Creating Landscape-Scale Site Index Maps for the Southeastern US Is Possible with Airborne LiDAR and Landsat Imagery

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dc.contributor.authorGopalakrishnan, Ranjithen
dc.contributor.authorKauffman, Jobriath S.en
dc.contributor.authorFagan, Matthew E.en
dc.contributor.authorCoulston, John W.en
dc.contributor.authorThomas, Valerie A.en
dc.contributor.authorWynne, Randolph H.en
dc.contributor.authorFox, Thomas R.en
dc.contributor.authorQuirino, Valquiria F.en
dc.contributor.departmentForest Resources and Environmental Conservationen
dc.date.accessioned2019-03-18T12:28:06Zen
dc.date.available2019-03-18T12:28:06Zen
dc.date.issued2019-03-06en
dc.date.updated2019-03-15T13:54:40Zen
dc.description.abstractSustainable forest management is hugely dependent on high-quality estimates of forest site productivity, but it is challenging to generate productivity maps over large areas. We present a method for generating site index (a measure of such forest productivity) maps for plantation loblolly pine (<i>Pinus taeda</i> L.) forests over large areas in the southeastern United States by combining airborne laser scanning (ALS) data from disparate acquisitions and Landsat-based estimates of forest age. For predicting canopy heights, a linear regression model was developed using ALS data and field measurements from the Forest Inventory and Analysis (FIA) program of the US Forest Service (<i>n</i> = 211 plots). The model was strong (<i>R</i><sup>2</sup> = 0.84, RMSE = 1.85 m), and applicable over a large area (~208,000 sq. km). To estimate the site index, we combined the ALS estimated heights with Landsat-derived maps of stand age and planted pine area. The estimated bias was low (&minus;0.28 m) and the RMSE (3.8 m, relative RMSE: 19.7%, base age 25 years) was consistent with other similar approaches. Due to Landsat-related constraints, our methodology is valid only for relatively young pine plantations established after 1984. We generated 30 m resolution site index maps over a large area (~832 sq. km). The site index distribution had a median value of 19.4 m, the 5th percentile value of 13.0 m and the 95th percentile value of 23.3 m. Further, using a watershed level analysis, we ranked these regions by their estimated productivity. These results demonstrate the potential and value of remote sensing based large-area site index maps.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationGopalakrishnan, R.; Kauffman, J.S.; Fagan, M.E.; Coulston, J.W.; Thomas, V.A.; Wynne, R.H.; Fox, T.R.; Quirino, V.F. Creating Landscape-Scale Site Index Maps for the Southeastern US Is Possible with Airborne LiDAR and Landsat Imagery. Forests 2019, 10, 234.en
dc.identifier.doihttps://doi.org/10.3390/f10030234en
dc.identifier.urihttp://hdl.handle.net/10919/88468en
dc.language.isoenen
dc.publisherMDPIen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectforestryen
dc.subjectforest site productivityen
dc.subjectsite indexen
dc.subjectLandsaten
dc.subjectairborne laser scanningen
dc.subjectforest productivity mappingen
dc.subjectFIAen
dc.titleCreating Landscape-Scale Site Index Maps for the Southeastern US Is Possible with Airborne LiDAR and Landsat Imageryen
dc.title.serialForestsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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