[{"type":"article-journal","title":"Influence of plot and sample sizes on aboveground biomass estimations in plantation forests using very high resolution stereo satellite imagery","issued":{"date-parts":[["2020"]]},"container-title":"Forestry","DOI":"10.1093\/forestry\/cpaa028","author":[{"family":"Hosseini","given":"Zahra"},{"family":"Latifi","given":"Hooman"},{"family":"Naghavi","given":"Hamed"},{"family":"Bakhtiarvand Bakhtiari","given":"Siavash"},{"family":"Fassnacht","given":"Fabian Ewald"}],"ISSN":"0015-752X, 1464-3626","abstract":"Regular biomass estimations for natural and plantation forests are important to support sustainable forestry and\r\nto calculate carbon-related statistics. The application of remote sensing data to estimate biomass of forests has\r\nbeen amply demonstrated but there is still space for increasing the efficiency of current approaches. Here, we\r\ninvestigated the influence of field plot and sample sizes on the accuracy of random forest models trained with\r\ninformation derived from Pl\u00e9iades very high resolution (VHR) stereo images applied to plantation forests in an\r\narid environment. We collected field data at 311 locations with three different plot area sizes (100, 300 and\r\n500 m2). In two experiments, we demonstrate how plot and sample sizes influence the accuracy of biomass\r\nestimation models. In the first experiment, we compared model accuracies obtained with varying plot sizes but\r\nconstant number of samples. In the second experiment, we fixed the total area to be sampled to account for\r\nthe additional effort to collect large field plots. Our results for the first experiment show that model performance\r\nmetrics Spearman\u2019s r, RMSErel and RMSEnor improve from 0.61, 0.70 and 0.36 at a sample size of 24\u20130.79, 0.51\r\nand 0.15 at a sample size of 192, respectively. In the second experiment, highest accuracies were obtained with\r\na plot size of 100 m2 (most samples) with Spearman\u2019s r =0.77, RMSErel =0.59 and RMSEnor =0.15. Results from\r\nan analysis of variance type-II suggest that the overall most important factors to explain model performance\r\nmetrics for our biomass models is sample size. Our results suggest no clear advantage for any plot size to reach\r\naccurate biomass estimates using VHR stereo imagery in plantations. This is an important finding, which partly\r\ncontradicts the suggestions of earlier studies but requires validation for other forest types and remote sensing\r\ndata types (e.g. LiDAR).","kit-publication-id":"1000124332"}]