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Integrating MODIS satellite imagery and proximal vegetation sensors to enable precision livestock management

机译:集成MODIS卫星图像和近端植被传感器以实现精确的牲畜管理

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In temperate and mediterranean regions of Australia, utilisation of pastures by grazing animals can often be as low as thirty percent. Feed budgeting is a critical strategy for improving feed utilisation and there are now pasture evaluation and monitoring programs available to farmers across Australia to enable them to estimate Pasture Growth Rate (PGR) and feed availability or Feed On Offer (FOO). Unfortunately, many farmers do not have the confidence or the time available to make regular accurate field estimates across large and remote paddocks. It is also extremely difficult to measure the spatial variation of FOO and PGR. Both satellite image-based systems, such as that derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and on-ground, active sensors and spectroradiometers have limitations. The MODIS sensor, although offering a daily acquisition interval, has a spatial resolution of 6.25ha which does create an issue in describing higher resolution spatial variation in fields and is susceptible to the unwanted artefacts associated with non-forageable vegetation such as trees. On-ground (proximal) sensors such as the Crop Circle™ instrument, can be integrated with with global positioning systems (GPS) and dataloggers and operated ‘on-the-go’, but field-coverage require a vast number of samples that in turn require tedious analysis and particularly if done at frequent intervals. The aim of this paper is to test whether the MODIS and Crop Circle™ data can be intergrated to provide the benefit of both high spatial and temporal resolution. Such information would be useful when determining feed on offer and pasture growth rate information at weekly or strategic times from MODIS acquisitions including other biophysical and physical relevant attributes. This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as- illustrated by the portions given in this document.
机译:在澳大利亚的温带和地中海地区,放牧动物对牧场的利用率通常可能低至30%。饲料预算是提高饲料利用率的关键策略,澳大利亚各地的牧场主现在可以使用牧场评估和监控程序,使他们能够估算牧场增长率(PGR)和饲料供应量或提供的饲料(FOO)。不幸的是,许多农民没有信心或没有时间对大型偏远围场进行定期的准确田间估计。测量FOO和PGR的空间变化也极其困难。基于卫星图像的系统(例如从中等分辨率成像光谱仪(MODIS)和地面,有源传感器和光谱仪获得的系统)都有局限性。尽管MODIS传感器提供每日采集间隔,但其空间分辨率为6.25ha,这确实在描述更高分辨率的田间空间变化方面产生了问题,并且容易受到与不可觅食的植被(例如树木)相关的不希望的伪影的影响。诸如Crop Circle™仪器之类的地面(近端)传感器可以与全球定位系统(GPS)和数据记录仪集成在一起,并且可以“随时随地”运行,但是现场覆盖需要大量样本,转弯需要繁琐的分析,尤其是如果频繁地进行。本文的目的是测试是否可以将MODIS和Crop Circle™数据整合在一起,以提供高空间和时间分辨率的好处。当从MODIS采集中每周或战略时间确定包括其他生物物理和物理相关属性的提供的饲料和牧场生长率信息时,此类信息将非常有用。该电子文档是“实时”模板。纸张的各种组成部分[标题,文本,标题等]已在样式表上定义,如本文档中给出的部分所示。

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