A1 Journal article (refereed), original research

Large tree diameter distribution modelling using sparse airborne laser scanning data in a subtropical forest in Nepal


Publication Details
Authors: Rana Parvez, Vauhkonen Jari, Junttila Virpi, Hou Zhengyang, Gautam Basanta, Cawkwell Fiona, Tokola Timo
Publisher: Elsevier
Publication year: 2017
Language: English
Related Journal or Series Information: ISPRS Journal of Photogrammetry and Remote Sensing
Volume number: 134
Start page: 86
End page: 95
Number of pages: 10
ISSN: 0924-2716
eISSN: 1872-8235
JUFO-Level of this publication: 1
Open Access: Not an Open Access publication

Abstract

Large-diameter trees (taking DBH > 30 cm to define large trees)
dominate the dynamics, function and structure of a forest ecosystem. The
aim here was to employ sparse airborne laser scanning (ALS) data with a
mean point density of 0.8 m−2 and the non-parametric k-most similar neighbour (k-MSN)
to predict tree diameter at breast height (DBH) distributions in a
subtropical forest in southern Nepal. The specific objectives were: (1)
to evaluate the accuracy of the large-tree fraction of the diameter
distribution; and (2) to assess the effect of the number of training
areas (sample size, n) on the accuracy of the predicted tree diameter
distribution. Comparison of the predicted distributions with empirical
ones indicated that the large tree diameter distribution can be derived
in a mixed species forest with a RMSE% of 66% and a bias%
of −1.33%. It was also feasible to downsize the sample size without
losing the interpretability capacity of the model. For large-diameter
trees, even a reduction of half of the training plots (n = 250), giving a
marginal increase in the RMSE% (1.12–1.97%) was reported
compared with the original training plots (n = 500). To be consistent
with these outcomes, the sample areas should capture the entire range of
spatial and feature variability in order to reduce the occurrence of
error.


Last updated on 2018-19-10 at 07:55