Publications

Mustafa, Yaseen T.; Tolpekin, Valentyn A.; Stein, Alfred (2014). Improvement of Spatio-temporal Growth Estimates in Heterogeneous Forests Using Gaussian Bayesian Networks. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 52(8), 4980-4991.

Abstract
Canopy leaf area index (LAI) is a quantitative measure of canopy foliar area. LAI values can be derived from Moderate Resolution Imaging Spectroradiometer (MODIS) images. In this paper, MODIS pixels from a heterogeneous forest located in The Netherlands were decomposed using the linear mixture model using class fractions derived from a high-resolution aerial image. Gaussian Bayesian networks (GBNs) were applied to improve the spatio-temporal estimation of LAI by combining the decomposed MODIS images with a spatial version of physiological principles predicting growth (3PG) model output at different moments in time. Results showed that the spatial-temporal output obtained with the GBN was 40% more accurate than the spatial 3PG, with a root-mean-square error below 0.25. We concluded that the GBNs improved the spatial estimation of LAI values of a heterogeneous forest by combining a spatial forest growth model with satellite imagery.

DOI:
10.1109/TGRS.2013.2286219

ISSN:
0196-2892; 1558-0644