Stand age information in forestry generally is collected by means of expensive, labour-intensive and time-consuming field surveys that are also often inconsistent in their analysis results. As an alternative, this research provides a solution to the problem of loblolly pine plantation stand age estimation, using Landsat Thematic Mapper (TM) images, Shuttle Radar Topography Mission (SRTM) and the National Elevation Dataset (NED). The multivariate and tree-based regression models were applied to the derived information, namely the normalized difference vegetation index (NDVI), tasselled cap (TC) transformation and interferometric tree heights. Time-series analyses were carried out to determine the relation of the optical and interferometric tree height data to the forest stand age. The feasibility of regression analysis for timber age estimation, for the two applied regression models, was supported by the high values of R2 and the low standard error of estimate (SEE). With regard to the multivariate regression model, the effects of average ground slope and forest stand size were confirmed to be important factors in timber age estimation. Further, the contributions of the tree height layer in the two regression processes were analysed, the results of which proved the effectiveness of the integration of optical satellite images and tree height information. The synergetic effects of the combined optical and radar data sets were established by comparison with the results of using either optical or radar interferometric data alone.
All Science Journal Classification (ASJC) codes
- Earth and Planetary Sciences(all)