A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems

Abstract

Interest in the use of unmanned aerial systems (UAS) to estimate the aboveground biomass (AGB) of vegetation in agricultural and non-agricultural settings is growing rapidly but there is no standardized methodology for planning, collecting and analyzing UAS data for this purpose. We synthesized 46 studies from the peer-reviewed literature to provide the first-ever review on the subject. Our analysis showed that spectral and structural data from UAS imagery can accurately estimate vegetation biomass in a variety of settings, especially when both data types are combined. Vegetation-height metrics are useful for trees, while metrics of variation in structure or volume are better for non-woody vegetation. Multispectral indices using NIR and red-edge wavelengths normally have strong relationships with AGB but RGB-based indices often outperform them in models. Including measures of image texture can improve model accuracy for vegetation with heterogeneous canopies. Vegetation growth structure and phenological stage strongly influence model accuracy and the selection of useful metrics and should be considered carefully. Additional factors related to the study environment, data collection and analytical approach also impact biomass estimation and need to be considered throughout the workflow. Our review shows that UASs provide a capable tool for fine-scale, spatially explicit estimations of vegetation AGB and are an ideal complement to existing ground- and satellite-based approaches. We recommend future studies aimed at emerging UAS technologies and at evaluating the effect of vegetation type and growth stages on AGB estimation.

Drone-derived canopy height predicts biomass across non-forest ecosystems globally

“…We considered ferns, forbs, graminoids, shrubs, trees and succulents as PFTs, and constrained the y-intercept to zero in order to ensure zero canopy height predicted zero biomass. Model performance was validated using leave-one-out crossvalidation (LOOCV) to compute the mean out-of-sample prediction error, which was divided by the model slope to obtain relative errors for each model 31,66 .…”

“…Low-stature ecosystems are understudied and tools for quantifying forest biomass are better represented in the existing literature 1,7,8 . While photogrammetry can be used to characterise forest canopies 30,31,35 , we consider forest ecosystems better candidates for observation with active remote sensing approaches such as synthetic-aperture radar 7 , vegetation optical depth and LiDAR 52 . We selected species that were regionally widespread, accessible and would inform ongoing research efforts, but excluded extensively modified vegetation such as managed hedges.…”

“…An essential requirement for deriving canopy height models from photogrammetry-derived point clouds is a digital terrain model, which must be sufficiently accurate and detailed with respect to canopy heights and topographic complexity 31 . We used terrain models interpolated with Delaunay triangulation between the GNSS-observations of the harvest plot corners (Fig.…”

“…Advances in photogrammetry, particularly structure-from-motion (SfM) with multi-view stereopsis 25 , have made it possible to capture 3D representations of plants, quantitatively describing their fine-scale structure with an unprecedented level of detail 26,27 . SfM allows objective measurements of canopy height at sub-decimetre spatial grain for a wide range of plant growth forms [18][19][20][27][28][29][30][31] .…”

“…Over the past few years, thousands of hectares of low-stature ecosystems have been surveyed with drones across the globe, yielding information-rich datasets. However, drone-photogrammetry products are sensitive to the ways in which data are (i) collected (e.g., ground sampling distance, image overlap, viewing geometry, spatial control, illumination conditions) 27,30,[34][35][36][37][38] , (ii) processed (e.g., software, lens model, specification of control accuracy, selection of processing quality, depth filtering) 27,[36][37][38] , and (iii) analysed (e.g., canopy height metrics, spatial grain and interpolation, statistical treatment) 20,[27][28][29]31 . These sensitivities are more pronounced for subjects with complex texture, such as vegetation, and hinder comparisons between products obtained from different workflows.…”