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Mangrove ecosystems provide critical goods and ecosystem services to coastal communities and contribute to climate change mitigation. Over four decades, remote sensing has proved its usefulness in monitoring mangrove ecosystems on a broad scale, over time, and at a lower cost than field observation. The increasing use of spectral indices has led to an expansion of the geographical context of mangrove studies from local-scale studies to intercontinental and global analyses over the past 20 years. In remote sensing, numerous spectral indices derived from multiple spectral bands of remotely sensed data have been developed and used for multiple studies on mangroves. In this paper, we review the range of spectral indices produced and utilised in mangrove remote sensing between 1996 and 2021. Our findings reveal that spectral indices have been used for a variety of mangrove aspects but excluded identification of mangrove species. The included aspects are mangrove extent, distribution, mangrove above ground parameters (e.g., carbon density, biomass, canopy height, and estimations of LAI), and changes to the aforementioned aspects over time. Normalised Difference Vegetation Index (NDVI) was found to be the most widely applied index in mangroves, used in 82% of the studies reviewed, followed by the Enhanced Vegetation Index (EVI) used in 28% of the studies. Development and application of potential indices for mangrove cover characterisation has increased (currently 6 indices are published), but NDVI remains the most popular index for mangrove remote sensing. Ultimately, we identify the limitations and gaps of current studies and suggest some future directions under the topic of spectral index application in connection to time series imagery and the fusion of optical sensors for mangrove studies in the digital era.

Mangrove ecosystems play a crucial role in coastal protection, biodiversity conservation, and climate regulation. Using satellite imagery, this study examines the seasonal changes in mangrove health in Abu Dhabi over the years 2013 to 2021. Landsat 8 data were analyzed to assess mangrove extent and condition, focusing on vegetation indices and land surface temperature to understand their response to environmental factors. Results indicate that mangrove health fluctuates with seasonal temperature variations, with vegetation indices ranging from 0.3 in summer to above 0.6 in winter. The highest values were recorded in December, indicating peak health, while August exhibited the lowest, reflecting stress from extreme temperatures exceeding 40°C. A strong negative correlation (−0.75) was observed between vegetation health and surface temperature, confirming that elevated temperatures reduce mangrove vitality. Over the study period, mangrove cover increased by approximately 12%, highlighting the success of conservation initiatives. This study underscores the importance of continuous monitoring for conservation planning and climate adaptation. Satellite-based assessments provide valuable insights into mangrove ecosystem dynamics, advocating for targeted preservation measures.

Mangrove ecosystems are critical for coastal resilience, biodiversity, and carbon sequestration, yet remain vulnerable to fragmentation, invasive species, and anthropogenic pressures. To address the need for more precise monitoring tools, this study introduces the Lagoon Mangrove Ecosystem Health Index (MEHI/HEMI), which integrates the Enhanced Mangrove Index (EMI), hexagonal grid-based spatial metrics, and anthropogenic disturbance indices. Validated in Segara Anakan, Cilacap, Indonesia, the MEHI was benchmarked against field observations and compared with existing indices. Results demonstrate that MEHI achieved an overall accuracy of 90.91% with a Kappa coefficient of 0.87, significantly outperforming (P < 0.05) the Mangrove Quality Index (MQI: 78%, Kappa = 0.70) and the Mangrove Vegetation Index (MVI: 82%, Kappa = 0.75). Spatial patterns revealed severe degradation in the western lagoon due to invasive species and human activity, while the eastern zone exhibited stronger ecological integrity supported by community-led conservation, notably the Simanja Ecotourism initiative. By delivering higher diagnostic accuracy and clear classification into Poor, Moderate, and Excellent categories, MEHI provides actionable insights for targeted restoration, conservation prioritization, and integration into national monitoring frameworks. This study advances mangrove health assessment by combining remote sensing diagnostics with participatory management perspectives, offering a scalable and policy-relevant approach to sustaining coastal ecosystems under increasing environmental pressures.

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.

Bangka’s mangroves contribute to Indonesia’s species-rich coastal ecosystems, yet they have experienced substantial degradation, largely driven by human activities such as tin mining. Establishing long-term records of mangrove extent is essential for understanding distribution dynamics, assessing impacts, and guiding conservation strategies. In this study, we applied change detection techniques, a random forest classifier, and the LandTrendr algorithm to analyze Landsat time-series data from 1994 to 2023 across Bangka Island. We quantified multi-decadal changes in mangrove extent, periods of disturbance and recovery, and discrepancies between local and global datasets. Mangrove dynamics were spatially heterogeneous, with both expansion and loss observed across regions in landward and seaward settings. Over the 30-year period, total gains reached 4956.39 ha (10.30% of the baseline), yet the net change indicated an overall loss of 1055.85 ha. LandTrendr analysis further revealed sustained mangrove expansion since 1989. Observed changes reflect the combined influence of natural processes, including accretion and erosion, and human pressures, particularly tin mining. Although net area loss aligns with national trends, the drivers in this mining-dominated region differ from those elsewhere, and some mangrove areas remain absent from global datasets. These findings emphasize the need to better capture local gain–loss dynamics to support effective management and conservation.

