Infrared imagery for limnology

Inferring microphyte density and net ecosystem production on soft sediments using infrared imaging

Anthony R. Ives, Emily L. Adler, K. Riley Book, Jamieson C. Botsch, Árni Einarsson, Ian S. Hart, Colin H. Ives, Ian Jin, Amanda R. McCormick, Joseph S. Phillips

Abstract
Measuring microphyte densities in soft-sediment benthos has challenges for even the most sophisticated methods. If the goal is to assess the photosynthetic potential of the microphyte community, then microphytes should be sampled only at the surface of the benthos to the depth of light penetration. Furthermore, microphyte density may show spatial and temporal variability that can only be captured by using many point samples and non-destructive sampling. Here, we use simple near-infrared (NIR) imagery to assess surface density of microphytes in soft underwater sediments and to infer their photosynthetic capacity. In lab studies, NIR imagery gives estimates of microphyte density that are strongly correlated to standard chl-a assays using pigment extraction and fluorometry (Radj2 = 0.7), but NIR imagery is better able to separate experimental treatments. In analyses of sediment samples from a lake, NIR imagery gives estimates of microphyte density strongly correlated to net ecosystem production (NEP). NIR imagery also gives a fine-grained assessment of the spatial distribution of microphytes that helps to explain the relationship between microphyte density and NEP. Finally, images from an underwater NIR camera over the course of a wind disturbance event give estimates of the relative density of microphytes that is buried and is likely to be, at least temporarily, photosynthetically inactive. These results show that NIR imagery provides an easy and non-destructive method for sampling surface densities of microphytes which is particularly suitable for remote field locations and for educational settings in which students can generate results with cheap and robust equipment.<br>

CODE FILES
Two code files contain code to process images or data:

"NIR image processing.R" takes a folder of images and calculates BGN_NDVI.
"NIR NEP processing.R" takes a file of data and calculates NEP.

The image and data files used to illustrate these functions are from the fourth experiment in the manuscript.

A final code file, "NIR data analyses.R", produces the figures and analyses in the manuscript.<br>DATA FILES
The data files are organized according to the experiments (1-5), and the final observational study.<br>

IMAGE FILES
The image files in the folder "Expt_4_images" are for the image processing for the fourth experiment by "NIR image processing.R". The images "GOPR5373.JPG", "GOPR5392.JPG", and "GOPR5414.JPG" are used to produce figure 7.