Effects of landscape fragmentation on land loss
Coastal Louisiana has been facing a serious land loss problem over the past several decades, and extensive research has been undertaken to address the problem. However, the importance of landscape fragmentation on land loss has seldom been examined. This paper evaluates the effects of landscape frag...
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| Veröffentlicht in: | Remote sensing of environment 2018-05, Vol.209, p.253-262 |
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| creator | Lam, Nina S.-N. Cheng, Weijia Zou, Lei Cai, Heng |
| description | Coastal Louisiana has been facing a serious land loss problem over the past several decades, and extensive research has been undertaken to address the problem. However, the importance of landscape fragmentation on land loss has seldom been examined. This paper evaluates the effects of landscape fragmentation on land loss in the Lower Mississippi River Basin region. The research hypothesis is that the higher the degree of fragmentation in a locality, the greater the amount of land loss in the next time period. We used Landsat-TM data with a pixel size of 30m×30m in 1996 and 2010 and transformed the images into either land or water pixels. We then calculated the fractal dimension and Moran's I spatial autocorrelation statistics and used them to represent the degree of landscape fragmentation. Four sample box sizes, including sizes of 101×101, 71×71, 51×51, and 31×31pixels, were used to detect if there is a relationship between fragmentation and land loss at different neighborhood (context) scales. For each box size, 100 samples were randomly selected. To isolate the fragmentation effect so that it can be better evaluated, we used only sample boxes with a 50% land-water ratio. Regression results between fragmentation and land loss show that the R2 values for box sizes of 71×71, 51×51 and 31×31 were statistically significant (0.20, 0.45, 0.35; p |
| doi_str_mv | 10.1016/j.rse.2017.12.034 |
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•Landscape fragmentation increased land loss in the Mississippi River Delta.•Effects of fragmentation changed with the neighborhood scale.•R2 between fragmentation and land loss reached the highest at scale of 1.5×1.5km2.</description><identifier>ISSN: 0034-4257</identifier><identifier>EISSN: 1879-0704</identifier><identifier>DOI: 10.1016/j.rse.2017.12.034</identifier><language>eng</language><publisher>New York: Elsevier Inc</publisher><subject>Coastal land loss ; Coasts ; Data processing ; Fractal dimension ; Fractals ; Fragmentation ; Landsat ; Landsat satellites ; Landscape ; Landscape ecology ; Landscape fragmentation ; Mississippi River Delta ; Pixels ; Remote sensing ; River basins ; Rivers ; Scale and context effects ; Spatial analysis ; Spatial autocorrelation ; Spatial distribution ; Statistical analysis ; Statistical methods</subject><ispartof>Remote sensing of environment, 2018-05, Vol.209, p.253-262</ispartof><rights>2017 Elsevier Inc.</rights><rights>Copyright Elsevier BV May 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c368t-dce914d08af8fc77d2ce9f7c20b09f695f0c75a1665949eb4808580673dd15183</citedby><cites>FETCH-LOGICAL-c368t-dce914d08af8fc77d2ce9f7c20b09f695f0c75a1665949eb4808580673dd15183</cites><orcidid>0000-0002-5344-9368</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0034425717306181$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65534</link.rule.ids></links><search><creatorcontrib>Lam, Nina S.-N.</creatorcontrib><creatorcontrib>Cheng, Weijia</creatorcontrib><creatorcontrib>Zou, Lei</creatorcontrib><creatorcontrib>Cai, Heng</creatorcontrib><title>Effects of landscape fragmentation on land loss</title><title>Remote sensing of environment</title><description>Coastal Louisiana has been facing a serious land loss problem over the past several decades, and extensive research has been undertaken to address the problem. However, the importance of landscape fragmentation on land loss has seldom been examined. This paper evaluates the effects of landscape fragmentation on land loss in the Lower Mississippi River Basin region. The research hypothesis is that the higher the degree of fragmentation in a locality, the greater the amount of land loss in the next time period. We used Landsat-TM data with a pixel size of 30m×30m in 1996 and 2010 and transformed the images into either land or water pixels. We then calculated the fractal dimension and Moran's I spatial autocorrelation statistics and used them to represent the degree of landscape fragmentation. Four sample box sizes, including sizes of 101×101, 71×71, 51×51, and 31×31pixels, were used to detect if there is a relationship between fragmentation and land loss at different neighborhood (context) scales. For each box size, 100 samples were randomly selected. To isolate the fragmentation effect so that it can be better evaluated, we used only sample boxes with a 50% land-water ratio. Regression results between fragmentation and land loss show that the R2 values for box sizes of 71×71, 51×51 and 31×31 were statistically significant (0.20, 0.45, 0.35; p<0.001 for Moran's I) but not for the 101×101 box size. These results imply that land protection may be most effective by prioritizing areas with land patches that have the least fragmentation. Furthermore, the neighborhood scale at which the R2 value is the highest indicates the scale at which the effects are most likely to be observed (51×51 box size, approximately 1.5×1.5km2, R2=0.45), which suggests that future land loss modeling using this neighborhood scale would be most effective.
