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Fusion of Ultrasonic and Spectral Sensor Data for Improving the Estimation of Biomass in Grasslands with Heterogeneous Sward Structure

机译:超声和光谱传感器数据的融合,以提高草地异质草地结构生物量的估计

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摘要

An accurate estimation of biomass is needed to understand the spatio-temporal changes of forage resources in pasture ecosystems and to support grazing management decisions. A timely evaluation of biomass is challenging, as it requires efficient means such as technical sensing methods to assess numerous data and create continuous maps. In order to calibrate ultrasonic and spectral sensors, a field experiment with heterogeneous pastures continuously stocked by cows at three grazing intensities was conducted. Sensor data fusion by combining ultrasonic sward height (USH) with narrow band normalized difference spectral index (NDSI) (R^2CV = 0.52) or simulated WorldView2 (WV2) (R^2CV = 0.48) satellite broad bands increased the prediction accuracy significantly, compared to the exclusive use of USH or spectral measurements. Some combinations were even better than the use of the full hyperspectral information (R^2CV = 0.48). Spectral regions related to plant water content were found to be of particular importance (996–1225 nm). Fusion of ultrasonic and spectral sensors is a promising approach to assess biomass even in heterogeneous pastures. However, the suggested technique may have limited usefulness in the second half of the growing season, due to an increasing abundance of senesced material.
机译:需要准确估算生物量,以了解牧场生态系统中饲草资源的时空变化并支持放牧管理决策。及时评估生物量具有挑战性,因为它需要有效的手段(例如技术传感方法)来评估大量数据并创建连续图。为了校准超声波和频谱传感器,进行了野外试验,以三种放牧强度连续放牧奶牛。通过将超声波草地高度(USH)与窄带归一化差异光谱指数(NDSI)(R ^ 2CV = 0.52)或模拟的WorldView2(WV2)(R ^ 2CV = 0.48)卫星宽带相结合,传感器数据融合显着提高了预测准确性,与专用USH或频谱测量相比。一些组合甚至比使用完整的高光谱信息更好(R ^ 2CV = 0.48)。发现与植物含水量有关的光谱区域特别重要(996-1225 nm)。超声和光谱传感器的融合是一种评估生物量的有前途的方法,即使在异质牧场中也是如此。但是,由于增加了感性物质的含量,建议的技术在生长期的后半段可能用途有限。

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