The Benefit of Spectral and Point-Cloud Data for Herbage Yield and Quality Assessment of Grasslands

Abstract

Grassland represents the largest single agricultural vegetation in Germany and provides a multitude of ecosystem services. Timely and accurate information about herbage yield and quality is essential for an efficient use of resources, e.g. to be able to match the actual available feed with a demand of animals or with other industrial uses. Grasslands frequently exhibit small-scale botanical and structural heterogeneity with pronounced spatio-temporal dynamics. These features present particular challenges for sensor applications, which, apart from limitations posed by high costs and low temporal and spatial resolutions of many available remote sensing (RS) systems, may be the reason for so far little commercial applications of RS in practical grassland farming. This paper considers recent developments in the use of spectral and point-cloud data for herbage yield and quality assessment of grasslands. Former research showed that single sensor systems mounted on unmanned aerial vehicles produce similar prediction errors in crude protein or acid detergent fibre concentrations as proximal sensing tools (e.g. field spectroscopy). However, further improvements are needed. Beside improvements of single sensor types, the development of systems with complementary sensors is seen as a promising research area. It will help to overcome the limitations of single sensors and provide better information about herbage yield and quality. From an agronomic point of view, thematic maps of farm fields are suggested as the central outcome of RS and data analysis. These maps are representing the relevant grassland features and therefore can be used as low-cost, appropriate and timely information to support farmers' decision-making.

Monitoring Invasive Plant Species Using Hyperspectral Remote Sensing Data

“…Cubert hyperspectral remote and proximal sensing proved to be an efficient platform for the detection of spectro-phenological parameters of different plantations [58], like raspberry varieties [59] or grassland agricultural vegetation type [60]. It was also tested as a unique technique in complex agro-ecosystem monitoring [61].…”