Department of Geography and Planning, University of Saskatchewan, Kirk Hall, 117 Science Place, Saskatoon, SK S7N 5C8, Canada.
Sensors (Basel). 2021 Nov 3;21(21):7310. doi: 10.3390/s21217310.
Fractional vegetation cover is a key indicator of rangeland health. However, survey techniques such as line-point intercept transect, pin frame quadrats, and visual cover estimates can be time-consuming and are prone to subjective variations. For this reason, most studies only focus on overall vegetation cover, ignoring variation in live and dead fractions. In the arid regions of the Canadian prairies, grass cover is typically a mixture of green and senescent plant material, and it is essential to monitor both green and senescent vegetation fractional cover. In this study, we designed and built a camera stand to acquire the close-range photographs of rangeland fractional vegetation cover. Photographs were processed by four approaches: SamplePoint software, object-based image analysis (OBIA), unsupervised and supervised classifications to estimate the fractional cover of green vegetation, senescent vegetation, and background substrate. These estimates were compared to in situ surveys. Our results showed that the SamplePoint software is an effective alternative to field measurements, while the unsupervised classification lacked accuracy and consistency. The Object-based image classification performed better than other image classification methods. Overall, SamplePoint and OBIA produced mean values equivalent to those produced by in situ assessment. These findings suggest an unbiased, consistent, and expedient alternative to in situ grassland vegetation fractional cover estimation, which provides a permanent image record.
植被盖度是衡量草原健康的关键指标。然而，像线点截距样带、针框样方和目视盖度估计等调查技术既费时又容易受到主观差异的影响。出于这个原因，大多数研究仅关注总体植被盖度，而忽略了活和死部分的变化。在加拿大草原的干旱地区，草的覆盖通常是绿色和衰老植物材料的混合物，因此必须监测绿色和衰老植被的分数盖度。在这项研究中，我们设计并建造了一个相机支架，以获取草原植被分数盖度的近景照片。通过四种方法处理照片：SamplePoint 软件、基于对象的图像分析（OBIA）、无监督和监督分类，以估计绿色植被、衰老植被和背景基质的分数覆盖。这些估计与现场调查进行了比较。我们的结果表明，SamplePoint 软件是现场测量的有效替代方法，而无监督分类缺乏准确性和一致性。基于对象的图像分类比其他图像分类方法表现更好。总的来说，SamplePoint 和 OBIA 产生的平均值与现场评估产生的平均值相当。这些发现表明，一种无偏见、一致和快捷的替代现场草地植被分数盖度估计方法，提供了永久的图像记录。
