The Spectral Response of Pastures in an Intensively Managed Dairy System

Handcock, R.N.; Mata, G.; Donald, G. E.; Edirisinghe, A.; Henry, D. A.; Gherardi, SG

doi:10.1007/978-3-540-93962-7_24

Cited by 4 publications

(7 citation statements)

References 13 publications

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“…This increase is needed to be quantified for the periods 'JunJul', 'Aug' and 'SepOct' for NDVI > 0.74. However, a significant increase in variance was not seen for SE8 (November 2006) andSE9 (December 2006) in Fig. 5.…”

Section: Extending the Relationship For Ndvi > 074contrasting

confidence: 56%

“…Elsewhere, Narrow band based NDVI studies and hyperspectral band depth analysis have been used to overcome saturation of broad band NDVI response from dense grass canopies to improve biomass assessment (Mutanga and Skidmore, 2004a,b) and alternative vegetation indices for vegetation biomass assessment were also considered (Huete et al, 2002). Satellite sensed spectral response of rotationally grazed dairy pasture systems has been reported by Handcock et al (2006) and Mata et al (2007). In a study of non-dairy extensive grazing systems in Australia NDVI has been related to the pasture biomass, through a systematic relationship (Edirisinghe et al, 2011(Edirisinghe et al, , 2000.…”

Section: Introductionmentioning

confidence: 96%

“…This can only be achieved by an accurate technique that delivers pasture biomass estimates "near-real-time" to large geographic areas with the minimum reliance on subjective human interventions. Developments in remote sensing technology over the last decade (Clark et al, 2006;Edirisinghe et al, 2011Edirisinghe et al, , 2000Handcock et al, 2006) have been useful in providing solutions to satisfy the demand of the dairy industry in New Zealand overcoming these difficulties. The potential of this technology to provide information on the time and spatial scales demanded by the industry over geographically extensive areas has made remote sensing solutions attractive for the dairy industry.…”

Section: Introductionmentioning

confidence: 98%

See 2 more Smart Citations

Spatio-temporal modelling of biomass of intensively grazed perennial dairy pastures using multispectral remote sensing

Edirisinghe

Clark

Waugh

2012

International Journal of Applied Earth Observation and Geoinfor

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Section: Extending the Relationship For Ndvi > 074contrasting

confidence: 56%

Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Spatio-temporal modelling of biomass of intensively grazed perennial dairy pastures using multispectral remote sensing

Edirisinghe

Clark

Waugh

2012

International Journal of Applied Earth Observation and Geoinfor

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“…If different pasture heights or biomass amounts are found at the same time, it can be indicative of rotational grazing management. Time series of high spatial resolution data can address this goal by measuring, for example, pasture growth rates or post-grazing regrowth [164][165][166]. On the other hand, the observation of the pasture subdivision into several paddocks forming spatial geometric patterns can indirectly indicate the adoption of some type of rotational grazing.…”

Section: Grazing Vs Mowingmentioning

confidence: 99%

Remote Sensing and Cropping Practices: A Review

et al. 2018

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For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on remote sensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing), cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry), and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures). We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remote sensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues.

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“…In grazing-based industries, gathering information on feed resources through traditional methods for measuring pasture biomass (e.g., pasture cuts, visual assessments and plate meters) [1] is time-consuming and error-prone. While pasture monitoring using remotely sensed data has been shown to be useful for measuring pasture characteristics such as growth rate [2] and biomass [3,4], the data requirements of image frequency and spatial scale do not always match with the operational needs of a rapidly changing agricultural system. Ground-based or proximal-sensing systems which collect data on vegetation status via multiple automated sensor nodes embedded within a wireless sensor network (WSN) have the potential to collect data with lower cost, and without the delays and access issues typical of repeated manual surveys [5].…”

Section: Introductionmentioning

confidence: 99%

Prototyping an Operational System with Multiple Sensors for Pasture Monitoring

Gobbett

Handcock

Zerger

et al. 2013

JSAN

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Combining multiple proximal sensors within a wireless sensor network (WSN) enhances our capacity to monitor vegetation, compared to using a single sensor or non-networked setup. Data from sensors with different spatial and temporal characteristics can provide complementary information. For example, point-based sensors such as multispectral sensors which monitor at high temporal frequency but, at a single point, can be complemented by array-based sensors such as digital cameras which have greater spatial resolution but may only gather data at infrequent intervals. In this article we describe the successful deployment of a prototype system for using multiple proximal sensors (multispectral sensors and digital cameras) for monitoring pastures. We show that there are many technical issues involved in such a deployment, and we share insights relevant for other researchers who may consider using WSNs for an operational deployment for pasture monitoring under often difficult environmental conditions. Although the sensors and infrastructure are important, we found that other issues arise and that an end-to-end workflow is an essential part of effectively capturing, processing and managing the data from a WSN. Our deployment highlights the importance of testing and ongoing monitoring of the entire workflow to ensure the quality of data captured. We demonstrate that the combination of different sensors enhances our ability to identify sensor problems necessary to collect accurate data for pasture monitoring

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The Spectral Response of Pastures in an Intensively Managed Dairy System

Cited by 4 publications

References 13 publications

Spatio-temporal modelling of biomass of intensively grazed perennial dairy pastures using multispectral remote sensing

Spatio-temporal modelling of biomass of intensively grazed perennial dairy pastures using multispectral remote sensing

Remote Sensing and Cropping Practices: A Review

Prototyping an Operational System with Multiple Sensors for Pasture Monitoring

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