•Landscape fragmentation increased land loss in the Mississippi River Delta.•Effects of fragmentation changed with the neighborhood scale.•R2 between fragmentation and land loss reached the highest at scale of 1.5×1.5km2.</description><subject>Coastal land loss</subject><subject>Coasts</subject><subject>Data processing</subject><subject>Fractal dimension</subject><subject>Fractals</subject><subject>Fragmentation</subject><subject>Landsat</subject><subject>Landsat satellites</subject><subject>Landscape</subject><subject>Landscape ecology</subject><subject>Landscape fragmentation</subject><subject>Mississippi River Delta</subject><subject>Pixels</subject><subject>Remote sensing</subject><subject>River basins</subject><subject>Rivers</subject><subject>Scale and context effects</subject><subject>Spatial analysis</subject><subject>Spatial autocorrelation</subject><subject>Spatial distribution</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><issn>0034-4257</issn><issn>1879-0704</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp9kE1Lw0AQhhdRsFZ_gLeA56Qzm-wXnqTUKhS86HnZ7ockpEndTQX_vVviWRgYmJl35p2HkHuECgH5qqti8hUFFBXSCurmgixQClWCgOaSLCCXyoYycU1uUuoAkEmBC7LahODtlIoxFL0ZXLLm6IsQzefBD5OZ2nEocpxbRT-mdEuugumTv_vLS_LxvHlfv5S7t-3r-mlX2prLqXTWK2wcSBNksEI4mgtBWAp7UIErFsAKZpBzphrl940EySRwUTuHDGW9JA_z3mMcv04-TbobT3HIJzXNLykqOYM8hfOUjdlb9EEfY3sw8Ucj6DMX3enMRZ-5aKQ6Q8iax1njs_3v1kedbOsH610bMwntxvYf9S8HPmlS</recordid><startdate>201805</startdate><enddate>201805</enddate><creator>Lam, Nina S.-N.</creator><creator>Cheng, Weijia</creator><creator>Zou, Lei</creator><creator>Cai, Heng</creator><general>Elsevier Inc</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SN</scope><scope>7SP</scope><scope>7SR</scope><scope>7TA</scope><scope>7TB</scope><scope>7TG</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KL.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><orcidid>https://orcid.org/0000-0002-5344-9368</orcidid></search><sort><creationdate>201805</creationdate><title>Effects of landscape fragmentation on land loss</title><author>Lam, Nina S.-N. ; Cheng, Weijia ; Zou, Lei ; Cai, Heng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c368t-dce914d08af8fc77d2ce9f7c20b09f695f0c75a1665949eb4808580673dd15183</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Coastal land loss</topic><topic>Coasts</topic><topic>Data processing</topic><topic>Fractal dimension</topic><topic>Fractals</topic><topic>Fragmentation</topic><topic>Landsat</topic><topic>Landsat satellites</topic><topic>Landscape</topic><topic>Landscape ecology</topic><topic>Landscape fragmentation</topic><topic>Mississippi River Delta</topic><topic>Pixels</topic><topic>Remote sensing</topic><topic>River basins</topic><topic>Rivers</topic><topic>Scale and context effects</topic><topic>Spatial analysis</topic><topic>Spatial autocorrelation</topic><topic>Spatial distribution</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lam, Nina S.-N.</creatorcontrib><creatorcontrib>Cheng, Weijia</creatorcontrib><creatorcontrib>Zou, Lei</creatorcontrib><creatorcontrib>Cai, Heng</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Ecology Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Remote sensing of environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lam, Nina S.-N.</au><au>Cheng, Weijia</au><au>Zou, Lei</au><au>Cai, Heng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Effects of landscape fragmentation on land loss</atitle><jtitle>Remote sensing of environment</jtitle><date>2018-05</date><risdate>2018</risdate><volume>209</volume><spage>253</spage><epage>262</epage><pages>253-262</pages><issn>0034-4257</issn><eissn>1879-0704</eissn><abstract>Coastal Louisiana has been facing a serious land loss problem over the past several decades, and extensive research has been undertaken to address the problem. However, the importance of landscape fragmentation on land loss has seldom been examined. This paper evaluates the effects of landscape fragmentation on land loss in the Lower Mississippi River Basin region. The research hypothesis is that the higher the degree of fragmentation in a locality, the greater the amount of land loss in the next time period. We used Landsat-TM data with a pixel size of 30m×30m in 1996 and 2010 and transformed the images into either land or water pixels. We then calculated the fractal dimension and Moran's I spatial autocorrelation statistics and used them to represent the degree of landscape fragmentation. Four sample box sizes, including sizes of 101×101, 71×71, 51×51, and 31×31pixels, were used to detect if there is a relationship between fragmentation and land loss at different neighborhood (context) scales. For each box size, 100 samples were randomly selected. To isolate the fragmentation effect so that it can be better evaluated, we used only sample boxes with a 50% land-water ratio. Regression results between fragmentation and land loss show that the R2 values for box sizes of 71×71, 51×51 and 31×31 were statistically significant (0.20, 0.45, 0.35; p<0.001 for Moran's I) but not for the 101×101 box size. These results imply that land protection may be most effective by prioritizing areas with land patches that have the least fragmentation. Furthermore, the neighborhood scale at which the R2 value is the highest indicates the scale at which the effects are most likely to be observed (51×51 box size, approximately 1.5×1.5km2, R2=0.45), which suggests that future land loss modeling using this neighborhood scale would be most effective.
•Landscape fragmentation increased land loss in the Mississippi River Delta.•Effects of fragmentation changed with the neighborhood scale.•R2 between fragmentation and land loss reached the highest at scale of 1.5×1.5km2.</abstract><cop>New York</cop><pub>Elsevier Inc</pub><doi>10.1016/j.rse.2017.12.034</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-5344-9368</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Coastal land loss Coasts Data processing Fractal dimension Fractals Fragmentation Landsat Landsat satellites Landscape Landscape ecology Landscape fragmentation Mississippi River Delta Pixels Remote sensing River basins Rivers Scale and context effects Spatial analysis Spatial autocorrelation Spatial distribution Statistical analysis Statistical methods |
| title | Effects of landscape fragmentation on land loss |
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