ASSESSMENT OF MANGROVE EXTENT EXTRACTION ACCURACY OF THRESHOLD SEGMENTATION-BASED INDICES USING SENTINEL IMAGERY

• Main Authors: Zablan, C. D. C., Blanco, A. C., Nadaoka, K., Martinez, K. P., Baloloy, A. B.
• Format: Conference Proceeding
• Language: English
• Subjects: Accuracy; Classification; Coastal zone management; Environmental protection; Image segmentation; Land cover; Machine learning; Mangroves; Mapping; Remote monitoring; Remote sensing; Satellite imagery; Satellites; Synthetic aperture radar; Vegetation index; Wildlife
• ISSN: 2194-9034; 1682-1750; 2194-9034
• DOI: 10.5194/isprs-archives-XLVIII-4-W6-2022-391-2023

Mangroves have been protecting coastlines, nourishing wildlife, and capturing carbon for climate regulation. The decline of mangroves calls for action to rapidly and accurately monitor them. Remote Sensing makes it possible to remotely monitor mangroves from images captured from space. Sentinel-1 and Sentinel-2 are examples of remote sensing satellites and there is extensive research on their land cover mapping capabilities, including mangrove mapping. While machine learning is a popular methodology for mangrove mapping (e.g., the use of Random Forest) there exist simpler techniques, i.e., utilizing threshold segmentation-based indices that only use a formula and a specific threshold to extract mangrove extents from satellite imagery (mostly Sentinel imagery). This study compared the products and the accuracy of different threshold segmentation-based mangrove mapping indices in four study areas in the Philippines and one in Indonesia. Results showed that the Mangrove Vegetation Index (MVI), Automatic Mangrove Map and Index (AMMI), and the Optical and SAR images Combined Mangrove Index (OSCMI) subindex SWIRB (full name of this subindex here) were the superior indices with overall accuracies (OA) greater than 80% in all study areas and reaching a maximum of 90%, 91% and 96%, respectively. By McNemar’s test showed that their results have insignificant differences. MVI, SWIRB, and AMMI only used Sentinel-2 optical imagery, which means the addition of Sentinel-1 SAR imagery was unnecessary. Since the validation data is a product of machine-learning classification, this shows that using threshold segmentation-based indices is promising as it is simpler, faster, and requires little skill compared to using classification techniques.