Estimating tree canopy cover using harmonic regression coefficients derived from multitemporal Landsat data

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dc.contributor.authorDerwin, Jill M.en
dc.contributor.authorThomas, Valerie A.en
dc.contributor.authorWynne, Randolph H.en
dc.contributor.authorCoulston, John W.en
dc.contributor.authorLiknes, Greg C.en
dc.contributor.authorBender, Stacieen
dc.contributor.authorBlinn, Christine E.en
dc.contributor.authorBrooks, Evan B.en
dc.contributor.authorRuefenacht, Bonnieen
dc.contributor.authorBenton, Roberten
dc.contributor.authorFinco, Mark V.en
dc.contributor.authorMegown, Kevinen
dc.contributor.departmentForest Resources and Environmental Conservationen
dc.date.accessioned2021-01-05T14:13:20Zen
dc.date.available2021-01-05T14:13:20Zen
dc.date.issued2020-04en
dc.description.abstractThe goal of this study was to evaluate whether harmonic regression coefficients derived using all available cloud free observations in a given Landsat pixel for a three-year period can be used to estimate tree canopy cover (TCC), and whether models developed using harmonic regression coefficients as predictor variables are better than models developed using median composite predictor variables, the previous operational standard for the National Land Cover Database (NLCD). The two study areas in the conterminous USA were as follows: West (Oregon), bounded by Landsat Worldwide Reference System 2 (WRS-2) paths/rows 43/30, 44/30, and 45/30; and South (Georgia/South Carolina), bounded by WRS-2 paths/rows 16/37, 17/37, and 18/37. Plot-specific tree canopy cover (the response variable) was collected by experienced interpreters using a dot grid overlaid on 1 m spatial resolution National Agricultural Imagery Program (NAIP) images at two different times per region, circa 2010 and circa 2014. Random forest model comparisons (using 500 independent model runs for each comparison) revealed the following (1) harmonic regression coefficients (one harmonic) are better predictors for every time/region of TCC than median composite focal means and standard deviations (across times/regions, mean increase in pseudo R-2 of 6.7% and mean decrease in RMSE of 1.7% TCC) and (2) harmonic regression coefficients (one harmonic, from NDVI, SWIR1, and SWIR2), when added to the full suite of median composite and terrain variables used for the NLCD 2011 product, improve the quality of TCC models for every time/region (mean increase in pseudo R-2 of 3.6% and mean decrease in RMSE of 1.0% TCC). The harmonic regression NDVI constant was always one of the top four most important predictors across times/regions, and is more correlated with TCC than the NDVI median composite focal mean. Eigen analysis revealed that there is little to no additional information in the full suite of predictor variables (47 bands) when compared to the harmonic regression coefficients alone (using NDVI, SWIR1, and SWIR2; 9 bands), a finding echoed by both model fit statistics and the resulting maps. We conclude that harmonic regression coefficients derived from Landsat (or, by extension, other comparable earth resource satellite data) can be used to map TCC, either alone or in combination with other TCC-related variables.en
dc.description.notesThis study was funded by the 'TCC 2021 Research and Development' grant, the 'NLCD Percent Tree Canopy Cover Layer - Evaluation of Data Sources to Predict Canopy Cover', and the 'NLCD Percent Tree Canopy Cover Layer - Evaluation of the 2011 Product and Alternative Approaches to Detect Change' grant from the USDA Forest Service. It was also supported in part by the Virginia Agricultural Experiment Station and the McIntire-Stennis Program of NIFA, USDA, "Detecting and Forecasting the Consequences of Subtle and Gross Disturbance on Forest Carbon Cycling").en
dc.description.sponsorship'TCC 2021 Research and Development' grant from the USDA Forest Service; the 'NLCD Percent Tree Canopy Cover Layer - Evaluation of Data Sources to Predict Canopy Cover' from the USDA Forest Service; 'NLCD Percent Tree Canopy Cover Layer - Evaluation of the 2011 Product and Alternative Approaches to Detect Change' grant from the USDA Forest Service; Virginia Agricultural Experiment Station; McIntire-Stennis Program of NIFA, USDAen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1016/j.jag.2019.101985en
dc.identifier.issn0303-2434en
dc.identifier.other101985en
dc.identifier.urihttp://hdl.handle.net/10919/101739en
dc.identifier.volume86en
dc.language.isoenen
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectTree Canopy Coveren
dc.subjectLandsat time seriesen
dc.subjectHarmonic regressionen
dc.subjectImage compositingen
dc.subjectRandom forest regression modelsen
dc.titleEstimating tree canopy cover using harmonic regression coefficients derived from multitemporal Landsat dataen
dc.title.serialInternational Journal of Applied Earth Observation and Geoinformationen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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