Sentinel-2 Leaf Area Index Estimation for Pine Plantations in the Southeastern United States

dc.contributor.authorCohrs, Chris W.en
dc.contributor.authorCook, Rachel L.en
dc.contributor.authorGray, Josh M.en
dc.contributor.authorAlbaugh, Timothy J.en
dc.contributor.departmentForest Resources and Environmental Conservationen
dc.date.accessioned2020-05-14T17:47:17Zen
dc.date.available2020-05-14T17:47:17Zen
dc.date.issued2020-04-29en
dc.date.updated2020-05-14T13:55:45Zen
dc.description.abstractLeaf area index (LAI) is an important biophysical indicator of forest health that is linearly related to productivity, serving as a key criterion for potential nutrient management. A single equation was produced to model surface reflectance values captured from the Sentinel-2 Multispectral Instrument (MSI) with a robust dataset of field observations of loblolly pine (<i>Pinus taeda</i> L.) LAI collected with a LAI-2200C plant canopy analyzer. Support vector machine (SVM)-supervised classification was used to improve the model fit by removing plots saturated with aberrant radiometric signatures that would not be captured in the association between Sentinel-2 and LAI-2200C. The resulting equation, LAI = 0.310<b><i>SR</i></b> &minus; 0.098 (where <b><i>SR</i></b> = the simple ratio between near-infrared (NIR) and red bands), displayed good performance (<inline-formula> <math display="inline"> <semantics> <mrow> <msup> <mi>R</mi> <mn>2</mn> </msup> </mrow> </semantics> </math> </inline-formula> = 0.81, RMSE = 0.36) at estimating the LAI for loblolly pine within the analyzed region at a 10 m spatial resolution. Our model incorporated a high number of validation plots (<i>n</i> = 292) spanning from southern Virginia to northern Florida across a range of soil textures (sandy to clayey), drainage classes (well drained to very poorly drained), and site characteristics common to pine forest plantations in the southeastern United States. The training dataset included plot-level treatment metrics&mdash;silviculture intensity, genetics, and density&mdash;on which sensitivity analysis was performed to inform model fit behavior. Plot density, particularly when there were &le;618 trees per hectare, was shown to impact model performance, causing LAI estimates to be overpredicted (to a maximum of <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>X</mi> <mi>i</mi> </msub> </mrow> </semantics> </math> </inline-formula> + 0.16). Silviculture intensity (competition control and fertilization rates) and genetics did not markedly impact the relationship between SR and LAI. Results indicate that Sentinel-2&rsquo;s improved spatial resolution and temporal revisit interval provide new opportunities for managers to detect within-stand variance and improve accuracy for LAI estimation over current industry standard models.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationCohrs, C.W.; Cook, R.L.; Gray, J.M.; Albaugh, T.J. Sentinel-2 Leaf Area Index Estimation for Pine Plantations in the Southeastern United States. Remote Sens. 2020, 12, 1406.en
dc.identifier.doihttps://doi.org/10.3390/rs12091406en
dc.identifier.urihttp://hdl.handle.net/10919/98389en
dc.language.isoenen
dc.publisherMDPIen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectleaf area indexen
dc.subjectLoblolly pineen
dc.subjectforestryen
dc.subjectsite variabilityen
dc.subjectforest site productivityen
dc.subjectremote sensingen
dc.subjectsilvicultureen
dc.subjectstand densityen
dc.subjectsupport vector machineen
dc.subjectsupervised classificationen
dc.titleSentinel-2 Leaf Area Index Estimation for Pine Plantations in the Southeastern United Statesen
dc.title.serialRemote Sensingen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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