Detecting Multi-Decadal Changes in Seagrass Cover in Tauranga Harbour, New Zealand, Using Landsat Imagery and Boosting Ensemble Classification Techniques
Bibliographic Data
| ID | 22034091 |
|---|---|
| Authors | Nam Thang Ha (0000-0002-4661-8602, Hue University), Merilyn Manley-Harris (0000-0001-5795-0208, University of Waikato), Tien-Dat Pham (0000-0002-6422-2847, University of Miami, corresponding author), I Hawes (0000-0003-2471-6903, University of Waikato) |
| Year | 2021 |
| Volume | 10 |
| Issue | 6 |
| Pages | 371 |
| Publication date | 2021-05-31 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi10060371 |
| OpenAlex | W3167113081 |
| Language | EN |
| Citations received | 1 |
| References cited | 61 |
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
Cartography · Climate change · Ecosystem · Geography · Habitat · Physical geography · Random forest · Remote sensing · Satellite imagery · Seagrass · Ungulate · Computer Science · Coral and Marine Ecosystems Studies · Environmental Science · Isotope Analysis in Ecology · Marine and coastal plant biology · Artificial Intelligence · Ecology · Geology · Oceanography
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,2 |
| Citation span | 2021 - 2021 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |