Using airborne laser scanning data and digital aerial photographs to estimate growing stock by tree species

Nowadays modern remote sensing techniques are seen as very attractive alternatives for expensive field measurements in forest inventories. The most promising remote sensing technique for forest inventory purposes is currently Airborne Laser Scanning (ALS). Several studies have indicated that essential forest characteristics such as mean height, basal area and stand volume can be predicted very accurately using ALS data. However, most ALS studies have concentrated on predicting total stand characteristics although species-specific characteristics are of primary interest in Finland. The aim of the thesis is to develop and evaluate methods for species-specific stand level forest inventories using remote sensing. The study was carried out in two test areas, both of which are located in eastern Finland and represent typical managed boreal forests in Finland. All the modelling was done at plot level and the tree species considered were Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.) and deciduous trees as a species group. The simultaneous use of ALS data and aerial photographs forms a basis of the work; the idea is that the ALS data bring information about dimensions and that aerial photographs are useful in order to discriminate between tree species. In the first sub-study regression modelling combined with fuzzy classification and a variant of the nearest neighbour method called k-MSN were compared for determining species-specific volumes. The k-MSN method provided promising results and in the second sub-study it was extended to simultaneously predict by tree species: volume, stem number, basal area, and diameter and height of the basal area median tree; and to calculate total characteristics as sums of the species-specific estimates. The third sub-study investigated the ability of remote sensing information to predict species-specific diameter distributions. The nearest neighbour approach in which field measured trees were used as such to predict diameter distribution outperformed a theoretical diameter distribution approach in which the parameters of the Weibull distribution were predicted using the k-MSN estimates. In the fourth sub-study a new method was presented which uses the unrectified aerial photographs with known external and internal orientation instead of orthorectified images. This overcomes certain issues that are inherent to the previously used approach: the radiometric correction of aerial photographs, and the proper linkage of ALS points and aerial photographs. The nearest neighbour method employed in this thesis turned out to be an efficient and versatile approach to estimating both total and species-specific forest characteristics and diameter distributions when using ALS data, aerial photographs and a set of sample plots as source data. The accuracies achieved in the estimation of species-specific characteristics were at least as good as those obtained by the current field inventory practise, and in the case of total characteristics the accuracy was even better.