Distinguishing Intensity Levels of Grassland Fertilization Using Vegetation Indices

Abstract

Abstract:Monitoring the reaction of grassland canopies on fertilizer application is of major importance to enable a well-adjusted management supporting a sustainable production of the grass crop. Up to date, grassland managers estimate the nutrient status and growth dynamics of grasslands by costly and time-consuming field surveys, which only provide low temporal and spatial data density. Grassland mapping using remotely-sensed Vegetation Indices (VIs) has the potential to contribute to solving these problems. In this study, we explored the potential of VIs for distinguishing five differently-fertilized grassland communities. Therefore, we collected spectral signatures of these communities in a long-term fertilization experiment (since 1941) in Germany throughout the growing seasons 2012-2014. Fifteen VIs were calculated and their seasonal developments investigated. Welch tests revealed that the accuracy of VIs for distinguishing these grassland communities varies throughout the growing season. Thus, the selection of the most promising single VI for grassland mapping was dependent on the date of the spectra acquisition. A random forests classification using all calculated VIs reduced variations in classification accuracy within the growing season and provided a higher overall precision of classification. Thus, we recommend a careful selection of VIs for grassland mapping or the utilization of temporally-stable methods, i.e., including a set of VIs in the random forests algorithm.

Assessing the Effect of Real Spatial Resolution of In Situ UAV Multispectral Images on Seedling Rapeseed Growth Monitoring

“…In order to evaluate the effect of VI types on canopy GS-NDVI and LAI estimation, the canopy reflectance measured by the ASD spectrometer was used to simulate the equivalent reflectance of the five bands by the following formula (1) [45]. The VIs (Table 3) were calculated based on the ASD data (ASD-VIs), and the coefficients of determination (R 2 ) between each of the two measured parameters (GS-NDVI and LAI) and each of the five ASD-VIs were also computed.…”

Relationship between Remote Sensing Data, Plant Biomass and Soil Nitrogen Dynamics in Intensively Managed Grasslands under Controlled Conditions

“…It has been shown that plant traits such as fresh plant biomass, plant dry matter, plant N and chlorophyll content can be easily determined using optical sensors able to detect light reflection in the range of red to near-infrared light [ 7 , 9 ]. Depending on the wavelength detected by the sensors, different vegetation indices can be calculated.…”

“…To this end, farmers have to better understand and be able to predict the grass’ biomass development, N demand, and N availability throughout the growing season, allowing for adjustments of fertilizer application in relation to the biomass development. One possibility to quickly estimate the plant biomass in the field is the use of remote sensing techniques, such as optical sensors based on measuring light reflectance from vegetation in the range of red to near-infrared light (approximately 630–900 nm), or hyperspectral sensors [ 7 , 8 , 9 ]. Ideally, these remote sensing data will, in future, feed into a predictive model integrating plant biomass development, plant N uptake, and N delivery from N mineralization.…”

Methods to describe the botanical composition of vegetation in grassland research

“…Strong emphasis is currently placed on spectrometry, a non-invasive method that allows analyzing biological systems in terms of components, structures and molecular functions. Vegetation and its growth dynamics can be described at a community level by means of various indices derived from field-spectrometric measurements and partly related also to vegetation traits (i.e., vegetation mapping, species richness) at a community level (Govender et al, 2007;Psomas et al, 2011;Hollberg and Schellberg, 2017). Spectrometric techniques can also be used for detecting weeds (Glenn et al, 2005) or, as expected in future, for estimating the optimal time of harvesting in combination with satellite data (Schaumberger and Schellberg, 2015).…”