Inter-annual changes and growth trends mapping of mangrove using Landsat time series imagery
Mangroves, as the most prolific but vulnerable ecosystems, necessitate continuous monitoring for effective conservation. However, continuous mangrove mapping is challenging due to extensive land use changes and highly intertidal dynamics. In this study, a novel mangrove index, named the Composite Ma...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/15481603.2025.2480422 |
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| Summary: | Mangroves, as the most prolific but vulnerable ecosystems, necessitate continuous monitoring for effective conservation. However, continuous mangrove mapping is challenging due to extensive land use changes and highly intertidal dynamics. In this study, a novel mangrove index, named the Composite Mangrove Index (CMI), was developed to map and assess the growth trends of mangroves, based on the truth that the spectral-temporal features of mangrove wetland environment related to greenness, moisture, and bare soil. To facilitate long-term mapping of mangroves, the Continuous Change Detection and Classification (CCDC) algorithm was utilized on the Google Earth Engine platform (GEE). The innovative mangrove mapping framework using the CMI based CCDC was applied in three diverse regions (i.e. Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in southern China, Sundarbans in India and Bangladesh, and Gulf of Paria (GP) in northern Venezuela). The results showed that annual mangrove maps of these three typical regions from 2000 to 2020 achieved overall accuracy exceeding 92%, indicating the ability of CMI to capture the temporal inter-annual characteristics of mangroves. The area of mangroves represented a total increase of 700 ha in the GBA, while slightly decreasing in the Sundarbans and the GP. By comparing Fractional Vegetation Cover (FVC) and CMI with two mangrove types, CMI proved to be effective in not only assessing mangrove growth status but also monitoring recovery and degradation growth trends of mangroves, surpassing some existing indices (MI, EVI, TCA). Controlled experiments also demonstrated that CMI outperformed in mangrove classification compared to some mangrove indices (MI, MVI, CMRI). The distinctive CMI values can offer a rapid method to detect and quantify the growth trends for mangrove afforestation, restoration and even degradation. Therefore, the proposed CMI with CCDC provides new slight to monitor the ongoing efforts in mangrove conservation and shed light on understanding the mangrove dynamics. |
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| ISSN: | 1548-1603 1943-7226 |