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Open access

Ladislav Kulla, Ivan Sačkov and Miroslav Juriš

Abstract

Even if stand inventories based on growth tables have been widely discussed over the last years, this method of forest mensuration is still widely applied due to favourable ratio between costs and achievable precision of stand growing stock estimation. The aim of the study was to verify the potential of airborne laser scanning data (ALS) for direct estimation of mean stand height and mean stand density (stocking) as fundamental inputs for forest mensuration based on yield tables. The material from two reference plots with substantially different stand structure was processed by REFLEX software, and confronted with the results of the precise terrestrial inventory. The number of detected tree tops decreased from 100% in the case of super-dominant trees to 30% and 5% in the case of supressed trees at the homogeneous and heterogeneous plot, respectively. The correlation of ALS heights with terrestrially measured heights was R = 0.88 at the homogenous plot, and R = 0.77 at the heterogeneous plot. The tendency to underestimate dominant and to overestimate suppressed trees was revealed at both plots, but was more pronounced at the heterogeneous one. Nevertheless we justified that the mean ALS height calculated from the heights of the detected trees represented the biometric mean stand height linked to the stem with the mean basal area quite well. The stocking estimated by REFLEX software according to delineated crowns´ area was also closer to the real value of stocking than the one obtained by the routine mensuration procedure. The results indicate promising potential of the ALS data processed by REFLEX software to rationalise forest mensuration based on yield tables in even-aged forest structures.

Open access

Maroš Sedliak, Ivan Sačkov and Ladislav Kulla

Abstract

Remote Sensing provides a variety of data and resources useful in mapping of forest. Currently, one of the common applications in forestry is the identification of individual trees and tree species composition, using the object-based image analysis, resulting from the classification of aerial or satellite imagery. In the paper, there is presented an approach to the identification of group of tree species (deciduous - coniferous trees) in diverse structures of close-to-nature mixed forests of beech, fir and spruce managed by selective cutting. There is applied the object-oriented classification based on multispectral images with and without the combination with airborne laser scanning data in the eCognition Developer 9 software. In accordance to the comparison of classification results, the using of the airborne laser scanning data allowed identifying ground of terrain and the overall accuracy of classification increased from 84.14% to 87.42%. Classification accuracy of class “coniferous” increased from 82.93% to 85.73% and accuracy of class “deciduous” increased from 84.79% to 90.16%.