Which Method for Which Purpose? A Comparison of Line Intercept Transect and Underwater Photogrammetry Methods for Coral Reef Surveys
Résumé
The choice of ecological monitoring methods and descriptors determines the effectiveness of a program designed to assess the state of coral reef ecosystems. Here, we comparer the relative performance of the traditional Line Intercept Transect (LIT) method with three methods derived from underwater photogrammetry: LIT on orthomosaics, photoquadrats from orthomosaics, and surface analyses on orthomosaics. The data were acquired at Reunion Island on five outer reef slopes and two coral communities on underwater lava-flows. Coral cover was estimated in situ using the LIT method and with LITs and photoquadrats digitized on orthomosaic. Surface analyses were done on the same orthomosaics. Structural complexity of the surveyed sites was calculated from digital elevation models using three physical descriptors (fractal dimension, slope, surface complexity), and used to explore their possible influence in coral cover estimates. We also compared the methods in terms of scientific outputs, the human expertise and time required. Coral cover estimates obtained with in situ LITs were higher than those obtained with digitized LITs and photoquadrats. Surfaces analyses on orthomosaics yielded the lowest but most the precise cover estimates (i.e., lowest sample dispersion). Sites with the highest coral cover also had the highest structural complexity. Finally, when we added scientific outputs, and requirements for human expertise and time to our comparisons between methods, we found that surface analysis on the orthomosaics was the most efficient method. Photoquadrats were more time-consuming than both in situ and digitized LITs, even though they provided coral cover estimates similar to those of digitized LITs and yielded more than one descriptor. The LIT in situ method remains the least time-consuming and most effective for species-level taxonomic identifications but is the most limited method in terms of data outputs and representativeness of the ecosystem.
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