Context . Hollow-bearing trees are an important breeding and shelter resource for wildlife in Australian native forests and hollow availability can influence species abundance and diversity in forest ecosystems. A persistent problem for forest managers is the ability to locate and survey hollow-bearing trees with a high level of accuracy at low cost over large areas of forest.

Aims . The aim of this study was to determine whether remote-sensing techniques could identify key variables useful in classifying the likelihood of a tree to contain hollows suitable for wildlife.

Methods . The data were high-resolution, multispectral aerial imagery and light detection and ranging (Lidar). A ground-based survey of 194 trees, 96 Eucalyptus crebra and 98 E. chloroclada and E. blakelyi, were used to train and validate tree-senescence classification models.

Key results . We found that trees in the youngest stage of tree senescence, which had a very low probability of hollow occurrence, could be distinguished using multispectral aerial imagery from trees in the later stages of tree senescence, which had a high probability of hollow occurrence. Independently, the canopy-height model used to estimate crown foliage density demonstrated the potential of Lidar-derived structural parameters as predictors of senescence and the hollow-bearing status of individual trees.

Conclusions . This study demonstrated a ‘proof of concept’ that remotely sensed tree parameters are suitable predictor variables for the hollow-bearing status of an individual tree.

Implications . Distinguishing early stage senescence trees from later-stage senescence trees using remote sensing offers potential as an efficient, repeatable and cost-effective way to map the distribution and abundance of hollow-bearing trees across the landscape. Further development is required to automate this process across the landscape, particularly the delineation of tree crowns. Further improvements may be obtained using a combination of these remote-sensing techniques. This information has important applications in commercial forest inventory and in biodiversity monitoring programs.