Predicting the cover and richness of intertidal macroalgae in remote areas: a case study in the Antarctic Peninsula

Abstract

Antarctica is an iconic region for scientific explorations as it is remote and a critical component of the global climate system. Recent climate change causes a dramatic retreat of ice in Antarctica with associated impacts to its coastal ecosystem. These anthropogenic impacts have a potential to increase habitat availability for Antarctic intertidal assemblages. Assessing the extent and ecological consequences of these changes requires us to develop accurate biotic baselines and quantitative predictive tools. In this study, we demonstrated that satellite‐based remote sensing, when used jointly with in situ ground‐truthing and machine learning algorithms, provides a powerful tool to predict the cover and richness of intertidal macroalgae. The salient finding was that the Sentinel‐based remote sensing described a significant proportion of variability in the cover and richness of Antarctic macroalgae. The highest performing models were for macroalgal richness and the cover of green algae as opposed to the model of brown and red algal cover. When expanding the geographical range of the ground‐truthing, even involving only a few sample points, it becomes possible to potentially map other Antarctic intertidal macroalgal habitats and monitor their dynamics. This is a significant milestone as logistical constraints are an integral part of the Antarctic expeditions. The method has also a potential in other remote coastal areas where extensive in situ mapping is not feasible.

Remote characterization of photosynthetic communities in the Fryxell basin of Taylor Valley, Antarctica

“…Previous investigations have studied the distribution and relative abundance of lichen, mosses and grasses on the Antarctic Peninsula using low-resolution satellite data (e.g. Haselwimmer & Fretwell 2009, Fretwell et al 2011, Casanovas et al 2015, Kotta et al 2018), while others have studied the spectral signatures of microbial mat communities in non-polar environments (e.g. Andréfouët et al 2003).…”

Predicting lake dissolved organic carbon at a global scale

“…Model performance was evaluated using the cross-validation statistics calculated during model fitting 56 . For more details on the BRT modeling see Kotta et al 57 . Standard errors for the predictions and pointwise standard errors for the partial dependence curves, produced by R package "pdp" 58 , were estimated using bootstrap (100 replications).…”

Combining unmanned aerial vehicles and satellite imagery to quantify areal extent of intertidal brown canopy‐forming macroalgae

“…In the marine environment such studies have generally compared classification results across the two types of imagery (Brodie et al, 2018). In Kotta et al (2018) strong relationship was found between the cover of algae (determined through quadrat data) and reflectance values from satellite imagery. This relationship was specifically for green algae, possibly due to the use of single reflectance bands from satellite imagery.…”