Estimating biomass in temperate grassland with high resolution canopy surface models from UAV-based RGB images and vegetation indices

Abstract

Monitoring grassland biomass throughout the growing season is of key importance in sustainable, site-specific management decisions. Precision agriculture applications can support these decisions. However, precision agriculture relies on timely and accurate information on plant parameters with a high spatial and temporal resolution. The use of structural and spectral features derived from unmanned aerial vehicle (UAV)-based image data from low-cost sensors is a promising nondestructive approach to assess plant traits such as above-ground biomass or plant height. Therefore, the main objectives were (1) to evaluate the potential of low-cost UAV-based canopy surface models to monitor sward height as an indicator of grassland biomass, (2) to evaluate the potential of vegetation indices from low-cost UAV-based red-greenblue (RGB) digital image data, and (3) to compare the mentioned methods with established methods for biomass monitoring such as rising plate meters and spectroradiometer-based narrowband vegetation indices over the growing season in 2017, including three cuts. We compared the accuracy of each single UAV-based height feature and vegetation index using a combined multivariate approach to estimate fresh and dry biomass. The heterogeneous sward structure with high spatiotemporal variability led to varying performance in biomass estimation depending on the growths (time between two cuts) and choice of predictor variable. The results showed that biomass prediction by height features provided moderate-to-good results (cross-validation R 2 ¼ 0.57 to 0.73 for dry biomass and 0.43 to 0.79 for fresh biomass), but reference measurements based on rising plate meters were more robust when estimating biomass. The spectral features (RGB-based vegetation indices and spectroradiometerbased vegetation indices) yielded varying accuracy and suitability for biomass prediction. Despite the variability, our findings indicate a promising approach for grassland biomass monitoring. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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

“…Our sample achieved a more than twenty-fold improvement in the coverage of harvest plots, species and sites compared to previous photogrammetry vegetation studies (Fig. 1C) 20,28,29 . We fitted plant functional type (PFT) and species-specific models that predict AGB from fine-grained canopy height as determined by SfM photogrammetry.…”

“…Any obviously implausible camera positions were refined after marker placement and optimisation. All cameras were usually aligned and used for multi-view stereopsis (dense point cloud generation), using the ultrahigh quality setting with mild depth filtering to preserve finer details of the vegetation 27,29,30 . For further discussion of some of the limitations of this approach, see Supplementary Note 1.…”

“…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.…”

UAV hyperspectral inversion modeling of rice nitrogen content based on WOA-ELM

“…However, the water availability should be considered when estimating the grain protein content. At present, in the research of UAV remote sensing inversion of rice, most of them first establish a statistical regression model using the vegetation index, and then invert the chlorophyll content, which have ideal inversion effect for specific varieties in specific areas, but there were still some deficiencies in the universality of the model [14][15][16] . The previous work of using hyperspectral analysis technology to detect chlorophyll content mainly focused on two aspects: establishing various vegetation indexes, using multiple linear or non-linear regression method to establish the inversion model between the index and chlorophyll content; or modeling all bands of hyperspectral data of rice canopy by PCA, PLS and other methods [17] .…”