Background
A number of data sources currently exist that can provide information on forest plantations at a range of scales over an entire rotation cycle. In particular, LiDAR is quickly becoming the technology of choice for harvest planning and providing local-scale estimates of forest structure. Its application is still limited as repeat annual acquisition at this scale is generally cost prohibitive. Development of temporally updateable models that can accurately project important metrics such as tree height between LiDAR acquisitions would be of considerable use to resource managers. The objective of this research was to develop models of Pinus radiata height using GIS spatial data supplemented with RapidEye satellite imagery.
Methods
Multiple regression models were constructed to describe maximum canopy height (Hm) derived from LiDAR at two relatively distant study sites located in Kaingaroa and Tairua forests. A randomised selection of 300 m2 circular plots was made at both sites and average values of Hm within these plots were used for the modelling. Sources of information used for predicting Hm included stand age and spatial information describing environmental variables and stand productivity. This information was supplemented with spectra and vegetation ratios derived from high resolution RapidEye satellite imagery.
Results
The most robust models of Hm that were developed for both sites included a combination of the crop age obtained from the stand GIS, Site Index (obtained from a GIS surface) and the red-edge vegetation ratio (REVI) The final models of Hm had respective R2 of 0.99 and 0.94 for the Kaingaroa and Tairua sites. At both sites, stand age was the strongest predictor of Hm. However, the inclusion of REVI from high resolution imagery did add an updatable temporal dimension to the model. Changes in REVI are sensitive to the impacts of abiotic and biotic factors that are not captured by stand age and Site Index.
Conclusion
Applied operationally, this model can be used in a GIS environment to estimate tree height and identify areas of anomalous growth or disturbance caused by wind, snow, fire or disease.