Abdullah Meshal M, Al-Ali Zahraa M, Abdullah Mansour T, Al-Anzi Bader
Department of Ecology and Conservation Biology, Texas A&M University, College Station, TX 77843, USA.
Natural Environmental Systems and Technologies (NEST) Research Group, Ecolife Sciences Research and Consultation, Hawally 30002, Kuwait.
Plants (Basel). 2021 May 13;10(5):977. doi: 10.3390/plants10050977.
The rapid assessment and monitoring of native desert plants are essential in restoration and revegetation projects to track the changes in vegetation patterns in terms of vegetation coverage and structure. This work investigated advanced vegetation monitoring methods utilizing UAVs and remote sensing techniques at the Al Abdali protected site in Kuwait. The study examined the effectiveness of using UAV techniques to assess the structure of desert plants. We specifically examined the use of very-high-resolution aerial imagery to estimate the vegetation structure of (perennial desert shrub), assess the vegetation cover density changes in desert plants after rainfall events, and investigate the relationship between the distribution of perennial shrub structure and vegetation cover density of annual plants. The images were classified using supervised classification techniques (the SVM method) to assess the changes in desert plants after extreme rainfall events. A digital terrain model (DTM) and a digital surface model (DSM) were also generated to estimate the maximum shrub heights. The classified imagery results show that a significant increase in vegetation coverage occurred in the annual plants after rainfall events. The results also show a reasonable correlation between the shrub heights estimated using UAVs and the ground-truth measurements ( = 0.66, < 0.01). The shrub heights were higher in the high-cover-density plots, with coverage >30% and an average height of 77 cm. However, in the medium-cover-density (MD) plots, the coverage was <30%, and the average height was 52 cm. Our study suggests that utilizing UAVs can provide several advantages to critically support future ecological studies and revegetation and restoration programs in desert ecosystems.
在恢复和植被重建项目中，对原生沙漠植物进行快速评估和监测对于追踪植被覆盖和结构方面的植被格局变化至关重要。这项工作在科威特的阿卜杜勒保护区研究了利用无人机和遥感技术的先进植被监测方法。该研究考察了使用无人机技术评估沙漠植物结构的有效性。我们特别研究了使用超高分辨率航空影像来估计（多年生沙漠灌木）的植被结构，评估降雨事件后沙漠植物的植被覆盖密度变化，并研究多年生灌木结构分布与一年生植物植被覆盖密度之间的关系。使用监督分类技术（支持向量机方法）对图像进行分类，以评估极端降雨事件后沙漠植物的变化。还生成了数字地形模型（DTM）和数字表面模型（DSM）来估计灌木的最大高度。分类后的影像结果表明，降雨事件后一年生植物的植被覆盖显著增加。结果还表明，使用无人机估计的灌木高度与实地测量值之间存在合理的相关性（= 0.66，< 0.01）。在高覆盖密度地块中，灌木高度较高，覆盖率> 30%，平均高度为77厘米。然而，在中等覆盖密度（MD）地块中，覆盖率< 30%，平均高度为52厘米。我们的研究表明，利用无人机可为沙漠生态系统未来的生态研究以及植被重建和恢复项目提供重要支持，具有诸多优势。
