Author:
Wigley Kathryn,Owens Jennifer L.,Westerschulte Matthias,Riding Paul,Fourie Jaco,Werner Armin
Abstract
New tools are required to provide estimates of pasture biomass as current methods are time consuming and labour intensive. This proof-of-concept study tested the suitability of photogrammetry to estimate pasture height in a grazed dairy pasture. Images were obtained using a digital camera from one site on two separate occasions (May and June 2017). Photogrammetry-derived pasture height was estimated from digital surface models created using the photos. Pasture indices were also measured using two currently available methods: a Rising Plate Meter (RPM), and Normalised Difference Vegetation Index (NDVI). Empirical pasture biomass measurements were taken using destructive sampling after all other measurements were made, and were used to evaluate the accuracy of the estimates from each method. There was a strong linear relationship between photogrammetry-derived plant height and actual biomass (R2=0.92May and 0.78June) and between RPM and actual biomass (R2=0.91May and 0.78June). The relationship between NDVI and actual biomass was relatively weaker (R2=0.65May and 0.66June). Photogrammetry could be an efficient way to measure pasture biomass with an accuracy comparable to that of the RPM but further work is required to confirm these preliminary findings.
Publisher
New Zealand Grassland Association
Subject
Nature and Landscape Conservation,Plant Science,Soil Science,Agronomy and Crop Science,Ecology, Evolution, Behavior and Systematics
Reference27 articles.
1. Álvarez F, Catanzarite T, Castellanos J, Blanco-Medina V 2010 Biomass Estimation using Digital Photogrammetric Cameras. Preasented at the International Calibration and Orientation Workshop EuroCOW, Castelldefels, Spain, 10-12 Feburary.
2. Bareth G, Bendig J, Tilly N, Hoffmeister D, Aasen H, Bolten A 2016. A comparison of UAV-and TLS-derived plant height for crop monitoring: using polygon grids for the analysis of crop surface models (CSMs). Photogrammetrie-Fernerkundung-Geoinformation 2016: 85-94.
3. Bendig J, Yu K, Aasen H, Bolten A, Bennertz S, Broscheit J, Gnyp ML, Bareth G 2015. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation 39: 79-87.
4. Beukes P, McCarthy S, Wims C, Romera A 2015. Regular estimates of paddock pasture mass can improve profitability on New Zealand dairy farms. Journal of New Zealand Grasslands 77: 29-34.
5. Cimbelli A, Vitale V 2017. Grassland height assessment by satellite images. Advances in Remote Sensing 6: 40.
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