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<p>Monitoring mangrove ecosystems is essential due to their numerous benefits, such as reducing coastal erosion, improving water quality, and possessing carbon storage capacity three to five times higher than other ecosystems. However, monitoring mangrove ecosystems using traditional methods such as field surveys is challenging, as mangroves are in intertidal zones. One alternative method is remote sensing, which allows observing mangrove areas and their spatial distribution based on pixel value. Moreover, the rapid development of mangrove indices from 2014 to 2023, the availability of open-access satellite imagery such as Landsat-8 and Sentinel-2, and the presence of the Google Earth Engine (GEE) platform, facilitate the calculation of mangrove indices by enabling pixel-level analysis of satellite images. Nevertheless, there is no dedicated Earth Engine app for calculating mangrove indices. Therefore, we aim to develop an application called ECOMangrove, built as an Earth Engine App, to calculate spectral mangrove indices, including MI (Mangrove Index), CMRI (Combined Mangrove Recognition Index), MVI (Mangrove Vegetation Index), NDMI (Normalised Difference Mangrove Index), and REMI (Red-Edge Mangrove Index) using the GEE platform. We further tested the ECOMangrove application in Baros, Yogyakarta, Indonesia, which demonstrated an effectiveness of 96.697% on processing time calculation than using the manual software. Moreover, application and classification in Baros yielded accuracy values of 87.30% for MI, 76.56% for CMRI, 85.93% for MVI, 65.07% for NDMI, and 92.18% for REMI.</p>

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ECOMangrove an interactive Earth engine apps for Mangrove index calculating and mapping

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月31日

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  • Agnes Risma Anggraeni,
  • Naufal Setiawan

摘要

Monitoring mangrove ecosystems is essential due to their numerous benefits, such as reducing coastal erosion, improving water quality, and possessing carbon storage capacity three to five times higher than other ecosystems. However, monitoring mangrove ecosystems using traditional methods such as field surveys is challenging, as mangroves are in intertidal zones. One alternative method is remote sensing, which allows observing mangrove areas and their spatial distribution based on pixel value. Moreover, the rapid development of mangrove indices from 2014 to 2023, the availability of open-access satellite imagery such as Landsat-8 and Sentinel-2, and the presence of the Google Earth Engine (GEE) platform, facilitate the calculation of mangrove indices by enabling pixel-level analysis of satellite images. Nevertheless, there is no dedicated Earth Engine app for calculating mangrove indices. Therefore, we aim to develop an application called ECOMangrove, built as an Earth Engine App, to calculate spectral mangrove indices, including MI (Mangrove Index), CMRI (Combined Mangrove Recognition Index), MVI (Mangrove Vegetation Index), NDMI (Normalised Difference Mangrove Index), and REMI (Red-Edge Mangrove Index) using the GEE platform. We further tested the ECOMangrove application in Baros, Yogyakarta, Indonesia, which demonstrated an effectiveness of 96.697% on processing time calculation than using the manual software. Moreover, application and classification in Baros yielded accuracy values of 87.30% for MI, 76.56% for CMRI, 85.93% for MVI, 65.07% for NDMI, and 92.18% for REMI.