Estimation of boreal forest canopy cover with ground measurements, statistical models and remote sensing

Abstract

Forest canopy cover (CC) is an important ecological variable and the basis for the international definition of forest. Canopy cover is defined as the proportion of forest floor covered by the vertical projection of the tree crowns. Thus, an unbiased estimation of CC requires that the area of interest is covered by vertical measurements, typically by using upward-looking sighting tubes. However, these measurements are very laborious. In practical forest inventories the estimate should be obtained as quickly as possible, but large errors should still be avoided. The aim of this thesis was to compare different quicker-toapply CC estimation techniques to more accurate sighting tube estimates. One alternative is to use instruments with an angle of view (AOV), such as cameras or spherical densiometers, instead of the sighting tubes. This may, however, lead to biased results when using large AOVs, because the sides of the crowns are also observed. The results showed that moderate (max. 40°) AOVs can be used to decrease the number of required sample points without causing a large bias, but more than 20 measurements per plot should be made to avoid large errors in all forests. A new instrument, the crown relascope, is potentially a good alternative in low cover forests where the trees are not very tall. Ocular estimates were found to depend on the observer, but considerable underestimation of CC was common. Furthermore, models for predicting CC based on commonly available forest metrics such as tree height and basal area were created, and reached a precision similar to the quicker field methods. Finally, airborne laser scanning data can be used to estimate CC from the proportion of pulses that hit the canopy above a predefined height limit. The laser method was found to have a high precision but resulted in a small overestimation of CC.