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Spatially optimizing vegetation indices integrated with sparse partial least squares regression to detect and map the effects of Gonipterus scutellatus on the chlorophyll content of eucalyptus plantations
Gonipterus scutellatus is a beetle causing severe defoliation to South Africa's eucalyptus plantations. This defoliation induced by the beetle inhibits the eucalypts ability to photosynthesize, by affecting its chlorophyll content. Therefore, this study integrates spatially optimized and the si...
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Published in: | International journal of remote sensing 2020-08, Vol.41 (16), p.6444-6459 |
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description | Gonipterus scutellatus is a beetle causing severe defoliation to South Africa's eucalyptus plantations. This defoliation induced by the beetle inhibits the eucalypts ability to photosynthesize, by affecting its chlorophyll content. Therefore, this study integrates spatially optimized and the single 0.5 m resolution vegetation indices with sparse partial least squares regression (SPLS-R) and partial least squares regression (PLS-R) to detect and map leaf chlorophyll content of defoliated eucalyptus plantations. The optimized vegetation indices were spatially resampled to resolutions that best paralleled varying levels of G. scutellatus defoliation. From the results, the 0.5 m resolution SPLS-R model (R
2
= 0.76; RMSE of 1.50 (2.88% of the mean measured chlorophyll)) outcompeted the 0.5 m resolution PLS-R (R
2
= 0.73; RMSE of 1.54 (2.95% of the mean measured chlorophyll)) model. Furthermore, the spatially optimized SPLS-R (R
2
= 0.81; RMSE of 1.44 (2.76% of the mean measured chlorophyll) model was more superior in detecting and mapping chlorophyll content of defoliated eucalyptus plantations when compared to the 0.5 m resolution SPLS-R model. The most significant variables selected by the optimized SPLS-R model were DMI, ARI, NDRE, GNDVI, and NDVI. In essence, this study has illustrated the significance of the spatial resolution in effectively detecting and mapping chlorophyll content of defoliated eucalyptus plantations. |
doi_str_mv | 10.1080/01431161.2020.1739350 |
format | article |
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2
= 0.76; RMSE of 1.50 (2.88% of the mean measured chlorophyll)) outcompeted the 0.5 m resolution PLS-R (R
2
= 0.73; RMSE of 1.54 (2.95% of the mean measured chlorophyll)) model. Furthermore, the spatially optimized SPLS-R (R
2
= 0.81; RMSE of 1.44 (2.76% of the mean measured chlorophyll) model was more superior in detecting and mapping chlorophyll content of defoliated eucalyptus plantations when compared to the 0.5 m resolution SPLS-R model. The most significant variables selected by the optimized SPLS-R model were DMI, ARI, NDRE, GNDVI, and NDVI. In essence, this study has illustrated the significance of the spatial resolution in effectively detecting and mapping chlorophyll content of defoliated eucalyptus plantations.</description><identifier>ISSN: 0143-1161</identifier><identifier>EISSN: 1366-5901</identifier><identifier>DOI: 10.1080/01431161.2020.1739350</identifier><language>eng</language><publisher>London: Taylor & Francis</publisher><subject>Chlorophyll ; Chlorophyll content ; Chlorophylls ; Defoliation ; Eucalyptus ; Least squares method ; Mapping ; Plantations ; Regression ; Resolution ; Spatial resolution ; Vegetation</subject><ispartof>International journal of remote sensing, 2020-08, Vol.41 (16), p.6444-6459</ispartof><rights>2020 Informa UK Limited, trading as Taylor & Francis Group 2020</rights><rights>2020 Informa UK Limited, trading as Taylor & Francis Group</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c201t-375f4cdb119b290bda978139077f3b24b0618113d0373be41595860668cb87903</cites><orcidid>0000-0003-2381-4915 ; 0000-0001-7842-6130</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Lottering, Romano</creatorcontrib><creatorcontrib>Mutanga, Onisimo</creatorcontrib><creatorcontrib>Peerbhay, Kabir</creatorcontrib><creatorcontrib>Lottering, Shenelle</creatorcontrib><title>Spatially optimizing vegetation indices integrated with sparse partial least squares regression to detect and map the effects of Gonipterus scutellatus on the chlorophyll content of eucalyptus plantations</title><title>International journal of remote sensing</title><description>Gonipterus scutellatus is a beetle causing severe defoliation to South Africa's eucalyptus plantations. This defoliation induced by the beetle inhibits the eucalypts ability to photosynthesize, by affecting its chlorophyll content. Therefore, this study integrates spatially optimized and the single 0.5 m resolution vegetation indices with sparse partial least squares regression (SPLS-R) and partial least squares regression (PLS-R) to detect and map leaf chlorophyll content of defoliated eucalyptus plantations. The optimized vegetation indices were spatially resampled to resolutions that best paralleled varying levels of G. scutellatus defoliation. From the results, the 0.5 m resolution SPLS-R model (R
2
= 0.76; RMSE of 1.50 (2.88% of the mean measured chlorophyll)) outcompeted the 0.5 m resolution PLS-R (R
2
= 0.73; RMSE of 1.54 (2.95% of the mean measured chlorophyll)) model. Furthermore, the spatially optimized SPLS-R (R
2
