Abstract
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. 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.
Highlights
The development of laser/radar ranging measurements without scanning and proper attitude control for forest inventory in 1970s–1990s [1,2,3,4,5,6,7,8,9,10,11] promoted the application of laser measurement in forestry and led to the rapid adaptation of airborne laser scanning (ALS) in forest inventory
Manually Extracted Trees (Manual) processing found 70% of the trees, which corresponds to the performance of one of the first automated tree extraction methods by Persson et al [30]
The results confirmed that the extraction method is the main factor impacting achieved accuracy, as proposed by Kaartinen and Hyyppä [27], and that laser point density has less impact on individual tree detection
Summary
The development of laser/radar ranging measurements without scanning and proper attitude control for forest inventory in 1970s–1990s [1,2,3,4,5,6,7,8,9,10,11] promoted the application of laser measurement in forestry and led to the rapid adaptation of airborne laser scanning (ALS) in forest inventory. ALS was applied in determining forest terrain elevations [12,13] This was immediately followed by standwise mean height and volume estimation [14,15,16], based on the data collected via ranging measurements, and very soon ALS was applied to inventorying, focusing on individual trees [17,18,19,20] with the advent of rapid image processing, tree species classification [21,22] and the measurement of tree growth and detection of harvested trees [23] based on bi-temporal data sets. Over 10 years, the extraction of forest variables has been divided into two categories: area-based inventories and inventories based on individual trees or groups of trees. With these developments, laser scanning has increasingly provided the core data set for mapping authorities. In addition to being used in forest inventory, ALS data from forested areas is used for purposes such as flight obstacle mapping, power line mapping, virtual city visualization and mapping, and telecommunication planning
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