Publication Date:
2022-10-14
Description:
© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Houskeeper, H. F., Rosenthal, I. S., Cavanaugh, K. C., Pawlak, C., Trouille, L., Byrnes, J. E. K., Bell, T. W., & Cavanaugh, K. C. Automated satellite remote sensing of giant kelp at the Falkland Islands (Islas Malvinas). Plos One, 17(1), (2022): e0257933, https://doi.org/10.1371/journal.pone.0257933.
Description:
Giant kelp populations that support productive and diverse coastal ecosystems at temperate and subpolar latitudes of both hemispheres are vulnerable to changing climate conditions as well as direct human impacts. Observations of giant kelp forests are spatially and temporally uneven, with disproportionate coverage in the northern hemisphere, despite the size and comparable density of southern hemisphere kelp forests. Satellite imagery enables the mapping of existing and historical giant kelp populations in understudied regions, but automating the detection of giant kelp using satellite imagery requires approaches that are robust to the optical complexity of the shallow, nearshore environment. We present and compare two approaches for automating the detection of giant kelp in satellite datasets: one based on crowd sourcing of satellite imagery classifications and another based on a decision tree paired with a spectral unmixing algorithm (automated using Google Earth Engine). Both approaches are applied to satellite imagery (Landsat) of the Falkland Islands or Islas Malvinas (FLK), an archipelago in the southern Atlantic Ocean that supports expansive giant kelp ecosystems. The performance of each method is evaluated by comparing the automated classifications with a subset of expert-annotated imagery (8 images spanning the majority of our continuous timeseries, cumulatively covering over 2,700 km of coastline, and including all relevant sensors). Using the remote sensing approaches evaluated herein, we present the first continuous timeseries of giant kelp observations in the FLK region using Landsat imagery spanning over three decades. We do not detect evidence of long-term change in the FLK region, although we observe a recent decline in total canopy area from 2017–2021. Using a nitrate model based on nearby ocean state measurements obtained from ships and incorporating satellite sea surface temperature products, we find that the area of giant kelp forests in the FLK region is positively correlated with the nitrate content observed during the prior year. Our results indicate that giant kelp classifications using citizen science are approximately consistent with classifications based on a state-of-the-art automated spectral approach. Despite differences in accuracy and sensitivity, both approaches find high interannual variability that impedes the detection of potential long-term changes in giant kelp canopy area, although recent canopy area declines are notable and should continue to be monitored carefully.
Description:
This work was funded by the National Aeronautics and Space Administration as part of the Citizen Science for Earth Systems Program (https://earthdata.nasa.gov/esds/competitive-programs/csesp) with grant #80NSSC18M0103 (awarded to JEKB), which also provided salary to HFH, and by the National Science Foundation through the Santa Barbara Coastal Long-Term Environmental Research (https://sbclter.msi.ucsb.edu) program with grants #OCE 0620276 and 1232779. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Repository Name:
Woods Hole Open Access Server
Type:
Article
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