Semi-automatic classification of tree species in different forest ecosystems by spectral and geometric variables derived from Airborne Digital Sensor (ADS40) and RC30 data

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Differentiating plant species within and across diverse ecosystems with imaging spectroscopy (2015, result): “Species that occur less frequently in the landscape, or occur only within small patches, were difficult to classify accurately (similar to findings by Waser & Asner, 2011).”
National mapping and estimation of forest area by dominant tree species using Sentinel-2 data (2020, background): “…which provides the broadest range of spectral information (e.g. Dalponte et al. 2012, Fricker et al. 2019); (2) multispectral airborne (e.g. Waser et al. 2011, Engler et al. 2013) with high spatial but limited spectral resolution, and passive spaceborne sensors, which provide a high…”
Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution (2013, background): “Waser et al. (2011), who also used ADS40 multi-spectral data obtained an overall CCR of 0.76, a Cohen’s kappa of 0.76 and percentages of correctly classified samples ranging between 25.4–84.7% depending on the species.”