ArticleFrontiers in plant science2022
A novel method for cliff vegetation estimation based on the unmanned aerial vehicle 3D modeling.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Broad-ranged, highly disjunct, locally rare and severely endangered: the challenging risk assessment and a global conservation strategy forPhytoKeys · 2025Article
- UAV telephotography elucidates floristic variability and beta diversity of island cliffs under grazing interventions.Scientific reports · 2024Article
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6 authors.
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Abstract
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.
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