Evaluating different spatial interpolation methods and modeling techniques for estimating spatial forest site index in pure beech forests: a case study from Turkey
Spatial interpolation methods are widely used to estimate some ecological and environmental parameters that are difficult to measure. One of these parameters is forest site index, which is a demonstration of forest productivity. The aim of this study was to estimate forest site index in a beech fore...
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| description | Spatial interpolation methods are widely used to estimate some ecological and environmental parameters that are difficult to measure. One of these parameters is forest site index, which is a demonstration of forest productivity. The aim of this study was to estimate forest site index in a beech forest ecosystem in Turkey. In this context, soil characteristics, stand parameters, and topographic features were measured in 70 temporary sample plots of beech forest stands. Forest site index of beech forest stands was predicted using different modeling techniques such as multiple regression analysis (MLR), multilayer perceptron (MLP), radial basis function (RBF), multiple regression kriging (MLRK), multilayer perceptron kriging (MLPK), and radial basis function kriging (RBFK). The results showed that the RBFK (
R
2
= 0.98) and MLRK (
R
2
= 0.96) outperformed the others to predict forest site index in the study area. The greatest improvement occurred when krigged residual used with MLR, which increase from 0.23 to 0.96. Thus, MLRK method significantly improved the prediction accuracy for site index. The models combined with krigged residuals were more successful than those used without krigged residuals. The results of this study suggest that the combined methods may help obtaining improved site index maps for forest management. |
| doi_str_mv | 10.1007/s10661-019-8028-5 |
| format | Article |
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R
2
= 0.98) and MLRK (
R
2
= 0.96) outperformed the others to predict forest site index in the study area. The greatest improvement occurred when krigged residual used with MLR, which increase from 0.23 to 0.96. Thus, MLRK method significantly improved the prediction accuracy for site index. The models combined with krigged residuals were more successful than those used without krigged residuals. The results of this study suggest that the combined methods may help obtaining improved site index maps for forest management.</description><identifier>ISSN: 0167-6369</identifier><identifier>EISSN: 1573-2959</identifier><identifier>DOI: 10.1007/s10661-019-8028-5</identifier><identifier>PMID: 31848720</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Atmospheric Protection/Air Quality Control/Air Pollution ; Beech ; Earth and Environmental Science ; Ecology ; Ecosystem ; Ecotoxicology ; Environment ; Environmental Management ; Environmental monitoring ; Environmental Monitoring - methods ; Environmental science ; Fagus ; Forest ecosystems ; Forest management ; Forest productivity ; Forests ; Interpolation ; Interpolation methods ; Monitoring/Environmental Analysis ; Site index ; Soil ; Spatial Analysis ; Terrestrial ecosystems ; Trees ; Turkey</subject><ispartof>Environmental monitoring and assessment, 2020-01, Vol.192 (1), p.53-53, Article 53</ispartof><rights>Springer Nature Switzerland AG 2019</rights><rights>Environmental Monitoring and Assessment is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c372t-75fb6aac2ce25cc650c2ebc96579c9f75db151621521e2c649178f7087f818963</citedby><cites>FETCH-LOGICAL-c372t-75fb6aac2ce25cc650c2ebc96579c9f75db151621521e2c649178f7087f818963</cites><orcidid>0000-0001-6149-0910</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10661-019-8028-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10661-019-8028-5$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27923,27924,41487,42556,51318</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31848720$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Günlü, Alkan</creatorcontrib><creatorcontrib>Bulut, Sinan</creatorcontrib><creatorcontrib>Keleş, Sedat</creatorcontrib><creatorcontrib>Ercanlı, İlker</creatorcontrib><title>Evaluating different spatial interpolation methods and modeling techniques for estimating spatial forest site index in pure beech forests: a case study from Turkey</title><title>Environmental monitoring and assessment</title><addtitle>Environ Monit Assess</addtitle><addtitle>Environ Monit Assess</addtitle><description>Spatial interpolation methods are widely used to estimate some ecological and environmental parameters that are difficult to measure. One of these parameters is forest site index, which is a demonstration of forest productivity. The aim of this study was to estimate forest site index in a beech forest ecosystem in Turkey. In this context, soil characteristics, stand parameters, and topographic features were measured in 70 temporary sample plots of beech forest stands. Forest site index of beech forest stands was predicted using different modeling techniques such as multiple regression analysis (MLR), multilayer perceptron (MLP), radial basis function (RBF), multiple regression kriging (MLRK), multilayer perceptron kriging (MLPK), and radial basis function kriging (RBFK). The results showed that the RBFK (
R
2
= 0.98) and MLRK (
R
2