= 0.81; RMSE of 1.44 (2.76% of the mean measured chlorophyll) model was more superior in detecting and mapping chlorophyll content of defoliated eucalyptus plantations when compared to the 0.5 m resolution SPLS-R model. The most significant variables selected by the optimized SPLS-R model were DMI, ARI, NDRE, GNDVI, and NDVI. In essence, this study has illustrated the significance of the spatial resolution in effectively detecting and mapping chlorophyll content of defoliated eucalyptus plantations.</description><subject>Chlorophyll</subject><subject>Chlorophyll content</subject><subject>Chlorophylls</subject><subject>Defoliation</subject><subject>Eucalyptus</subject><subject>Least squares method</subject><subject>Mapping</subject><subject>Plantations</subject><subject>Regression</subject><subject>Resolution</subject><subject>Spatial resolution</subject><subject>Vegetation</subject><issn>0143-1161</issn><issn>1366-5901</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kdGO1SAQhonRxOPqI5iQeN2VKW0pd5qNriabeKFeE0qn57DhAAvUTX1GH0qas956w5DJ9w0TfkLeArsGNrL3DDoOMMB1y9raElzynj0jB-DD0PSSwXNy2Jlmh16SVznfM8YG0YsD-fM96mK1cxsNsdiz_W39kf7CI5baD55aP1uDudaCx6QLzvTRlhPNUaeMtJ67Th3qXGh-WHWqcKoo5rz7JdAZC5pCtZ_pWUdaTkhxWWor07DQ2-BtLJjWTLNZCzqnS73vagXNyYUU4mlzjppQd_Bll3A12m1xB6PT_rJrfk1eLNplfPNUr8jPz59-3Hxp7r7dfr35eNeYlkFpuOiXzswTgJxayaZZSzECl0yIhU9tN7EBRgA-My74hB30sh8HNgyjmUYhGb8i7y5zYwoPK-ai7sOafH1StR0IkG0PfaX6C2VSyDnhomKyZ502BUztwal_wak9OPUUXPU-XDzrl5DO-jEkN6uit_oTS9Le2Kz4_0f8BUPRpMw</recordid><startdate>20200817</startdate><enddate>20200817</enddate><creator>Lottering, Romano</creator><creator>Mutanga, Onisimo</creator><creator>Peerbhay, Kabir</creator><creator>Lottering, Shenelle</creator><general>Taylor & Francis</general><general>Taylor & Francis Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>7TN</scope><scope>8FD</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0003-2381-4915</orcidid><orcidid>https://orcid.org/0000-0001-7842-6130</orcidid></search><sort><creationdate>20200817</creationdate><title>Spatially optimizing vegetation indices integrated with sparse partial least squares regression to detect and map the effects of Gonipterus scutellatus on the chlorophyll content of eucalyptus plantations</title><author>Lottering, Romano ; Mutanga, Onisimo ; Peerbhay, Kabir ; Lottering, Shenelle</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c201t-375f4cdb119b290bda978139077f3b24b0618113d0373be41595860668cb87903</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Chlorophyll</topic><topic>Chlorophyll content</topic><topic>Chlorophylls</topic><topic>Defoliation</topic><topic>Eucalyptus</topic><topic>Least squares method</topic><topic>Mapping</topic><topic>Plantations</topic><topic>Regression</topic><topic>Resolution</topic><topic>Spatial resolution</topic><topic>Vegetation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lottering, Romano</creatorcontrib><creatorcontrib>Mutanga, Onisimo</creatorcontrib><creatorcontrib>Peerbhay, Kabir</creatorcontrib><creatorcontrib>Lottering, Shenelle</creatorcontrib><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Technology Research Database</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>International journal of remote sensing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lottering, Romano</au><au>Mutanga, Onisimo</au><au>Peerbhay, Kabir</au><au>Lottering, Shenelle</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Spatially optimizing vegetation indices integrated with sparse partial least squares regression to detect and map the effects of Gonipterus scutellatus on the chlorophyll content of eucalyptus plantations</atitle><jtitle>International journal of remote sensing</jtitle><date>2020-08-17</date><risdate>2020</risdate><volume>41</volume><issue>16</issue><spage>6444</spage><epage>6459</epage><pages>6444-6459</pages><issn>0143-1161</issn><eissn>1366-5901</eissn><abstract>Gonipterus scutellatus is a beetle causing severe defoliation to South Africa's eucalyptus plantations. This defoliation induced by the beetle inhibits the eucalypts ability to photosynthesize, by affecting its chlorophyll content. Therefore, this study integrates spatially optimized and the single 0.5 m resolution vegetation indices with sparse partial least squares regression (SPLS-R) and partial least squares regression (PLS-R) to detect and map leaf chlorophyll content of defoliated eucalyptus plantations. The optimized vegetation indices were spatially resampled to resolutions that best paralleled varying levels of G. scutellatus defoliation. From the results, the 0.5 m resolution SPLS-R model (R
2
= 0.76; RMSE of 1.50 (2.88% of the mean measured chlorophyll)) outcompeted the 0.5 m resolution PLS-R (R
2
= 0.73; RMSE of 1.54 (2.95% of the mean measured chlorophyll)) model. Furthermore, the spatially optimized SPLS-R (R
2
= 0.81; RMSE of 1.44 (2.76% of the mean measured chlorophyll) model was more superior in detecting and mapping chlorophyll content of defoliated eucalyptus plantations when compared to the 0.5 m resolution SPLS-R model. The most significant variables selected by the optimized SPLS-R model were DMI, ARI, NDRE, GNDVI, and NDVI. In essence, this study has illustrated the significance of the spatial resolution in effectively detecting and mapping chlorophyll content of defoliated eucalyptus plantations.</abstract><cop>London</cop><pub>Taylor & Francis</pub><doi>10.1080/01431161.2020.1739350</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0003-2381-4915</orcidid><orcidid>https://orcid.org/0000-0001-7842-6130</orcidid></addata></record> |
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subjects | Chlorophyll Chlorophyll content Chlorophylls Defoliation Eucalyptus Least squares method Mapping Plantations Regression Resolution Spatial resolution Vegetation |
title | Spatially optimizing vegetation indices integrated with sparse partial least squares regression to detect and map the effects of Gonipterus scutellatus on the chlorophyll content of eucalyptus plantations |
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