Comparison of individual tree detection and canopy height distribution approaches: a case study in Finland.

The two main approaches in ALS based prediction of growing stock characteristics of forests have been individual tree detection (ITD) and canopy height distribution based modelling (CHD). There are numerous studies, in which either of these approaches have been used with a particular test area and dataset. However, the results obtained are not directly comparable between different datasets and areas. In this paper we present a comparison of ITD and CHD using the same validation dataset. The validation data consisted of 41 sample plots, located in a boreal managed forest. ITD and CHD produced equally accurate estimates with respect to stem volume and Lorey’s height. The RMSE was about 22% for volume and about 8% for Lorey’s height. The residuals were also similar with both methods. Stem number estimates were less accurate with both approaches; particularly ITD had a large RMSE and bias in the form of underestimation. This study indicated that, when considering total stem volume, both ITD and CHD are potential inventory approaches in managed boreal forests. CHD has a cost benefit in the acquisition of ALS data but, on the other hand, it requires more field work in the collection of modelling data.

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