利用固定翼无人机和光谱仪多光谱图像估算米草孢子菌生物量

N. D. Leo, Emiliano Jozami, Enrique Montero Bulacio, N. Rigalli, M. Romagnoli, Ivana G. Barbona, M. Portapila, S. Feldman
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引用次数: 0

摘要

spartinus(三)下午Peterson & Saarela (=Spartina argentinensis Parodi,通用名称:espartiillo)是一种多年生C4草,在圣达菲省北部名为“Bajos潜水”的地区的许多社区中占主导地位。它产生大量的生物质,但由于其消化率低,养牛者经常燃烧它,以刺激更嫩的叶子的生长。这种燃烧的生物质可以用来生产生物能源,因此了解其可用性是强制性的,以便选择具有较高生物质的地区。在这项研究中,研究人员在2019年10月和2020年3月两个不同的日期,分别用安装在无人机上的多光谱相机和光谱仪上的高光谱相机测量了加尔韦兹镇附近一个农场的S. spartinus生物量反射率。10月米草总生物量较高(平均干物质7700 kg /公顷),其中大部分(67%)为衰老生物量,3月总生物量为5300 kg /公顷,枯死生物量仅占38%。两种仪器的归一化指数都能解释33%的总生物量变异。由于绿色或衰老的生物质都可以用于生物能源,我们的目的是利用遥感多光谱图像来估计这些生物质。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Biomass Estimation of Sporobolus spartinus with Multispectral Images from a Fixed-Wing Unmanned Aerial Vehicle and a Spectrometer
Sporobolus spartinus (Trin.) P.M. Peterson & Saarela (=Spartina argentinensis Parodi, common name: espartillo) is a perennial C4 grass that dominates in many communities from a region named "Bajos Submeridionales" in the northern part of Santa Fe province. It produces high quantity of biomass but, due to its low digestibility, cattle risers often burn it in order to stimulate the growth of more tender leaves. This burnt biomass could be used in order to produce bioenergy, thus knowing its availability is mandatory in order to select areas with higher biomass. In this research, S. spartinus biomass reflectance from a farm near the town of Galvez was measured with i: a multispectral camera mounted in a Drone and ii: a hyperspectral camera in a spectrometer in two different dates, one in October of 2019 and the other in March of 2020. S. spartinus biomass presented higher total biomass (average of 7700 kg of dry matter per hectare) in October and most of it (67%) was senescent biomass while in March, total biomass accounted for 5300 kg per hectare and only 38% of it was standing dead biomass. A Normalized index was able to explain 33% of total biomass variability with both instruments. Due to the fact that either green or senescent biomass can be used for bioenergy, our aim was to estimate those using remotely sensed multispectral images.
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