Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/84192
Type of publication: Straipsnis konferencijos medžiagoje Clarivate Analytics Web of Science ar/ir Scopus / Article in Clarivate Analytics Web of Science or Scopus DB conference proceedings (P1a)
Field of Science: Miškotyra / Forestry (A004)
Author(s): Bikuvienė, Ina;Mozgeris, Gintautas
Title: Testing the simultaneous use of laser scanning and aerial image data for estimation of tree crown density
Is part of: Research for rural development 2010 : annual 16th international scientific conference proceedings, Jelgava, Latvia, 19-21 May, 2010. Jelgava, 2010, Vol. 1
Extent: p. 201-207
Date: 2010
Keywords: Sample plot;Crown density;LiDAR;Aerial image;Non parametric methods
Abstract: This paper introduces the first test results to use laser scanning and high resolution digital colour infrared aerial image data to estimate average tree crown density at a sample plot level. General methodological framework based on two-phase sampling schemes, non-parametric estimators and satellite images as the auxiliary data sets was adopted for the use with airborne data sources. More than 400 circular sample plots were established and measured in a special research forest area near Kaunas, the central part of Lithuania. The tree crown density was visually estimated for every coniferous tree belonging to the 500 square m plot together with other conventional forest parameters. Two variants of digital colour infrared aerial images (ground sampling density 15 and 40 cm), LiDAR point clouds, based on 1 point/square m scanning density and two phase sampling approach with non-parametric k-nearest neighbour and most similar neighbour estimators were used to test the accuracies of tree crown density estimation at a sample plot level. Reliable estimates were found to be possible on pure coniferous stands only. Average tree crown density was estimated with the root mean square error around 17.5-18% at a sample plot level, bearing in mind average crown density around 64% for the whole study area. The estimates were unbiased. Integration of laser scanning based variables with the ones available from digital aerial images resulted in lowest estimation root mean square errors. Laser scanning based variables used as the auxiliary data set independently resulted in better estimation errors than the variables available from digital colour infrared images
Internet: http://www.llu.lv/getfile.php?id=27929
http://www.llu.lv/getfile.php?id=27929
Affiliation(s): Vytauto Didžiojo universitetas
Žemės ūkio akademija
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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