monitoring of forest biomass, which will ultimately result in
the production of accurate and comprehensive information
for use in forest management.
Conclusions
This study proposed the use of a
MVRVR
model for
AGB
estima-
tion. In addition, to validate the use of multi-temporal
ALOS
PALSAR
FBD
data, a comparison of
MLR
,
MLPNN
,
SVR
, and
MVRVR
models was performed.
AGB
values were obtained from forest
inventories which were in the range of 11 to 392 Mg/ha. The
results from the various models showed that the
MVRVR
model
featured the highest R2 value and the lowest errors; the
MLR
model had the lowest R2 value and the highest errors with a
high overestimation in the biomass range between 0 and 200
Mg/ha. It was also indicated that the
MLPNN
model had a rela-
tively good saturation point, but the results was not able to
follow the trend of validation data. The superior performance
of the
MVRVR
model is related to the fact that it is able to fol-
low the trend of validation data using limited ground data.
In addition, the output values of the validation data were
synthesized using only a limited number of relevance vectors
(12 percent of training data). Furthermore, the
MVRVR
model
has the highest saturation point (297.81 Mg/ha) in compari-
son with those of the
MLPNN
(264.69 Mg/ha),
MLR
(255.66
Mg/ha), and
SVR
(224.75 Mg/ha) methods. It is evident that
although each approach has positive properties, none of them
is able to thoroughly solve the problem of underestimation. In
future work, we will focus on machine learning techniques,
such as deep learning algorithms, in an attempt to capture all
the details relating to
AGB
data, with the aim of avoiding the
problem of underestimation at higher levels.
Acknowledgments
The authors would like to thank Dr. Sumantyo and Dr. Pour-
shakouri for supplying the
ALOS
PALSAR
data and the forest
biomass data, respectively.
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