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.

Giant kelp populations support productive and diverse coastal ecosystems in both hemispheres at temperate and subpolar latitudes but are vulnerable to changing climate conditions as well as direct human impacts. Observations of giant kelp forests are spatially and temporally patchy, 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 in large satellite datasets 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 cumulatively spanning over 2,700 km of coastline. Using the remote sensing approaches evaluated herein, we present the first continuous time series 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.

There is a critical need for research-based active learning instructional materials for the teaching and learning of STEM in online courses. Every year, hundreds of thousands of undergraduate non-science majors enroll in general education astronomy courses to fulfill their institution’s liberal arts requirements. When designing instructional materials for this population of learners, a central focus must be to help learners become more scientifically and data literate. As such, we developed a new, three-part, curricular model that was used to inform the creation of active-learning instructional materials designed for use in online courses to help introductory astronomy students improve their ability to make evidence-based conclusions when presented with a variety of data representations, while increasing their self-efficacy with respect to engaging meaningfully in science. We conducted a pilot study of these instructional materials at nine different colleges and universities to better understand whether students’ engagement with these materials lead to increases in self-efficacy, and whether faculty who implemented the materials were able to easily incorporate our active learning materials into their existing online astronomy courses. Overall, we found a statistically significant improvement in students' self-efficacy after engaging with our instructional materials in their online courses. The results of the item-by-item analysis indicated that students’ beliefs improved most on the questions that assessed their ability to make meaningful contributions to scientific research, and their confidence using data representations to interpret an array of scientific questions. The instructor feedback emphasized that our curriculum development model could successfully inform the creation of instructional materials that were easy to implement in existing online astronomy classes, and supported course learning objectives, creating the potential for widespread dissemination and use at the undergraduate level.

The onset of the COVID-19 pandemic and the Black Lives Matter movement urged institutions to redress shortcomings in their diversity, equity, and inclusion goals and initiatives. The School for the Environment (SFE) at the University of Massachusetts Boston (UMass Boston), a public research minority serving university in the United States of America, responded to this call through launching the Online Conversations for Equity, Action, and Networking (OCEAN) program. This pilot project funded by Woods Hole Sea Grant aimed to amplify the voices of Black, Indigenous, and People of Color (BIPOC) in the marine sciences. A collective of SFE undergraduate and graduate students hosted virtual department seminars, undergraduate meet and greets, and podcast interviews for invited BIPOC speakers. Pre-and post-surveys were developed to evaluate the efficacy and reach of the OCEAN programming and the results indicate that the program had an overall positive effect on the UMass Boston community. Ultimately, the OCEAN program provides an example for launching and evaluating virtual BIPOC science engagement and outreach initiatives.