The capability of species-related forest stand characteristics determination with the use of hyperspectral data

The primary goal of this study was to bridge the gap between hyperspectral data classifications and their practical implementation for forest management. Information on the distributions and abundances of tree species can help forest managers understand the spatial coverage of tree species and manage the species composition by establishing a specific strategy. For this purpose, classification of tree species was applied to airborne hyperspectral imagery, and a determination of selected forest stand characteristics (i.e., main tree species, species cover proportions, and species mixture patterns) was made.We selected the Milicz Forest District of southwestern Poland as our study area. Sixteen airborne hyperspectral images were acquired with the HySpex VNIR-1800 and SWIR-384 sensors on 19 August 2015. We classified four deciduous tree species (Black alder, Pedunculate oak, Silver birch, and European beech) and coniferous species using a support vector machine (SVM) classifier. To properly evaluate the classification accuracy, the level of crown visibility from above was determined during ground measurements. The obtained information was used to create a reference set integrated with the Remote Sensing data.The classification accuracy was 91%. The main tree species found in the sample plots were correctly assessed based on the coverage area for 92% of the sample plots. The species cover proportions were estimated correctly for 75–94% of the sample plots with a tolerance threshold ≤10 percentage points depending on the species considered. Similarities of the species structures shown in the sample plots between the classification map and reference field data were found by Morisita's index (0.92). Spatial mixture pattern detection was performed for 316 forest stands. The results of the developed method were found to agree to field data with 69% accuracy. This study shows that airborne hyperspectral data serve as a reliable source for the precise description of forest characteristics, such as the main tree species, tree species cover proportions and mixture patterns.