Abstract
Seagrass provides a wide range of essential ecosystem services, supports climate change mitigation, and contributes to blue carbon sequestration. This resource, however, is undergoing significant declines across the globe, and there is an urgent need to develop change detection techniques appropriate to the scale of loss and applicable to the complex coastal marine environment. Our work aimed to develop remote-sensing-based techniques for detection of changes between 1990 and 2019 in the area of seagrass meadows in Tauranga Harbour, New Zealand. Four state-of-the-art machine-learning models, Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boost (XGB), and CatBoost (CB), were evaluated for classification of seagrass cover (presence/absence) in a Landsat 8 image from 2019, using near-concurrent Ground-Truth Points (GTPs). We then used the most accurate one of these models, CB, with historic Landsat imagery supported by classified aerial photographs for an estimation of change in cover over time. The CB model produced the highest accuracies (precision, recall, F1 scores of 0.94, 0.96, and 0.95 respectively). We were able to use Landsat imagery to document the trajectory and spatial distribution of an approximately 50% reduction in seagrass area from 2237 ha to 1184 ha between the years 1990–2019. Our illustration of change detection of seagrass in Tauranga Harbour suggests that machine-learning techniques, coupled with historic satellite imagery, offers potential for evaluation of historic as well as ongoing seagrass dynamics.
Highlights
Seagrass provides a number of valuable ecosystem services in coastal areas, including primary production, biogenic habitat production, water filtering, wave energy attenuation, and sediment trapping [1,2]
The Random Forest (RF) and XGB techniques showed an equivalent performance (Table 3) with F1 score of 0.93, while the Support Vector Machine (SVM) model underperformed the other models with a F1 score of 0.91
We have demonstrated the use of machine-learning approaches successfully to classify seagrass in Landsat images of Tauranga Harbour, and to use this classification to detect changes in seagrass cover over a period of 29 years
Summary
Seagrass provides a number of valuable ecosystem services in coastal areas, including primary production, biogenic habitat production, water filtering, wave energy attenuation, and sediment trapping [1,2]. An accurate and rapid technique to inventory this resource is in high demand [5,11,12], to contribute baseline data for the evaluation of coastal ecosystem dynamics, establishment of marine protected areas, and functional zoning fitting to the local conditions. Where this can include a historic perspective, it can provide a comprehensive understanding of system change
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