Testing the quality of forest variable estimation using dense image matching: a comparison with airborne laser scanning in a Mediterranean pine forest

Abstract

ABSTRACTAirborne laser scanning (ALS) is commonly used in forest mapping. Full coverage of ALS is already available in some countries to provide high-detailed terrain elevation models. These kinds of data sets have been shown to offer great potential in forest mapping. However, it presents some drawbacks such as the resampling periods may be longer than recommended for forestry purposes or unexpected data updates. The recent development of digital photogrammetric algorithms makes dense image matching (DIM) point clouds an alternative to ALS in forest monitoring and management. Area-based approach estimations from ALS and DIM-based point clouds in a Pinus pinaster Ait. forest of Central Iberia were compared. Heights from image matching were normalized by an ALS-derived digital elevation model (DEM). A total of 50 sampling plots were used to fit non-parametric models for the estimation of forest structure variables. Plot-level validation revealed that DIM-based models predicted dominant height, stem number, basal area, and stem volume with root mean square error of 10.71%, 43.02%, 27.02%, and 26.80%, respectively. The corresponding results from ALS data were 11.06% for dominant height, 39.71% for stem number, 25.07% for basal area, and 25.60% for stem volume. This study demonstrates the usefulness of the combination of DIM with ALS-derived DEM to develop forest metrics and high-quality inventories in Mediterranean pine forests.