4 August 2017 Remote sensing of key grassland nutrients using hyperspectral techniques in KwaZulu-Natal, South Africa
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Abstract
The concentration of forage fiber content is critical in explaining the palatability of forage quality for livestock grazers in tropical grasslands. Traditional methods of determining forage fiber content are usually time consuming, costly, and require specialized laboratory analysis. With the potential of remote sensing technologies, determination of key fiber attributes can be made more accurately. This study aims to determine the effectiveness of known absorption wavelengths for detecting forage fiber biochemicals, neutral detergent fiber, acid detergent fiber, and lignin using hyperspectral data. Hyperspectral reflectance spectral measurements (350 to 2500 nm) of grass were collected and implemented within the random forest (RF) ensemble. Results show successful correlations between the known absorption features and the biochemicals with coefficients of determination (R2) ranging from 0.57 to 0.81 and root mean square errors ranging from 6.97 to 3.03  g/kg. In comparison, using the entire dataset, the study identified additional wavelengths for detecting fiber biochemicals, which contributes to the accurate determination of forage quality in a grassland environment. Overall, the results showed that hyperspectral remote sensing in conjunction with the competent RF ensemble could discriminate each key biochemical evaluated. This study shows the potential to upscale the methodology to a space-borne multispectral platform with similar spectral configurations for an accurate and cost effective mapping analysis of forage quality.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Leeth Singh, Onisimo Mutanga, Paramu Mafongoya, Kabir Peerbhay, "Remote sensing of key grassland nutrients using hyperspectral techniques in KwaZulu-Natal, South Africa," Journal of Applied Remote Sensing 11(3), 036005 (4 August 2017). https://doi.org/10.1117/1.JRS.11.036005 . Submission: Received: 19 January 2017; Accepted: 6 July 2017
Received: 19 January 2017; Accepted: 6 July 2017; Published: 4 August 2017
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