The coppice-with-standards silvicultural system as applied to Eucalyptus plantations-a review

We review the management of Eucalyptus species under a coppice-with-standards （CWS） silvicultural system. CWS management results in product diversification, permitting production of small and large scale timber from the same stand. Eucalyptus species are suitable candidates for CWS management because： there are large worldwide plantation areas, sprouting capacity is high, and eucalypts are multipur- pose species. We discuss （1） short rotation Eucalyptus coppice manage- ment for energy and pulping and （2） Eucalyptus seedling management for solid wood products. We review the literature and discuss experi- ences with Eucalyptus managed under the CWS system. We also assess projects dealing with Eucalyptus coppice management, stand density regulation, pruning, and stand and wood quality. The growth environ- ment of the standard trees （heavy competition up to the first harvest, free growth afterwards） coupled with long rotations （〉20 years） results in high quality logs for solid wood products. Early pruning should be ap- plied to enhance wood quality. We propose a system for the silvicultural management of Eucalyptus under the CWS system, elaborating on the consequences of initial planting density, site productivity, and standard tree densities as well as timing of basic silvicultural applications.

Most similar neighbor imputation of forest attributes using metrics derived from combined airborne LIDAR and multispectral sensors

In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral(MS)sensors,we suggest the inclusion of normalized difference vegetation index(NDVI)metrics along with the more traditional LIDAR height metrics.Here the data fusion method consists of back-projecting LIDAR returns onto original MS images,avoiding co-registration errors.The prediction method is based on nonparametric imputation(the most similar neighbor).Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods.Results show improvements when using combinations of LIDAR and MS compared to using either of them alone.The MS sensor has little explanatory capacity for forest variables dependent on tree height,already well determined from LIDAR alone.However,there is potential for variables dependent on tree diameters and their density.The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity,which are best described from synergies between LIDAR heights and NDVI dispersion.Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes.Their inclusion in the predictor dataset may,however,in a few cases be detrimental to accuracy,and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis.