| Abstrakt: |
Mangrove forests in West Asia are pure and sparse to dense communities in contact with high salinity. However, they have been rapidly declining during the previous two decades. What is critical to understanding the growth of mangrove species and better assessing the value of their ecological services is the knowledge gained about mangrove mapping. Since mangrove forests grow primarily in tropical and subtropical regions, and in these areas, the sky is usually cloudy, we used a combination of Sentinel-1 and Sentinel-2. A random forest algorithm is used to investigate the potential of Sentinel images for the extraction of mangrove forests in southern Iran, and seven spectral indices were used for better monitoring of mangrove forests. Our research was focused on three questions: (1) For mangrove forests mapping, whether Sentinel imagery, Sentinel-1 SAR data, or Sentinel-2 multispectral data, is more accurate; (2) which Sentinel imaging feature combination produces the best appropriate mangrove forests map; (3) how does a 10-m resolution map increase our understanding of the distribution of mangrove forests when compared to 30-m resolution mangrove products obtained from Landsat imagery? This study showed that the Sentinel-2 time-series images with an F1-score (0.89) are more effective in extracting the mangrove map than the Sentinel-1 time-series images with an F1-score (0.83). According to this study, it can be stated that the combination of multispectral and SAR images had an acceptable performance in separating areas with an area of less than 1 hectare of mangrove forests. [ABSTRACT FROM AUTHOR] |
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