Estimation of Alpine Forest Structural Variables from Imaging Spectrometer Data
AbstractSpatial information of forest structural variables is crucial for sustainable forest management planning, forest monitoring, and the assessment of forest ecosystem productivity. We investigate a complex alpine forest ecosystem located in the Swiss National Park (SNP) and apply empirical models to retrieve the structural variables canopy closure, basal area, and timber volume at plot scale. We used imaging spectrometer (IS) data from the Airborne Prism EXperiment (APEX) in combination with in-situ measurements of forest structural variables to develop empirical models. These models are based on simple and stepwise multiple regressions, while all potential two narrow-band combinations of the Simple Ratio (SR), the Normalized Difference Vegetation Index (NDVI), the perpendicular vegetation index (PVI), the second soil-adjusted vegetation index (SAVI2), and band depth indices were tested. The accuracy of the estimated structural attributes was evaluated using a leave-one-out cross-validation technique. Using stepwise multiple regression models, we obtained a moderate to good accuracy when estimating canopy closure (R2 = 0.81, rRMSE = 10%), basal area (R2 = 0.68, rRMSE = 20%), and timber volume (R2 = 0.73, rRMSE = 22%). We discuss the reliability of empirical approaches for estimates of canopy structural parameters considering the causality of light interaction and surface information. View Full-Text
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Fatehi, P.; Damm, A.; Schaepman, M.E.; Kneubühler, M. Estimation of Alpine Forest Structural Variables from Imaging Spectrometer Data. Remote Sens. 2015, 7, 16315-16338.
Fatehi P, Damm A, Schaepman ME, Kneubühler M. Estimation of Alpine Forest Structural Variables from Imaging Spectrometer Data. Remote Sensing. 2015; 7(12):16315-16338.Chicago/Turabian Style
Fatehi, Parviz; Damm, Alexander; Schaepman, Michael E.; Kneubühler, Mathias. 2015. "Estimation of Alpine Forest Structural Variables from Imaging Spectrometer Data." Remote Sens. 7, no. 12: 16315-16338.