We examined the effect of mangrove, seagrass meadows, reef characteristics, and complexity of seascape heterogeneity on reef fish assemblages in Ternate, Natuna and Bintan-Indonesia. The analytical approach of seascape ecology was undertaken, using field survey data of reef fish community and maps of mangrove covers and habitat benthic. The fish data had been collected during reef health monitoring in 2015 through the Coral Reef Rehabilitation and Management Program-Coral Triangle Initiative. Map of benthic habitats and mangroves was provided by Indonesia’s data custodians in 2015-2016. Generalized Additive Models were performed to analyze non-linear and non-monotonic relationships. The results showed that mangroves and seagrasses were essential for reef fishes, and as expected, reef characteristics were also important. Accordingly, the conservations of coral reefs should consider mangrove and seagrass protection and vice versa. Therefore, this information could be considered for managers of marine protected areas (MPA) to better practice MPA management.

The only place for estuarine-mangroves in Java Island, Segara Anakan Lagoon, experiences the vast decline of mangrove cover. Satellite remote sensing has a critical role in monitoring that change as it allows to record vast areas over time. However, most studies tend to utilize satellite data to investigate the change of mangrove areas into other land-use types rather than identify the mangrove community’s shifting. This study utilized the mangrove vegetation index (MVI) for monitoring the changes of mangrove communities at the life-form level using satellite data. The study used multi-temporal Landsat images as it has historical systematic archive data. The threshold value of the index for each class is defined by referring to the field data. The class referred to the life-form classification consisting of mangrove trees, Nypa, and understorey. The image analysis was conducted using Google Earth Engine (GEE), while R software was used for determining threshold values through statistical analysis. The result shows that the MVI can differentiate between some life forms of mangroves, with the overall accuracy reaching 78.79% and a kappa coefficient of 0.729. Further, the multi-temporal maps showed the decline of mangrove tree areas, which the understorey and Nypa community have replaced.

Mapping mangroves using satellite imagery has been done for decades. It helps reduce obstacles in inaccessible places caused by the mangroves’ intricate root system, thick mud, and loss of position signals. There is an urgent need to produce a mangrove map that automatically and accurately covers the mangroves with the density index of the canopy as visually represented in satellite imagery. The research was conducted through an analytical desk study of the mangrove features from space. The study aims to develop a simple formula for automatically tracing, capturing, and mapping mangroves and determining the canopy density index from open access of satellite data to eliminate manual digitization work, make it easy to use, and save cost and time. The goal is to monitor, assess, and manage the condition of mangroves for anyone interested in mangroves, including the central government, local authorities, and local communities. As a result, the authors proposed an algorithm: (ρNIR − ρRed)/(ρRed + ρSWIR1) ∗ (ρNIR − ρSWIR1)/(ρSWIR1 − 0.65 ∗ ρRed). Experimental results in many mangrove forests using Landsat 5 TM, Landsat 7 ETM, Landsat 8 OLI, and Sentinel 2 imageries show satisfactory performance. The maps capture the spatial extent of the mangroves automatically and match the satellite imagery visually. The index correlates significantly with the Normalized Difference Water Index (NDWI), with R2 reaching 0.99. The research will apply the formula of the Musi Delta mangrove complex in South Sumatra, Indonesia. The advantage of the algorithm is that it works well, is easy to use, produces mangrove maps faster, informs the index, and efficiently monitors the change in mangrove conditions from time to time.

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