Comparative testing of single-tree detection algorithms under different types of forest

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A Benchmark of Lidar-Based Single Tree Detection Methods Using Heterogeneous Forest Data from the Alpine Space (2015, methodology): “[26] focused on the effect of different study areas and forest types on the detection results of different algorithms.”
Predicting the spatial pattern of trees by airborne laser scanning (2013, background): “5D (cf. Larsen et al. 2011; Vauhkonen et al. 2012), is the limitation that it can detect only those trees that are clearly visible from above.”
Comparison of airborne laser scanning methods for estimating forest structure indicators based on Lorenz curves (2014, methodology): “The performance of the ITD algorithms typically depends on tree density and spatial distribution of trees, i.e. clustering patterns (Vauhkonen et al., 2012).”
Mapping local density of young Eucalyptus plantations by individual tree detection in high spatial resolution satellite images (2013, background): “In comparison, Vauhkonen et al. (2011) concluded that airborne scanning laser data allowed tree detection during the whole rotation time in 4x3m Eucalyptus plantations.”
Outlook for the Next Generation’s Precision Forestry in Finland (2014, background): “For example, in ABA, stem-quality attributes required by the forest industry, such as species-specific timber assortments, cannot be obtained accurately [5,8–10].”