Abstract
A high resolution mangrove map (e.g., 10-m), including mangrove patches with small size, is urgently needed for mangrove protection and ecosystem function estimation, because more small mangrove patches have disappeared with influence of human disturbance and sea-level rise. However, recent national-scale mangrove forest maps are mainly derived from 30-m Landsat imagery, and their spatial resolution is relatively coarse to accurately characterize the extent of mangroves, especially those with small size. Now, Sentinel imagery with 10-m resolution provides an opportunity for generating high-resolution mangrove maps containing these small mangrove patches. Here, we used spectral/backscatter-temporal variability metrics (quantiles) derived from Sentinel-1 SAR (Synthetic Aperture Radar) and/or Sentinel-2 MSI (Multispectral Instrument) time-series imagery as input features of random forest to classify mangroves in China. We found that Sentinel-2 (F1-Score of 0.895) is more effective than Sentinel-1 (F1-score of 0.88) in mangrove extraction, and a combination of SAR and MSI imagery can get the best accuracy (F1-score of 0.94). The 10-m mangrove map was derived by combining SAR and MSI data, which identified 20003 ha mangroves in China, and the area of small mangrove patches (<1 ha) is 1741 ha, occupying 8.7% of the whole mangrove area. At the province level, Guangdong has the largest area (819 ha) of small mangrove patches, and in Fujian, the percentage of small mangrove patches is the highest (11.4%). A comparison with existing 30-m mangrove products showed noticeable disagreement, indicating the necessity for generating mangrove extent product with 10-m resolution. This study demonstrates the significant potential of using Sentinel-1 and Sentinel-2 images to produce an accurate and high-resolution mangrove forest map with Google Earth Engine (GEE). The mangrove forest map is expected to provide critical information to conservation managers, scientists, and other stakeholders in monitoring the dynamics of the mangrove forest.
Highlights
Mangrove forest ecosystems, which are a major component of coastal and estuarine zones in tropical and subtropical regions, provide substantial socio-economic functions, such as supplying nutrients and grounds for fish and shellfish, and producing wood and non-wood goods [1,2,3]
This study advances mangrove forest mapping in China by using time-series Sentinel-1 C-band SAR and Sentinel-2 MSI imagery based on Google Earth Engine (GEE), and demonstrates that Sentinel imagery with 10-m resolution has the capability of accurately detecting national-scale mangrove forests
When applying Sentinel imagery to mapping national-scale mangroves, we found that the combination of Sentinel-1/2 time-series data is the optimal approach
Summary
Mangrove forest ecosystems, which are a major component of coastal and estuarine zones in tropical and subtropical regions, provide substantial socio-economic functions, such as supplying nutrients and grounds for fish and shellfish, and producing wood and non-wood goods [1,2,3]. Mangrove forests provide important environmental functions [4,5,6,7,8]. Recent studies have shown that, over the past two decades, global mangrove forests have been declining rapidly and are gradually fragmenting into small patches. This can be attributed to anthropogenic activities, such as aquaculture and land reclamation [15,16,17,18]. There is an urgent need for advancing the monitoring capabilities of mangrove forests to prevent the extinction of mangroves and assess changes in mangrove ecosystem functions
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