%0 Articles
%T Selection of training areas for remote sensing-based forest above-ground biomass estimation
%A Rana, Md Parvez
%D 2016
%J Dissertationes Forestales
%V 2016
%N 227
%R doi:10.14214/df.227
%U http://dissertationesforestales.fi/article/2009
%X The aim of this work was to estimate forest above-ground biomass (AGB) – one of the fundamental parameters used in the forest inventory for measurement, reporting and verification (MRV) under the Reducing Emissions from Deforestation and Forest Degradation (REDD) and sustainable forest management (REDD+) mechanisms. In particular, this work examined the training area concept in a two-step approach for AGB estimation using airborne laser scanning (ALS) and RapidEye satellite data in the eastern area of Finland (Study I), the effect of the training area location (Study II), and the effect of sample size for the training area (Study III) using ALS, RapidEye and Landsat data in southern Nepal. The AGB model was fitted using simple linear regression (Study I) and the sparse Bayesian method (Study II-III). The AGB model performance was validated using an independent validation dataset, and the performance was evaluated by assessing the root mean square error (RMSE) and mean deviation. The findings of Study I show that the RapidEye model had a promising accuracy with a relative RMSE of 20% against an independent validation set. Study II findings showed that distance from road and the degree of slope in the training area had a considerable effect on the accuracy of the AGB estimation because the forest structure varied according to the level of accessibility. The findings of Study III indicated that an adequate coverage of the variability in tree height and density was an important condition for selecting the training areas. Only a minor increase in relative RMSE is observed when reducing the total number of training areas. ALS-based prediction required the smallest number of training areas when compared to the RapidEye and Landsat data.

To conclude: (i) ALS-simulated training areas could be an alternative to expensive field sample plots using a two-step approach; (ii) the training area should cover a full range of variability in respect to accessibility factors and forest structures such as height, density; (iii) the ALS-based prediction outperformed RapidEye and Landsat data with reasonable accuracy. These evaluated concepts and issues of forest AGB inventory are likely to be useful in supporting future forest monitoring and decision making for the sustainable use of forest resources and REDD.