Extracting LiDAR indices to characterise multilayered forest structure using mixture distribution functions
Jaskierniak, D and Lane, PNJ and Robinson, A and Lucieer, A, Extracting LiDAR indices to characterise multilayered forest structure using mixture distribution functions, Remote Sensing of Environment, 115, (2) pp. 573-585. ISSN 0034-4257 (2011) [Refereed Article]
Discrete Light Detection and Ranging (LiDAR) data is used to stratify a multilayered eucalyptus forest and characterise the structure of the vertical profile. We present a methodology that may prove useful for a very broad range of forest management applications, particularly for timber inventory evaluation and forest growth modelling. In this study, we use LiDAR data to stratify a multilayered eucalyptus forest and characterise the structure of specific vegetation layers for forest hydrology research, as vegetation dynamics influence a catchment's streamflow yield. A forest stand's crown height, density, depth, and closure, influence aerodynamic properties of the forest structure and the amount of transpiring leaf area, which in turn determine evapotranspiration rates. We present a methodology that produces canopy profile indices of understorey and overstorey vegetation using mixture models with a wide range of theoretical distribution functions. Mixture models provide a mechanism to summarise complex canopy attributes into a short list of
parameters that can be empirically analysed against stand characteristics.
Few studies have explored theoretical distribution functions to represent the vertical profile of vegetation structure in LiDAR data. All prior studies have focused on a Weibull distribution function, which is unimodal. In a complex native forest ecosystem, the form of the distribution of LiDAR points may be highly variable between forest types and age classes. We compared 44 probability distributions within a two component mixture model to determine the most suitable bimodal distributions for representing LiDAR density estimates
of Mountain Ash forests in south-eastern Australia. An elimination procedure identified eleven candidate distributions for representing the eucalyptus component of the mixture model. We demonstrate the methodology on a sample of plots to predict overstorey stand volumes and basal area, and understorey basal area of 18-, 37-, and 70-year old Mountain Ash forest with variable density classes. The 70-year old forest has been subjected to a range of treatments including: thinning of the eucalyptus layer with two distinct retention rates, removal of the understorey, and clear felling of patches that have 37 year old
regenerating forest. We demonstrate that the methodology has clear potential, as observed versus predicted values of eucalyptus basal area and stand volume were highly correlated, with bootstrap based r2 ranging from 0.61 to 0.89 and 0.67 to 0.88 respectively. Non-eucalyptus basal area r2 ranged from 0.5 to 0.91.