Comparing mobile and static assessment of biomass in heterogeneous grassland with a multi-sensor system
| Autor: | Thomas Möckel, Hanieh Safari, Björn Reddersen, Michael Wachendorf, Thomas Fricke |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0106 biological sciences
business.industry lcsh:T Biomass 04 agricultural and veterinary sciences 01 natural sciences lcsh:Technology Position (vector) 040103 agronomy & agriculture Range (statistics) Global Positioning System 0401 agriculture forestry and fisheries Environmental science Ultrasonic sensor Electrical and Electronic Engineering business Instrumentation Mobile device Predictive modelling Simulation Intensity (heat transfer) 010606 plant biology & botany Remote sensing |
| Zdroj: | Journal of Sensors and Sensor Systems, Vol 5, Iss 2, Pp 301-312 (2016) |
| ISSN: | 2194-878X |
| Popis: | The present study aimed to test a mobile device equipped with ultrasonic and spectral sensors for the assessment of biomass from diverse pastures and to compare its prediction accuracy to that from static measurements. Prediction of biomass by mobile application of sensors explained > 63 % of the variation in manually determined reference plots representing the biomass range of each paddock. Accuracy of biomass prediction improved with increasing grazing intensity. A slight overestimation of the true values was observed at low levels of biomass, whereas an underestimation occurred at high values, irrespective of stocking rate and years. Prediction accuracy with a mobile application of sensors was always lower than when sensors were applied statically. Differences between mobile and static measurements may be caused by position errors, which accounted for 8.5 cm on average. Beside GPS errors (±1–2 cm horizontal accuracy and twice that vertically), position inaccuracy predominantly originated from undirected vehicle movements due to heaps and hollows on the ground surface. However, the mobile sensor system in connection with biomass prediction models may provide acceptable prediction accuracies for practical application, such as mapping. The findings also show the limits even sophisticated sensor combinations have in the assessment of biomass of extremely heterogeneous grasslands, which is typical for very leniently stocked pastures. Thus, further research is needed to develop improved sensor systems for supporting practical grassland farming. |
| Databáze: | OpenAIRE |
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