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Assessing the transferability of statistical predictive models for leaf area index between two airborne discrete return LiDAR sensor designs within multiple intensely managed Loblolly pine forest locations in the south-eastern USA

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dc.contributor.authorSumnall, Matthewen
dc.contributor.authorPeduzzi, Aliciaen
dc.contributor.authorFox, Thomas R.en
dc.contributor.authorWynne, Randolph H.en
dc.contributor.authorThomas, Valerie A.en
dc.contributor.authorCook, Bruceen
dc.contributor.departmentForest Resources and Environmental Conservationen
dc.date.accessioned2020-04-22T18:25:05Zen
dc.date.available2020-04-22T18:25:05Zen
dc.date.issued2016-04en
dc.description.abstractLeaf area is an important forest structural variable which serves as the primary means of mass and energy exchange within vegetated ecosystems. The objective of the current study was to determine if leaf area index (LAI) could be estimated accurately and consistently in five intensively managed pine plantation forests using two multiple-return airborne LiDAR datasets. Field measurements of LAI were made using the LiCOR LAI2000 and LAI2200 instruments within 116 plots were established of varying size and within a variety of stand conditions (i.e. stand age, nutrient regime and stem density) in North Carolina and Virginia in 2008 and 2013. A number of common LiDAR return height and intensity distribution metrics were calculated (e.g. average return height), in addition to ten indices, with two additional variants, utilized in the surrounding literature which have been used to estimate LAI and fractional cover, were calculated from return heights and intensity, for each plot extent. Each of the indices was assessed for correlation with each other, and was used as independent variables in linear regression analysis with field LAI as the dependent variable. All LiDAR derived metrics were also entered into a forward stepwise linear regression. The results from each of the indices varied from an R-2 of 0.33 (S.E. 0.87) to 0.89 (S.E. 0.36). Those indices calculated using ratios of all returns produced the strongest correlations, such as the Above and Below Ratio Index (ABRI) and Laser Penetration Index 1 ( LPI1). The regression model produced from a combination of three metrics did not improve correlations greatly (R-2 0.90; S.E. 0.35). The results indicate that LAI can be predicted over a range of intensively managed pine plantation forest environments accurately when using different LiDAR sensor designs. Those indices which incorporated counts of specific return numbers (e.g. first returns) or return intensity correlated poorly with field measurements. There were disparities between the number of different types of returns and intensity values when comparing the results from two LiDAR sensors, indicating that predictive models developed using such metrics are not transferable between datasets with different acquisition parameters. Each of the indices were significantly correlated with one another, with one exception (LAI proxy), in particular those indices calculated from all returns, which indicates similarities in information content for those indices. It can then be argued that LiDAR indices have reached a similar stage in development to those calculated from optical-spectral sensors, but which offer a number of advantages, such as the reduction or removal of saturation issues in areas of high biomass.en
dc.description.adminPublic domain – authored by a U.S. government employeeen
dc.description.notesThis research was possible thanks to the support from the Forest Productivity Cooperative, the help in field data collection provided by Beth Stein and the assistance Alica Peduzzi, who provided the field data for validation of the 2008 LiDAR acquisition assessed in the project. We gratefully acknowledge the support and LiDAR data supplied by NASA, in addition to the support of the staff members of the Duke Forest teaching and research laboratory, the Northern Research Station Strategic Foresight Group of the North Carolina Forest service and the landowner Weyerhaeuser. Funding for this work was provided in part by the Virginia Agricultural Experiment Station (Project Number VA-136623) and the Program McIntire Stennis of the National Institute of Food and Agriculture, U.S. Department of Agriculture.en
dc.description.sponsorshipVirginia Agricultural Experiment Station [VA-136623]; Program McIntire Stennis of the National Institute of Food and Agriculture, U.S. Department of Agriculture; Forest Productivity Cooperative; NASANational Aeronautics & Space Administration (NASA)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1016/j.rse.2016.02.012en
dc.identifier.eissn1879-0704en
dc.identifier.issn0034-4257en
dc.identifier.urihttp://hdl.handle.net/10919/97884en
dc.identifier.volume176en
dc.language.isoenen
dc.rightsCreative Commons CC0 1.0 Universal Public Domain Dedicationen
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/en
dc.subjectLoblolly pineen
dc.subjectForest managementen
dc.subjectLiDARen
dc.subjectRemote sensingen
dc.subjectLeaf area indexen
dc.titleAssessing the transferability of statistical predictive models for leaf area index between two airborne discrete return LiDAR sensor designs within multiple intensely managed Loblolly pine forest locations in the south-eastern USAen
dc.title.serialRemote Sensing of Environmenten
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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