Remote sensing of species mixtures in conifer plantations using LiDAR height and intensity data

• Published in: Remote sensing of environment 2007-10, Vol.110 (4), p.509-522
• Main Authors: Donoghue, Daniel N.M., Watt, Peter J., Cox, Nicholas J., Wilson, Jimmy
• Format: Article
• Language: English
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Earth sciences; Earth, ocean, space; Exact sciences and technology; Forestry; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Internal geophysics; LiDAR; Species mixtures; Teledetection and vegetation maps
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2007.02.032

In even-aged, single species conifer plantations LiDAR height data can be modelled to provide accurate estimates of tree height and volume. However, it is apparent that growth models developed for single species stands are not directly transferable to a more general situation of mixed species plantations. This paper evaluates the ability of small footprint, dual-return, pulsed airborne LiDAR data to estimate the proportion of the productive species when mixed with a nurse crop in closed canopy plantations. A study area located in Galloway Forest District in Scotland is used as an example of Lodgepole pine and Sitka spruce mixed plantation; this area contains good examples of a wide range of pure and mixed species plantation types. Three species groups are studied: areas of pure Sitka spruce, areas of pure Lodgepole pine and areas where the two species have been planted together. Two approaches are assessed for detection of plantation mixtures: the first uses LiDAR intensity data to separate spruce and pine species and the second uses LiDAR-derived canopy density measures, coefficient of variation, skewness, percent of ground returns (which provides a measure of canopy openness) and the mean canopy height, which enables areas with height variations to be identified. From analysis of LiDAR data extracted from 54 study plots using logistic regression, the coefficient of variation and LiDAR intensity data provide the most useful predictors of the proportion of spruce in a pine/spruce mixture with coefficients of determination ( R 2) of 0.914 and 0.930 respectively. The method could be developed as a mapping tool, which in combination with existing inventory data should help to improve timber volume forecasting for mixed species even-aged plantations.