Abstract

With the rapid development of photogrammetric software and accessible camera technology, land surveys and other mapping organizations now provide various point cloud and digital surface model products from aerial images, often including spectral information. In this study, methods for colouring the point cloud and the importance of different metrics were compared for tree species-specific estimates at a coniferous hemi-boreal test site in southern Sweden. A total of three different data sets of aerial image-based products and one multi-spectral lidar data set were used to estimate tree species-specific proportion and stem volume using an area-based approach. Metrics were calculated for 156 field plots (10 m radius) from point cloud data and used in a Random Forest analysis. Plot level accuracy was evaluated using leave-one-out cross-validation. The results showed small differences in estimation accuracy of species-specific variables between the colouring methods. Simple averages of the spectral metrics had the highest importance and using spectral data from two seasons improved species prediction, especially deciduous proportion. Best tree species-specific proportion was estimated using multi-spectral lidar with 0.22 root mean square error (RMSE) for pine, 0.22 for spruce and 0.16 for deciduous. Corresponding RMSE for aerial images was 0.24, 0.23 and 0.20 for pine, spruce and deciduous, respectively. For the species-specific stem volume at plot level using image data, the RMSE in percent of surveyed mean was 129% for pine, 60% for spruce and 118% for deciduous.

Keywords

aerial images; multi-spectral lidar; Optec Titan; photogrammetry; species-specific proportion; stem volume; UltraCam

Published in

Remote Sensing
2021, Volume: 13, number: 4, article number: 720
Publisher: MDPI

SLU Authors

• Bohlin, Jonas

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

• Wallerman, Jörgen

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

• Fransson, Johan

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

Associated SLU-program

Remningstorp


UKÄ Subject classification

Remote Sensing
Forest Science


Publication identifier

DOI: https://doi.org/10.3390/rs13040720

Permanent link to this page (URI)

https://res.slu.se/id/publ/111374