Abstract
Seagrass has been acknowledged as a productive blue carbon ecosystem that is in significant decline across much of the world. A first step toward conservation is the mapping and monitoring of extant seagrass meadows. Several methods are currently in use, but mapping the resource from satellite images using machine learning is not widely applied, despite its successful use in various comparable applications. This research aimed to develop a novel approach for seagrass monitoring using state-of-the-art machine learning with data from Sentinel–2 imagery. We used Tauranga Harbor, New Zealand as a validation site for which extensive ground truth data are available to compare ensemble machine learning methods involving random forests (RF), rotation forests (RoF), and canonical correlation forests (CCF) with the more traditional maximum likelihood classifier (MLC) technique. Using a group of validation metrics including F1, precision, recall, accuracy, and the McNemar test, our results indicated that machine learning techniques outperformed the MLC with RoF as the best performer (F1 scores ranging from 0.75–0.91 for sparse and dense seagrass meadows, respectively). Our study is the first comparison of various ensemble-based methods for seagrass mapping of which we are aware, and promises to be an effective approach to enhance the accuracy of seagrass monitoring.
Highlights
Together with mangrove and salt marsh, seagrass has been evaluated as an effective coastal ecosystem for blue carbon storage [1,2,3]
Our results show that random forests (RF), rotation forests (RoF), and canonical correlation forests (CCF) are good performers with a balance of high precision and recall scores, whilst very low precision scores of dense and sparse seagrass classes ranging from 0.34–0.63 were found for maximum likelihood classifier (MLC)
We tested the performance of machine learning (ML) ensemble-based and MLC methods for seagrass mapping from Sentinel-2 data
Summary
Together with mangrove and salt marsh, seagrass has been evaluated as an effective coastal ecosystem for blue carbon storage [1,2,3]. Sentinel-2 data have been distributed free-of-charge at the top-of-atmosphere corrected level (level 1C) for blue, green, red, and near infrared (NIR) bands at 10 m resolution, and provides a very good resource for intertidal and subtidal ecosystem mapping. Using these data to derive ecosystem spatial properties requires classification algorithms and overfitting, and the inaccurate edge detection of different substrata remains a limitation of traditional classification methods [6,8,9,10]. The utilization of the linear or quadratic discrimination functions of a MLC may not work when the boundaries of classes are not well defined [15]
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