Detecting Multi-Decadal Changes in Seagrass Cover in Tauranga Harbour, New Zealand, Using Landsat Imagery and Boosting Ensemble Classification Techniques
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 appropriat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/10/6/371 |
| _version_ | 1797531831840538624 |
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| author | Nam-Thang Ha Merilyn Manley-Harris Tien-Dat Pham Ian Hawes |
| author_facet | Nam-Thang Ha Merilyn Manley-Harris Tien-Dat Pham Ian Hawes |
| author_sort | Nam-Thang Ha |
| collection | DOAJ |
| description | 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, <i>F</i><sub>1</sub> 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. |
| first_indexed | 2024-03-10T10:50:23Z |
| format | Article |
| id | doaj.art-05ea5ff77b54418eb3cecd10b4e1da62 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T10:50:23Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-05ea5ff77b54418eb3cecd10b4e1da622023-11-21T22:14:50ZengMDPI AGISPRS International Journal of Geo-Information2220-99642021-05-0110637110.3390/ijgi10060371Detecting Multi-Decadal Changes in Seagrass Cover in Tauranga Harbour, New Zealand, Using Landsat Imagery and Boosting Ensemble Classification TechniquesNam-Thang Ha0Merilyn Manley-Harris1Tien-Dat Pham2Ian Hawes3Environmental Research Institute, School of Science, University of Waikato, Hamilton 3260, New ZealandEnvironmental Research Institute, School of Science, University of Waikato, Hamilton 3260, New ZealandDepartment of Biological Sciences, Florida International University (FIU), Miami, FL 33199, USAEnvironmental Research Institute, School of Science, University of Waikato, Hamilton 3260, New ZealandSeagrass 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, <i>F</i><sub>1</sub> 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.https://www.mdpi.com/2220-9964/10/6/371seagrass mappingTauranga Harbourchange detectionlandsatrandom forestsupport vector machine |
| spellingShingle | Nam-Thang Ha Merilyn Manley-Harris Tien-Dat Pham Ian Hawes Detecting Multi-Decadal Changes in Seagrass Cover in Tauranga Harbour, New Zealand, Using Landsat Imagery and Boosting Ensemble Classification Techniques ISPRS International Journal of Geo-Information seagrass mapping Tauranga Harbour change detection landsat random forest support vector machine |
| title | Detecting Multi-Decadal Changes in Seagrass Cover in Tauranga Harbour, New Zealand, Using Landsat Imagery and Boosting Ensemble Classification Techniques |
| title_full | Detecting Multi-Decadal Changes in Seagrass Cover in Tauranga Harbour, New Zealand, Using Landsat Imagery and Boosting Ensemble Classification Techniques |
| title_fullStr | Detecting Multi-Decadal Changes in Seagrass Cover in Tauranga Harbour, New Zealand, Using Landsat Imagery and Boosting Ensemble Classification Techniques |
| title_full_unstemmed | Detecting Multi-Decadal Changes in Seagrass Cover in Tauranga Harbour, New Zealand, Using Landsat Imagery and Boosting Ensemble Classification Techniques |
| title_short | Detecting Multi-Decadal Changes in Seagrass Cover in Tauranga Harbour, New Zealand, Using Landsat Imagery and Boosting Ensemble Classification Techniques |
| title_sort | detecting multi decadal changes in seagrass cover in tauranga harbour new zealand using landsat imagery and boosting ensemble classification techniques |
| topic | seagrass mapping Tauranga Harbour change detection landsat random forest support vector machine |
| url | https://www.mdpi.com/2220-9964/10/6/371 |
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