Detection of sea urchins (Diadema setosum) colony distribution on UAV imagery data using fuzzy logic approach
Unmanned aerial vehicle (UAV) technology has been widely used to identify the spatial distribution of marine and coastal resources. Diadema setosum plays a crucial role in controlling algae populations in coral reef ecosystems, which would otherwise compete with corals for sunlight. Diadema setosum...
Saved in:
| Format: | Article |
|---|---|
| Language: | English |
| Published: |
Elsevier
2025-04-01
|
| Series: | Kuwait Journal of Science |
| Subjects: | |
| Online Access: | https://www.sciencedirect.com/science/article/pii/S2307410825000331 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Unmanned aerial vehicle (UAV) technology has been widely used to identify the spatial distribution of marine and coastal resources. Diadema setosum plays a crucial role in controlling algae populations in coral reef ecosystems, which would otherwise compete with corals for sunlight. Diadema setosum lives in colonies and generally exists in shallow water areas such as coral, seagrass, and sand. Due to its ecological importance in controlling algae and its economic value as a high-nutrient food source, monitoring and managing the distribution and abundance of Diadema setosum is crucial for sustaining coastal ecosystems. This study aimed to assess the ability of UAV imagery to detect the spatial distribution of Diadema setosum colonies using a simple index ratio approach and fuzzy logic. The study was conducted on the southwest coast of Lancang Island, Seribu Islands, Indonesia, from May 3 to 12, 2018. The results showed that the red-green ratio index (RGRI) band combination from the UAV image was better at detecting Diadema setosum colonies than the green-blue ratio index (GBRI) was. The range of index values for Diadema setosum obtained from GBRI and RGRI transformations were 0.717887–1.989796 and 0.510457–1.183333, respectively. The combination of fuzzy logic and the RGRI band index demonstrated strong performance in detecting the distribution of Diadema setosum colonies, achieving an overall classification accuracy exceeding 90%. © 2025 The Authors |
|---|---|
| ISSN: | 2307-4108 2307-4116 |