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Mapping grassland biomass and nitrogen from hyperspectral UAV images

Published on
March 14, 2022

For the West-European dairy agriculture systems, adequate grassland management is required to ensure sustainable and rentable cattle production, especially taking into account the potentially high environmental impacts of this activity.

The management of pasture areas is complex, in particular for intensive production systems with multiple mowing and/or grazing events over the growing season. For farmers, determining the quantity and quality of forage available in-situ is essential since it is the least expensive source of feed and optimizing the utilization of this resource is crucial to reduce production costs.

As part of the Interreg project Spectors, German and Dutch research institutes investigated the opportunities to use optical imaging systems on Unmanned Aerial Vehicles (UAV) for the characterization of forage traits. A long-term grassland experiment established at Haus Riswick in Kleve in Germany was monitored for several years and grassland traits were measured and hyperspectral images using the UAV-based Hyperspectral Mapping System (HYMSY) were acquired.

One of the challenges is to develop multi-annual models of grassland traits retrieval which can be applied for different years under varying environmental conditions. In his research Marston Franceschini and colleagues demonstrate that considerable simple approaches applied to small datasets can result in relatively accurate predictions of grassland traits, indicating their spatial patterns and providing initial site-specific information to assist farmers in pasture management. Special attention was given to model calibration transfer by selecting and adding samples from the target data to the training dataset. This procedure searched to adequately represent new observations, which was not possible based on the limited number of samples available before transfer. By adding only a limited number of samples from the target date to a pre-existing dataset it was possible to reduce the number of samples that would need to be collected and analysed in a laboratory by up to 77%, depending on the trait of interest.

The results of this study are published in the journal Drones in a paper entitled ‘Quantification of Grassland Biomass and Nitrogen Content through UAV Hyperspectral Imagery—Active Sample Selection for Model Transfer’. The paper is open-source and can be accessed through the following link: https://doi.org/10.3390/drones6030073

In addition, the dataset comprising the grassland trait measurements and the UAV hyperspectral images used in this research was recently published online under https://doi.org/10.4121/19188872.v1. In addition, the main python scripts used in the analysis will soon be available in this repository (https://github.com/mhdf/Uncertainty-based_Model_Transfer).

Abstract
Accurate retrieval of grassland traits is important to support management of pasture production and phenotyping studies. In general, conventional methods used to measure forage yield and quality rely on costly destructive sampling and laboratory analysis, which is often not viable in practical applications. Optical imaging systems carried as payload in Unmanned Aerial Vehicles (UAVs) platforms have increasingly been proposed as alternative non-destructive solutions for crop characterization and monitoring. The vegetation spectral response in the visible and near-infrared wavelengths provides information on many aspects of its composition and structure. Combining spectral measurements and multivariate modelling approaches it is possible to represent the often complex relationship between canopy reflectance and specific plant traits. However, empirical models are limited and strictly represent characteristics of the observations used during model training, therefore having low generalization potential. A method to mitigate this issue consists of adding informative samples from the target domain (i.e., new observations) to the training dataset. This approach searches for a compromise between representing the variability in new data and selecting only a minimal number of additional samples for calibration transfer. In this study, a method to actively choose new training samples based on their spectral diversity and prediction uncertainty was implemented and tested using a multi-annual dataset. Accurate predictions were obtained using hyperspectral imagery and linear multivariate models (Partial Least Squares Regression—PLSR) for grassland dry matter (DM; R2 = 0.92, RMSE = 3.25 dt ha−1), nitrogen (N) content in % of DM (R2 = 0.58, RMSE = 0.27%) and N-uptake (R2 = 0.91, RMSE = 6.50 kg ha−1). In addition, the number of samples from the target dates added to the training dataset could be reduced by up to 77% and 74% for DM and N-related traits, respectively, after model transfer. Despite this reduction, RMSE values for optimal transfer sets (identified after validation and used as benchmark) were only 20–30% lower than those values obtained after model transfer based on prediction uncertainty reduction, indicating that loss of accuracy was relatively small. These results demonstrate that considerably simple approaches based on UAV hyperspectral data can be applied in preliminary grassland monitoring frameworks, even with limited datasets.