Influence of plot and sample sizes on aboveground biomass estimations in plantation forests using very high resolution stereo satellite imagery

Regular biomass estimations for natural and plantation forests are important to support sustainable forestry and
to calculate carbon-related statistics. The application of remote sensing data to estimate biomass of forests has
been amply demonstrated but there is still space for increasing the efficiency of current approaches. Here, we
investigated the influence of field plot and sample sizes on the accuracy of random forest models trained with
information derived from Pléiades very high resolution (VHR) stereo images applied to plantation forests in an
arid environment. We collected field data at 311 locations with three different plot area sizes (100, 300 and
500 m2). In two experiments, we demonstrate how plot and sample sizes influence the accuracy of biomass
estimation models. In the first experiment, we compared model accuracies obtained with varying plot sizes but
constant number of samples. In the second experiment, we fixed the total area to be sampled to account for
the additional effort to collect large field plots. Our results for the first experiment show that model performance
metrics Spearman’s r, RMSErel and RMSEnor improve from 0.61, 0.70 and 0.36 at a sample size of 24–0.79, 0.51
... mehr
and 0.15 at a sample size of 192, respectively. In the second experiment, highest accuracies were obtained with
a plot size of 100 m2 (most samples) with Spearman’s r =0.77, RMSErel =0.59 and RMSEnor =0.15. Results from
an analysis of variance type-II suggest that the overall most important factors to explain model performance
metrics for our biomass models is sample size. Our results suggest no clear advantage for any plot size to reach
accurate biomass estimates using VHR stereo imagery in plantations. This is an important finding, which partly
contradicts the suggestions of earlier studies but requires validation for other forest types and remote sensing
data types (e.g. LiDAR).