Light detection and ranging (LiDAR) data and large scale (1:4000) photography (LSP) were investigated for their potential to quantify the floristics and structure of mixed species forests near Injune, central east Queensland, and to scale these up to the region for purposes of baseline assessment and on-going monitoring. For a 220,000 hectare (ha) area, LiDAR and LSP were acquired over 150 500 m × 150 m (7.5 ha) primary sampling units (PSUs) located on a not, vert, similar4 km systematic grid. Based on LSP interpretation, 292 species combinations were observed, although forests were dominated or co-dominated primarily by Callitris glaucophylla, Eucalyptus melanaphloia, Eucalyptus populnea and Angophora Leiocarpa. Comparisons with species distributions mapped using LSP and in the field suggested a 79% correspondence for dominant species. Robust relationships were observed between LiDAR and field measurements of individual tree (r2 = 0.91, S.E. = 1.34 m, n = 100) and stand (r2 = 0.84, S.E. = 2.07 m, n = 32) height. LiDAR-derived estimates of plot level foliage/branch projected cover (FBPC), defined by the percentage of returns >2 m, compared well (r2 of 0.74, S.E. = 8.1%, n = 29) with estimates based on field transects. When translated to foliage projected cover (FPC), a close correspondence with field measurements (r2 = 0.62, S.E. = 6.2%, n = 29) was observed. Using these relationships, floristics and both height and FPC distributions were estimated for forests contained with the PSU grid and extrapolated to the study area. Comparisons with National Forest Inventory (NFI), National Vegetation Information System (NVIS) and Queensland Herbarium data suggested that sampling using LSP and LiDAR aggregated to the landscape provided similar estimates at the broad level but allowed access to a permanent and more detailed record. Observed differences were attributed to different scales of data acquisition and mapping. The cost of survey was also reduced compared to more traditional methods. The method outlined in the paper has relevance to national forest monitoring initiatives, such as the Continental Forest Monitoring Framework currently being evaluated in Australia.