Li Minghui, Yan Enping, Zhou Hui, Zhu Jiaxing, Jiang Jiawei, Mo Dengkui
Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha, China.
Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha, China.
Front Plant Sci. 2022 Sep 23;13:1006795. doi: 10.3389/fpls.2022.1006795. eCollection 2022.
The cliff ecosystem is one of the least human-disturbed ecosystems in nature, and its inaccessible and often extreme habitats are home to many ancient and unique plant species. Because of the harshness of cliff habitats, their high elevation, steepness of slopes, and inaccessibility to humans, surveying cliffs is incredibly challenging. Comprehensive and systematic information on cliff vegetation cover is not unavailable but obtaining such information on these cliffs is fundamentally important and of high priority for environmentalists. Traditional coverage survey methods-such as large-area normalized difference vegetation index (NDVI) statistics and small-area quadratic sampling surveys-are not suitable for cliffs that are close to vertical. This paper presents a semi-automatic systematic investigation and a three-dimensional reconstruction of karst cliffs for vegetation cover evaluation. High-resolution imagery with structure from motion (SFM) was captured by a smart unmanned aerial vehicle (UAV). Using approximately 13,000 records retrieved from high-resolution images of 16 cliffs in the karst region Guilin, China, 16 models of cliffs were reconstructed. The results show that this optimized UAV photogrammetry method greatly improves modeling efficiency and the vegetation cover from the bottom to the top of cliffs is high-low-high, and very few cliffs have high-low cover at the top. This study highlights the unique vegetation cover of karst cliffs, which warrants further research on the use of SFM to retrieve cliff vegetation cover at large and global scales.
悬崖生态系统是自然界中受人类干扰最少的生态系统之一，其难以到达且往往极端的栖息地是许多古老而独特的植物物种的家园。由于悬崖栖息地条件恶劣，海拔高、坡度陡且人类难以到达，对悬崖进行调查极具挑战性。关于悬崖植被覆盖的全面系统信息并非不可获取，但获取这些悬崖的此类信息对环保主义者来说至关重要且具有高度优先性。传统的覆盖度调查方法，如大面积归一化植被指数（NDVI）统计和小面积二次抽样调查，并不适用于近乎垂直的悬崖。本文提出了一种用于评估植被覆盖的喀斯特悬崖半自动系统调查和三维重建方法。通过智能无人机（UAV）获取了具有运动结构（SFM）的高分辨率图像。利用从中国桂林喀斯特地区16座悬崖的高分辨率图像中检索到的约13000条记录，重建了16座悬崖模型。结果表明，这种优化的无人机摄影测量方法大大提高了建模效率，悬崖从底部到顶部的植被覆盖呈现高 - 低 - 高的情况，且很少有悬崖顶部植被覆盖为高 - 低情况。本研究突出了喀斯特悬崖独特的植被覆盖情况，这值得进一步研究在大尺度和全球尺度上利用SFM获取悬崖植被覆盖信息。
