Getting to the Point: Accuracy of Point Count in Monitoring Ecosystem Change

Abstract

Ecological monitoring programs depend on the robust estimation of descriptive parameters. Percent cover, gleaned from transects sampled with video imagery, is a popular benthic ecology descriptor often estimated using point counting, an image-based method for identifying substrate types beneath random points. We tested the hypothesis that the number of points needed to robustly estimate benthic cover in video imagery transects depends on cover itself, predicting that lower cover will require more points/frame to be accurately estimated. While this point may seem obvious to the statistically inclined, the justification of point density has been largely ignored in the literature. We examined the statistical behavior of point count estimates using computer-simulated 20 m-long transects patterned after data from a Bahamian reef. The minimum number of points necessary to insure accurate percent cover estimation, the Optimal Point Count (OPC), is a function of mean percent cover and spatial heterogeneity of the benthic community. More points are required to characterize reefs with lower cover and more homogeneously distributed coral colonies. These results show that careful consideration must be given to sampling design and data analysis prior to attempting to estimate benthic cover, especially in the context of long-term monitoring of degrading coral reef ecosystems.

Scaling up Ecological Measurements of Coral Reefs Using Semi-Automated Field Image Collection and Analysis

“…All manual image annotations were conducted using point-sampling methods, sensu the Coral Point Count Method [71], adapted to GBR species and functional groups ( Table 1). In this method a number of points are overlaid over the image at random locations, and the substrate types at each point location are assigned to one of the labels (Table 1).…”

“…Although the automated annotation method can technically annotate every single pixel in every single remaining image, here we followed the standard point sampling protocol and only created automated annotations at 100 randomly selected points per image. The reasoning for this was twofold: (1) to ensure straight-forward comparisons to percentage cover estimates from the human annotations; and (2) because studies indicate that the estimation error is small if the number of points per image is sufficiently large (>10) [71].…”

Optimization of Coral Video Transect Technique for Coral Reef Survey: Comparison with Intercept Transect Technique

“…The percentage major benthic cover (coral, algae, other invertebrates and dead coral) and number of benthic categories (coral genera, coral growth form and others) were calculated from different camera distance from the substrate (1.0 m, 0.5 m and 0.2 m), number of frames (20%, 30%, 50%, 80% and 100% frames extracted from 20 m transect) and number of points analyzed (5,10,20,30,50,75, 100, 150, and 300 points per frame). The percentage cover was automatically calculated by Coral Point Count with Excel Extension (CPCe) software [21].…”

Automated annotation of coral reef survey images

“…Statistically valid sampling of benthic habitats with image-and point-based tools requires careful attention to the primary purpose of the study, choice of the experimental and statistical approaches necessary to answer the questions being addressed, and the statistical power (i.e., a function of sample size, variance, and desired difference to be detected) required to test the relevant hypotheses. These experimental design components are not the focus of this paper, and interested readers are referred to the many excellent texts on these subjects [104,90]. Instead, we focus on the means and accuracy by which quantitative information can be manually and automatically extracted from underwater images using random point annotation, specifically for near-shore tropical marine environments.…”

“…However, obtaining ecological data, such as percent cover of key benthic groups, from the collected images requires time-consuming and expensive manual image analysis, often in the form of point annotation [104].…”