= 0.96) outperformed the others to predict forest site index in the study area. The greatest improvement occurred when krigged residual used with MLR, which increase from 0.23 to 0.96. Thus, MLRK method significantly improved the prediction accuracy for site index. The models combined with krigged residuals were more successful than those used without krigged residuals. The results of this study suggest that the combined methods may help obtaining improved site index maps for forest management.</description><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Beech</subject><subject>Earth and Environmental Science</subject><subject>Ecology</subject><subject>Ecosystem</subject><subject>Ecotoxicology</subject><subject>Environment</subject><subject>Environmental Management</subject><subject>Environmental monitoring</subject><subject>Environmental Monitoring - methods</subject><subject>Environmental science</subject><subject>Fagus</subject><subject>Forest ecosystems</subject><subject>Forest management</subject><subject>Forest productivity</subject><subject>Forests</subject><subject>Interpolation</subject><subject>Interpolation methods</subject><subject>Monitoring/Environmental Analysis</subject><subject>Site index</subject><subject>Soil</subject><subject>Spatial Analysis</subject><subject>Terrestrial ecosystems</subject><subject>Trees</subject><subject>Turkey</subject><issn>0167-6369</issn><issn>1573-2959</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kctu1TAURa0KRC8tH9AJssSEScCP6xczVJWHVIlJO7Yc57hNSeJgOxX3e_hRHOUWJCQmtnzO2vvY3ghdUPKOEqLeZ0qkpA2hptGE6UacoB0VijfMCPMM7QiVqpFcmlP0MucHQohRe_MCnXKq91oxskO_rh7dsLjST3e460OABFPBea4VN-B-KpDmONRTnPAI5T52Gbupw2PsYFhFBfz91P9YIOMQE4Zc-nGzezKp5VrFuS9QDTv4WVc8LwlwC1V87OcP2GHvMuBclu6AQ4ojvlnSdzico-fBDRleHfczdPvp6ubyS3P97fPXy4_XjeeKlUaJ0ErnPPPAhPdSEM-g9UYKZbwJSnQtFVQyKhgF5uXeUKWDIloFTbWR_Ay93XznFNcHFTv22cMwuAniki3jTPO9ZJxU9M0_6ENc0lRvt1JKE04prxTdKJ9izgmCnVP9nXSwlNg1QbslaGuCdk3Qiqp5fXRe2hG6P4qnyCrANiDX1nQH6e_o_7v-BsdgqTA</recordid><startdate>20200101</startdate><enddate>20200101</enddate><creator>Günlü, Alkan</creator><creator>Bulut, Sinan</creator><creator>Keleş, Sedat</creator><creator>Ercanlı, İlker</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QH</scope><scope>7QL</scope><scope>7SN</scope><scope>7ST</scope><scope>7T7</scope><scope>7TG</scope><scope>7TN</scope><scope>7U7</scope><scope>7UA</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X7</scope><scope>7XB</scope><scope>87Z</scope><scope>88E</scope><scope>88I</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FR3</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H97</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>K9.</scope><scope>KL.</scope><scope>L.-</scope><scope>L.G</scope><scope>M0C</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7N</scope><scope>P64</scope><scope>PATMY</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>SOI</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-6149-0910</orcidid></search><sort><creationdate>20200101</creationdate><title>Evaluating different spatial interpolation methods and modeling techniques for estimating spatial forest site index in pure beech forests: a case study from Turkey</title><author>Günlü, Alkan ; Bulut, Sinan ; Keleş, Sedat ; Ercanlı, İlker</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c372t-75fb6aac2ce25cc650c2ebc96579c9f75db151621521e2c649178f7087f818963</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Atmospheric Protection/Air Quality Control/Air Pollution</topic><topic>Beech</topic><topic>Earth and Environmental Science</topic><topic>Ecology</topic><topic>Ecosystem</topic><topic>Ecotoxicology</topic><topic>Environment</topic><topic>Environmental Management</topic><topic>Environmental monitoring</topic><topic>Environmental Monitoring - 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Academic</collection><jtitle>Environmental monitoring and assessment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Günlü, Alkan</au><au>Bulut, Sinan</au><au>Keleş, Sedat</au><au>Ercanlı, İlker</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluating different spatial interpolation methods and modeling techniques for estimating spatial forest site index in pure beech forests: a case study from Turkey</atitle><jtitle>Environmental monitoring and assessment</jtitle><stitle>Environ Monit Assess</stitle><addtitle>Environ Monit Assess</addtitle><date>2020-01-01</date><risdate>2020</risdate><volume>192</volume><issue>1</issue><spage>53</spage><epage>53</epage><pages>53-53</pages><artnum>53</artnum><issn>0167-6369</issn><eissn>1573-2959</eissn><abstract>Spatial interpolation methods are widely used to estimate some ecological and environmental parameters that are difficult to measure. One of these parameters is forest site index, which is a demonstration of forest productivity. The aim of this study was to estimate forest site index in a beech forest ecosystem in Turkey. In this context, soil characteristics, stand parameters, and topographic features were measured in 70 temporary sample plots of beech forest stands. Forest site index of beech forest stands was predicted using different modeling techniques such as multiple regression analysis (MLR), multilayer perceptron (MLP), radial basis function (RBF), multiple regression kriging (MLRK), multilayer perceptron kriging (MLPK), and radial basis function kriging (RBFK). The results showed that the RBFK (
R
2
= 0.98) and MLRK (
R
2
= 0.96) outperformed the others to predict forest site index in the study area. The greatest improvement occurred when krigged residual used with MLR, which increase from 0.23 to 0.96. Thus, MLRK method significantly improved the prediction accuracy for site index. The models combined with krigged residuals were more successful than those used without krigged residuals. The results of this study suggest that the combined methods may help obtaining improved site index maps for forest management.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>31848720</pmid><doi>10.1007/s10661-019-8028-5</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-6149-0910</orcidid></addata></record> |
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| ispartof | Environmental monitoring and assessment, 2020-01, Vol.192 (1), p.53-53, Article 53 |
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| source | MEDLINE; SpringerLink Journals - AutoHoldings |
| subjects | Atmospheric Protection/Air Quality Control/Air Pollution Beech Earth and Environmental Science Ecology Ecosystem Ecotoxicology Environment Environmental Management Environmental monitoring Environmental Monitoring - methods Environmental science Fagus Forest ecosystems Forest management Forest productivity Forests Interpolation Interpolation methods Monitoring/Environmental Analysis Site index Soil Spatial Analysis Terrestrial ecosystems Trees Turkey |
| title | Evaluating different spatial interpolation methods and modeling techniques for estimating spatial forest site index in pure beech forests: a case study from Turkey |
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