Remote Sensing, 2012, 4(4), 950-974; published online 30 March 2012

Harri Kaartinen , Juha Hyyppä , Xiaowei Yu , Mikko Vastaranta , Hannu Hyyppä , Antero Kukko , Markus Holopainen , Christian Heipke , Manuela Hirschmugl , Felix Morsdorf , Erik Næsset , Juho Pitkänen , Sorin Popescu , Svein Solberg , Bernd Michael Wolf, and Jee-Cheng Wu
Article:

“The objective of the “Tree Extraction” project organized by EuroSDR (European Spatial data Research) and ISPRS (International Society of Photogrammetry and Remote Sensing) was to evaluate the quality, accuracy, and feasibility of automatic tree extraction methods, mainly based on laser scanner data. In the final report of the project, Kaartinen and Hyyppä (2008) reported a high variation in the quality of the published methods under boreal forest conditions and with varying laser point densities. This paper summarizes the findings beyond the final report after analyzing the results obtained in different tree height classes. Omission/Commission statistics as well as neighborhood relations are taken into account. Additionally, four automatic tree detection and extraction techniques were added to the test.

Site A (Left) and Site B (Right), tree heights shown as color-coded canopy
height model (CHM).

“Several methods in this experiment were superior to manual processing in the dominant, co-dominant and suppressed tree storeys. In general, as expected, the taller the tree, the better the location accuracy. The accuracy of tree height, after removing gross errors, was better than 0.5 m in all tree height classes with the best methods investigated in this experiment. For forest inventory, minimum curvature-based tree detection accompanied by point cloud-based cluster detection for suppressed trees is a solution that deserves attention in the future.”

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