Evaluating an active optical sensor for quantifying and mapping green herbage mass and growth in a perennial grass pasture

Abstract

Efficiently measuring and mapping green herbage mass using remote sensing devices offers substantial potential benefits for improved management of grazed pastures over space and time. Several techniques and instruments have been developed for estimating herbage mass, however, they face similar limitations in terms of their ability to distinguish green and senescent material and their use over large areas. In this study we explore the application of an active, near infrared and red reflectance sensor to quantify and map pasture herbage mass using a range of derived spectral indices. The Soil Adjusted Vegetation Index offered the best correlation with green dry matter (GDM), with a root mean square error of prediction of 288 kg/ha. The calibrated sensor was integrated with a Global Positioning System on a 4-wheel motor bike to map green herbage mass. An evaluation of representative, truncated transects indicated the potential to conduct rapid assessments of the GDM in a paddock, without the need for full paddock surveys.

Reflectance indices as a diagnostic tool for weed control performed by multipurpose equipment in precision agriculture

“…In the soybean experiment, the high weed control obtained since the low herbicide doses used did not provide the variability of weed leaf coverage necessary to obtain this information. The implementation of this methodology is derived from one of the main uses of reflectance sensors related to the prediction of nitrogen fertilization in the top dressing of corn, wheat, and barley (Grohs et al, 2009;Rambo et al, 2010) and for pasture utilization (Trotter et al, 2010). Similarly, the prediction of weed leaf coverage through the NDVI index was conducted on cotton with the WeedSeeker TM equipment, resulting in a linear model with a determination coefficient of 0.69 (Sui et al, 2008).…”

A Combination of Plant NDVI and LiDAR Measurements Improve the Estimation of Pasture Biomass in Tall Fescue (Festuca arundinacea var. Fletcher)

“…To address this problem, a variety of other spatially-enabled, "on-the-go" pasture biomass measuring techniques have been developed. Trotter et al [9] provides a review of these techniques which include visual assessment, pasture height recording devices, weighted plate meters, combinations of height and weighted plate meters, electrical capacitance probes, pendulum sensors as well as active optical sensors (AOS). Each of these techniques have their own advantages and disadvantages, however common issues included limited accuracy when dealing with spatially-variable phenology, morphology, species composition and green vs. dry fraction, e.g., [10][11][12][13].…”

“…Spectral reflectance indices that are calculated by measuring the reflectance of vegetation at certain wavelengths, (such as the normalised difference vegetation index (NDVI), simple ratio (SR), soil adjusted vegetation index (SAVI) to name a few) have been used to successfully estimate the biomass content of vegetation, e.g., [9,[14][15][16][17][18]. In recent times, a certain class of sensors that have been used for estimating biomass in pastures are AOS [15,16,19].…”

Examination of the Potential of Terrestrial Laser Scanning and Structure-from-Motion Photogrammetry for Rapid Nondestructive Field Measurement of Grass Biomass

“…Quantification of the above ground biomass (AGB, units g m −2 ) of grasslands is important for a number of applications including pasture management [1], wildlife habitat monitoring [2,3], fire management [4,5], carbon storage [6,7], and understanding the implications of biophysical and ecological processes that influence grass production [8,9]. Destructive methods of AGB estimation require cutting, drying, and weighing grass samples, which is time consuming, inefficient for large area application, and may preclude repeat temporal estimation as the grass is physically removed.…”