Knowledge of forest structures is critical to understanding and preserving the biodiversity of forests. Tremendous advances are being made in the use of 3D mapping from lidar to better understand forests at a very large scale.
Peter Krzystek, Professor for Photogrammetry and Remote Sensing at Munich University of Applied Sciences has written an important article on his research into using lidar to map forests at very large scales. This is state of the art.
In the article in Open Access Government Peter makes a compelling case for the importance of better understanding the ability of forests to support biodiversity which includes not only living trees, but also dead wood. He states that 14% of total carbon stocks in forests are contained within dead wood.
Peter explains, “We have been focusing our recent research at the Munich University of Applied Sciences on developing innovative methods of the 3D mapping of trees, by applying advanced computer techniques such as machine learning and computer vision. We were extremely successful in demonstrating that forest areas can be completely and automatically processed with LiDAR to produce 3D maps of individual trees, even at very large scales. The results of our methods enable an area-wide 3D vegetation mapping and provide precise information about the percentage of tree species, stock of wood, wood growth, wood harvest in forests and biomass.”
He continues, “The key method is a new approach for single tree detection from the LiDAR point clouds, which turned out as the breakthrough idea in 3D forest mapping. The newly patented technique approach takes advantage of a special segmentation technique adapted from image analysis. This technique subdivides the forest area into voxels or supervoxels, labels every point in the point cloud and groups these points into disjoint tree segments.
The new full waveform LiDAR data helps to significantly improve the detection rate. Based on that technique, coniferous and deciduous trees can be classified with excellent accuracy. As well as the classification of multiple tree species could be significantly improved using multispectral data from LiDAR and aerial imagery.”
For the full article click here.
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