Coupling ICESat-2 and Sentinel-2 data for inversion of mangrove tidal flat to predict future distribution pattern of mangroves
Tidal flats represent one of the Earth’s most critical ecosystems characterized by substantial ecological value, but these areas are also among the most fragile ecosystems. A detailed topography survey of tidal flat is essential for exploring how tidal flat ecosystems respond to environmental change...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
|
| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225000457 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1825206959193718784 |
|---|---|
| author | Xiaoyong Ming Yichao Tian Qiang Zhang Yali Zhang Jin Tao Junliang Lin |
| author_facet | Xiaoyong Ming Yichao Tian Qiang Zhang Yali Zhang Jin Tao Junliang Lin |
| author_sort | Xiaoyong Ming |
| collection | DOAJ |
| description | Tidal flats represent one of the Earth’s most critical ecosystems characterized by substantial ecological value, but these areas are also among the most fragile ecosystems. A detailed topography survey of tidal flat is essential for exploring how tidal flat ecosystems respond to environmental changes and for predicting morphological shifts, thereby impacting the protection and restoration of mangrove ecosystems. However, there is still a dearth of data available for mangrove tidal flat topography, as the majority of measurements primarily rely on traditional cartographic methods or small-scale surveys. Therefore, we aim to rely entirely on Earth observation satellite platforms, combining satellite-based Light Detection and Ranging (LiDAR) and optical remote sensing to monitor extensive mangrove tidal flat topography. This methodology was rigorously applied and validated on China’s largest and most representative mangrove tidal flats, revealing a Root Mean Square Error (RMSE) not exceeding 7.5 cm and an R-squared value surpassing 0.89 when compared to airborne LiDAR data. We use the inundation frequency derived from the long-term Sentinel-2 image sequences and elevation data extracted from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) to establish a specific relationship between inundation frequency and ground elevation using both classical and generalized regression models, a mangrove tidal flat topography covering 76.9 km2 was generated. Our findings delineate suitable distribution areas for mangroves in the Maowei Sea, covering an expansive 18.2 km2. |
| format | Article |
| id | doaj-art-89c6daab5e4d4a0d866b64af7de30e67 |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-89c6daab5e4d4a0d866b64af7de30e672025-02-07T04:47:19ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-02-01136104398Coupling ICESat-2 and Sentinel-2 data for inversion of mangrove tidal flat to predict future distribution pattern of mangrovesXiaoyong Ming0Yichao Tian1Qiang Zhang2Yali Zhang3Jin Tao4Junliang Lin5School of Resources and Environment, Beibu Gulf University, Qinzhou 535011, ChinaSchool of Resources and Environment, Beibu Gulf University, Qinzhou 535011, China; Beibu Gulf Ocean Development Research Center, Beibu Gulf University, Qinzhou 535011, China; Guangxi Key Laboratory of Marine Environmental Change and Disaster in the Beibu Gulf, Qinzhou 535011, China; Key Laboratory of Marine Geographic Information Resources Development and Utilization in the Beibu Gulf, Beibu Gulf University, Qinzhou 535011, China; Corresponding author at: School of Resources and Environment, Beibu Gulf University, No.12, Binhai Avenue, Qinzhou, Guangxi, China.School of Resources and Environment, Beibu Gulf University, Qinzhou 535011, ChinaSchool of Resources and Environment, Beibu Gulf University, Qinzhou 535011, ChinaSchool of Resources and Environment, Beibu Gulf University, Qinzhou 535011, ChinaSchool of Resources and Environment, Beibu Gulf University, Qinzhou 535011, ChinaTidal flats represent one of the Earth’s most critical ecosystems characterized by substantial ecological value, but these areas are also among the most fragile ecosystems. A detailed topography survey of tidal flat is essential for exploring how tidal flat ecosystems respond to environmental changes and for predicting morphological shifts, thereby impacting the protection and restoration of mangrove ecosystems. However, there is still a dearth of data available for mangrove tidal flat topography, as the majority of measurements primarily rely on traditional cartographic methods or small-scale surveys. Therefore, we aim to rely entirely on Earth observation satellite platforms, combining satellite-based Light Detection and Ranging (LiDAR) and optical remote sensing to monitor extensive mangrove tidal flat topography. This methodology was rigorously applied and validated on China’s largest and most representative mangrove tidal flats, revealing a Root Mean Square Error (RMSE) not exceeding 7.5 cm and an R-squared value surpassing 0.89 when compared to airborne LiDAR data. We use the inundation frequency derived from the long-term Sentinel-2 image sequences and elevation data extracted from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) to establish a specific relationship between inundation frequency and ground elevation using both classical and generalized regression models, a mangrove tidal flat topography covering 76.9 km2 was generated. Our findings delineate suitable distribution areas for mangroves in the Maowei Sea, covering an expansive 18.2 km2.http://www.sciencedirect.com/science/article/pii/S1569843225000457ICESat-2Sentinel 2MangroveTidal Flat TopographyChina Maowei SeaGoogle Earth Engine |
| spellingShingle | Xiaoyong Ming Yichao Tian Qiang Zhang Yali Zhang Jin Tao Junliang Lin Coupling ICESat-2 and Sentinel-2 data for inversion of mangrove tidal flat to predict future distribution pattern of mangroves International Journal of Applied Earth Observations and Geoinformation ICESat-2 Sentinel 2 Mangrove Tidal Flat Topography China Maowei Sea Google Earth Engine |
| title | Coupling ICESat-2 and Sentinel-2 data for inversion of mangrove tidal flat to predict future distribution pattern of mangroves |
| title_full | Coupling ICESat-2 and Sentinel-2 data for inversion of mangrove tidal flat to predict future distribution pattern of mangroves |
| title_fullStr | Coupling ICESat-2 and Sentinel-2 data for inversion of mangrove tidal flat to predict future distribution pattern of mangroves |
| title_full_unstemmed | Coupling ICESat-2 and Sentinel-2 data for inversion of mangrove tidal flat to predict future distribution pattern of mangroves |
| title_short | Coupling ICESat-2 and Sentinel-2 data for inversion of mangrove tidal flat to predict future distribution pattern of mangroves |
| title_sort | coupling icesat 2 and sentinel 2 data for inversion of mangrove tidal flat to predict future distribution pattern of mangroves |
| topic | ICESat-2 Sentinel 2 Mangrove Tidal Flat Topography China Maowei Sea Google Earth Engine |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225000457 |
| work_keys_str_mv | AT xiaoyongming couplingicesat2andsentinel2dataforinversionofmangrovetidalflattopredictfuturedistributionpatternofmangroves AT yichaotian couplingicesat2andsentinel2dataforinversionofmangrovetidalflattopredictfuturedistributionpatternofmangroves AT qiangzhang couplingicesat2andsentinel2dataforinversionofmangrovetidalflattopredictfuturedistributionpatternofmangroves AT yalizhang couplingicesat2andsentinel2dataforinversionofmangrovetidalflattopredictfuturedistributionpatternofmangroves AT jintao couplingicesat2andsentinel2dataforinversionofmangrovetidalflattopredictfuturedistributionpatternofmangroves AT junlianglin couplingicesat2andsentinel2dataforinversionofmangrovetidalflattopredictfuturedistributionpatternofmangroves |