranks in order of importance their anthropogenic and climatological causes. It especially focuses on the savanna transition zone west of Beijing to see if climate factors or increasing population density can be correlated to land cover change. GIS (Geographic Information Systems) software is used to recognize locations of rapid land cover change. Statistical tests, such as unbalanced multi-way ANOVA, determine if climatic or anthropogenic factors can predict if an area is undergoing rapid land cover change. The climate and population data is resampled to an uniform 0.5' scale and converted into qualitative, data before statistical testing. This project tests if land cover change, a more difficult indicator to measure, can be predicted by analyzing trends in vegetation, precipitation, temperature, wind and population. Desertification is more likely and more severe in climates with low precipitation. Areas with low population density tend to have less severe land degradation than areas with medium or high density; this may be due to more intense land use in high population areas.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="statementofresponsibility" lang="en_US">by Leah Hutchison.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="degree" lang="en_US">S.B. in Geosciences</dim:field>
   <dim:field mdschema="dc" element="format" qualifier="extent" lang="en_US">64 pages</dim:field>
   <dim:field mdschema="dc" element="language" qualifier="iso" lang="en_US">eng</dim:field>
   <dim:field mdschema="dc" element="publisher" lang="en_US">Massachusetts Institute of Technology</dim:field>
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   <dim:field mdschema="dc" element="subject" lang="en_US">Earth, Atmospheric, and Planetary Sciences.</dim:field>
   <dim:field mdschema="dc" element="title" lang="en_US">Determining land use change and desertification in China using remote sensing data</dim:field>
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   	&lt;Title>Determining land use change and desertification in China using remote sensing data&lt;/Title>
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   	&lt;PublicationDate>2004&lt;/PublicationDate>
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        	&lt;DisplayName>Hutchison, Leah (Leah Ellen Ann)&lt;/DisplayName>
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    &lt;Keyword>Earth, Atmospheric, and Planetary Sciences.&lt;/Keyword>
   	&lt;Abstract>Desertification, the spread of desert-like conditions in arid or semiarid areas due to human influence or to climatic change, affects most arable land in arid and semi-arid China. This project provides an analysis of desertification in northeastern arid and semi-arid China to determine its spatial distribution, severity, and causes. It locates areas of desertification and identifies and ranks in order of importance their anthropogenic and climatological causes. It especially focuses on the savanna transition zone west of Beijing to see if climate factors or increasing population density can be correlated to land cover change. GIS (Geographic Information Systems) software is used to recognize locations of rapid land cover change. Statistical tests, such as unbalanced multi-way ANOVA, determine if climatic or anthropogenic factors can predict if an area is undergoing rapid land cover change. The climate and population data is resampled to an uniform 0.5&amp;apos; scale and converted into qualitative, data before statistical testing. This project tests if land cover change, a more difficult indicator to measure, can be predicted by analyzing trends in vegetation, precipitation, temperature, wind and population. Desertification is more likely and more severe in climates with low precipitation. Areas with low population density tend to have less severe land degradation than areas with medium or high density; this may be due to more intense land use in high population areas.&lt;/Abstract>
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