Biophysical parameters obtained from woody vegetation are commonly measured using field based techniques which require significant investment in resources. Quantitative measurements of woody vegetation provide important information for ecological studies investigating landscape change. The fine spatial resolution of aerial photography enables identification of features such as trees and shrubs. Improvements in spatial and spectral resolution of digital aerial photographic sensors have increased the possibility of using these data in quantitative remote sensing. Obtaining biophysical measurements from aerial photography has the potential to enable it to be used as a surrogate for the collection of field data. In this study quantitative measurements obtained from digital aerial photography captured at ground sampling distance (GSD) of 15 cm (n = 50) and 30 cm (n = 52) were compared to woody biophysical parameters measured from 1 ha field plots. Supervised classification of the aerial photography using object based image analysis was used to quantify woody and non-woody vegetation components in the imagery. There was a high correlation (r ≥ 0.92) between all field measured woody canopy parameters and aerial derived green woody cover measurements, however only foliage projective cover (FPC) was found to be statistically significant (paired t-test; α = 0.01). There was no significant difference between measurements derived from imagery captured at either GSD of 15 cm and 30 cm over the same field site (n = 20). Live stand basal area (SBA) (m2 ha−1) was predicted from the aerial photographs by applying an allometric equation developed between field-measured live SBA and woody FPC. The results show that there was very little difference between live SBA predicted from FPC measured in the field or from aerial photography. The results of this study show that accurate woody biophysical parameters can be obtained from aerial photography from a range of woody vegetation communities across the Northern Territory.
|Number of pages||17|
|Journal||International Journal of Applied Earth Observation and Geoinformation|
|Publication status||Published - 1 Oct 2016|