The expansion of human activities and environmental changes in coastal desert and semi-desert ecosystems significantly impact habitat quality. This study used remote sensing data on the Google Earth Engine (GEE) platform to generate layers representing threats to mangrove ecosystems, including human construction, runoff, maximum daily temperature, wind speed, access roads, population density, drought severity, NO₂, SO₂, land surface temperature, soil moisture deficiency, actual evapotranspiration, and dust. These layers were analyzed for current and future conditions, and a target layer for mangrove change detection was created using a variable sampling method and the Random Forest algorithm for both time periods. The Mangrove Vegetation Index (MVI) and Enhanced Mangrove Vegetation Index (EMVI) were included as auxiliary data. The resulting data were analyzed using the InVEST model to generate maps of habitat quality and mangrove degradation, and habitat quality was further assessed using machine learning models. The results showed that Linear Trend, Neural Network and SVR models performed better than other machine learning models. Also, in the study area, habitat quality will decrease according to the Otsu threshold in the future years. The results reveal that, under current conditions, 35.43% of the mangrove forest area is classified as experiencing low-intensity degradation. Projections indicate that, under future conditions, 33.33% of the area will shift to high-intensity degradation, especially in southern regions near Qeshm Island. Habitat quality is also to decline, with the “good and suitable” habitat quality category expected to decrease by 3.52%. Additionally, human activity indices in medium and high categories are projected to increase by 5.38 km² and 4.48 km², respectively. These results can be used to guide future coastal management and conservation strategies by identifying areas at highest risk of mangrove degradation and declining habitat quality under increasing human pressure and environmental change.

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.

Mangrove forests distributed along the coast of southern Iran are an important resource and a vital habitat for species communities and the local people. In this study, accurate mapping and spatiotemporal change detection were conducted on Iran’s mangroves for three decades, using the Landsat imagery available for the years 1990, 2000, 2010, and 2020. Four general vegetation indices and eight mangrove-specific indices were employed for mangrove mapping in three study sites. Additionally, six important landscape metrics were implemented to quantify the spatiotemporal alteration of the mangrove forests during the study period. Our results showed the robustness of the submerged mangrove recognition index (SMRI), validated as the most effective index (F1-score ≥ 0.89), which was used for mangrove identification within all nine sites. The mangrove area of southern Iran was estimated at approximately 13,000 ha in 2020, with an overall increase of 2313 ha over the whole period. A similar trend could be observed for both the landscape connectivity and complexity. Our results revealed that a stronger connectivity and higher complexity could be detected in most sites, while there was increased fragmentation and a weaker connection in some locations. This study provides an accurate map of Iran’s mangrove forests over time and space.

Indonesian mangrove declined significantly in the last two decades which has been considered to deliver a negative impact for adjacent communities in small islands. Mangrove quality monitoring was conducted during Nusa Manggala Expedition in 2018, which was aimed to analyze forest structure and healthiness using spatial-temporal investigation in Liki island, Papua. Field data were collected from 10m-×-10m quadratic plots which were distributed following stratified purposive sampling method. Spatial and temporal was implemented using Sentinel 2 imagery on this area from 2016 to 2021. The result of this field study had considered that mangrove in Liki island was in moderate healthiness since the MHI value was between 33.33%-66.67%. It was supported by remote sensing analysis in 2018 which showed that the moderate MHI area was dominant by approximately 42% compared to the excellent area in about 33%. Liki’s mangrove had experienced a declining trend of excellent category from 2016 and reached the lowest area of its category in 2018. In the last four-year observation, excellent areas gradually increased which was covering 57.68% of forest MHI. The dynamic of mangrove healthiness on this island tended to be delivered by natural events.

Continuous monitoring of mangrove forest conditions is essential to support a sustainable blue economy through informed land use planning, conservation, and rehabilitation strategies. This study investigates the spatial and temporal dynamics of the Mangrove Health Index (MHI) in the Sunda Strait region using Sentinel-2 satellite imagery for the years 2015, 2020, and 2025, focusing on the Sumur Coastal Area, Pandeglang, Banten. The analysis utilized three vegetation indices: the Normalized Burn Ratio (NBR), Green Chlorophyll Index (GCI), and Structure-Insensitive Pigment Index (SIPI). Results indicate a positive trend in ecosystem health. The average NBR increased from 0.416 in 2015 to 0.450 in 2025, GCI from 1.14 to 4.88, and SIPI from 0.84 to 1.04. Mangrove areas in poor condition (MHI < 33.3%) decreased from 684.15 ha to 376.78 ha, while areas in very good condition (MHI > 66.8%) increased from 548.39 ha to 945.29 ha. The moderate condition category (33.3% ≤ MHI ≤ 66.8%) declined from 3,124.11 ha to 2,159.87 ha. These findings highlight significant mangrove vegetation recovery, though some degradation persists due to land conversion. This research provides a scientific contribution to the development of a remote sensing-based mangrove health monitoring method with the integration of multitemporal vegetation indices and field survey validation. This approach can be replicated in other coastal areas as a basis for decision-making in sustainable mangrove ecosystem management.