Using Calibrated RGB Imagery From Low-Cost Uavs for Grassland Monitoring: Case Study at the Rengen Grassland Experiment (Rge), Germany

Abstract

ABSTRACT:Monitoring the spectral response of intensively managed grassland throughout the growing season allows optimizing fertilizer inputs by monitoring plant growth. For example, site-specific fertilizer application as part of precision agriculture (PA) management requires information within short time. But, this requires field-based measurements with hyper-or multispectral sensors, which may not be feasible on a day to day farming practice. Exploiting the information of RGB images from consumer grade cameras mounted on unmanned aerial vehicles (UAV) can offer cost-efficient as well as near-real time analysis of grasslands with high temporal and spatial resolution. The potential of RGB imagery-based vegetation indices (VI) from consumer grade cameras mounted on UAVs has been explored recently in several. However, for multitemporal analyses it is desirable to calibrate the digital numbers (DN) of RGB-images to physical units. In this study, we explored the comparability of the RGBVI from a consumer grade camera mounted on a low-cost UAV to well established vegetation indices from hyperspectral field measurements for applications in grassland. The study was conducted in 2014 on the Rengen Grassland Experiment (RGE) in Germany. Image DN values were calibrated into reflectance by using the Empirical Line Method (Smith & Milton 1999). Depending on sampling date and VI the correlation between the UAV-based RGBVI and VIs such as the NDVI resulted in varying R 2 values from no correlation to up to 0.9. These results indicate, that calibrated RGB-based VIs have the potential to support or substitute hyperspectral field measurements to facilitate management decisions on grasslands.

Use of consumer-grade cameras to assess wheat N status and grain yield

“…Finally, differences in band amplitude, light conditions and geometry of acquisition are known to introduce some variability in the relationships between vegetation indices and either chlorophyll and N content. In our study, data were corrected to account for varying light conditions at the time of acquisition using a reference panel which might have partially improved the capability of vegetation indices at assessing chlorophyll content [51].…”

UAS-Based Plant Phenotyping for Research and Breeding Applications

“…Additionally, due to their similarity to the electromagnetic spectrum over which the human eye operates, RGB camera-based UAS image data has been successfully used for automated phenotyping of features that have traditionally been manually performed. Examples of morphological traits include height, leaf area, shape, organ detection and counting, plant density estimation, and plant/weeds discrimination, among others [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]. Most popular UAS systems are integrated with a RGB camera system, thus allowing real-time image preview, seamless camera configuration management, and simple remote trigger control by the operator.…”

Estimation of Grassland Canopy Height and Aboveground Biomass at the Quadrat Scale Using Unmanned Aerial Vehicle

“…When the quality of the generated point cloud data is insufficient for distinguishing the ground and vegetation point clouds, the algorithm cannot accurately invert the vegetation height information. As a result, few studies have been conducted over grassland [29,54] and wetland [55], since the height of herbage is much smaller than that of forest and shrub. In this study, we focused on the development of a novel method for estimating the quadrat-scale aboveground biomass of low-statute vegetation.…”