Digital colour-infrared (CIR) aerial photographs, which have been collected routinely in many parts of the world, are an invaluable data source for the monitoring and assessment of forest resources. Yet, the potential of these data for automated individual tree species mapping remains largely unexplored. One way to maximize the usefulness of digital CIR aerial photographs for individual tree species mapping is to integrate them with modern and complementary remote sensing technologies such as the light detection and ranging (LiDAR) system and 3D segmentation algorithms. In this study, we examined whether multi-temporal digital CIR orthophotos could be used to further increase the accuracy of airborne LiDAR-based individual tree species mapping for a temperate mixed forest in eastern Germany. Our results showed that the texture features captured by multi-temporal digital CIR orthophotos under different view-illumination conditions were species-specific. As a consequence, combining these texture features with LiDAR metrics significantly improved tree species mapping accuracy (overall accuracy: 77.4%, kappa: 0.68) compared to using LiDAR data alone (overall accuracy: 69.3%, kappa: 0.58). Among various texture features, the average gray level in the near-infrared band was found to contribute most to the classification. Our results suggest that the synergic use of multi-temporal digital aerial photographs and airborne LiDAR data has the potential to accurately classify individual tree species in Central European mixed forests.
|Number of pages||10|
|Journal||International Journal of Applied Earth Observation and Geoinformation (JAG)|
|Early online date||12 Sep 2019|
|Publication status||Published - 2020|
Shi, Y., Wang, T., Skidmore, A. K., & Heurich, M. (2020). Improving LiDAR-based tree species mapping in Central European mixed forests using multi-temporal digital aerial colour-infrared photographs. International Journal of Applied Earth Observation and Geoinformation (JAG), 84, 1-10. . https://doi.org/10.1016/j.jag.2019.101970