Remotely sensed assisted forest inventory has emerged in the past decade as a robust and
cost efficient method for generating accurate information on forest biophysical parameters. The
launching and public access of ALOS PALSAR-2, Sentinel-1 (SAR), and Sentinel-2 together with the
associated open-source software, has further increased the opportunity for application of remotely
sensed data in forest inventories. In this study, we evaluated the ability of ALOS PALSAR-2, Sentinel-1
(SAR) and Sentinel-2 and their combinations to predict growing stock volume in small-scale forest
plantations of Tanzania. The effects of two variable extraction approaches (i.e., centroid and weighted
mean), seasonality (i.e., rainy and dry), and tree species on the prediction accuracy of growing
stock volume when using each of the three remotely sensed data were also investigated. Statistical
models relating growing stock volume and remotely sensed predictor variables at the plot-level were
fitted using multiple linear regression. The models were evaluated using the k-fold cross validation
and judged based on the relative root mean square error values (RMSEr). The results showed that:
Sentinel-2 (RMSEr = 42.03% and pseudo − R
2 = 0.63) and the combination of Sentinel-1 and Sentinel-2
(RMSEr = 46.98% and pseudo − R
2 = 0.52), had better performance in predicting growing stock
volume, as compared to Sentinel-1 (RMSEr = 59.48% and pseudo − R
2 = 0.18) alone. Models fitted
with variables extracted from the weighted mean approach, turned out to have relatively lower
RMSEr % values, as compared to centroid approaches. Sentinel-2 rainy season based models had
slightly smaller RMSEr values, as compared to dry season based models. Dense time series (i.e.,
annual) data resulted to the models with relatively lower RMSEr values, as compared to seasonal
based models when using variables extracted from the weighted mean approach. For the centroid
approach there was no notable difference between the models fitted using dense time series versus
rain season based predictor variables. Stratifications based on tree species resulted into lower RMSEr
values for Pinus patula tree species, as compared to other tree species. Finally, our study concluded that
combination of Sentinel-1&2 as well as the use Sentinel-2 alone can be considered for remote-sensing
assisted forest inventory in the small-scale plantation forests of Tanzania. Further studies on the effect
of field plot size, stratification and statistical methods on the prediction accuracy are recommended. </p
