Forest height estimation using semi-individual tree detection in multi-spectral 3D aerial DMC data

Abstract

The increasing availability of accurate Digital Elevation Models (DEMs) of nation-wide cover has opened new possibilities to produce accurate forest variable estimation using 3D data acquired from aerial imagery. Such data can be produced by automatic matching of stereo images and photogrammetric modeling of the forest canopy height. Using existing accurate DEM information, the forest canopy height above ground is then easily assessed. Today, Airborne Laser Scanning (ALS) is frequently used to capture data for accurate estimation of variables to be used in forest management planning. Recent studies in Scandinavia show estimation accuracies almost as accurate as ALS, using 3D data obtained from standard aerial imagery, at least for the most important forest variables. So far mainly area-based estimation methods at field plot or raster cell level have been studied. This paper reports early results from applying a single-tree modeling approach, corresponding to the Semi-ITC (Individual Tree Crown) method, commonly used in ALS-based applications, using 3D data acquired from aerial DMC imagery. Here, a simplified Semi-ITC method was used to estimate tree height at segment level. The Root Mean Square Error of estimating the maximum tree height was 34% (of the true mean maximum tree height). Clearly, the methodology used shows promising results and has potential to be used in forest management planning.