Exploiting tree shadows on snow for estimating forest basal area using Landsat data

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2012-06-01
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Wolter, P.
Berkley, E. A.
Peckham, S. D.
Singh, A.
Townsend, P. A.
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Wolter, Peter
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Natural Resource Ecology and Management
The Department of Natural Resource Ecology and Management is dedicated to the understanding, effective management, and sustainable use of our renewable natural resources through the land-grant missions of teaching, research, and extension.
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Basal area (BA) is a basic structural and ecological attribute of forests that is often used to describe forest composition, estimate volume of wood, and guide management decisions. BA is the sum of cross-sectional area of trees measured at 1.37 m above ground surface, per unit area, and is most commonly measured in-situ. The objective of this study was to supply estimates of BA for oak woodlands and savannas on the 12,828.5 ha Sherburne National Wildlife Refuge in Central Minnesota to guide management efforts. We used winter and summer Landsat imagery, combined with field measurements, to assess the potential for improving forest BA estimates by taking advantage of the high spectral contrast between sunlit snow, forest canopy elements, and shadows projected onto snow ground cover. We explained up to 90% of measured variation in BA using partial least squares regression models calibrated using single- and multiple-date winter Landsat data (R2 = 0.898, RMSE = 2.79 m2ha− 1), which performed better than models calibrated using summer imagery (R2 = 0.762, RMSE = 3.85 m2ha− 1). Success of the winter-based BA models may be driven, in part, by potential geometric/allometric relationships between cast shadow and forest BA, but definitive proof of this is a topic for future research. This method of BA estimation is not refuge-specific and may be extended for regional use to manage oak forest wherever winter snow coverage is consistent. Additional research is needed to determine the degree of robustness to variations in the empirical relationship between BA and tree shading patterns across different forest functional types.

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This article is from Remote Sensing of Environment 121 (2012): 69, doi: 10.1016/j.rse.2012.01.008.

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