For P. menziesii, growth responses to temperature and precipitation did not vary between canopy classes.
We found that climate growth relationships varied significantly between canopy classes and across habitat types but that these effects were highly species-specific.
Largest patch index (LPI), mean patch size (MPS), area-weighted mean shape index (AWMSI) and edge density (ED) were calculated for the no-canopy class as an average of the sampled squares at the selected scales.
The effect of harvesting was examined by classifying the forest land into no-canopy and closed-canopy classes.
Most classification errors were observed in the 10 20% canopy cover class, with a misclassification error of around 20 25%.
Woody canopy height class distributions differed significantly between protected and accessible areas, with the tallest vegetation (>10 m) occurring on protected termite mounds.
Timber volume estimates are generated for 16 forest classes, i.e., four forest cover types × four canopy density classes, across this 811,414 km2 area and compared with a ground-based regional volume estimate.
Conifers in the higher canopy cover classes (16 − 50% and > 50% canopy cover) were scarce (< 2% and 1% canopy cover), as was mesquite (< 5% and 1% canopy cover).
Landsat TM satellite imagery, topographic and climate ancillary data are used to build binary (forest/non-forest) and multiclass (forest canopy cover classes) classification models, trained using sample aerial photograph maps, across Victoria, Australia.
We applied a stratified random sampling inventory design, based on five forest canopy height classes, derived from Ice, Cloud, and Land Elevation Satellite/Geoscience Laser Altimeter System (ICE Sat/GLAS) and the Shuttle Radar Topography Mission (SRTM) data, and a Spatial Decision Support System to allocate inventory plots.
