Influence of Plot Size on Efficiency of Biomass Estimates in Inventories of Dry Tropical Forests Assisted by Photogrammetric Data from an Unmanned Aircraft System

Abstract

Abstract:Applications of unmanned aircraft systems (UASs) to assist in forest inventories have provided promising results in biomass estimation for different forest types. Recent studies demonstrating use of different types of remotely sensed data to assist in biomass estimation have shown that accuracy and precision of estimates are influenced by the size of field sample plots used to obtain reference values for biomass. The objective of this case study was to assess the influence of sample plot size on efficiency of UAS-assisted biomass estimates in the dry tropical miombo woodlands of Malawi. The results of a design-based field sample inventory assisted by three-dimensional point clouds obtained from aerial imagery acquired with a UAS showed that the root mean square errors as well as the standard error estimates of mean biomass decreased as sample plot sizes increased. Furthermore, relative efficiency values over different sample plot sizes were above 1.0 in a design-based and model-assisted inferential framework, indicating that UAS-assisted inventories were more efficient than purely field-based inventories. The results on relative costs for UAS-assisted and pure field-based sample plot inventories revealed that there is a trade-off between inventory costs and required precision. For example, in our study if a standard error of less than approximately 3 Mg ha −1 was targeted, then a UAS-assisted forest inventory should be applied to ensure more cost effective and precise estimates. Future studies should therefore focus on finding optimum plot sizes for particular applications, like for example in projects under the Reducing Emissions from Deforestation and Forest Degradation, plus forest conservation, sustainable management of forest and enhancement of carbon stocks (REDD+) mechanism with different geographical scales.

Effects of Sample Plot Size and GPS Location Errors on Aboveground Biomass Estimates from LiDAR in Tropical Dry Forests

“…These findings concur with previous studies in both temperate [20,21,38] and tropical forests [39,40] highlighting the importance of selecting an appropriate plot size for forest inventories used for estimating forest biomass. Studies in tropical dry forests in Malawi using photogrammetric data showed the same trend of increasing accuracy with increasing sample plot size [41].…”

“…Thus, the R 2 values of cross validations increased by 0.25 (a 76% increase) from 80 m 2 (0.33) to 400 m 2 (0.58) but only by 0.17 (a 29% increase) from 400 m 2 to 1000 m 2 (0.75) in Kiuic, and by 0.28 (a 120% increase) from 80 m 2 (0.23) to 400 m 2 (0.51) but only by 0.16 (a 31% increase) from 400 m 2 to 1000 m 2 (0.67) in FCP. These results are relevant because, as plot sizes increases, the cost of field sampling also increases [41]. Therefore, the selection of an optimal plot size for AGB estimation from LiDAR data should take into account a trade-off between estimation accuracy and plot establishment cost.…”

“…Although there are alternative approaches trying to link field-surveyed tree locations with positions of trees identified by LiDAR and using an automatic procedure in order to reduce co-registration errors [23,45,46], the use of large plots has the advantage of capturing an adequate amount of structural variability in the field [22], reducing edge effects and increasing overlap area [41].…”

Canopy Height and Above-Ground Biomass Retrieval in Tropical Forests Using Multi-Pass X- and C-Band Pol-InSAR Data

“…The precision and accuracy of AGB extrapolated from field plot data are affected by the size and shape of the plots [64]. Most of the used field plots in the present study exceed an area of 1000 m 2 , a size large enough to be more robust against boundary effects and less sensitive to individual trees [65][66][67]. Aside from the size, the shape affects the results of extrapolated AGB.…”

Evaluation of Ground Surface Models Derived from Unmanned Aerial Systems with Digital Aerial Photogrammetry in a Disturbed Conifer Forest

“…Nevertheless, the low cost and increased repeatability of DAP relative to ALS, for stand-level applications, shows significant potential [27,29]. Recent studies indicate that accurate DEM generation from the unsupervised classification of DAP point clouds under open forest canopies is achievable [29,31,46,47]. For example Guerra-Hernández et al [31] used 20 high precision GPS checkpoints and found RMSE of 0.046 m, 0.018 m and 0.033 m in the X, Y and Z directions respectively.…”