TY - JOUR
T1 - Improvement of spatio-temporal growth estimates in heterogeneous forests using Gaussian Bayesian networks
AU - Mustafa, Y.T.
AU - Tolpekin, V.A.
AU - Stein, A.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - 22/4 OA procedure
UR - https://ezproxy2.utwente.nl/login?url=https://doi.org/10.1109/TGRS.2013.2286219
U2 - 10.1109/TGRS.2013.2286219
DO - 10.1109/TGRS.2013.2286219
M3 - Article
SN - 0196-2892
VL - 52
SP - 4980
EP - 4991
JO - IEEE transactions on geoscience and remote sensing
JF - IEEE transactions on geoscience and remote sensing
IS - 8
ER -