Abstract
A tree list is a list of trees in the area of interest containing, for example, the species, diameter, height, and stem volume of each tree. Tree lists can be used to derive various characteristics of the growing stock, and are therefore versatile and informative sources of data for several forest management purposes. Especially in heterogonous and unmanaged forest structures with multiple species, tree list estimates imputed from local reference field data can provide an alternative to mean value estimates of growing stock (e.g., basal area, total stem volume, mean tree diameter, mean tree height, and number of trees). In this study, reference field plots, airborne laser scanning (ALS) data, and SPOT 5 satellite (Satellite Pour l’Observation de la Terre) imagery were used for tree list imputation applying the k most similar neighbors (k-MSN) estimation method in the West Ural taiga region of the Russian Federation for diameter distribution estimation. In k-MSN, weighted average of k field reference plots with highest similarity between field reference plot and target (forest grid cell, or field plot) based on ALS and SPOT 5 features were used to predict the mean values of growing stock and tree lists for the target object simultaneously. Diameter distributions were then constructed from the predicted tree lists. The prediction of mean values and diameter distributions was tested in 18 independent validation plots of 0.25–0.5 ha in size, whose species specific diameter distributions were measured in the field and grouped into three functional groups (Pines, Spruce/Fir, Broadleaf Group), each containing several species. In terms of root mean squared error relative to mean of validation plots, the accuracy of estimation was 0.14 and 0.17 for basal area and total stem volume, respectively. Reynolds error index values and visual inspection showed encouraging results in evaluating the goodness-of-fit statistics of the estimated diameter distributions. Although estimation accuracy was worse for functional group mean values and diameter distributions, the results indicate that it is possible to predict diameter distributions in forests of the test area with the tested methodology and materials.
Highlights
Parameters describing tree diameter distribution are commonly used in ecological and economic analysis in forestry
Inventory methods based on Airborne laser scanning (ALS) can be roughly divided into two categories: area based approach (ABA) and detecting individual tree crowns (ITC); a third possible category is a mixture of ABA and ITC
The reference data validation allows for the comparison of the k most similar neighbors (k-most similar neighbors (MSN)) estimates with the sparse Bayesian estimates
Summary
Parameters describing tree diameter distribution (mean and standard deviation of diameter, shape of distribution) are commonly used in ecological and economic analysis in forestry. The distribution of trees in forest stands in terms of their diameter at breast height (DBH) provides useful information. Compartment level forest inventory information of the growing stock is very often presented as mean values. The mean values of parameters, such as basal area, total stem volume and stem count per ha, mean tree diameter, mean tree height, and forest age are listed by species and sometimes by age or canopy layer class (dominant, sub dominant, and under canopy trees). Tree lists are very versatile input data for various applications They can be used to derive mean values by species or by other classification as input data for applying tree level growth models or estimating amounts of timber sortiments in case of harvesting. It is possible to use ABA for estimating the mean values of the growing stock (for example [9,10,11]) or in the simultaneous estimation of mean values and tree lists (for example, [12,13]), while ITC always produces complete tree lists if all the trees are correctly detected (for example [14])
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