Abstract
The logistical challenges of Antarctic field work and the increasing availability of very high resolution commercial imagery have driven an interest in more efficient search and classification of remotely sensed imagery. This exploratory study employed geographic object-based analysis (GEOBIA) methods to classify guano stains, indicative of chinstrap and Adélie penguin breeding areas, from very high spatial resolution (VHSR) satellite imagery and closely examined the transferability of knowledge-based GEOBIA rules across different study sites focusing on the same semantic class. We systematically gauged the segmentation quality, classification accuracy, and the reproducibility of fuzzy rules. A master ruleset was developed based on one study site and it was re-tasked “without adaptation” and “with adaptation” on candidate image scenes comprising guano stains. Our results suggest that object-based methods incorporating the spectral, textural, spatial, and contextual characteristics of guano are capable of successfully detecting guano stains. Reapplication of the master ruleset on candidate scenes without modifications produced inferior classification results, while adapted rules produced comparable or superior results compared to the reference image. This work provides a road map to an operational “image-to-assessment pipeline” that will enable Antarctic wildlife researchers to seamlessly integrate VHSR imagery into on-demand penguin population census.
Highlights
Climate change, fisheries, cetacean recovery, and human disturbance are all hypothesized as drivers of ecological change in Antarctica [1,2,3,4]
When analyzing scores reported for the Quality metric when the reference ruleset is executed without adaptations, Baily Head (BAIL) had the highest classification accuracy (36.8%) while Zavodovski Island (ZAVO) reported the lowest accuracy (0.0%)
We tasked object-based methods closely examined the transferability of knowledge-based GEographic Object-Based Image Analysis (GEOBIA) rules across different study sites to classify chinstrap and Adélie guano stains from very high spatial resolution satellite imagery focusing on the same semantic class
Summary
Fisheries, cetacean recovery, and human disturbance are all hypothesized as drivers of ecological change in Antarctica [1,2,3,4]. The consequences of climate change may be seen across the marine food web from phytoplankton to top predators [3,10,11,12,13], though study of these processes has been traditionally limited by the challenges of doing field research across most of the Antarctic continent. Adélie penguins are found around the entire continent, while chinstrap penguins and gentoo penguins are restricted to the Antarctic Peninsula and sub-Antarctic islands further north [14]. Owing to their narrow diets and the relative tractability of monitoring their populations at the breeding colony, penguins can serve as indicators of environmental and ecological change
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