King Diana H, Wasley Jane, Ashcroft Michael B, Ryan-Colton Ellen, Lucieer Arko, Chisholm Laurie A, Robinson Sharon A
Centre for Sustainable Ecosystem Solutions, School of Earth, Atmospheric and Life Sciences, University of Wollongong, Wollongong, NSW, Australia.
Global Challenges Program, University of Wollongong, Wollongong, NSW, Australia.
Front Plant Sci. 2020 Jun 9;11:766. doi: 10.3389/fpls.2020.00766. eCollection 2020.
Climate change is affecting Antarctica and minimally destructive long-term monitoring of its unique ecosystems is vital to detect biodiversity trends, and to understand how change is affecting these communities. The use of automated or semi-automated methods is especially valuable in harsh polar environments, as access is limited and conditions extreme. We assessed moss health and cover at six time points between 2003 and 2014 at two East Antarctic sites. Semi-automatic object-based image analysis (OBIA) was used to classify digital photographs using a set of rules based on digital red, green, blue (RGB) and hue-saturation-intensity (HSI) value thresholds, assigning vegetation to categories of healthy, stressed or moribund moss and lichens. Comparison with traditional visual estimates showed that estimates of percent cover using semi-automated OBIA classification fell within the range of variation determined by visual methods. Overall moss health, as assessed using the mean percentages of healthy, stressed and moribund mosses within quadrats, changed over the 11 years at both sites. A marked increase in stress and decline in health was observed across both sites in 2008, followed by recovery to baseline levels of health by 2014 at one site, but with significantly more stressed or moribund moss remaining within the two communities at the other site. Our results confirm that vegetation cover can be reliably estimated using semi-automated OBIA, providing similar accuracy to visual estimation by experts. The resulting vegetation cover estimates provide a sensitive measure to assess change in vegetation health over time and have informed a conceptual framework for the changing condition of Antarctic mosses. In demonstrating that this method can be used to monitor ground cover vegetation at small scales, we suggest it may also be suitable for other extreme environments where repeat monitoring via images is required.
气候变化正在影响南极洲，对其独特生态系统进行最小程度破坏性的长期监测对于检测生物多样性趋势以及了解变化如何影响这些群落至关重要。在恶劣的极地环境中，使用自动化或半自动化方法尤其有价值，因为进入受限且条件极端。我们在2003年至2014年期间的六个时间点，对南极东部两个地点的苔藓健康状况和覆盖度进行了评估。基于对象的半自动图像分析（OBIA）被用于根据一组基于数字红、绿、蓝（RGB）和色调-饱和度-亮度（HSI）值阈值的规则对数码照片进行分类，将植被分为健康、受胁迫或濒死的苔藓和地衣类别。与传统视觉估计的比较表明，使用半自动OBIA分类得出的覆盖百分比估计值落在视觉方法确定的变化范围内。在这两个地点，使用样方内健康、受胁迫和濒死苔藓的平均百分比评估的总体苔藓健康状况在11年中发生了变化。2008年，两个地点都观察到胁迫显著增加且健康状况下降，随后其中一个地点到2014年恢复到健康基线水平，但另一个地点的两个群落中仍有明显更多受胁迫或濒死的苔藓。我们的结果证实，使用半自动OBIA可以可靠地估计植被覆盖度，其准确性与专家的视觉估计相似。由此得出的植被覆盖度估计值提供了一种敏感的措施来评估植被健康随时间的变化，并为南极苔藓状况变化的概念框架提供了依据。在证明该方法可用于小尺度监测地面覆盖植被时，我们认为它也可能适用于其他需要通过图像进行重复监测的极端环境。
