2.3 Forest evaluation and land cover classification
For the forest evaluation, we used a simple random sampling design using the same four 1 ha random plots per condition as sampling units, measuring every tree above 1.3 m height and breast height diameter (BHD) in the plot (DBH >1cm). Additionally, we used partial data from a paired A.araucana regeneration study project with the Chilean national forest corporation described in (Montecino et al., In Prep), which consisted of four more 1 ha random plots, which allowed us to reduce error and to represent forest stand conditions better
Forest structure was evaluated by analyzing the observed stand diameter distribution and fitting the most common probability density functions such as lognormal, negative exponential, Weibull, and gamma functions (Newton, 2007). Following goodness-of-fit indices such as the Akaike Information Criterion (AIC) and Kolmogorov- Smirnov (K-S) test to determine the best-fit diameter distribution models.
A high-resolution multispectral satellite image was used to generate an unsupervised land cover classification of the area. The image was obtained from the World View II satellite with a 0.46m resolution from February 2015. These images supply great detail and geospatial accuracy, allowing to conduct an unsupervised classification in ENVI 4.7 software, using the ISODATA clustering algorithm.