There is a well-established need within the remote sensing community for improved estimation of canopy structure and understanding of its influence on the retrieval of leaf biochemical properties. The aim of this project was to evaluate the estimation of structural properties directly from hyperspectral data, with the broader goal that these might be used to constrain retrievals of canopy chemistry. We used NASA’s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) to discriminate different canopy structural types, defined in terms of biomass, canopy height and vegetation complexity, and compared them to estimates of these properties measured by LiDAR data. We tested a large number of optical metrics, including single narrow band reflectance and 1st derivative, sub-pixel cover fractions, narrow-band indices, spectral absorption features, and Principal Component Analysis components. Canopy structural types were identified and classified from different forest types by integrating structural traits measured by optical metrics using the Random Forest (RF) classifier. The classification accuracy was above 70% in most of the vegetation scenarios. The best overall accuracy was achieved for hardwood forest (>80% accuracy) and the lowest accuracy was found in mixed forest (~70% accuracy). Furthermore, similarly high accuracy was found when the RF classifier was applied to a spatially independent dataset, showing significant portability for the method used. Results show that all spectral regions played a role in canopy structure assessment, thus the whole spectrum is required. Furthermore, optical metrics derived from AVIRIS proved to be a powerful technique for structural attribute mapping. This research illustrates the potential for using optical properties to distinguish several canopy structural types in different forest types, and these may be used to constrain quantitative measurements of absorbing properties in future research.
|Number of pages||1|
|Publication status||Published - 15 Dec 2015|
|Event||AGU fall meeting 2015 - San Francisco, United States|
Duration: 14 Dec 2015 → 18 Dec 2015
|Conference||AGU fall meeting 2015|
|Period||14/12/15 → 18/12/15|