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-green-blue (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 R2 = 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 spectroradiometer-based vegetation indices) yielded varying accuracy and suitability for biomass prediction. Despite the variability, our findings indicate a promising approach for grassland biomass monitoring.
Climate change, food insecurity and limited land and water resources strengthen the need for operational and spatially explicit information on vegetation condition and dynamics. The detection of vegetation condition as well as multiannual and seasonal changes using satellite remote sensing, however, depends on the choice of data including length and frequency of time series. Thus, this contribution focuses on the derivation of the optimal remotely sensed data for vegetation monitoring and extraction of relevant metrics. Time series of satellite data from Landsat-8, Sentinel-1/2, and MODIS were used to identify characteristics of vegetation at different spatiotemporal scales. We derived parameters, such as: maximum and amplitude based on vegetation index time series, as well as Land Surface Temperature (LST). Along with optical data, we used backscattering intensity over consecutive vegetation growing seasons. The analysis was carried out using Google Earth Engine, a cloud computing platform which allows to access various data archives and conduct data-intensive analysis. Taking advantage of this platform, we developed a web-based application named GreenLeaf. The application is computing metrics and plotting time series, based on parameters defined by the user. The derived vegetation condition parameters provide sufficient information to detect vegetation change. In addition, the images acquired from near-coincident dates provide similar information over continuous surfaces. The developed application contributes to the use of satellite data and the simplification of data access for users with limited remote sensing experience and/or restricted processing power. Aiming at providing this knowledge to stakeholders can further support decision making on multiple scales.