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

Abstract

The goal of this study was to evaluate whether harmonic regression coefficients derived using all available cloudfreeobservations 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 betterthan models developed using median composite predictor variables, the previous operational standard for theNational 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 treecanopy cover (the response variable) was collected by experienced interpreters using a dot grid overlaid on 1mspatial resolution National Agricultural Imagery Program (NAIP) images at two different times per region, circa2010 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 forevery time/region of TCC than median composite focal means and standard deviations (across times/regions,mean increase in pseudo R2 of 6.7% and mean decrease in RMSE of 1.7% TCC) and (2) harmonic regressioncoefficients (one harmonic, from NDVI, SWIR1, and SWIR2), when added to the full suite of median compositeand terrain variables used for the NLCD 2011 product, improve the quality of TCC models for every time/region(mean increase in pseudo R2 of 3.6% and mean decrease in RMSE of 1.0% TCC). The harmonic regression NDVIconstant was always one of the top four most important predictors across times/regions, and is more correlatedwith TCC than the NDVI median composite focal mean. Eigen analysis revealed that there is little to no additionalinformation in the full suite of predictor variables (47 bands) when compared to the harmonic regressioncoefficients alone (using NDVI, SWIR1, and SWIR2; 9 bands), a finding echoed by both model fit statistics andthe 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 withother TCC-related variables.

Citation

Derwin, Jill M.; Thomas, Valerie A.; Wynne, Randolph H.; Coulston, John W.; Liknes, Greg C.; Bender, Stacie; Blinn, Christine E.; Brooks, Evan B.; Ruefenacht, Bonnie; Benton, Robert; Finco, Mark V.; Megown, Kevin. 2020. Estimating tree canopy cover using harmonic regression coefficients derived from multitemporal Landsat data. International Journal of Applied Earth Observation and Geoinformation. 86: 101985-. https://doi.org/10.1016/j.jag.2019.101985.